diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml index d787d786..49d50bb2 100644 --- a/.github/workflows/docker-publish.yml +++ b/.github/workflows/docker-publish.yml @@ -3,6 +3,7 @@ name: Build and Push Docker Image on: release: types: [published] + workflow_dispatch: permissions: contents: read @@ -38,6 +39,7 @@ jobs: uses: docker/build-push-action@v5 with: context: . + platforms: linux/amd64,linux/arm64 push: true tags: ${{ steps.meta.outputs.tags }} labels: ${{ steps.meta.outputs.labels }} diff --git a/.gitignore b/.gitignore index dd1c386b..a1b10a90 100644 --- a/.gitignore +++ b/.gitignore @@ -49,6 +49,7 @@ inputs/ rag_storage/ examples/input/ examples/output/ +output*/ # Miscellaneous .DS_Store @@ -59,6 +60,8 @@ ignore_this.txt # Project-specific files dickens*/ book.txt +LightRAG.pdf +download_models_hf.py lightrag-dev/ gui/ diff --git a/README-zh.md b/README-zh.md index 5851c480..45335489 100644 --- a/README-zh.md +++ b/README-zh.md @@ -1,9 +1,56 @@ -# LightRAG: Simple and Fast Retrieval-Augmented Generation +
-LightRAG Diagram +
+ LightRAG Logo +
+ +# 🚀 LightRAG: Simple and Fast Retrieval-Augmented Generation + +
+ HKUDS%2FLightRAG | Trendshift +
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+ LightRAG Diagram +
+ +--- ## 🎉 新闻 +- [X] [2025.06.05]🎯📢LightRAG现已集成RAG-Anything，支持全面的多模态文档解析与RAG能力（PDF、图片、Office文档、表格、公式等）。详见下方[多模态处理模块](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#多模态文档处理rag-anything集成)。 - [X] [2025.03.18]🎯📢LightRAG现已支持引文功能。 - [X] [2025.02.05]🎯📢我们团队发布了[VideoRAG](https://github.com/HKUDS/VideoRAG)，用于理解超长上下文视频。 - [X] [2025.01.13]🎯📢我们团队发布了[MiniRAG](https://github.com/HKUDS/MiniRAG)，使用小型模型简化RAG。 @@ -43,6 +90,8 @@ LightRAG服务器旨在提供Web UI和API支持。Web UI便于文档索引、知 ```bash pip install "lightrag-hku[api]" +cp env.example .env +lightrag-server ``` * 从源代码安装 @@ -53,6 +102,8 @@ cd LightRAG # 如有必要，创建Python虚拟环境 # 以可编辑模式安装并支持API pip install -e ".[api]" +cp env.example .env +lightrag-server ``` * 使用 Docker Compose 启动 LightRAG 服务器 @@ -111,6 +162,8 @@ python examples/lightrag_openai_demo.py ## 使用LightRAG Core进行编程 +> 如果您希望将LightRAG集成到您的项目中，建议您使用LightRAG Server提供的REST API。LightRAG Core通常用于嵌入式应用，或供希望进行研究与评估的学者使用。 + ### 一个简单程序 以下Python代码片段演示了如何初始化LightRAG、插入文本并进行查询： @@ -708,6 +761,8 @@ async def initialize_rag():
使用Faiss进行存储 +在使用Faiss向量数据库之前必须手工安装`faiss-cpu`或`faiss-gpu`。 + - 安装所需依赖： @@ -929,6 +984,94 @@ rag.insert_custom_kg(custom_kg)
+## 删除功能 + +LightRAG提供了全面的删除功能，允许您删除文档、实体和关系。 + +
+ 删除实体 + +您可以通过实体名称删除实体及其所有关联关系： + +```python +# 删除实体及其所有关系（同步版本） +rag.delete_by_entity("Google") + +# 异步版本 +await rag.adelete_by_entity("Google") +``` + +删除实体时会： +- 从知识图谱中移除该实体节点 +- 删除该实体的所有关联关系 +- 从向量数据库中移除相关的嵌入向量 +- 保持知识图谱的完整性 + +
+ +
+ 删除关系 + +您可以删除两个特定实体之间的关系： + +```python +# 删除两个实体之间的关系（同步版本） +rag.delete_by_relation("Google", "Gmail") + +# 异步版本 +await rag.adelete_by_relation("Google", "Gmail") +``` + +删除关系时会： +- 移除指定的关系边 +- 从向量数据库中删除关系的嵌入向量 +- 保留两个实体节点及其他关系 + +
+ +
+ 通过文档ID删除 + +您可以通过文档ID删除整个文档及其相关的所有知识： + +```python +# 通过文档ID删除（异步版本） +await rag.adelete_by_doc_id("doc-12345") +``` + +通过文档ID删除时的优化处理： +- **智能清理**：自动识别并删除仅属于该文档的实体和关系 +- **保留共享知识**：如果实体或关系在其他文档中也存在，则会保留并重新构建描述 +- **缓存优化**：清理相关的LLM缓存以减少存储开销 +- **增量重建**：从剩余文档重新构建受影响的实体和关系描述 + +删除过程包括： +1. 删除文档相关的所有文本块 +2. 识别仅属于该文档的实体和关系并删除 +3. 重新构建在其他文档中仍存在的实体和关系 +4. 更新所有相关的向量索引 +5. 清理文档状态记录 + +注意：通过文档ID删除是一个异步操作，因为它涉及复杂的知识图谱重构过程。 + +
+ +
+ 删除注意事项 + +**重要提醒：** + +1. **不可逆操作**：所有删除操作都是不可逆的，请谨慎使用 +2. **性能考虑**：删除大量数据时可能需要一些时间，特别是通过文档ID删除 +3. **数据一致性**：删除操作会自动维护知识图谱和向量数据库之间的一致性 +4. **备份建议**：在执行重要删除操作前建议备份数据 + +**批量删除建议：** +- 对于批量删除操作，建议使用异步方法以获得更好的性能 +- 大规模删除时，考虑分批进行以避免系统负载过高 + +
+ ## 实体合并
@@ -1000,6 +1143,120 @@ rag.merge_entities(
+## 多模态文档处理（RAG-Anything集成） + +LightRAG 现已与 [RAG-Anything](https://github.com/HKUDS/RAG-Anything) 实现无缝集成，这是一个专为 LightRAG 构建的**全能多模态文档处理RAG系统**。RAG-Anything 提供先进的解析和检索增强生成（RAG）能力，让您能够无缝处理多模态文档，并从各种文档格式中提取结构化内容——包括文本、图片、表格和公式——以集成到您的RAG流程中。 + +**主要特性：** +- **端到端多模态流程**：从文档摄取解析到智能多模态问答的完整工作流程 +- **通用文档支持**：无缝处理PDF、Office文档（DOC/DOCX/PPT/PPTX/XLS/XLSX）、图片和各种文件格式 +- **专业内容分析**：针对图片、表格、数学公式和异构内容类型的专用处理器 +- **多模态知识图谱**：自动实体提取和跨模态关系发现以增强理解 +- **混合智能检索**：覆盖文本和多模态内容的高级搜索能力，具备上下文理解 + +**快速开始：** +1. 安装RAG-Anything： + ```bash + pip install raganything + ``` +2. 处理多模态文档： +
+ RAGAnything 使用示例 + + ```python + import asyncio + from raganything import RAGAnything + from lightrag import LightRAG + from lightrag.llm.openai import openai_complete_if_cache, openai_embed + from lightrag.utils import EmbeddingFunc + import os + + async def load_existing_lightrag(): + # 首先，创建或加载现有的 LightRAG 实例 + lightrag_working_dir = "./existing_lightrag_storage" + + # 检查是否存在之前的 LightRAG 实例 + if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir): + print("✅ Found existing LightRAG instance, loading...") + else: + print("❌ No existing LightRAG instance found, will create new one") + + # 使用您的配置创建/加载 LightRAG 实例 + lightrag_instance = LightRAG( + working_dir=lightrag_working_dir, + llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key="your-api-key", + **kwargs, + ), + embedding_func=EmbeddingFunc( + embedding_dim=3072, + max_token_size=8192, + func=lambda texts: openai_embed( + texts, + model="text-embedding-3-large", + api_key=api_key, + base_url=base_url, + ), + ) + ) + + # 初始化存储（如果有现有数据，这将加载现有数据） + await lightrag_instance.initialize_storages() + + # 现在使用现有的 LightRAG 实例初始化 RAGAnything + rag = RAGAnything( + lightrag=lightrag_instance, # 传递现有的 LightRAG 实例 + # 仅需要视觉模型用于多模态处理 + vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache( + "gpt-4o", + "", + system_prompt=None, + history_messages=[], + messages=[ + {"role": "system", "content": system_prompt} if system_prompt else None, + {"role": "user", "content": [ + {"type": "text", "text": prompt}, + {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}} + ]} if image_data else {"role": "user", "content": prompt} + ], + api_key="your-api-key", + **kwargs, + ) if image_data else openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key="your-api-key", + **kwargs, + ) + # 注意：working_dir、llm_model_func、embedding_func 等都从 lightrag_instance 继承 + ) + + # 查询现有的知识库 + result = await rag.query_with_multimodal( + "What data has been processed in this LightRAG instance?", + mode="hybrid" + ) + print("Query result:", result) + + # 向现有的 LightRAG 实例添加新的多模态文档 + await rag.process_document_complete( + file_path="path/to/new/multimodal_document.pdf", + output_dir="./output" + ) + + if __name__ == "__main__": + asyncio.run(load_existing_lightrag()) + ``` + +
+ +如需详细文档和高级用法，请参阅 [RAG-Anything 仓库](https://github.com/HKUDS/RAG-Anything)。 + ## Token统计功能
diff --git a/README.md b/README.md index af6f50a7..e812e8df 100644 --- a/README.md +++ b/README.md @@ -1,45 +1,55 @@ -

🚀 LightRAG: Simple and Fast Retrieval-Augmented Generation

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+# 🚀 LightRAG: Simple and Fast Retrieval-Augmented Generation
HKUDS%2FLightRAG | Trendshift
-## 🎉 News +
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+ +--- +## 🎉 News +- [X] [2025.06.16]🎯📢Our team has released [RAG-Anything](https://github.com/HKUDS/RAG-Anything) an All-in-One Multimodal RAG System for seamless text, image, table, and equation processing. +- [X] [2025.06.05]🎯📢LightRAG now supports comprehensive multimodal data handling through [RAG-Anything](https://github.com/HKUDS/RAG-Anything) integration, enabling seamless document parsing and RAG capabilities across diverse formats including PDFs, images, Office documents, tables, and formulas. Please refer to the new [multimodal section](https://github.com/HKUDS/LightRAG/?tab=readme-ov-file#multimodal-document-processing-rag-anything-integration) for details. - [X] [2025.03.18]🎯📢LightRAG now supports citation functionality, enabling proper source attribution. - [X] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos. - [X] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models. @@ -79,6 +89,8 @@ The LightRAG Server is designed to provide Web UI and API support. The Web UI fa ```bash pip install "lightrag-hku[api]" +cp env.example .env +lightrag-server ``` * Installation from Source @@ -89,6 +101,8 @@ cd LightRAG # create a Python virtual enviroment if neccesary # Install in editable mode with API support pip install -e ".[api]" +cp env.example .env +lightrag-server ``` * Launching the LightRAG Server with Docker Compose @@ -147,6 +161,14 @@ For a streaming response implementation example, please see `examples/lightrag_o ## Programing with LightRAG Core +> If you would like to integrate LightRAG into your project, we recommend utilizing the REST API provided by the LightRAG Server. LightRAG Core is typically intended for embedded applications or for researchers who wish to conduct studies and evaluations. + +### ⚠️ Important: Initialization Requirements + +**LightRAG requires explicit initialization before use.** You must call both `await rag.initialize_storages()` and `await initialize_pipeline_status()` after creating a LightRAG instance, otherwise you will encounter errors like: +- `AttributeError: __aenter__` - if storages are not initialized +- `KeyError: 'history_messages'` - if pipeline status is not initialized + ### A Simple Program Use the below Python snippet to initialize LightRAG, insert text to it, and perform queries: @@ -171,8 +193,9 @@ async def initialize_rag(): embedding_func=openai_embed, llm_model_func=gpt_4o_mini_complete, ) - await rag.initialize_storages() - await initialize_pipeline_status() + # IMPORTANT: Both initialization calls are required! + await rag.initialize_storages() # Initialize storage backends + await initialize_pipeline_status() # Initialize processing pipeline return rag async def main(): @@ -182,7 +205,7 @@ async def main(): rag.insert("Your text") # Perform hybrid search - mode="hybrid" + mode = "hybrid" print( await rag.query( "What are the top themes in this story?", @@ -800,6 +823,8 @@ For production level scenarios you will most likely want to leverage an enterpri
Using Faiss for Storage +You must manually install faiss-cpu or faiss-gpu before using FAISS vector db. +Manually install `faiss-cpu` or `faiss-gpu` before using FAISS vector db. - Install the required dependencies: @@ -898,59 +923,66 @@ All operations are available in both synchronous and asynchronous versions. The ```python custom_kg = { - "chunks": [ - { - "content": "Alice and Bob are collaborating on quantum computing research.", - "source_id": "doc-1" - } - ], - "entities": [ - { - "entity_name": "Alice", - "entity_type": "person", - "description": "Alice is a researcher specializing in quantum physics.", - "source_id": "doc-1" - }, - { - "entity_name": "Bob", - "entity_type": "person", - "description": "Bob is a mathematician.", - "source_id": "doc-1" - }, - { - "entity_name": "Quantum Computing", - "entity_type": "technology", - "description": "Quantum computing utilizes quantum mechanical phenomena for computation.", - "source_id": "doc-1" - } - ], - "relationships": [ - { - "src_id": "Alice", - "tgt_id": "Bob", - "description": "Alice and Bob are research partners.", - "keywords": "collaboration research", - "weight": 1.0, - "source_id": "doc-1" - }, - { - "src_id": "Alice", - "tgt_id": "Quantum Computing", - "description": "Alice conducts research on quantum computing.", - "keywords": "research expertise", - "weight": 1.0, - "source_id": "doc-1" - }, - { - "src_id": "Bob", - "tgt_id": "Quantum Computing", - "description": "Bob researches quantum computing.", - "keywords": "research application", - "weight": 1.0, - "source_id": "doc-1" - } - ] -} + "chunks": [ + { + "content": "Alice and Bob are collaborating on quantum computing research.", + "source_id": "doc-1", + "file_path": "test_file", + } + ], + "entities": [ + { + "entity_name": "Alice", + "entity_type": "person", + "description": "Alice is a researcher specializing in quantum physics.", + "source_id": "doc-1", + "file_path": "test_file" + }, + { + "entity_name": "Bob", + "entity_type": "person", + "description": "Bob is a mathematician.", + "source_id": "doc-1", + "file_path": "test_file" + }, + { + "entity_name": "Quantum Computing", + "entity_type": "technology", + "description": "Quantum computing utilizes quantum mechanical phenomena for computation.", + "source_id": "doc-1", + "file_path": "test_file" + } + ], + "relationships": [ + { + "src_id": "Alice", + "tgt_id": "Bob", + "description": "Alice and Bob are research partners.", + "keywords": "collaboration research", + "weight": 1.0, + "source_id": "doc-1", + "file_path": "test_file" + }, + { + "src_id": "Alice", + "tgt_id": "Quantum Computing", + "description": "Alice conducts research on quantum computing.", + "keywords": "research expertise", + "weight": 1.0, + "source_id": "doc-1", + "file_path": "test_file" + }, + { + "src_id": "Bob", + "tgt_id": "Quantum Computing", + "description": "Bob researches quantum computing.", + "keywords": "research application", + "weight": 1.0, + "source_id": "doc-1", + "file_path": "test_file" + } + ] + } rag.insert_custom_kg(custom_kg) ``` @@ -971,6 +1003,89 @@ These operations maintain data consistency across both the graph database and ve
+## Delete Functions + +LightRAG provides comprehensive deletion capabilities, allowing you to delete documents, entities, and relationships. + +
+ Delete Entities + +You can delete entities by their name along with all associated relationships: + +```python +# Delete entity and all its relationships (synchronous version) +rag.delete_by_entity("Google") + +# Asynchronous version +await rag.adelete_by_entity("Google") +``` + +When deleting an entity: +- Removes the entity node from the knowledge graph +- Deletes all associated relationships +- Removes related embedding vectors from the vector database +- Maintains knowledge graph integrity + +
+ +
+ Delete Relations + +You can delete relationships between two specific entities: + +```python +# Delete relationship between two entities (synchronous version) +rag.delete_by_relation("Google", "Gmail") + +# Asynchronous version +await rag.adelete_by_relation("Google", "Gmail") +``` + +When deleting a relationship: +- Removes the specified relationship edge +- Deletes the relationship's embedding vector from the vector database +- Preserves both entity nodes and their other relationships + +
+ +
+ Delete by Document ID + +You can delete an entire document and all its related knowledge through document ID: + +```python +# Delete by document ID (asynchronous version) +await rag.adelete_by_doc_id("doc-12345") +``` + +Optimized processing when deleting by document ID: +- **Smart Cleanup**: Automatically identifies and removes entities and relationships that belong only to this document +- **Preserve Shared Knowledge**: If entities or relationships exist in other documents, they are preserved and their descriptions are rebuilt +- **Cache Optimization**: Clears related LLM cache to reduce storage overhead +- **Incremental Rebuilding**: Reconstructs affected entity and relationship descriptions from remaining documents + +The deletion process includes: +1. Delete all text chunks related to the document +2. Identify and delete entities and relationships that belong only to this document +3. Rebuild entities and relationships that still exist in other documents +4. Update all related vector indexes +5. Clean up document status records + +Note: Deletion by document ID is an asynchronous operation as it involves complex knowledge graph reconstruction processes. + +
+ +**Important Reminders:** + +1. **Irreversible Operations**: All deletion operations are irreversible, please use with caution +2. **Performance Considerations**: Deleting large amounts of data may take some time, especially deletion by document ID +3. **Data Consistency**: Deletion operations automatically maintain consistency between the knowledge graph and vector database +4. **Backup Recommendations**: Consider backing up data before performing important deletion operations + +**Batch Deletion Recommendations:** +- For batch deletion operations, consider using asynchronous methods for better performance +- For large-scale deletions, consider processing in batches to avoid excessive system load + ## Entity Merging
@@ -1042,6 +1157,119 @@ When merging entities:
+## Multimodal Document Processing (RAG-Anything Integration) + +LightRAG now seamlessly integrates with [RAG-Anything](https://github.com/HKUDS/RAG-Anything), a comprehensive **All-in-One Multimodal Document Processing RAG system** built specifically for LightRAG. RAG-Anything enables advanced parsing and retrieval-augmented generation (RAG) capabilities, allowing you to handle multimodal documents seamlessly and extract structured content—including text, images, tables, and formulas—from various document formats for integration into your RAG pipeline. + +**Key Features:** +- **End-to-End Multimodal Pipeline**: Complete workflow from document ingestion and parsing to intelligent multimodal query answering +- **Universal Document Support**: Seamless processing of PDFs, Office documents (DOC/DOCX/PPT/PPTX/XLS/XLSX), images, and diverse file formats +- **Specialized Content Analysis**: Dedicated processors for images, tables, mathematical equations, and heterogeneous content types +- **Multimodal Knowledge Graph**: Automatic entity extraction and cross-modal relationship discovery for enhanced understanding +- **Hybrid Intelligent Retrieval**: Advanced search capabilities spanning textual and multimodal content with contextual understanding + +**Quick Start:** +1. Install RAG-Anything: + ```bash + pip install raganything + ``` +2. Process multimodal documents: +
+ RAGAnything Usage Example + + ```python + import asyncio + from raganything import RAGAnything + from lightrag import LightRAG + from lightrag.llm.openai import openai_complete_if_cache, openai_embed + from lightrag.utils import EmbeddingFunc + import os + + async def load_existing_lightrag(): + # First, create or load an existing LightRAG instance + lightrag_working_dir = "./existing_lightrag_storage" + + # Check if previous LightRAG instance exists + if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir): + print("✅ Found existing LightRAG instance, loading...") + else: + print("❌ No existing LightRAG instance found, will create new one") + + # Create/Load LightRAG instance with your configurations + lightrag_instance = LightRAG( + working_dir=lightrag_working_dir, + llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key="your-api-key", + **kwargs, + ), + embedding_func=EmbeddingFunc( + embedding_dim=3072, + max_token_size=8192, + func=lambda texts: openai_embed( + texts, + model="text-embedding-3-large", + api_key=api_key, + base_url=base_url, + ), + ) + ) + + # Initialize storage (this will load existing data if available) + await lightrag_instance.initialize_storages() + + # Now initialize RAGAnything with the existing LightRAG instance + rag = RAGAnything( + lightrag=lightrag_instance, # Pass the existing LightRAG instance + # Only need vision model for multimodal processing + vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache( + "gpt-4o", + "", + system_prompt=None, + history_messages=[], + messages=[ + {"role": "system", "content": system_prompt} if system_prompt else None, + {"role": "user", "content": [ + {"type": "text", "text": prompt}, + {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}} + ]} if image_data else {"role": "user", "content": prompt} + ], + api_key="your-api-key", + **kwargs, + ) if image_data else openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key="your-api-key", + **kwargs, + ) + # Note: working_dir, llm_model_func, embedding_func, etc. are inherited from lightrag_instance + ) + + # Query the existing knowledge base + result = await rag.query_with_multimodal( + "What data has been processed in this LightRAG instance?", + mode="hybrid" + ) + print("Query result:", result) + + # Add new multimodal documents to the existing LightRAG instance + await rag.process_document_complete( + file_path="path/to/new/multimodal_document.pdf", + output_dir="./output" + ) + + if __name__ == "__main__": + asyncio.run(load_existing_lightrag()) + ``` +
+ +For detailed documentation and advanced usage, please refer to the [RAG-Anything repository](https://github.com/HKUDS/RAG-Anything). + ## Token Usage Tracking
@@ -1183,6 +1411,33 @@ Valid modes are:
+## Troubleshooting + +### Common Initialization Errors + +If you encounter these errors when using LightRAG: + +1. **`AttributeError: __aenter__`** + - **Cause**: Storage backends not initialized + - **Solution**: Call `await rag.initialize_storages()` after creating the LightRAG instance + +2. **`KeyError: 'history_messages'`** + - **Cause**: Pipeline status not initialized + - **Solution**: Call `await initialize_pipeline_status()` after initializing storages + +3. **Both errors in sequence** + - **Cause**: Neither initialization method was called + - **Solution**: Always follow this pattern: + ```python + rag = LightRAG(...) + await rag.initialize_storages() + await initialize_pipeline_status() + ``` + +### Model Switching Issues + +When switching between different embedding models, you must clear the data directory to avoid errors. The only file you may want to preserve is `kv_store_llm_response_cache.json` if you wish to retain the LLM cache. + ## LightRAG API The LightRAG Server is designed to provide Web UI and API support. **For more information about LightRAG Server, please refer to [LightRAG Server](./lightrag/api/README.md).** @@ -1234,7 +1489,7 @@ Output the results in the following structure: ### Batch Eval -To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `example/batch_eval.py`. +To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `reproduce/batch_eval.py`.
Prompt @@ -1448,7 +1703,47 @@ def extract_queries(file_path):
-## Star History +## 🔗 Related Projects + +*Ecosystem & Extensions* + +
+ + + + + + +
+ +
+ 📸 +
+ RAG-Anything
+ Multimodal RAG + + 		+ +
+ 🎥 +
+ VideoRAG
+ Extreme Long-Context Video RAG + + 		+ +
+ ✨ +
+ MiniRAG
+ Extremely Simple RAG + +
+
+ +--- + +## ⭐ Star History @@ -1458,15 +1753,22 @@ def extract_queries(file_path): -## Contribution +## 🤝 Contribution -Thank you to all our contributors! +
+ We thank all our contributors for their valuable contributions. +
- - - +
+ + + +
-## 🌟Citation +--- + + +## 📖 Citation ```python @article{guo2024lightrag, @@ -1479,4 +1781,31 @@ primaryClass={cs.IR} } ``` -**Thank you for your interest in our work!** +--- + +
+
+ +
+
+ + + + + + + + + +
+
+ +
+
+
+ ⭐ + Thank you for visiting LightRAG! + ⭐ +
+
+
diff --git a/docker-compose.yml b/docker-compose.yml index d620a4f0..da71faf8 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,10 +1,12 @@ services: lightrag: + container_name: lightrag + image: ghcr.io/hkuds/lightrag:latest build: context: . dockerfile: Dockerfile tags: - - lightrag:latest + - ghcr.io/hkuds/lightrag:latest ports: - "${PORT:-9621}:9621" volumes: diff --git a/docs/LightRAG_concurrent_explain.md b/docs/LightRAG_concurrent_explain.md new file mode 100644 index 00000000..5f55b0a9 --- /dev/null +++ b/docs/LightRAG_concurrent_explain.md @@ -0,0 +1,281 @@ +# LightRAG Multi-Document Processing: Concurrent Control Strategy Analysis + +LightRAG employs a multi-layered concurrent control strategy when processing multiple documents. This article provides an in-depth analysis of the concurrent control mechanisms at document level, chunk level, and LLM request level, helping you understand why specific concurrent behaviors occur. + +## Overview + +LightRAG's concurrent control is divided into three layers: + +1. **Document-level concurrency**: Controls the number of documents processed simultaneously +2. **Chunk-level concurrency**: Controls the number of chunks processed simultaneously within a single document +3. **LLM request-level concurrency**: Controls the global concurrent number of LLM requests + +## 1. Document-Level Concurrent Control + +**Control Parameter**: `max_parallel_insert` + +Document-level concurrency is controlled by the `max_parallel_insert` parameter, with a default value of 2. + +```python +# lightrag/lightrag.py +max_parallel_insert: int = field(default=int(os.getenv("MAX_PARALLEL_INSERT", 2))) +``` + +### Implementation Mechanism + +In the `apipeline_process_enqueue_documents` method, a semaphore is used to control document concurrency: + +```python +# lightrag/lightrag.py - apipeline_process_enqueue_documents method +async def process_document( + doc_id: str, + status_doc: DocProcessingStatus, + split_by_character: str | None, + split_by_character_only: bool, + pipeline_status: dict, + pipeline_status_lock: asyncio.Lock, + semaphore: asyncio.Semaphore, # Document-level semaphore +) -> None: + """Process single document""" + async with semaphore: # 🔥 Document-level concurrent control + # ... Process all chunks of a single document + +# Create document-level semaphore +semaphore = asyncio.Semaphore(self.max_parallel_insert) # Default 2 + +# Create processing tasks for each document +doc_tasks = [] +for doc_id, status_doc in to_process_docs.items(): + doc_tasks.append( + process_document( + doc_id, status_doc, split_by_character, split_by_character_only, + pipeline_status, pipeline_status_lock, semaphore + ) + ) + +# Wait for all documents to complete processing +await asyncio.gather(*doc_tasks) +``` + +## 2. Chunk-Level Concurrent Control + +**Control Parameter**: `llm_model_max_async` + +**Key Point**: Each document independently creates its own chunk semaphore! + +```python +# lightrag/lightrag.py +llm_model_max_async: int = field(default=int(os.getenv("MAX_ASYNC", 4))) +``` + +### Implementation Mechanism + +In the `extract_entities` function, **each document independently creates** its own chunk semaphore: + +```python +# lightrag/operate.py - extract_entities function +async def extract_entities(chunks: dict[str, TextChunkSchema], global_config: dict[str, str], ...): + # 🔥 Key: Each document independently creates this semaphore! + llm_model_max_async = global_config.get("llm_model_max_async", 4) + semaphore = asyncio.Semaphore(llm_model_max_async) # Chunk semaphore for each document + + async def _process_with_semaphore(chunk): + async with semaphore: # 🔥 Chunk concurrent control within document + return await _process_single_content(chunk) + + # Create tasks for each chunk + tasks = [] + for c in ordered_chunks: + task = asyncio.create_task(_process_with_semaphore(c)) + tasks.append(task) + + # Wait for all chunks to complete processing + done, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_EXCEPTION) + chunk_results = [task.result() for task in tasks] + return chunk_results +``` + +### Important Inference: System Overall Chunk Concurrency + +Since each document independently creates chunk semaphores, the theoretical chunk concurrency of the system is: + +**Theoretical Chunk Concurrency = max_parallel_insert × llm_model_max_async** + +For example: +- `max_parallel_insert = 2` (process 2 documents simultaneously) +- `llm_model_max_async = 4` (maximum 4 chunk concurrency per document) +- **Theoretical result**: Maximum 2 × 4 = 8 chunks simultaneously in "processing" state + +## 3. LLM Request-Level Concurrent Control (The Real Bottleneck) + +**Control Parameter**: `llm_model_max_async` (globally shared) + +**Key**: Although there might be 8 chunks "in processing", all LLM requests share the same global priority queue! + +```python +# lightrag/lightrag.py - __post_init__ method +self.llm_model_func = priority_limit_async_func_call(self.llm_model_max_async)( + partial( + self.llm_model_func, + hashing_kv=hashing_kv, + **self.llm_model_kwargs, + ) +) +# 🔥 Global LLM queue size = llm_model_max_async = 4 +``` + +### Priority Queue Implementation + +```python +# lightrag/utils.py - priority_limit_async_func_call function +def priority_limit_async_func_call(max_size: int, max_queue_size: int = 1000): + def final_decro(func): + queue = asyncio.PriorityQueue(maxsize=max_queue_size) + tasks = set() + + async def worker(): + """Worker that processes tasks in the priority queue""" + while not shutdown_event.is_set(): + try: + priority, count, future, args, kwargs = await asyncio.wait_for(queue.get(), timeout=1.0) + result = await func(*args, **kwargs) # 🔥 Actual LLM call + if not future.done(): + future.set_result(result) + except Exception as e: + # Error handling... + finally: + queue.task_done() + + # 🔥 Create fixed number of workers (max_size), this is the real concurrency limit + for _ in range(max_size): + task = asyncio.create_task(worker()) + tasks.add(task) +``` + +## 4. Chunk Internal Processing Mechanism (Serial) + +### Why Serial? + +Internal processing of each chunk strictly follows this serial execution order: + +```python +# lightrag/operate.py - _process_single_content function +async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]): + # Step 1: Initial entity extraction + hint_prompt = entity_extract_prompt.format(**{**context_base, "input_text": content}) + final_result = await use_llm_func_with_cache(hint_prompt, use_llm_func, ...) + + # Process initial extraction results + maybe_nodes, maybe_edges = await _process_extraction_result(final_result, chunk_key, file_path) + + # Step 2: Gleaning phase + for now_glean_index in range(entity_extract_max_gleaning): + # 🔥 Serial wait for gleaning results + glean_result = await use_llm_func_with_cache( + continue_prompt, use_llm_func, + llm_response_cache=llm_response_cache, + history_messages=history, cache_type="extract" + ) + + # Process gleaning results + glean_nodes, glean_edges = await _process_extraction_result(glean_result, chunk_key, file_path) + + # Merge results... + + # Step 3: Determine whether to continue loop + if now_glean_index == entity_extract_max_gleaning - 1: + break + + # 🔥 Serial wait for loop decision results + if_loop_result = await use_llm_func_with_cache( + if_loop_prompt, use_llm_func, + llm_response_cache=llm_response_cache, + history_messages=history, cache_type="extract" + ) + + if if_loop_result.strip().strip('"').strip("'").lower() != "yes": + break + + return maybe_nodes, maybe_edges +``` + +## 5. Complete Concurrent Hierarchy Diagram +![lightrag_indexing.png](assets%2Flightrag_indexing.png) + +### Chunk Internal Processing (Serial) +``` +Initial Extraction → Gleaning → Loop Decision → Complete +``` + +## 6. Real-World Scenario Analysis + +### Scenario 1: Single Document with Multiple Chunks +Assume 1 document with 6 chunks: + +- **Document level**: Only 1 document, not limited by `max_parallel_insert` +- **Chunk level**: Maximum 4 chunks processed simultaneously (limited by `llm_model_max_async=4`) +- **LLM level**: Global maximum 4 LLM requests concurrent + +**Expected behavior**: 4 chunks process concurrently, remaining 2 chunks wait. + +### Scenario 2: Multiple Documents with Multiple Chunks +Assume 3 documents, each with 10 chunks: + +- **Document level**: Maximum 2 documents processed simultaneously +- **Chunk level**: Maximum 4 chunks per document processed simultaneously +- **Theoretical Chunk concurrency**: 2 × 4 = 8 chunks processed simultaneously +- **Actual LLM concurrency**: Only 4 LLM requests actually execute + +**Actual state distribution**: +``` +# Possible system state: +Document 1: 4 chunks "processing" (2 executing LLM, 2 waiting for LLM response) +Document 2: 4 chunks "processing" (2 executing LLM, 2 waiting for LLM response) +Document 3: Waiting for document-level semaphore + +Total: +- 8 chunks in "processing" state +- 4 LLM requests actually executing +- 4 chunks waiting for LLM response +``` + +## 7. Performance Optimization Recommendations + +### Understanding the Bottleneck + +The real bottleneck is the global LLM queue, not the chunk semaphores! + +### Adjustment Strategies + +**Strategy 1: Increase LLM Concurrent Capacity** + +```bash +# Environment variable configuration +export MAX_PARALLEL_INSERT=2 # Keep document concurrency +export MAX_ASYNC=8 # 🔥 Increase LLM request concurrency +``` + +**Strategy 2: Balance Document and LLM Concurrency** + +```python +rag = LightRAG( + max_parallel_insert=3, # Moderately increase document concurrency + llm_model_max_async=12, # Significantly increase LLM concurrency + entity_extract_max_gleaning=0, # Reduce serial steps within chunks +) +``` + +## 8. Summary + +Key characteristics of LightRAG's multi-document concurrent processing mechanism: + +### Concurrent Layers +1. **Inter-document competition**: Controlled by `max_parallel_insert`, default 2 documents concurrent +2. **Theoretical Chunk concurrency**: Each document independently creates semaphores, total = max_parallel_insert × llm_model_max_async +3. **Actual LLM concurrency**: All chunks share global LLM queue, controlled by `llm_model_max_async` +4. **Intra-chunk serial**: Multiple LLM requests within each chunk execute strictly serially + +### Key Insights +- **Theoretical vs Actual**: System may have many chunks "in processing", but only few are actually executing LLM requests +- **Real Bottleneck**: Global LLM request queue is the performance bottleneck, not chunk semaphores +- **Optimization Focus**: Increasing `llm_model_max_async` is more effective than increasing `max_parallel_insert` diff --git a/docs/assets/lightrag_indexing.png b/docs/assets/lightrag_indexing.png new file mode 100644 index 00000000..6ef0491a Binary files /dev/null and b/docs/assets/lightrag_indexing.png differ diff --git a/docs/zh/LightRAG_concurrent_explain_zh.md b/docs/zh/LightRAG_concurrent_explain_zh.md new file mode 100644 index 00000000..d49a629c --- /dev/null +++ b/docs/zh/LightRAG_concurrent_explain_zh.md @@ -0,0 +1,277 @@ +# LightRAG 多文档并发控制机制详解 + +LightRAG 在处理多个文档时采用了多层次的并发控制策略。本文将深入分析文档级别、chunk级别和LLM请求级别的并发控制机制，帮助您理解为什么会出现特定的并发行为。 + +## 概述 + +LightRAG 的并发控制分为三个层次： + +1. 文档级别并发：控制同时处理的文档数量 +2. Chunk级别并发：控制单个文档内同时处理的chunk数量 +3. LLM请求级别并发：控制全局LLM请求的并发数量 + +## 1. 文档级别并发控制 + +**控制参数**：`max_parallel_insert` + +文档级别的并发由 `max_parallel_insert` 参数控制，默认值为2。 + +```python +# lightrag/lightrag.py +max_parallel_insert: int = field(default=int(os.getenv("MAX_PARALLEL_INSERT", 2))) +``` + +### 实现机制 + +在 `apipeline_process_enqueue_documents` 方法中，使用信号量控制文档并发： + +```python +# lightrag/lightrag.py - apipeline_process_enqueue_documents方法 +async def process_document( + doc_id: str, + status_doc: DocProcessingStatus, + split_by_character: str | None, + split_by_character_only: bool, + pipeline_status: dict, + pipeline_status_lock: asyncio.Lock, + semaphore: asyncio.Semaphore, # 文档级别信号量 +) -> None: + """Process single document""" + async with semaphore: # 🔥 文档级别并发控制 + # ... 处理单个文档的所有chunks + +# 创建文档级别信号量 +semaphore = asyncio.Semaphore(self.max_parallel_insert) # 默认2 + +# 为每个文档创建处理任务 +doc_tasks = [] +for doc_id, status_doc in to_process_docs.items(): + doc_tasks.append( + process_document( + doc_id, status_doc, split_by_character, split_by_character_only, + pipeline_status, pipeline_status_lock, semaphore + ) + ) + +# 等待所有文档处理完成 +await asyncio.gather(*doc_tasks) +``` + +## 2. Chunk级别并发控制 + +**控制参数**：`llm_model_max_async` + +**关键点**：每个文档都会独立创建自己的chunk信号量！ + +```python +# lightrag/lightrag.py +llm_model_max_async: int = field(default=int(os.getenv("MAX_ASYNC", 4))) +``` + +### 实现机制 + +在 `extract_entities` 函数中，**每个文档独立创建**自己的chunk信号量： + +```python +# lightrag/operate.py - extract_entities函数 +async def extract_entities(chunks: dict[str, TextChunkSchema], global_config: dict[str, str], ...): + # 🔥 关键：每个文档都会独立创建这个信号量！ + llm_model_max_async = global_config.get("llm_model_max_async", 4) + semaphore = asyncio.Semaphore(llm_model_max_async) # 每个文档的chunk信号量 + + async def _process_with_semaphore(chunk): + async with semaphore: # 🔥 文档内部的chunk并发控制 + return await _process_single_content(chunk) + + # 为每个chunk创建任务 + tasks = [] + for c in ordered_chunks: + task = asyncio.create_task(_process_with_semaphore(c)) + tasks.append(task) + + # 等待所有chunk处理完成 + done, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_EXCEPTION) + chunk_results = [task.result() for task in tasks] + return chunk_results +``` + +### 重要推论：系统整体Chunk并发数 + +由于每个文档独立创建chunk信号量，系统理论上的chunk并发数是： + +**理论Chunk并发数 = max_parallel_insert × llm_model_max_async** + +例如： +- `max_parallel_insert = 2`（同时处理2个文档） +- `llm_model_max_async = 4`（每个文档最多4个chunk并发） +- 理论结果：最多 2 × 4 = 8个chunk同时处于"处理中"状态 + +## 3. LLM请求级别并发控制（真正的瓶颈） + +**控制参数**：`llm_model_max_async`（全局共享） + +**关键**：尽管可能有8个chunk在"处理中"，但所有LLM请求共享同一个全局优先级队列！ + +```python +# lightrag/lightrag.py - __post_init__方法 +self.llm_model_func = priority_limit_async_func_call(self.llm_model_max_async)( + partial( + self.llm_model_func, + hashing_kv=hashing_kv, + **self.llm_model_kwargs, + ) +) +# 🔥 全局LLM队列大小 = llm_model_max_async = 4 +``` + +### 优先级队列实现 + +```python +# lightrag/utils.py - priority_limit_async_func_call函数 +def priority_limit_async_func_call(max_size: int, max_queue_size: int = 1000): + def final_decro(func): + queue = asyncio.PriorityQueue(maxsize=max_queue_size) + tasks = set() + + async def worker(): + """Worker that processes tasks in the priority queue""" + while not shutdown_event.is_set(): + try: + priority, count, future, args, kwargs = await asyncio.wait_for(queue.get(), timeout=1.0) + result = await func(*args, **kwargs) # 🔥 实际LLM调用 + if not future.done(): + future.set_result(result) + except Exception as e: + # 错误处理... + finally: + queue.task_done() + + # 🔥 创建固定数量的worker（max_size个），这是真正的并发限制 + for _ in range(max_size): + task = asyncio.create_task(worker()) + tasks.add(task) +``` + +## 4. Chunk内部处理机制（串行） + +### 为什么是串行？ + +每个chunk内部的处理严格按照以下顺序串行执行： + +```python +# lightrag/operate.py - _process_single_content函数 +async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]): + # 步骤1：初始实体提取 + hint_prompt = entity_extract_prompt.format(**{**context_base, "input_text": content}) + final_result = await use_llm_func_with_cache(hint_prompt, use_llm_func, ...) + + # 处理初始提取结果 + maybe_nodes, maybe_edges = await _process_extraction_result(final_result, chunk_key, file_path) + + # 步骤2：Gleaning（深挖）阶段 + for now_glean_index in range(entity_extract_max_gleaning): + # 🔥 串行等待gleaning结果 + glean_result = await use_llm_func_with_cache( + continue_prompt, use_llm_func, + llm_response_cache=llm_response_cache, + history_messages=history, cache_type="extract" + ) + + # 处理gleaning结果 + glean_nodes, glean_edges = await _process_extraction_result(glean_result, chunk_key, file_path) + + # 合并结果... + + # 步骤3：判断是否继续循环 + if now_glean_index == entity_extract_max_gleaning - 1: + break + + # 🔥 串行等待循环判断结果 + if_loop_result = await use_llm_func_with_cache( + if_loop_prompt, use_llm_func, + llm_response_cache=llm_response_cache, + history_messages=history, cache_type="extract" + ) + + if if_loop_result.strip().strip('"').strip("'").lower() != "yes": + break + + return maybe_nodes, maybe_edges +``` + +## 5. 完整的并发层次图 +![lightrag_indexing.png](..%2Fassets%2Flightrag_indexing.png) + + +## 6. 实际运行场景分析 + +### 场景1：单文档多Chunk +假设有1个文档，包含6个chunks： + +- 文档级别：只有1个文档，不受 `max_parallel_insert` 限制 +- Chunk级别：最多4个chunks同时处理（受 `llm_model_max_async=4` 限制） +- LLM级别：全局最多4个LLM请求并发 + +**预期行为**：4个chunks并发处理，剩余2个chunks等待。 + +### 场景2：多文档多Chunk +假设有3个文档，每个文档包含10个chunks： + +- 文档级别：最多2个文档同时处理 +- Chunk级别：每个文档最多4个chunks同时处理 +- 理论Chunk并发：2 × 4 = 8个chunks同时处理 +- 实际LLM并发：只有4个LLM请求真正执行 + +**实际状态分布**： +``` +# 可能的系统状态： +文档1: 4个chunks"处理中"（其中2个在执行LLM，2个在等待LLM响应） +文档2: 4个chunks"处理中"（其中2个在执行LLM，2个在等待LLM响应） +文档3: 等待文档级别信号量 + +总计： +- 8个chunks处于"处理中"状态 +- 4个LLM请求真正执行 +- 4个chunks等待LLM响应 +``` + +## 7. 性能优化建议 + +### 理解瓶颈 + +**真正的瓶颈是全局LLM队列，而不是chunk信号量！** + +### 调整策略 + +**策略1：提高LLM并发能力** + +```bash +# 环境变量配置 +export MAX_PARALLEL_INSERT=2 # 保持文档并发 +export MAX_ASYNC=8 # 🔥 增加LLM请求并发数 +``` + +**策略2：平衡文档和LLM并发** + +```python +rag = LightRAG( + max_parallel_insert=3, # 适度增加文档并发 + llm_model_max_async=12, # 大幅增加LLM并发 + entity_extract_max_gleaning=0, # 减少chunk内串行步骤 +) +``` + +## 8. 总结 + +LightRAG的多文档并发处理机制的关键特点： + +### 并发层次 +1. **文档间争抢**：受 `max_parallel_insert` 控制，默认2个文档并发 +2. **理论Chunk并发**：每个文档独立创建信号量，总数 = `max_parallel_insert × llm_model_max_async` +3. **实际LLM并发**：所有chunk共享全局LLM队列，受 `llm_model_max_async` 控制 +4. **单Chunk内串行**：每个chunk内的多个LLM请求严格串行执行 + +### 关键洞察 +- **理论vs实际**：系统可能有很多chunk在"处理中"，但只有少数在真正执行LLM请求 +- **真正瓶颈**：全局LLM请求队列是性能瓶颈，而不是chunk信号量 +- **优化重点**：提高 `llm_model_max_async` 比增加 `max_parallel_insert` 更有效 diff --git a/env.example b/env.example index 16b96ed9..f759ea92 100644 --- a/env.example +++ b/env.example @@ -1,6 +1,5 @@ ### This is sample file of .env - ### Server Configuration HOST=0.0.0.0 PORT=9621 @@ -51,13 +50,15 @@ OLLAMA_EMULATING_MODEL_TAG=latest # MAX_TOKEN_RELATION_DESC=4000 # MAX_TOKEN_ENTITY_DESC=4000 -### Entity and ralation summarization configuration +### Entity and relation summarization configuration ### Language: English, Chinese, French, German ... SUMMARY_LANGUAGE=English ### Number of duplicated entities/edges to trigger LLM re-summary on merge ( at least 3 is recommented) # FORCE_LLM_SUMMARY_ON_MERGE=6 ### Max tokens for entity/relations description after merge # MAX_TOKEN_SUMMARY=500 +### Maximum number of entity extraction attempts for ambiguous content +# MAX_GLEANING=1 ### Number of parallel processing documents(Less than MAX_ASYNC/2 is recommended) # MAX_PARALLEL_INSERT=2 @@ -74,16 +75,20 @@ TIMEOUT=240 TEMPERATURE=0 ### Max concurrency requests of LLM MAX_ASYNC=4 -### Max tokens send to LLM for entity relation summaries (less than context size of the model) +### MAX_TOKENS: max tokens send to LLM for entity relation summaries (less than context size of the model) +### MAX_TOKENS: set as num_ctx option for Ollama by API Server MAX_TOKENS=32768 -### LLM Binding type: openai, ollama, lollms +### LLM Binding type: openai, ollama, lollms, azure_openai LLM_BINDING=openai LLM_MODEL=gpt-4o LLM_BINDING_HOST=https://api.openai.com/v1 LLM_BINDING_API_KEY=your_api_key +### Optional for Azure +# AZURE_OPENAI_API_VERSION=2024-08-01-preview +# AZURE_OPENAI_DEPLOYMENT=gpt-4o ### Embedding Configuration -### Embedding Binding type: openai, ollama, lollms +### Embedding Binding type: openai, ollama, lollms, azure_openai EMBEDDING_BINDING=ollama EMBEDDING_MODEL=bge-m3:latest EMBEDDING_DIM=1024 @@ -91,26 +96,51 @@ EMBEDDING_BINDING_API_KEY=your_api_key # If the embedding service is deployed within the same Docker stack, use host.docker.internal instead of localhost EMBEDDING_BINDING_HOST=http://localhost:11434 ### Num of chunks send to Embedding in single request -# EMBEDDING_BATCH_NUM=32 +# EMBEDDING_BATCH_NUM=10 ### Max concurrency requests for Embedding # EMBEDDING_FUNC_MAX_ASYNC=16 ### Maximum tokens sent to Embedding for each chunk (no longer in use?) # MAX_EMBED_TOKENS=8192 +### Optional for Azure +# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large +# AZURE_EMBEDDING_API_VERSION=2023-05-15 +# AZURE_EMBEDDING_ENDPOINT=your_endpoint +# AZURE_EMBEDDING_API_KEY=your_api_key +########################### ### Data storage selection +########################### +### In-memory database with local file persistence(Recommended for small scale deployment) +# LIGHTRAG_KV_STORAGE=JsonKVStorage +# LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage +# LIGHTRAG_GRAPH_STORAGE=NetworkXStorage +# LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage +# LIGHTRAG_VECTOR_STORAGE=FaissVectorDBStorage +### PostgreSQL # LIGHTRAG_KV_STORAGE=PGKVStorage -# LIGHTRAG_VECTOR_STORAGE=PGVectorStorage # LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage +# LIGHTRAG_GRAPH_STORAGE=PGGraphStorage +# LIGHTRAG_VECTOR_STORAGE=PGVectorStorage +### MongoDB (Vector storage only available on Atlas Cloud) +# LIGHTRAG_KV_STORAGE=MongoKVStorage +# LIGHTRAG_DOC_STATUS_STORAGE=MongoDocStatusStorage +# LIGHTRAG_GRAPH_STORAGE=MongoGraphStorage +# LIGHTRAG_VECTOR_STORAGE=MongoVectorDBStorage +### Redis Storage (Recommended for production deployment) +# LIGHTRAG_KV_STORAGE=RedisKVStorage +# LIGHTRAG_DOC_STATUS_STORAGE=RedisDocStatusStorage +### Vector Storage (Recommended for production deployment) +# LIGHTRAG_VECTOR_STORAGE=MilvusVectorDBStorage +# LIGHTRAG_VECTOR_STORAGE=QdrantVectorDBStorage +### Graph Storage (Recommended for production deployment) # LIGHTRAG_GRAPH_STORAGE=Neo4JStorage -### TiDB Configuration (Deprecated) -# TIDB_HOST=localhost -# TIDB_PORT=4000 -# TIDB_USER=your_username -# TIDB_PASSWORD='your_password' -# TIDB_DATABASE=your_database -### separating all data from difference Lightrag instances(deprecating) -# TIDB_WORKSPACE=default +#################################################################### +### Default workspace for all storage types +### For the purpose of isolation of data for each LightRAG instance +### Valid characters: a-z, A-Z, 0-9, and _ +#################################################################### +# WORKSPACE=doc— ### PostgreSQL Configuration POSTGRES_HOST=localhost @@ -119,30 +149,19 @@ POSTGRES_USER=your_username POSTGRES_PASSWORD='your_password' POSTGRES_DATABASE=your_database POSTGRES_MAX_CONNECTIONS=12 -### separating all data from difference Lightrag instances(deprecating) -# POSTGRES_WORKSPACE=default +# POSTGRES_WORKSPACE=forced_workspace_name ### Neo4j Configuration NEO4J_URI=neo4j+s://xxxxxxxx.databases.neo4j.io NEO4J_USERNAME=neo4j NEO4J_PASSWORD='your_password' - -### Independent AGM Configuration(not for AMG embedded in PostreSQL) -# AGE_POSTGRES_DB= -# AGE_POSTGRES_USER= -# AGE_POSTGRES_PASSWORD= -# AGE_POSTGRES_HOST= -# AGE_POSTGRES_PORT=8529 - -# AGE Graph Name(apply to PostgreSQL and independent AGM) -### AGE_GRAPH_NAME is precated -# AGE_GRAPH_NAME=lightrag +# NEO4J_WORKSPACE=forced_workspace_name ### MongoDB Configuration MONGO_URI=mongodb://root:root@localhost:27017/ +#MONGO_URI=mongodb+srv://xxxx MONGO_DATABASE=LightRAG -### separating all data from difference Lightrag instances(deprecating) -# MONGODB_GRAPH=false +# MONGODB_WORKSPACE=forced_workspace_name ### Milvus Configuration MILVUS_URI=http://localhost:19530 @@ -150,10 +169,13 @@ MILVUS_DB_NAME=lightrag # MILVUS_USER=root # MILVUS_PASSWORD=your_password # MILVUS_TOKEN=your_token +# MILVUS_WORKSPACE=forced_workspace_name ### Qdrant -QDRANT_URL=http://localhost:16333 +QDRANT_URL=http://localhost:6333 # QDRANT_API_KEY=your-api-key +# QDRANT_WORKSPACE=forced_workspace_name ### Redis REDIS_URI=redis://localhost:6379 +# REDIS_WORKSPACE=forced_workspace_name diff --git a/examples/graph_visual_with_neo4j.py b/examples/graph_visual_with_neo4j.py index 35ef85a8..1cd2e7a3 100644 --- a/examples/graph_visual_with_neo4j.py +++ b/examples/graph_visual_with_neo4j.py @@ -1,6 +1,6 @@ import os import json -from lightrag.utils import xml_to_json +import xml.etree.ElementTree as ET from neo4j import GraphDatabase # Constants @@ -14,6 +14,66 @@ NEO4J_USERNAME = "neo4j" NEO4J_PASSWORD = "your_password" +def xml_to_json(xml_file): + try: + tree = ET.parse(xml_file) + root = tree.getroot() + + # Print the root element's tag and attributes to confirm the file has been correctly loaded + print(f"Root element: {root.tag}") + print(f"Root attributes: {root.attrib}") + + data = {"nodes": [], "edges": []} + + # Use namespace + namespace = {"": "http://graphml.graphdrawing.org/xmlns"} + + for node in root.findall(".//node", namespace): + node_data = { + "id": node.get("id").strip('"'), + "entity_type": node.find("./data[@key='d1']", namespace).text.strip('"') + if node.find("./data[@key='d1']", namespace) is not None + else "", + "description": node.find("./data[@key='d2']", namespace).text + if node.find("./data[@key='d2']", namespace) is not None + else "", + "source_id": node.find("./data[@key='d3']", namespace).text + if node.find("./data[@key='d3']", namespace) is not None + else "", + } + data["nodes"].append(node_data) + + for edge in root.findall(".//edge", namespace): + edge_data = { + "source": edge.get("source").strip('"'), + "target": edge.get("target").strip('"'), + "weight": float(edge.find("./data[@key='d5']", namespace).text) + if edge.find("./data[@key='d5']", namespace) is not None + else 0.0, + "description": edge.find("./data[@key='d6']", namespace).text + if edge.find("./data[@key='d6']", namespace) is not None + else "", + "keywords": edge.find("./data[@key='d7']", namespace).text + if edge.find("./data[@key='d7']", namespace) is not None + else "", + "source_id": edge.find("./data[@key='d8']", namespace).text + if edge.find("./data[@key='d8']", namespace) is not None + else "", + } + data["edges"].append(edge_data) + + # Print the number of nodes and edges found + print(f"Found {len(data['nodes'])} nodes and {len(data['edges'])} edges") + + return data + except ET.ParseError as e: + print(f"Error parsing XML file: {e}") + return None + except Exception as e: + print(f"An error occurred: {e}") + return None + + def convert_xml_to_json(xml_path, output_path): """Converts XML file to JSON and saves the output.""" if not os.path.exists(xml_path): diff --git a/examples/modalprocessors_example.py b/examples/modalprocessors_example.py new file mode 100644 index 00000000..b25c12c2 --- /dev/null +++ b/examples/modalprocessors_example.py @@ -0,0 +1,229 @@ +""" +Example of directly using modal processors + +This example demonstrates how to use LightRAG's modal processors directly without going through MinerU. +""" + +import asyncio +import argparse +from lightrag.llm.openai import openai_complete_if_cache, openai_embed +from lightrag.kg.shared_storage import initialize_pipeline_status +from lightrag import LightRAG +from lightrag.utils import EmbeddingFunc +from raganything.modalprocessors import ( + ImageModalProcessor, + TableModalProcessor, + EquationModalProcessor, +) + +WORKING_DIR = "./rag_storage" + + +def get_llm_model_func(api_key: str, base_url: str = None): + return ( + lambda prompt, + system_prompt=None, + history_messages=[], + **kwargs: openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=api_key, + base_url=base_url, + **kwargs, + ) + ) + + +def get_vision_model_func(api_key: str, base_url: str = None): + return ( + lambda prompt, + system_prompt=None, + history_messages=[], + image_data=None, + **kwargs: openai_complete_if_cache( + "gpt-4o", + "", + system_prompt=None, + history_messages=[], + messages=[ + {"role": "system", "content": system_prompt} if system_prompt else None, + { + "role": "user", + "content": [ + {"type": "text", "text": prompt}, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{image_data}" + }, + }, + ], + } + if image_data + else {"role": "user", "content": prompt}, + ], + api_key=api_key, + base_url=base_url, + **kwargs, + ) + if image_data + else openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=api_key, + base_url=base_url, + **kwargs, + ) + ) + + +async def process_image_example(lightrag: LightRAG, vision_model_func): + """Example of processing an image""" + # Create image processor + image_processor = ImageModalProcessor( + lightrag=lightrag, modal_caption_func=vision_model_func + ) + + # Prepare image content + image_content = { + "img_path": "image.jpg", + "img_caption": ["Example image caption"], + "img_footnote": ["Example image footnote"], + } + + # Process image + description, entity_info = await image_processor.process_multimodal_content( + modal_content=image_content, + content_type="image", + file_path="image_example.jpg", + entity_name="Example Image", + ) + + print("Image Processing Results:") + print(f"Description: {description}") + print(f"Entity Info: {entity_info}") + + +async def process_table_example(lightrag: LightRAG, llm_model_func): + """Example of processing a table""" + # Create table processor + table_processor = TableModalProcessor( + lightrag=lightrag, modal_caption_func=llm_model_func + ) + + # Prepare table content + table_content = { + "table_body": """ + | Name | Age | Occupation | + |------|-----|------------| + | John | 25 | Engineer | + | Mary | 30 | Designer | + """, + "table_caption": ["Employee Information Table"], + "table_footnote": ["Data updated as of 2024"], + } + + # Process table + description, entity_info = await table_processor.process_multimodal_content( + modal_content=table_content, + content_type="table", + file_path="table_example.md", + entity_name="Employee Table", + ) + + print("\nTable Processing Results:") + print(f"Description: {description}") + print(f"Entity Info: {entity_info}") + + +async def process_equation_example(lightrag: LightRAG, llm_model_func): + """Example of processing a mathematical equation""" + # Create equation processor + equation_processor = EquationModalProcessor( + lightrag=lightrag, modal_caption_func=llm_model_func + ) + + # Prepare equation content + equation_content = {"text": "E = mc^2", "text_format": "LaTeX"} + + # Process equation + description, entity_info = await equation_processor.process_multimodal_content( + modal_content=equation_content, + content_type="equation", + file_path="equation_example.txt", + entity_name="Mass-Energy Equivalence", + ) + + print("\nEquation Processing Results:") + print(f"Description: {description}") + print(f"Entity Info: {entity_info}") + + +async def initialize_rag(api_key: str, base_url: str = None): + rag = LightRAG( + working_dir=WORKING_DIR, + embedding_func=EmbeddingFunc( + embedding_dim=3072, + max_token_size=8192, + func=lambda texts: openai_embed( + texts, + model="text-embedding-3-large", + api_key=api_key, + base_url=base_url, + ), + ), + llm_model_func=lambda prompt, + system_prompt=None, + history_messages=[], + **kwargs: openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=api_key, + base_url=base_url, + **kwargs, + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + """Main function to run the example""" + parser = argparse.ArgumentParser(description="Modal Processors Example") + parser.add_argument("--api-key", required=True, help="OpenAI API key") + parser.add_argument("--base-url", help="Optional base URL for API") + parser.add_argument( + "--working-dir", "-w", default=WORKING_DIR, help="Working directory path" + ) + + args = parser.parse_args() + + # Run examples + asyncio.run(main_async(args.api_key, args.base_url)) + + +async def main_async(api_key: str, base_url: str = None): + # Initialize LightRAG + lightrag = await initialize_rag(api_key, base_url) + + # Get model functions + llm_model_func = get_llm_model_func(api_key, base_url) + vision_model_func = get_vision_model_func(api_key, base_url) + + # Run examples + await process_image_example(lightrag, vision_model_func) + await process_table_example(lightrag, llm_model_func) + await process_equation_example(lightrag, llm_model_func) + + +if __name__ == "__main__": + main() diff --git a/examples/raganything_example.py b/examples/raganything_example.py new file mode 100644 index 00000000..f61274a8 --- /dev/null +++ b/examples/raganything_example.py @@ -0,0 +1,286 @@ +#!/usr/bin/env python +""" +Example script demonstrating the integration of MinerU parser with RAGAnything + +This example shows how to: +1. Process parsed documents with RAGAnything +2. Perform multimodal queries on the processed documents +3. Handle different types of content (text, images, tables) +""" + +import os +import argparse +import asyncio +import logging +import logging.config +from pathlib import Path + +# Add project root directory to Python path +import sys + +sys.path.append(str(Path(__file__).parent.parent)) + +from lightrag.llm.openai import openai_complete_if_cache, openai_embed +from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug +from raganything import RAGAnything, RAGAnythingConfig + + +def configure_logging(): + """Configure logging for the application""" + # Get log directory path from environment variable or use current directory + log_dir = os.getenv("LOG_DIR", os.getcwd()) + log_file_path = os.path.abspath(os.path.join(log_dir, "raganything_example.log")) + + print(f"\nRAGAnything example log file: {log_file_path}\n") + os.makedirs(os.path.dirname(log_dir), exist_ok=True) + + # Get log file max size and backup count from environment variables + log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760)) # Default 10MB + log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5)) # Default 5 backups + + logging.config.dictConfig( + { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "format": "%(levelname)s: %(message)s", + }, + "detailed": { + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + }, + }, + "handlers": { + "console": { + "formatter": "default", + "class": "logging.StreamHandler", + "stream": "ext://sys.stderr", + }, + "file": { + "formatter": "detailed", + "class": "logging.handlers.RotatingFileHandler", + "filename": log_file_path, + "maxBytes": log_max_bytes, + "backupCount": log_backup_count, + "encoding": "utf-8", + }, + }, + "loggers": { + "lightrag": { + "handlers": ["console", "file"], + "level": "INFO", + "propagate": False, + }, + }, + } + ) + + # Set the logger level to INFO + logger.setLevel(logging.INFO) + # Enable verbose debug if needed + set_verbose_debug(os.getenv("VERBOSE", "false").lower() == "true") + + +async def process_with_rag( + file_path: str, + output_dir: str, + api_key: str, + base_url: str = None, + working_dir: str = None, +): + """ + Process document with RAGAnything + + Args: + file_path: Path to the document + output_dir: Output directory for RAG results + api_key: OpenAI API key + base_url: Optional base URL for API + working_dir: Working directory for RAG storage + """ + try: + # Create RAGAnything configuration + config = RAGAnythingConfig( + working_dir=working_dir or "./rag_storage", + mineru_parse_method="auto", + enable_image_processing=True, + enable_table_processing=True, + enable_equation_processing=True, + ) + + # Define LLM model function + def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs): + return openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=api_key, + base_url=base_url, + **kwargs, + ) + + # Define vision model function for image processing + def vision_model_func( + prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs + ): + if image_data: + return openai_complete_if_cache( + "gpt-4o", + "", + system_prompt=None, + history_messages=[], + messages=[ + {"role": "system", "content": system_prompt} + if system_prompt + else None, + { + "role": "user", + "content": [ + {"type": "text", "text": prompt}, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{image_data}" + }, + }, + ], + } + if image_data + else {"role": "user", "content": prompt}, + ], + api_key=api_key, + base_url=base_url, + **kwargs, + ) + else: + return llm_model_func(prompt, system_prompt, history_messages, **kwargs) + + # Define embedding function + embedding_func = EmbeddingFunc( + embedding_dim=3072, + max_token_size=8192, + func=lambda texts: openai_embed( + texts, + model="text-embedding-3-large", + api_key=api_key, + base_url=base_url, + ), + ) + + # Initialize RAGAnything with new dataclass structure + rag = RAGAnything( + config=config, + llm_model_func=llm_model_func, + vision_model_func=vision_model_func, + embedding_func=embedding_func, + ) + + # Process document + await rag.process_document_complete( + file_path=file_path, output_dir=output_dir, parse_method="auto" + ) + + # Example queries - demonstrating different query approaches + logger.info("\nQuerying processed document:") + + # 1. Pure text queries using aquery() + text_queries = [ + "What is the main content of the document?", + "What are the key topics discussed?", + ] + + for query in text_queries: + logger.info(f"\n[Text Query]: {query}") + result = await rag.aquery(query, mode="hybrid") + logger.info(f"Answer: {result}") + + # 2. Multimodal query with specific multimodal content using aquery_with_multimodal() + logger.info( + "\n[Multimodal Query]: Analyzing performance data in context of document" + ) + multimodal_result = await rag.aquery_with_multimodal( + "Compare this performance data with any similar results mentioned in the document", + multimodal_content=[ + { + "type": "table", + "table_data": """Method,Accuracy,Processing_Time + RAGAnything,95.2%,120ms + Traditional_RAG,87.3%,180ms + Baseline,82.1%,200ms""", + "table_caption": "Performance comparison results", + } + ], + mode="hybrid", + ) + logger.info(f"Answer: {multimodal_result}") + + # 3. Another multimodal query with equation content + logger.info("\n[Multimodal Query]: Mathematical formula analysis") + equation_result = await rag.aquery_with_multimodal( + "Explain this formula and relate it to any mathematical concepts in the document", + multimodal_content=[ + { + "type": "equation", + "latex": "F1 = 2 \\cdot \\frac{precision \\cdot recall}{precision + recall}", + "equation_caption": "F1-score calculation formula", + } + ], + mode="hybrid", + ) + logger.info(f"Answer: {equation_result}") + + except Exception as e: + logger.error(f"Error processing with RAG: {str(e)}") + import traceback + + logger.error(traceback.format_exc()) + + +def main(): + """Main function to run the example""" + parser = argparse.ArgumentParser(description="MinerU RAG Example") + parser.add_argument("file_path", help="Path to the document to process") + parser.add_argument( + "--working_dir", "-w", default="./rag_storage", help="Working directory path" + ) + parser.add_argument( + "--output", "-o", default="./output", help="Output directory path" + ) + parser.add_argument( + "--api-key", + default=os.getenv("OPENAI_API_KEY"), + help="OpenAI API key (defaults to OPENAI_API_KEY env var)", + ) + parser.add_argument("--base-url", help="Optional base URL for API") + + args = parser.parse_args() + + # Check if API key is provided + if not args.api_key: + logger.error("Error: OpenAI API key is required") + logger.error("Set OPENAI_API_KEY environment variable or use --api-key option") + return + + # Create output directory if specified + if args.output: + os.makedirs(args.output, exist_ok=True) + + # Process with RAG + asyncio.run( + process_with_rag( + args.file_path, args.output, args.api_key, args.base_url, args.working_dir + ) + ) + + +if __name__ == "__main__": + # Configure logging first + configure_logging() + + print("RAGAnything Example") + print("=" * 30) + print("Processing document with multimodal RAG pipeline") + print("=" * 30) + + main() diff --git a/examples/unofficial-sample/copy_llm_cache_to_another_storage.py b/examples/unofficial-sample/copy_llm_cache_to_another_storage.py index 60fa6192..1671b5d5 100644 --- a/examples/unofficial-sample/copy_llm_cache_to_another_storage.py +++ b/examples/unofficial-sample/copy_llm_cache_to_another_storage.py @@ -52,18 +52,23 @@ async def copy_from_postgres_to_json(): embedding_func=None, ) + # Get all cache data using the new flattened structure + all_data = await from_llm_response_cache.get_all() + + # Convert flattened data to hierarchical structure for JsonKVStorage kv = {} - for c_id in await from_llm_response_cache.all_keys(): - print(f"Copying {c_id}") - workspace = c_id["workspace"] - mode = c_id["mode"] - _id = c_id["id"] - postgres_db.workspace = workspace - obj = await from_llm_response_cache.get_by_mode_and_id(mode, _id) - if mode not in kv: - kv[mode] = {} - kv[mode][_id] = obj[_id] - print(f"Object {obj}") + for flattened_key, cache_entry in all_data.items(): + # Parse flattened key: {mode}:{cache_type}:{hash} + parts = flattened_key.split(":", 2) + if len(parts) == 3: + mode, cache_type, hash_value = parts + if mode not in kv: + kv[mode] = {} + kv[mode][hash_value] = cache_entry + print(f"Copying {flattened_key} -> {mode}[{hash_value}]") + else: + print(f"Skipping invalid key format: {flattened_key}") + await to_llm_response_cache.upsert(kv) await to_llm_response_cache.index_done_callback() print("Mission accomplished!") @@ -85,13 +90,24 @@ async def copy_from_json_to_postgres(): db=postgres_db, ) - for mode in await from_llm_response_cache.all_keys(): - print(f"Copying {mode}") - caches = await from_llm_response_cache.get_by_id(mode) - for k, v in caches.items(): - item = {mode: {k: v}} - print(f"\tCopying {item}") - await to_llm_response_cache.upsert(item) + # Get all cache data from JsonKVStorage (hierarchical structure) + all_data = await from_llm_response_cache.get_all() + + # Convert hierarchical data to flattened structure for PGKVStorage + flattened_data = {} + for mode, mode_data in all_data.items(): + print(f"Processing mode: {mode}") + for hash_value, cache_entry in mode_data.items(): + # Determine cache_type from cache entry or use default + cache_type = cache_entry.get("cache_type", "extract") + # Create flattened key: {mode}:{cache_type}:{hash} + flattened_key = f"{mode}:{cache_type}:{hash_value}" + flattened_data[flattened_key] = cache_entry + print(f"\tConverting {mode}[{hash_value}] -> {flattened_key}") + + # Upsert the flattened data + await to_llm_response_cache.upsert(flattened_data) + print("Mission accomplished!") if __name__ == "__main__": diff --git a/examples/unofficial-sample/lightrag_llamaindex_litellm_demo.py b/examples/unofficial-sample/lightrag_llamaindex_litellm_demo.py index 6e738628..3d0c69db 100644 --- a/examples/unofficial-sample/lightrag_llamaindex_litellm_demo.py +++ b/examples/unofficial-sample/lightrag_llamaindex_litellm_demo.py @@ -53,7 +53,6 @@ async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwar prompt, system_prompt=system_prompt, history_messages=history_messages, - **kwargs, ) return response except Exception as e: diff --git a/examples/unofficial-sample/lightrag_llamaindex_litellm_opik_demo.py b/examples/unofficial-sample/lightrag_llamaindex_litellm_opik_demo.py new file mode 100644 index 00000000..700f6209 --- /dev/null +++ b/examples/unofficial-sample/lightrag_llamaindex_litellm_opik_demo.py @@ -0,0 +1,155 @@ +import os +from lightrag import LightRAG, QueryParam +from lightrag.llm.llama_index_impl import ( + llama_index_complete_if_cache, + llama_index_embed, +) +from lightrag.utils import EmbeddingFunc +from llama_index.llms.litellm import LiteLLM +from llama_index.embeddings.litellm import LiteLLMEmbedding +import asyncio +import nest_asyncio + +nest_asyncio.apply() + +from lightrag.kg.shared_storage import initialize_pipeline_status + +# Configure working directory +WORKING_DIR = "./index_default" +print(f"WORKING_DIR: {WORKING_DIR}") + +# Model configuration +LLM_MODEL = os.environ.get("LLM_MODEL", "gemma-3-4b") +print(f"LLM_MODEL: {LLM_MODEL}") +EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "arctic-embed") +print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}") +EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192)) +print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}") + +# LiteLLM configuration +LITELLM_URL = os.environ.get("LITELLM_URL", "http://localhost:4000") +print(f"LITELLM_URL: {LITELLM_URL}") +LITELLM_KEY = os.environ.get("LITELLM_KEY", "sk-4JdvGFKqSA3S0k_5p0xufw") + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +# Initialize LLM function +async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs): + try: + # Initialize LiteLLM if not in kwargs + if "llm_instance" not in kwargs: + llm_instance = LiteLLM( + model=f"openai/{LLM_MODEL}", # Format: "provider/model_name" + api_base=LITELLM_URL, + api_key=LITELLM_KEY, + temperature=0.7, + ) + kwargs["llm_instance"] = llm_instance + + chat_kwargs = {} + chat_kwargs["litellm_params"] = { + "metadata": { + "opik": { + "project_name": "lightrag_llamaindex_litellm_opik_demo", + "tags": ["lightrag", "litellm"], + } + } + } + + response = await llama_index_complete_if_cache( + kwargs["llm_instance"], + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + chat_kwargs=chat_kwargs, + ) + return response + except Exception as e: + print(f"LLM request failed: {str(e)}") + raise + + +# Initialize embedding function +async def embedding_func(texts): + try: + embed_model = LiteLLMEmbedding( + model_name=f"openai/{EMBEDDING_MODEL}", + api_base=LITELLM_URL, + api_key=LITELLM_KEY, + ) + return await llama_index_embed(texts, embed_model=embed_model) + except Exception as e: + print(f"Embedding failed: {str(e)}") + raise + + +# Get embedding dimension +async def get_embedding_dim(): + test_text = ["This is a test sentence."] + embedding = await embedding_func(test_text) + embedding_dim = embedding.shape[1] + print(f"embedding_dim={embedding_dim}") + return embedding_dim + + +async def initialize_rag(): + embedding_dimension = await get_embedding_dim() + + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=embedding_dimension, + max_token_size=EMBEDDING_MAX_TOKEN_SIZE, + func=embedding_func, + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + # Initialize RAG instance + rag = asyncio.run(initialize_rag()) + + # Insert example text + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + # Test different query modes + print("\nNaive Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="naive") + ) + ) + + print("\nLocal Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="local") + ) + ) + + print("\nGlobal Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="global") + ) + ) + + print("\nHybrid Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="hybrid") + ) + ) + + +if __name__ == "__main__": + main() diff --git a/examples/unofficial-sample/lightrag_openai_neo4j_milvus_redis_demo.py b/examples/unofficial-sample/lightrag_openai_neo4j_milvus_redis_demo.py index cac0133d..38d89218 100644 --- a/examples/unofficial-sample/lightrag_openai_neo4j_milvus_redis_demo.py +++ b/examples/unofficial-sample/lightrag_openai_neo4j_milvus_redis_demo.py @@ -18,12 +18,12 @@ os.environ["REDIS_URI"] = "redis://localhost:6379" # neo4j BATCH_SIZE_NODES = 500 BATCH_SIZE_EDGES = 100 -os.environ["NEO4J_URI"] = "bolt://117.50.173.35:7687" +os.environ["NEO4J_URI"] = "neo4j://localhost:7687" os.environ["NEO4J_USERNAME"] = "neo4j" os.environ["NEO4J_PASSWORD"] = "12345678" # milvus -os.environ["MILVUS_URI"] = "http://117.50.173.35:19530" +os.environ["MILVUS_URI"] = "http://localhost:19530" os.environ["MILVUS_USER"] = "root" os.environ["MILVUS_PASSWORD"] = "Milvus" os.environ["MILVUS_DB_NAME"] = "lightrag" diff --git a/k8s-deploy/README-zh.md b/k8s-deploy/README-zh.md new file mode 100644 index 00000000..e5eb697d --- /dev/null +++ b/k8s-deploy/README-zh.md @@ -0,0 +1,191 @@ +# LightRAG Helm Chart + +这是用于在Kubernetes集群上部署LightRAG服务的Helm chart。 + +LightRAG有两种推荐的部署方法： +1. **轻量级部署**：使用内置轻量级存储，适合测试和小规模使用 +2. **生产环境部署**：使用外部数据库（如PostgreSQL和Neo4J），适合生产环境和大规模使用 + +> 如果您想要部署过程的视频演示，可以查看[bilibili](https://www.bilibili.com/video/BV1bUJazBEq2/)上的视频教程，对于喜欢视觉指导的用户可能会有所帮助。 + +## 前提条件 + +确保安装和配置了以下工具： + +* **Kubernetes集群** + * 需要一个运行中的Kubernetes集群。 + * 对于本地开发或演示，可以使用[Minikube](https://minikube.sigs.k8s.io/docs/start/)（需要≥2个CPU，≥4GB内存，以及Docker/VM驱动支持）。 + * 任何标准的云端或本地Kubernetes集群（EKS、GKE、AKS等）也可以使用。 + +* **kubectl** + * Kubernetes命令行工具，用于管理集群。 + * 按照官方指南安装：[安装和设置kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl)。 + +* **Helm**（v3.x+） + * Kubernetes包管理器，用于安装LightRAG。 + * 通过官方指南安装：[安装Helm](https://helm.sh/docs/intro/install/)。 + +## 轻量级部署（无需外部数据库） + +这种部署选项使用内置的轻量级存储组件，非常适合测试、演示或小规模使用场景。无需外部数据库配置。 + +您可以使用提供的便捷脚本或直接使用Helm命令部署LightRAG。两种方法都配置了`lightrag/values.yaml`文件中定义的相同环境变量。 + +### 使用便捷脚本（推荐）： + +```bash +export OPENAI_API_BASE=<您的OPENAI_API_BASE> +export OPENAI_API_KEY=<您的OPENAI_API_KEY> +bash ./install_lightrag_dev.sh +``` + +### 或直接使用Helm： + +```bash +# 您可以覆盖任何想要的环境参数 +helm upgrade --install lightrag ./lightrag \ + --namespace rag \ + --set-string env.LIGHTRAG_KV_STORAGE=JsonKVStorage \ + --set-string env.LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage \ + --set-string env.LIGHTRAG_GRAPH_STORAGE=NetworkXStorage \ + --set-string env.LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage \ + --set-string env.LLM_BINDING=openai \ + --set-string env.LLM_MODEL=gpt-4o-mini \ + --set-string env.LLM_BINDING_HOST=$OPENAI_API_BASE \ + --set-string env.LLM_BINDING_API_KEY=$OPENAI_API_KEY \ + --set-string env.EMBEDDING_BINDING=openai \ + --set-string env.EMBEDDING_MODEL=text-embedding-ada-002 \ + --set-string env.EMBEDDING_DIM=1536 \ + --set-string env.EMBEDDING_BINDING_API_KEY=$OPENAI_API_KEY +``` + +### 访问应用程序： + +```bash +# 1. 在终端中运行此端口转发命令： +kubectl --namespace rag port-forward svc/lightrag-dev 9621:9621 + +# 2. 当命令运行时，打开浏览器并导航到： +# http://localhost:9621 +``` + +## 生产环境部署（使用外部数据库） + +### 1. 安装数据库 +> 如果您已经准备好了数据库，可以跳过此步骤。详细信息可以在：[README.md](databases%2FREADME.md)中找到。 + +我们推荐使用KubeBlocks进行数据库部署。KubeBlocks是一个云原生数据库操作符，可以轻松地在Kubernetes上以生产规模运行任何数据库。 + +首先，安装KubeBlocks和KubeBlocks-Addons（如已安装可跳过）： +```bash +bash ./databases/01-prepare.sh +``` + +然后安装所需的数据库。默认情况下，这将安装PostgreSQL和Neo4J，但您可以修改[00-config.sh](databases%2F00-config.sh)以根据需要选择不同的数据库： +```bash +bash ./databases/02-install-database.sh +``` + +验证集群是否正在运行： +```bash +kubectl get clusters -n rag +# 预期输出： +# NAME CLUSTER-DEFINITION TERMINATION-POLICY STATUS AGE +# neo4j-cluster Delete Running 39s +# pg-cluster postgresql Delete Running 42s + +kubectl get po -n rag +# 预期输出： +# NAME READY STATUS RESTARTS AGE +# neo4j-cluster-neo4j-0 1/1 Running 0 58s +# pg-cluster-postgresql-0 4/4 Running 0 59s +# pg-cluster-postgresql-1 4/4 Running 0 59s +``` + +### 2. 安装LightRAG + +LightRAG及其数据库部署在同一Kubernetes集群中，使配置变得简单。 +安装脚本会自动从KubeBlocks获取所有数据库连接信息，无需手动设置数据库凭证： + +```bash +export OPENAI_API_BASE=<您的OPENAI_API_BASE> +export OPENAI_API_KEY=<您的OPENAI_API_KEY> +bash ./install_lightrag.sh +``` + +### 访问应用程序： + +```bash +# 1. 在终端中运行此端口转发命令： +kubectl --namespace rag port-forward svc/lightrag 9621:9621 + +# 2. 当命令运行时，打开浏览器并导航到： +# http://localhost:9621 +``` + +## 配置 + +### 修改资源配置 + +您可以通过修改`values.yaml`文件来配置LightRAG的资源使用： + +```yaml +replicaCount: 1 # 副本数量，可根据需要增加 + +resources: + limits: + cpu: 1000m # CPU限制，可根据需要调整 + memory: 2Gi # 内存限制，可根据需要调整 + requests: + cpu: 500m # CPU请求，可根据需要调整 + memory: 1Gi # 内存请求，可根据需要调整 +``` + +### 修改持久存储 + +```yaml +persistence: + enabled: true + ragStorage: + size: 10Gi # RAG存储大小，可根据需要调整 + inputs: + size: 5Gi # 输入数据存储大小，可根据需要调整 +``` + +### 配置环境变量 + +`values.yaml`文件中的`env`部分包含LightRAG的所有环境配置，类似于`.env`文件。当使用helm upgrade或helm install命令时，可以使用--set标志覆盖这些变量。 + +```yaml +env: + HOST: 0.0.0.0 + PORT: 9621 + WEBUI_TITLE: Graph RAG Engine + WEBUI_DESCRIPTION: Simple and Fast Graph Based RAG System + + # LLM配置 + LLM_BINDING: openai # LLM服务提供商 + LLM_MODEL: gpt-4o-mini # LLM模型 + LLM_BINDING_HOST: # API基础URL（可选） + LLM_BINDING_API_KEY: # API密钥 + + # 嵌入配置 + EMBEDDING_BINDING: openai # 嵌入服务提供商 + EMBEDDING_MODEL: text-embedding-ada-002 # 嵌入模型 + EMBEDDING_DIM: 1536 # 嵌入维度 + EMBEDDING_BINDING_API_KEY: # API密钥 + + # 存储配置 + LIGHTRAG_KV_STORAGE: PGKVStorage # 键值存储类型 + LIGHTRAG_VECTOR_STORAGE: PGVectorStorage # 向量存储类型 + LIGHTRAG_GRAPH_STORAGE: Neo4JStorage # 图存储类型 + LIGHTRAG_DOC_STATUS_STORAGE: PGDocStatusStorage # 文档状态存储类型 +``` + +## 注意事项 + +- 在部署前确保设置了所有必要的环境变量（API密钥和数据库密码） +- 出于安全原因，建议使用环境变量传递敏感信息，而不是直接写入脚本或values文件 +- 轻量级部署适合测试和小规模使用，但数据持久性和性能可能有限 +- 生产环境部署（PostgreSQL + Neo4J）推荐用于生产环境和大规模使用 +- 有关更多自定义配置，请参考LightRAG官方文档 diff --git a/k8s-deploy/README.md b/k8s-deploy/README.md new file mode 100644 index 00000000..cc1f2720 --- /dev/null +++ b/k8s-deploy/README.md @@ -0,0 +1,191 @@ +# LightRAG Helm Chart + +This is the Helm chart for LightRAG, used to deploy LightRAG services on a Kubernetes cluster. + +There are two recommended deployment methods for LightRAG: +1. **Lightweight Deployment**: Using built-in lightweight storage, suitable for testing and small-scale usage +2. **Production Deployment**: Using external databases (such as PostgreSQL and Neo4J), suitable for production environments and large-scale usage + +> If you'd like a video walkthrough of the deployment process, feel free to check out this optional [video tutorial](https://youtu.be/JW1z7fzeKTw?si=vPzukqqwmdzq9Q4q) on YouTube. It might help clarify some steps for those who prefer visual guidance. + +## Prerequisites + +Make sure the following tools are installed and configured: + +* **Kubernetes cluster** + * A running Kubernetes cluster is required. + * For local development or demos you can use [Minikube](https://minikube.sigs.k8s.io/docs/start/) (needs ≥ 2 CPUs, ≥ 4 GB RAM, and Docker/VM-driver support). + * Any standard cloud or on-premises Kubernetes cluster (EKS, GKE, AKS, etc.) also works. + +* **kubectl** + * The Kubernetes command-line tool for managing your cluster. + * Follow the official guide: [Install and Set Up kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl). + +* **Helm** (v3.x+) + * Kubernetes package manager used to install LightRAG. + * Install it via the official instructions: [Installing Helm](https://helm.sh/docs/intro/install/). + +## Lightweight Deployment (No External Databases Required) + +This deployment option uses built-in lightweight storage components that are perfect for testing, demos, or small-scale usage scenarios. No external database configuration is required. + +You can deploy LightRAG using either the provided convenience script or direct Helm commands. Both methods configure the same environment variables defined in the `lightrag/values.yaml` file. + +### Using the convenience script (recommended): + +```bash +export OPENAI_API_BASE= +export OPENAI_API_KEY= +bash ./install_lightrag_dev.sh +``` + +### Or using Helm directly: + +```bash +# You can override any env param you want +helm upgrade --install lightrag ./lightrag \ + --namespace rag \ + --set-string env.LIGHTRAG_KV_STORAGE=JsonKVStorage \ + --set-string env.LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage \ + --set-string env.LIGHTRAG_GRAPH_STORAGE=NetworkXStorage \ + --set-string env.LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage \ + --set-string env.LLM_BINDING=openai \ + --set-string env.LLM_MODEL=gpt-4o-mini \ + --set-string env.LLM_BINDING_HOST=$OPENAI_API_BASE \ + --set-string env.LLM_BINDING_API_KEY=$OPENAI_API_KEY \ + --set-string env.EMBEDDING_BINDING=openai \ + --set-string env.EMBEDDING_MODEL=text-embedding-ada-002 \ + --set-string env.EMBEDDING_DIM=1536 \ + --set-string env.EMBEDDING_BINDING_API_KEY=$OPENAI_API_KEY +``` + +### Accessing the application: + +```bash +# 1. Run this port-forward command in your terminal: +kubectl --namespace rag port-forward svc/lightrag-dev 9621:9621 + +# 2. While the command is running, open your browser and navigate to: +# http://localhost:9621 +``` + +## Production Deployment (Using External Databases) + +### 1. Install Databases +> You can skip this step if you've already prepared databases. Detailed information can be found in: [README.md](databases%2FREADME.md). + +We recommend KubeBlocks for database deployment. KubeBlocks is a cloud-native database operator that makes it easy to run any database on Kubernetes at production scale. + +First, install KubeBlocks and KubeBlocks-Addons (skip if already installed): +```bash +bash ./databases/01-prepare.sh +``` + +Then install the required databases. By default, this will install PostgreSQL and Neo4J, but you can modify [00-config.sh](databases%2F00-config.sh) to select different databases based on your needs: +```bash +bash ./databases/02-install-database.sh +``` + +Verify that the clusters are up and running: +```bash +kubectl get clusters -n rag +# Expected output: +# NAME CLUSTER-DEFINITION TERMINATION-POLICY STATUS AGE +# neo4j-cluster Delete Running 39s +# pg-cluster postgresql Delete Running 42s + +kubectl get po -n rag +# Expected output: +# NAME READY STATUS RESTARTS AGE +# neo4j-cluster-neo4j-0 1/1 Running 0 58s +# pg-cluster-postgresql-0 4/4 Running 0 59s +# pg-cluster-postgresql-1 4/4 Running 0 59s +``` + +### 2. Install LightRAG + +LightRAG and its databases are deployed within the same Kubernetes cluster, making configuration straightforward. +The installation script automatically retrieves all database connection information from KubeBlocks, eliminating the need to manually set database credentials: + +```bash +export OPENAI_API_BASE= +export OPENAI_API_KEY= +bash ./install_lightrag.sh +``` + +### Accessing the application: + +```bash +# 1. Run this port-forward command in your terminal: +kubectl --namespace rag port-forward svc/lightrag 9621:9621 + +# 2. While the command is running, open your browser and navigate to: +# http://localhost:9621 +``` + +## Configuration + +### Modifying Resource Configuration + +You can configure LightRAG's resource usage by modifying the `values.yaml` file: + +```yaml +replicaCount: 1 # Number of replicas, can be increased as needed + +resources: + limits: + cpu: 1000m # CPU limit, can be adjusted as needed + memory: 2Gi # Memory limit, can be adjusted as needed + requests: + cpu: 500m # CPU request, can be adjusted as needed + memory: 1Gi # Memory request, can be adjusted as needed +``` + +### Modifying Persistent Storage + +```yaml +persistence: + enabled: true + ragStorage: + size: 10Gi # RAG storage size, can be adjusted as needed + inputs: + size: 5Gi # Input data storage size, can be adjusted as needed +``` + +### Configuring Environment Variables + +The `env` section in the `values.yaml` file contains all environment configurations for LightRAG, similar to a `.env` file. When using helm upgrade or helm install commands, you can override these with the --set flag. + +```yaml +env: + HOST: 0.0.0.0 + PORT: 9621 + WEBUI_TITLE: Graph RAG Engine + WEBUI_DESCRIPTION: Simple and Fast Graph Based RAG System + + # LLM Configuration + LLM_BINDING: openai # LLM service provider + LLM_MODEL: gpt-4o-mini # LLM model + LLM_BINDING_HOST: # API base URL (optional) + LLM_BINDING_API_KEY: # API key + + # Embedding Configuration + EMBEDDING_BINDING: openai # Embedding service provider + EMBEDDING_MODEL: text-embedding-ada-002 # Embedding model + EMBEDDING_DIM: 1536 # Embedding dimension + EMBEDDING_BINDING_API_KEY: # API key + + # Storage Configuration + LIGHTRAG_KV_STORAGE: PGKVStorage # Key-value storage type + LIGHTRAG_VECTOR_STORAGE: PGVectorStorage # Vector storage type + LIGHTRAG_GRAPH_STORAGE: Neo4JStorage # Graph storage type + LIGHTRAG_DOC_STATUS_STORAGE: PGDocStatusStorage # Document status storage type +``` + +## Notes + +- Ensure all necessary environment variables (API keys and database passwords) are set before deployment +- For security reasons, it's recommended to pass sensitive information using environment variables rather than writing them directly in scripts or values files +- Lightweight deployment is suitable for testing and small-scale usage, but data persistence and performance may be limited +- Production deployment (PostgreSQL + Neo4J) is recommended for production environments and large-scale usage +- For more customized configurations, please refer to the official LightRAG documentation diff --git a/k8s-deploy/databases/00-config.sh b/k8s-deploy/databases/00-config.sh new file mode 100755 index 00000000..9431a3d3 --- /dev/null +++ b/k8s-deploy/databases/00-config.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +source "$DATABASE_SCRIPT_DIR/scripts/common.sh" + +# Namespace configuration +NAMESPACE="rag" +# version +KB_VERSION="1.0.0-beta.48" +ADDON_CLUSTER_CHART_VERSION="1.0.0-alpha.0" +# Helm repository +HELM_REPO="https://apecloud.github.io/helm-charts" + +# Set to true to enable the database, false to disable +ENABLE_POSTGRESQL=true +ENABLE_REDIS=false +ENABLE_QDRANT=false +ENABLE_NEO4J=true +ENABLE_ELASTICSEARCH=false +ENABLE_MONGODB=false diff --git a/k8s-deploy/databases/01-prepare.sh b/k8s-deploy/databases/01-prepare.sh new file mode 100755 index 00000000..f43257a4 --- /dev/null +++ b/k8s-deploy/databases/01-prepare.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +check_dependencies + +# Check if KubeBlocks is already installed, install it if it is not. +source "$DATABASE_SCRIPT_DIR/install-kubeblocks.sh" + +# Create namespaces +print "Creating namespaces..." +kubectl create namespace $NAMESPACE 2>/dev/null || true + +# Install database addons +print "Installing KubeBlocks database addons..." + +# Add and update Helm repository +print "Adding and updating KubeBlocks Helm repository..." +helm repo add kubeblocks $HELM_REPO +helm repo update +# Install database addons based on configuration +[ "$ENABLE_POSTGRESQL" = true ] && print "Installing PostgreSQL addon..." && helm upgrade --install kb-addon-postgresql kubeblocks/postgresql --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_REDIS" = true ] && print "Installing Redis addon..." && helm upgrade --install kb-addon-redis kubeblocks/redis --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_ELASTICSEARCH" = true ] && print "Installing Elasticsearch addon..." && helm upgrade --install kb-addon-elasticsearch kubeblocks/elasticsearch --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_QDRANT" = true ] && print "Installing Qdrant addon..." && helm upgrade --install kb-addon-qdrant kubeblocks/qdrant --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_MONGODB" = true ] && print "Installing MongoDB addon..." && helm upgrade --install kb-addon-mongodb kubeblocks/mongodb --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_NEO4J" = true ] && print "Installing Neo4j addon..." && helm upgrade --install kb-addon-neo4j kubeblocks/neo4j --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION + +print_success "KubeBlocks database addons installation completed!" +print "Now you can run 02-install-database.sh to install database clusters" diff --git a/k8s-deploy/databases/02-install-database.sh b/k8s-deploy/databases/02-install-database.sh new file mode 100755 index 00000000..f19a08d7 --- /dev/null +++ b/k8s-deploy/databases/02-install-database.sh @@ -0,0 +1,62 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +print "Installing database clusters..." + +# Install database clusters based on configuration +[ "$ENABLE_POSTGRESQL" = true ] && print "Installing PostgreSQL cluster..." && helm upgrade --install pg-cluster kubeblocks/postgresql-cluster -f "$DATABASE_SCRIPT_DIR/postgresql/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_REDIS" = true ] && print "Installing Redis cluster..." && helm upgrade --install redis-cluster kubeblocks/redis-cluster -f "$DATABASE_SCRIPT_DIR/redis/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_ELASTICSEARCH" = true ] && print "Installing Elasticsearch cluster..." && helm upgrade --install es-cluster kubeblocks/elasticsearch-cluster -f "$DATABASE_SCRIPT_DIR/elasticsearch/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_QDRANT" = true ] && print "Installing Qdrant cluster..." && helm upgrade --install qdrant-cluster kubeblocks/qdrant-cluster -f "$DATABASE_SCRIPT_DIR/qdrant/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_MONGODB" = true ] && print "Installing MongoDB cluster..." && helm upgrade --install mongodb-cluster kubeblocks/mongodb-cluster -f "$DATABASE_SCRIPT_DIR/mongodb/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_NEO4J" = true ] && print "Installing Neo4j cluster..." && helm upgrade --install neo4j-cluster kubeblocks/neo4j-cluster -f "$DATABASE_SCRIPT_DIR/neo4j/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION + +# Wait for databases to be ready +print "Waiting for databases to be ready..." +TIMEOUT=600 # Set timeout to 10 minutes +START_TIME=$(date +%s) + +while true; do + CURRENT_TIME=$(date +%s) + ELAPSED=$((CURRENT_TIME - START_TIME)) + + if [ $ELAPSED -gt $TIMEOUT ]; then + print_error "Timeout waiting for databases to be ready. Please check database status manually and try again" + exit 1 + fi + + # Build wait conditions for enabled databases + WAIT_CONDITIONS=() + [ "$ENABLE_POSTGRESQL" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=pg-cluster -n $NAMESPACE --timeout=10s") + [ "$ENABLE_REDIS" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=redis-cluster -n $NAMESPACE --timeout=10s") + [ "$ENABLE_ELASTICSEARCH" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=es-cluster -n $NAMESPACE --timeout=10s") + [ "$ENABLE_QDRANT" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=qdrant-cluster -n $NAMESPACE --timeout=10s") + [ "$ENABLE_MONGODB" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=mongodb-cluster -n $NAMESPACE --timeout=10s") + [ "$ENABLE_NEO4J" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=neo4j-cluster -n $NAMESPACE --timeout=10s") + + # Check if all enabled databases are ready + ALL_READY=true + for CONDITION in "${WAIT_CONDITIONS[@]}"; do + if ! eval "$CONDITION &> /dev/null"; then + ALL_READY=false + break + fi + done + + if [ "$ALL_READY" = true ]; then + print "All database pods are ready, continuing with deployment..." + break + fi + + print "Waiting for database pods to be ready (${ELAPSED}s elapsed)..." + sleep 10 +done + +print_success "Database clusters installation completed!" +print "Use the following command to check the status of installed clusters:" +print "kubectl get clusters -n $NAMESPACE" diff --git a/k8s-deploy/databases/03-uninstall-database.sh b/k8s-deploy/databases/03-uninstall-database.sh new file mode 100755 index 00000000..be0503c5 --- /dev/null +++ b/k8s-deploy/databases/03-uninstall-database.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +print "Uninstalling database clusters..." + +# Uninstall database clusters based on configuration +[ "$ENABLE_POSTGRESQL" = true ] && print "Uninstalling PostgreSQL cluster..." && helm uninstall pg-cluster --namespace $NAMESPACE 2>/dev/null || true +[ "$ENABLE_REDIS" = true ] && print "Uninstalling Redis cluster..." && helm uninstall redis-cluster --namespace $NAMESPACE 2>/dev/null || true +[ "$ENABLE_ELASTICSEARCH" = true ] && print "Uninstalling Elasticsearch cluster..." && helm uninstall es-cluster --namespace $NAMESPACE 2>/dev/null || true +[ "$ENABLE_QDRANT" = true ] && print "Uninstalling Qdrant cluster..." && helm uninstall qdrant-cluster --namespace $NAMESPACE 2>/dev/null || true +[ "$ENABLE_MONGODB" = true ] && print "Uninstalling MongoDB cluster..." && helm uninstall mongodb-cluster --namespace $NAMESPACE 2>/dev/null || true +[ "$ENABLE_NEO4J" = true ] && print "Uninstalling Neo4j cluster..." && helm uninstall neo4j-cluster --namespace $NAMESPACE 2>/dev/null || true + +print_success "Database clusters uninstalled" +print "To uninstall database addons and KubeBlocks, run 04-cleanup.sh" diff --git a/k8s-deploy/databases/04-cleanup.sh b/k8s-deploy/databases/04-cleanup.sh new file mode 100755 index 00000000..12493501 --- /dev/null +++ b/k8s-deploy/databases/04-cleanup.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +print "Uninstalling KubeBlocks database addons..." + +# Uninstall database addons based on configuration +[ "$ENABLE_POSTGRESQL" = true ] && print "Uninstalling PostgreSQL addon..." && helm uninstall kb-addon-postgresql --namespace kb-system 2>/dev/null || true +[ "$ENABLE_REDIS" = true ] && print "Uninstalling Redis addon..." && helm uninstall kb-addon-redis --namespace kb-system 2>/dev/null || true +[ "$ENABLE_ELASTICSEARCH" = true ] && print "Uninstalling Elasticsearch addon..." && helm uninstall kb-addon-elasticsearch --namespace kb-system 2>/dev/null || true +[ "$ENABLE_QDRANT" = true ] && print "Uninstalling Qdrant addon..." && helm uninstall kb-addon-qdrant --namespace kb-system 2>/dev/null || true +[ "$ENABLE_MONGODB" = true ] && print "Uninstalling MongoDB addon..." && helm uninstall kb-addon-mongodb --namespace kb-system 2>/dev/null || true +[ "$ENABLE_NEO4J" = true ] && print "Uninstalling Neo4j addon..." && helm uninstall kb-addon-neo4j --namespace kb-system 2>/dev/null || true + +print_success "Database addons uninstallation completed!" + +source "$DATABASE_SCRIPT_DIR/uninstall-kubeblocks.sh" + +kubectl delete namespace $NAMESPACE +kubectl delete namespace kb-system + +print_success "KubeBlocks uninstallation completed!" diff --git a/k8s-deploy/databases/README.md b/k8s-deploy/databases/README.md new file mode 100644 index 00000000..1e0002d0 --- /dev/null +++ b/k8s-deploy/databases/README.md @@ -0,0 +1,170 @@ +# Using KubeBlocks to Deploy and Manage Databases + +Learn how to quickly deploy and manage various databases in a Kubernetes (K8s) environment through KubeBlocks. + +## Introduction to KubeBlocks + +KubeBlocks is a production-ready, open-source toolkit that runs any database--SQL, NoSQL, vector, or document--on Kubernetes. +It scales smoothly from quick dev tests to full production clusters, making it a solid choice for RAG workloads like FastGPT that need several data stores working together. + +## Prerequisites + +Make sure the following tools are installed and configured: + +* **Kubernetes cluster** + * A running Kubernetes cluster is required. + * For local development or demos you can use [Minikube](https://minikube.sigs.k8s.io/docs/start/) (needs ≥ 2 CPUs, ≥ 4 GB RAM, and Docker/VM-driver support). + * Any standard cloud or on-premises Kubernetes cluster (EKS, GKE, AKS, etc.) also works. + +* **kubectl** + * The Kubernetes command-line interface. + * Follow the official guide: [Install and Set Up kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl). + +* **Helm** (v3.x+) + * Kubernetes package manager used by the scripts below. + * Install it via the official instructions: [Installing Helm](https://helm.sh/docs/intro/install/). + +## Installing + +1. **Configure the databases you want** + Edit `00-config.sh` file. Based on your requirements, set the variable to `true` for the databases you want to install. + For example, to install PostgreSQL and Neo4j: + + ```bash + ENABLE_POSTGRESQL=true + ENABLE_REDIS=false + ENABLE_ELASTICSEARCH=false + ENABLE_QDRANT=false + ENABLE_MONGODB=false + ENABLE_NEO4J=true + ``` + +2. **Prepare the environment and install KubeBlocks add-ons** + + ```bash + bash ./01-prepare.sh + ``` + + *What the script does* + `01-prepare.sh` performs basic pre-checks (Helm, kubectl, cluster reachability), adds the KubeBlocks Helm repo, and installs any core CRDs or controllers that KubeBlocks itself needs. It also installs the addons for every database you enabled in `00-config.sh`, but **does not** create the actual database clusters yet. + +3. **(Optional) Modify database settings** + Before deployment you can edit the `values.yaml` file inside each `/` directory to change `version`, `replicas`, `CPU`, `memory`, `storage size`, etc. + +4. **Install the database clusters** + + ```bash + bash ./02-install-database.sh + ``` + + *What the script does* + `02-install-database.sh` **actually deploys the chosen databases to Kubernetes**. + + When the script completes, confirm that the clusters are up. It may take a few minutes for all the clusters to become ready, + especially if this is the first time running the script as Kubernetes needs to pull container images from registries. + You can monitor the progress using the following commands: + + ```bash + kubectl get clusters -n rag + NAME CLUSTER-DEFINITION TERMINATION-POLICY STATUS AGE + es-cluster Delete Running 11m + mongodb-cluster mongodb Delete Running 11m + pg-cluster postgresql Delete Running 11m + qdrant-cluster qdrant Delete Running 11m + redis-cluster redis Delete Running 11m + ``` + + You can see all the Database `Pods` created by KubeBlocks. + Initially, you might see pods in `ContainerCreating` or `Pending` status - this is normal while images are being pulled and containers are starting up. + Wait until all pods show `Running` status: + + ```bash + kubectl get po -n rag + NAME READY STATUS RESTARTS AGE + es-cluster-mdit-0 2/2 Running 0 11m + mongodb-cluster-mongodb-0 2/2 Running 0 11m + pg-cluster-postgresql-0 4/4 Running 0 11m + pg-cluster-postgresql-1 4/4 Running 0 11m + qdrant-cluster-qdrant-0 2/2 Running 0 11m + redis-cluster-redis-0 2/2 Running 0 11m + ``` + + You can also check the detailed status of a specific pod if it's taking longer than expected: + + ```bash + kubectl describe pod -n rag + ``` + +## Connect to Databases + +To connect to your databases, follow these steps to identify available accounts, retrieve credentials, and establish connections: + +### 1. List Available Database Clusters + +First, view the database clusters running in your namespace: + +```bash +kubectl get cluster -n rag +``` + +### 2. Retrieve Authentication Credentials + +For PostgreSQL, retrieve the username and password from Kubernetes secrets: + +```bash +# Get PostgreSQL username +kubectl get secrets -n rag pg-cluster-postgresql-account-postgres -o jsonpath='{.data.username}' | base64 -d +# Get PostgreSQL password +kubectl get secrets -n rag pg-cluster-postgresql-account-postgres -o jsonpath='{.data.password}' | base64 -d +``` + +If you have trouble finding the correct secret name, list all secrets: + +```bash +kubectl get secrets -n rag +``` + +### 3. Port Forward to Local Machine + +Use port forwarding to access PostgreSQL from your local machine: + +```bash +# Forward PostgreSQL port (5432) to your local machine +# You can see all services with: kubectl get svc -n rag +kubectl port-forward -n rag svc/pg-cluster-postgresql-postgresql 5432:5432 +``` + +### 4. Connect Using Database Client + +Now you can connect using your preferred PostgreSQL client with the retrieved credentials: + +```bash +# Example: connecting with psql +export PGUSER=$(kubectl get secrets -n rag pg-cluster-postgresql-account-postgres -o jsonpath='{.data.username}' | base64 -d) +export PGPASSWORD=$(kubectl get secrets -n rag pg-cluster-postgresql-account-postgres -o jsonpath='{.data.password}' | base64 -d) +psql -h localhost -p 5432 -U $PGUSER +``` + +Keep the port-forwarding terminal running while you're connecting to the database. + + +## Uninstalling + +1. **Remove the database clusters** + + ```bash + bash ./03-uninstall-database.sh + ``` + + The script deletes the database clusters that were enabled in `00-config.sh`. + +2. **Clean up KubeBlocks add-ons** + + ```bash + bash ./04-cleanup.sh + ``` + + This removes the addons installed by `01-prepare.sh`. + +## Reference +* [Kubeblocks Documentation](https://kubeblocks.io/docs/preview/user_docs/overview/introduction) diff --git a/k8s-deploy/databases/elasticsearch/values.yaml b/k8s-deploy/databases/elasticsearch/values.yaml new file mode 100644 index 00000000..4563ad2c --- /dev/null +++ b/k8s-deploy/databases/elasticsearch/values.yaml @@ -0,0 +1,36 @@ +## description: The version of ElasticSearch. +## default: 8.8.2 +version: "8.8.2" + +## description: Mode for ElasticSearch +## default: multi-node +## one of: [single-node, multi-node] +mode: single-node + +## description: The number of replicas, for single-node mode, the replicas is 1, for multi-node mode, the default replicas is 3. +## default: 1 +## minimum: 1 +## maximum: 5 +replicas: 1 + +## description: CPU cores. +## default: 1 +## minimum: 0.5 +## maximum: 64 +cpu: 1 + +## description: Memory, the unit is Gi. +## default: 2 +## minimum: 1 +## maximum: 1000 +memory: 2 + +## description: Storage size, the unit is Gi. +## default: 20 +## minimum: 1 +## maximum: 10000 +storage: 5 + +extra: + terminationPolicy: Delete + disableExporter: true diff --git a/k8s-deploy/databases/install-kubeblocks.sh b/k8s-deploy/databases/install-kubeblocks.sh new file mode 100755 index 00000000..838413ac --- /dev/null +++ b/k8s-deploy/databases/install-kubeblocks.sh @@ -0,0 +1,52 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +# Check dependencies +check_dependencies + +# Function for installing KubeBlocks +install_kubeblocks() { + print "Ready to install KubeBlocks." + + # Install CSI Snapshotter CRDs + kubectl create -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshotclasses.yaml + kubectl create -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshots.yaml + kubectl create -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshotcontents.yaml + + # Add and update Piraeus repository + helm repo add piraeus-charts https://piraeus.io/helm-charts/ + helm repo update + + # Install snapshot controller + helm install snapshot-controller piraeus-charts/snapshot-controller -n kb-system --create-namespace + kubectl wait --for=condition=ready pods -l app.kubernetes.io/name=snapshot-controller -n kb-system --timeout=60s + print_success "snapshot-controller installation complete!" + + # Install KubeBlocks CRDs + kubectl create -f https://github.com/apecloud/kubeblocks/releases/download/v${KB_VERSION}/kubeblocks_crds.yaml + + # Add and update KubeBlocks repository + helm repo add kubeblocks $HELM_REPO + helm repo update + + # Install KubeBlocks + helm install kubeblocks kubeblocks/kubeblocks --namespace kb-system --create-namespace --version=${KB_VERSION} + + # Verify installation + print "Waiting for KubeBlocks to be ready..." + kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=kubeblocks -n kb-system --timeout=120s + print_success "KubeBlocks installation complete!" +} + +# Check if KubeBlocks is already installed +print "Checking if KubeBlocks is already installed in kb-system namespace..." +if kubectl get namespace kb-system &>/dev/null && kubectl get deployment kubeblocks -n kb-system &>/dev/null; then + print_success "KubeBlocks is already installed in kb-system namespace." +else + # Call the function to install KubeBlocks + install_kubeblocks +fi diff --git a/k8s-deploy/databases/mongodb/values.yaml b/k8s-deploy/databases/mongodb/values.yaml new file mode 100644 index 00000000..e4840a32 --- /dev/null +++ b/k8s-deploy/databases/mongodb/values.yaml @@ -0,0 +1,34 @@ +## description: Cluster version. +## default: 6.0.16 +## one of: [8.0.8, 8.0.6, 8.0.4, 7.0.19, 7.0.16, 7.0.12, 6.0.22, 6.0.20, 6.0.16, 5.0.30, 5.0.28, 4.4.29, 4.2.24, 4.0.28] +version: 6.0.16 + +## description: Cluster topology mode. +## default: standalone +## one of: [standalone, replicaset] +mode: standalone + +## description: CPU cores. +## default: 0.5 +## minimum: 0.5 +## maximum: 64 +cpu: 1 + +## description: Memory, the unit is Gi. +## default: 0.5 +## minimum: 0.5 +## maximum: 1000 +memory: 1 + +## description: Storage size, the unit is Gi. +## default: 20 +## minimum: 1 +## maximum: 10000 +storage: 20 + +## default: enabled +## one of: [enabled, disabled] +hostnetwork: "disabled" + +extra: + terminationPolicy: Delete diff --git a/k8s-deploy/databases/neo4j/values.yaml b/k8s-deploy/databases/neo4j/values.yaml new file mode 100644 index 00000000..2e26479a --- /dev/null +++ b/k8s-deploy/databases/neo4j/values.yaml @@ -0,0 +1,46 @@ +# Version +# description: Cluster version. +# default: 5.26.5 +# one of: [5.26.5, 4.4.42] +version: 5.26.5 + +# Mode +# description: Cluster topology mode. +# default: singlealone +# one of: [singlealone] +mode: singlealone + +# CPU +# description: CPU cores. +# default: 2 +# minimum: 2 +# maximum: 64 +cpu: 2 + +# Memory(Gi) +# description: Memory, the unit is Gi. +# default: 2 +# minimum: 2 +# maximum: 1000 +memory: 4 + +# Storage(Gi) +# description: Storage size, the unit is Gi. +# default: 20 +# minimum: 1 +# maximum: 10000 +storage: 20 + +# Replicas +# description: The number of replicas, for standalone mode, the replicas is 1, for replicaset mode, the default replicas is 3. +# default: 1 +# minimum: 1 +# maximum: 5 +replicas: 1 + +# Storage Class Name +# description: Storage class name of the data volume +storageClassName: "" + +extra: + terminationPolicy: Delete diff --git a/k8s-deploy/databases/postgresql/values.yaml b/k8s-deploy/databases/postgresql/values.yaml new file mode 100644 index 00000000..d706593b --- /dev/null +++ b/k8s-deploy/databases/postgresql/values.yaml @@ -0,0 +1,33 @@ +## description: service version. +## default: 15.7.0 +version: 16.4.0 + +## mode postgresql cluster topology mode replication +mode: replication + +## description: The number of replicas, for standalone mode, the replicas is 1, for replication mode, the default replicas is 2. +## default: 1 +## minimum: 1 +## maximum: 5 +replicas: 2 + +## description: CPU cores. +## default: 0.5 +## minimum: 0.5 +## maximum: 64 +cpu: 1 + +## description: Memory, the unit is Gi. +## default: 0.5 +## minimum: 0.5 +## maximum: 1000 +memory: 1 + +## description: Storage size, the unit is Gi. +## default: 20 +## minimum: 1 +## maximum: 10000 +storage: 5 + +## terminationPolicy define Cluster termination policy. One of DoNotTerminate, Delete, WipeOut. +terminationPolicy: Delete diff --git a/k8s-deploy/databases/qdrant/values.yaml b/k8s-deploy/databases/qdrant/values.yaml new file mode 100644 index 00000000..c49d9960 --- /dev/null +++ b/k8s-deploy/databases/qdrant/values.yaml @@ -0,0 +1,31 @@ +## description: The version of Qdrant. +## default: 1.10.0 +version: 1.10.0 + +## description: The number of replicas. +## default: 1 +## minimum: 1 +## maximum: 16 +replicas: 1 + +## description: CPU cores. +## default: 1 +## minimum: 0.5 +## maximum: 64 +cpu: 1 + +## description: Memory, the unit is Gi. +## default: 2 +## minimum: 0.5 +## maximum: 1000 +memory: 1 + +## description: Storage size, the unit is Gi. +## default: 20 +## minimum: 1 +## maximum: 10000 +storage: 20 + +## customized default values to override kblib chart's values +extra: + terminationPolicy: Delete diff --git a/k8s-deploy/databases/redis/values.yaml b/k8s-deploy/databases/redis/values.yaml new file mode 100644 index 00000000..356e35f7 --- /dev/null +++ b/k8s-deploy/databases/redis/values.yaml @@ -0,0 +1,34 @@ +## description: Cluster version. +## default: 7.2.7 +version: 7.2.7 + +## description: Cluster topology mode. +## default: replication +## one of: [standalone, replication, cluster, replication-twemproxy] +mode: standalone + +## description: The number of replicas, for standalone mode, the replicas is 1, for replication mode, the default replicas is 2. +## default: 1 +## minimum: 1 +## maximum: 5 +replicas: 1 + +## description: CPU cores. +## default: 0.5 +## minimum: 0.5 +## maximum: 64 +cpu: 0.5 + +## description: Memory, the unit is Gi. +## default: 0.5 +## minimum: 0.5 +## maximum: 1000 +memory: 1 + +## description: Storage size, the unit is Gi. +## default: 20 +## minimum: 1 +## maximum: 10000 +storage: 20 +extra: + disableExporter: true diff --git a/k8s-deploy/databases/scripts/common.sh b/k8s-deploy/databases/scripts/common.sh new file mode 100755 index 00000000..3ffb4b08 --- /dev/null +++ b/k8s-deploy/databases/scripts/common.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +print_title() { + echo "============================================" + echo "$1" + echo "============================================" +} + +print_success() { + echo "✅ $1" +} + +print_error() { + echo "❌ $1" +} + +print_warning() { + echo "⚠️ $1" +} + +print_info() { + echo "🔹 $1" +} + +print() { + echo "$1" +} + +# Check dependencies +check_dependencies(){ + print "Checking dependencies..." + command -v kubectl >/dev/null 2>&1 || { print "Error: kubectl command not found"; exit 1; } + command -v helm >/dev/null 2>&1 || { print "Error: helm command not found"; exit 1; } + + # Check if Kubernetes is available + print "Checking if Kubernetes is available..." + kubectl cluster-info &>/dev/null + if [ $? -ne 0 ]; then + print "Error: Kubernetes cluster is not accessible. Please ensure you have proper access to a Kubernetes cluster." + exit 1 + fi + print_success "Kubernetes cluster is accessible." +} diff --git a/k8s-deploy/databases/uninstall-kubeblocks.sh b/k8s-deploy/databases/uninstall-kubeblocks.sh new file mode 100755 index 00000000..3b5ffd1f --- /dev/null +++ b/k8s-deploy/databases/uninstall-kubeblocks.sh @@ -0,0 +1,51 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +# Check dependencies +print "Checking dependencies..." +command -v kubectl >/dev/null 2>&1 || { print "Error: kubectl command not found"; exit 1; } +command -v helm >/dev/null 2>&1 || { print "Error: helm command not found"; exit 1; } + +print "Checking if Kubernetes is available..." +if ! kubectl cluster-info &>/dev/null; then + print "Error: Kubernetes cluster is not accessible. Please ensure you have proper access to a Kubernetes cluster." + exit 1 +fi + +print "Checking if KubeBlocks is installed in kb-system namespace..." +if ! kubectl get namespace kb-system &>/dev/null; then + print "KubeBlocks is not installed in kb-system namespace." + exit 0 +fi + +# Function for uninstalling KubeBlocks +uninstall_kubeblocks() { + print "Uninstalling KubeBlocks..." + + # Uninstall KubeBlocks Helm chart + helm uninstall kubeblocks -n kb-system + + # Uninstall snapshot controller + helm uninstall snapshot-controller -n kb-system + + # Delete KubeBlocks CRDs + kubectl delete -f https://github.com/apecloud/kubeblocks/releases/download/v${KB_VERSION}/kubeblocks_crds.yaml --ignore-not-found=true + + # Delete CSI Snapshotter CRDs + kubectl delete -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshotclasses.yaml --ignore-not-found=true + kubectl delete -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshots.yaml --ignore-not-found=true + kubectl delete -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshotcontents.yaml --ignore-not-found=true + + # Delete the kb-system namespace + print "Waiting for resources to be removed..." + kubectl delete namespace kb-system --timeout=180s + + print "KubeBlocks has been successfully uninstalled!" +} + +# Call the function to uninstall KubeBlocks +uninstall_kubeblocks diff --git a/k8s-deploy/install_lightrag.sh b/k8s-deploy/install_lightrag.sh new file mode 100755 index 00000000..763521e7 --- /dev/null +++ b/k8s-deploy/install_lightrag.sh @@ -0,0 +1,95 @@ +#!/bin/bash + +NAMESPACE=rag + +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +if [ -z "$OPENAI_API_KEY" ]; then + echo "OPENAI_API_KEY environment variable is not set" + read -s -p "Enter your OpenAI API key: " OPENAI_API_KEY + if [ -z "$OPENAI_API_KEY" ]; then + echo "Error: OPENAI_API_KEY must be provided" + exit 1 + fi + export OPENAI_API_KEY=$OPENAI_API_KEY +fi + +if [ -z "$OPENAI_API_BASE" ]; then + echo "OPENAI_API_BASE environment variable is not set, will use default value" + read -p "Enter OpenAI API base URL (press Enter to skip if not needed): " OPENAI_API_BASE + export OPENAI_API_BASE=$OPENAI_API_BASE +fi + +# Install KubeBlocks (if not already installed) +bash "$SCRIPT_DIR/databases/01-prepare.sh" + +# Install database clusters +bash "$SCRIPT_DIR/databases/02-install-database.sh" + +# Create vector extension in PostgreSQL if enabled +print "Waiting for PostgreSQL pods to be ready..." +if kubectl wait --for=condition=ready pods -l kubeblocks.io/role=primary,app.kubernetes.io/instance=pg-cluster -n $NAMESPACE --timeout=300s; then + print "Creating vector extension in PostgreSQL..." + kubectl exec -it $(kubectl get pods -l kubeblocks.io/role=primary,app.kubernetes.io/instance=pg-cluster -n $NAMESPACE -o name) -n $NAMESPACE -- psql -c "CREATE EXTENSION vector;" + print_success "Vector extension created successfully." +else + print "Warning: PostgreSQL pods not ready within timeout. Vector extension not created." +fi + +# Get database passwords from Kubernetes secrets +echo "Retrieving database credentials from Kubernetes secrets..." +POSTGRES_PASSWORD=$(kubectl get secrets -n rag pg-cluster-postgresql-account-postgres -o jsonpath='{.data.password}' | base64 -d) +if [ -z "$POSTGRES_PASSWORD" ]; then + echo "Error: Could not retrieve PostgreSQL password. Make sure PostgreSQL is deployed and the secret exists." + exit 1 +fi +export POSTGRES_PASSWORD=$POSTGRES_PASSWORD + +NEO4J_PASSWORD=$(kubectl get secrets -n rag neo4j-cluster-neo4j-account-neo4j -o jsonpath='{.data.password}' | base64 -d) +if [ -z "$NEO4J_PASSWORD" ]; then + echo "Error: Could not retrieve Neo4J password. Make sure Neo4J is deployed and the secret exists." + exit 1 +fi +export NEO4J_PASSWORD=$NEO4J_PASSWORD + +#REDIS_PASSWORD=$(kubectl get secrets -n rag redis-cluster-redis-account-default -o jsonpath='{.data.password}' | base64 -d) +#if [ -z "$REDIS_PASSWORD" ]; then +# echo "Error: Could not retrieve Redis password. Make sure Redis is deployed and the secret exists." +# exit 1 +#fi +#export REDIS_PASSWORD=$REDIS_PASSWORD + +echo "Deploying production LightRAG (using external databases)..." + +if ! kubectl get namespace rag &> /dev/null; then + echo "creating namespace 'rag'..." + kubectl create namespace rag +fi + +helm upgrade --install lightrag $SCRIPT_DIR/lightrag \ + --namespace $NAMESPACE \ + --set-string env.POSTGRES_PASSWORD=$POSTGRES_PASSWORD \ + --set-string env.NEO4J_PASSWORD=$NEO4J_PASSWORD \ + --set-string env.LLM_BINDING=openai \ + --set-string env.LLM_MODEL=gpt-4o-mini \ + --set-string env.LLM_BINDING_HOST=$OPENAI_API_BASE \ + --set-string env.LLM_BINDING_API_KEY=$OPENAI_API_KEY \ + --set-string env.EMBEDDING_BINDING=openai \ + --set-string env.EMBEDDING_MODEL=text-embedding-ada-002 \ + --set-string env.EMBEDDING_DIM=1536 \ + --set-string env.EMBEDDING_BINDING_API_KEY=$OPENAI_API_KEY +# --set-string env.REDIS_URI="redis://default:${REDIS_PASSWORD}@redis-cluster-redis-redis:6379" + +# Wait for LightRAG pod to be ready +echo "" +echo "Waiting for lightrag pod to be ready..." +kubectl wait --for=condition=ready pod -l app.kubernetes.io/instance=lightrag --timeout=300s -n rag +echo "lightrag pod is ready" +echo "" +echo "Running Port-Forward:" +echo " kubectl --namespace rag port-forward svc/lightrag 9621:9621" +echo "===========================================" +echo "" +echo "✅ You can visit LightRAG at: http://localhost:9621" +echo "" +kubectl --namespace rag port-forward svc/lightrag 9621:9621 diff --git a/k8s-deploy/install_lightrag_dev.sh b/k8s-deploy/install_lightrag_dev.sh new file mode 100755 index 00000000..3316f8a2 --- /dev/null +++ b/k8s-deploy/install_lightrag_dev.sh @@ -0,0 +1,81 @@ +#!/bin/bash + +NAMESPACE=rag + +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +check_dependencies(){ + echo "Checking dependencies..." + command -v kubectl >/dev/null 2>&1 || { echo "Error: kubectl command not found"; exit 1; } + command -v helm >/dev/null 2>&1 || { echo "Error: helm command not found"; exit 1; } + + # Check if Kubernetes is available + echo "Checking if Kubernetes is available..." + kubectl cluster-info &>/dev/null + if [ $? -ne 0 ]; then + echo "Error: Kubernetes cluster is not accessible. Please ensure you have proper access to a Kubernetes cluster." + exit 1 + fi + echo "Kubernetes cluster is accessible." +} + +check_dependencies + +if [ -z "$OPENAI_API_KEY" ]; then + echo "OPENAI_API_KEY environment variable is not set" + read -s -p "Enter your OpenAI API key: " OPENAI_API_KEY + if [ -z "$OPENAI_API_KEY" ]; then + echo "Error: OPENAI_API_KEY must be provided" + exit 1 + fi + export OPENAI_API_KEY=$OPENAI_API_KEY +fi + +if [ -z "$OPENAI_API_BASE" ]; then + echo "OPENAI_API_BASE environment variable is not set, will use default value" + read -p "Enter OpenAI API base URL (press Enter to skip if not needed): " OPENAI_API_BASE + export OPENAI_API_BASE=$OPENAI_API_BASE +fi + +required_env_vars=("OPENAI_API_BASE" "OPENAI_API_KEY") + +for var in "${required_env_vars[@]}"; do + if [ -z "${!var}" ]; then + echo "Error: $var environment variable is not set" + exit 1 + fi +done + +if ! kubectl get namespace rag &> /dev/null; then + echo "creating namespace 'rag'..." + kubectl create namespace rag +fi + +helm upgrade --install lightrag-dev $SCRIPT_DIR/lightrag \ + --namespace rag \ + --set-string env.LIGHTRAG_KV_STORAGE=JsonKVStorage \ + --set-string env.LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage \ + --set-string env.LIGHTRAG_GRAPH_STORAGE=NetworkXStorage \ + --set-string env.LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage \ + --set-string env.LLM_BINDING=openai \ + --set-string env.LLM_MODEL=gpt-4o-mini \ + --set-string env.LLM_BINDING_HOST=$OPENAI_API_BASE \ + --set-string env.LLM_BINDING_API_KEY=$OPENAI_API_KEY \ + --set-string env.EMBEDDING_BINDING=openai \ + --set-string env.EMBEDDING_MODEL=text-embedding-ada-002 \ + --set-string env.EMBEDDING_DIM=1536 \ + --set-string env.EMBEDDING_BINDING_API_KEY=$OPENAI_API_KEY + +# Wait for LightRAG pod to be ready +echo "" +echo "Waiting for lightrag-dev pod to be ready..." +kubectl wait --for=condition=ready pod -l app.kubernetes.io/instance=lightrag-dev --timeout=300s -n rag +echo "lightrag-dev pod is ready" +echo "" +echo "Running Port-Forward:" +echo " kubectl --namespace rag port-forward svc/lightrag-dev 9621:9621" +echo "===========================================" +echo "" +echo "✅ You can visit LightRAG at: http://localhost:9621" +echo "" +kubectl --namespace rag port-forward svc/lightrag-dev 9621:9621 diff --git a/k8s-deploy/lightrag/.helmignore b/k8s-deploy/lightrag/.helmignore new file mode 100644 index 00000000..0e8a0eb3 --- /dev/null +++ b/k8s-deploy/lightrag/.helmignore @@ -0,0 +1,23 @@ +# Patterns to ignore when building packages. +# This supports shell glob matching, relative path matching, and +# negation (prefixed with !). Only one pattern per line. +.DS_Store +# Common VCS dirs +.git/ +.gitignore +.bzr/ +.bzrignore +.hg/ +.hgignore +.svn/ +# Common backup files +*.swp +*.bak +*.tmp +*.orig +*~ +# Various IDEs +.project +.idea/ +*.tmproj +.vscode/ diff --git a/k8s-deploy/lightrag/Chart.yaml b/k8s-deploy/lightrag/Chart.yaml new file mode 100644 index 00000000..19d68d9b --- /dev/null +++ b/k8s-deploy/lightrag/Chart.yaml @@ -0,0 +1,10 @@ +apiVersion: v2 +name: lightrag +description: A Helm chart for LightRAG, an efficient and lightweight RAG system +type: application +version: 0.1.0 +appVersion: "1.0.0" +maintainers: + - name: LightRAG Team + - name: earayu + email: earayu@gmail.com diff --git a/k8s-deploy/lightrag/templates/NOTES.txt b/k8s-deploy/lightrag/templates/NOTES.txt new file mode 100644 index 00000000..6b81aca0 --- /dev/null +++ b/k8s-deploy/lightrag/templates/NOTES.txt @@ -0,0 +1,38 @@ +=========================================== + LightRAG has been successfully deployed! +=========================================== + +View application logs: + kubectl logs -f --namespace {{ .Release.Namespace }} deploy/{{ include "lightrag.fullname" . }} + +=========================================== + +Access the application: +{{- if contains "NodePort" .Values.service.type }} + Run these commands to get access information: + ----------------------------------------- + export NODE_PORT=$(kubectl get --namespace {{ .Release.Namespace }} -o jsonpath="{.spec.ports[0].nodePort}" services {{ include "lightrag.fullname" . }}) + export NODE_IP=$(kubectl get nodes --namespace {{ .Release.Namespace }} -o jsonpath="{.items[0].status.addresses[0].address}") + echo "LightRAG is accessible at: http://$NODE_IP:$NODE_PORT" + ----------------------------------------- +{{- else if contains "LoadBalancer" .Values.service.type }} + Run these commands to get access information (external IP may take a minute to assign): + ----------------------------------------- + export SERVICE_IP=$(kubectl get svc --namespace {{ .Release.Namespace }} {{ include "lightrag.fullname" . }} --template "{{ "{{ range (index .status.loadBalancer.ingress 0) }}{{ . }}{{ end }}" }}") + echo "LightRAG is accessible at: http://$SERVICE_IP:{{ .Values.service.port }}" + ----------------------------------------- + If SERVICE_IP is empty, retry the command or check service status with: + kubectl get svc --namespace {{ .Release.Namespace }} {{ include "lightrag.fullname" . }} +{{- else if contains "ClusterIP" .Values.service.type }} + For development environments, to access LightRAG from your local machine: + + 1. Run this port-forward command in your terminal: + kubectl --namespace {{ .Release.Namespace }} port-forward svc/{{ include "lightrag.fullname" . }} {{ .Values.service.port }}:{{ .Values.env.PORT }} + + 2. While the command is running, open your browser and navigate to: + http://localhost:{{ .Values.service.port }} + + Note: To stop port-forwarding, press Ctrl+C in the terminal. +{{- end }} + +=========================================== diff --git a/k8s-deploy/lightrag/templates/_helpers.tpl b/k8s-deploy/lightrag/templates/_helpers.tpl new file mode 100644 index 00000000..2b2c23ae --- /dev/null +++ b/k8s-deploy/lightrag/templates/_helpers.tpl @@ -0,0 +1,42 @@ +{{/* +Application name +*/}} +{{- define "lightrag.name" -}} +{{- default .Chart.Name .Values.nameOverride | trunc 63 | trimSuffix "-" }} +{{- end }} + +{{/* +Full application name +*/}} +{{- define "lightrag.fullname" -}} +{{- default .Release.Name .Values.fullnameOverride | trunc 63 | trimSuffix "-" }} +{{- end }} + +{{/* +Common labels +*/}} +{{- define "lightrag.labels" -}} +app.kubernetes.io/name: {{ include "lightrag.name" . }} +app.kubernetes.io/instance: {{ .Release.Name }} +app.kubernetes.io/managed-by: {{ .Release.Service }} +{{- end }} + +{{/* +Selector labels +*/}} +{{- define "lightrag.selectorLabels" -}} +app.kubernetes.io/name: {{ include "lightrag.name" . }} +app.kubernetes.io/instance: {{ .Release.Name }} +{{- end }} + +{{/* +.env file content +*/}} +{{- define "lightrag.envContent" -}} +{{- $first := true -}} +{{- range $key, $val := .Values.env -}} +{{- if not $first -}}{{- "\n" -}}{{- end -}} +{{- $first = false -}} +{{ $key }}={{ $val }} +{{- end -}} +{{- end -}} diff --git a/k8s-deploy/lightrag/templates/deployment.yaml b/k8s-deploy/lightrag/templates/deployment.yaml new file mode 100644 index 00000000..fe63c1ac --- /dev/null +++ b/k8s-deploy/lightrag/templates/deployment.yaml @@ -0,0 +1,62 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: {{ include "lightrag.fullname" . }} + labels: + {{- include "lightrag.labels" . | nindent 4 }} +spec: + replicas: {{ .Values.replicaCount }} + selector: + matchLabels: + {{- include "lightrag.selectorLabels" . | nindent 6 }} + template: + metadata: + annotations: + checksum/config: {{ include "lightrag.envContent" . | sha256sum }} + labels: + {{- include "lightrag.selectorLabels" . | nindent 8 }} + spec: + containers: + - name: {{ .Chart.Name }} + image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default .Chart.AppVersion }}" + imagePullPolicy: IfNotPresent + ports: + - name: http + containerPort: {{ .Values.env.PORT }} + protocol: TCP + readinessProbe: + httpGet: + path: /health + port: http + initialDelaySeconds: 10 + periodSeconds: 5 + timeoutSeconds: 2 + successThreshold: 1 + failureThreshold: 3 + resources: + {{- toYaml .Values.resources | nindent 12 }} + volumeMounts: + - name: rag-storage + mountPath: /app/data/rag_storage + - name: inputs + mountPath: /app/data/inputs + - name: env-file + mountPath: /app/.env + subPath: .env + volumes: + - name: env-file + secret: + secretName: {{ include "lightrag.fullname" . }}-env + {{- if .Values.persistence.enabled }} + - name: rag-storage + persistentVolumeClaim: + claimName: {{ include "lightrag.fullname" . }}-rag-storage + - name: inputs + persistentVolumeClaim: + claimName: {{ include "lightrag.fullname" . }}-inputs + {{- else }} + - name: rag-storage + emptyDir: {} + - name: inputs + emptyDir: {} + {{- end }} diff --git a/k8s-deploy/lightrag/templates/pvc.yaml b/k8s-deploy/lightrag/templates/pvc.yaml new file mode 100644 index 00000000..570474bc --- /dev/null +++ b/k8s-deploy/lightrag/templates/pvc.yaml @@ -0,0 +1,28 @@ +{{- if .Values.persistence.enabled }} +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: {{ include "lightrag.fullname" . }}-rag-storage + labels: + {{- include "lightrag.labels" . | nindent 4 }} +spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: {{ .Values.persistence.ragStorage.size }} +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: {{ include "lightrag.fullname" . }}-inputs + labels: + {{- include "lightrag.labels" . | nindent 4 }} +spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: {{ .Values.persistence.inputs.size }} +{{- end }} diff --git a/k8s-deploy/lightrag/templates/secret.yaml b/k8s-deploy/lightrag/templates/secret.yaml new file mode 100644 index 00000000..8f403ab8 --- /dev/null +++ b/k8s-deploy/lightrag/templates/secret.yaml @@ -0,0 +1,10 @@ +apiVersion: v1 +kind: Secret +metadata: + name: {{ include "lightrag.fullname" . }}-env + labels: + {{- include "lightrag.labels" . | nindent 4 }} +type: Opaque +stringData: + .env: |- + {{- include "lightrag.envContent" . | nindent 4 }} diff --git a/k8s-deploy/lightrag/templates/service.yaml b/k8s-deploy/lightrag/templates/service.yaml new file mode 100644 index 00000000..e5525e18 --- /dev/null +++ b/k8s-deploy/lightrag/templates/service.yaml @@ -0,0 +1,15 @@ +apiVersion: v1 +kind: Service +metadata: + name: {{ include "lightrag.fullname" . }} + labels: + {{- include "lightrag.labels" . | nindent 4 }} +spec: + type: {{ .Values.service.type }} + ports: + - port: {{ .Values.service.port }} + targetPort: {{ .Values.env.PORT }} + protocol: TCP + name: http + selector: + {{- include "lightrag.selectorLabels" . | nindent 4 }} diff --git a/k8s-deploy/lightrag/values.yaml b/k8s-deploy/lightrag/values.yaml new file mode 100644 index 00000000..fb1bbe31 --- /dev/null +++ b/k8s-deploy/lightrag/values.yaml @@ -0,0 +1,58 @@ +replicaCount: 1 + +image: + repository: ghcr.io/hkuds/lightrag + tag: latest + +service: + type: ClusterIP + port: 9621 + +resources: + limits: + cpu: 1000m + memory: 2Gi + requests: + cpu: 500m + memory: 1Gi + +persistence: + enabled: true + ragStorage: + size: 10Gi + inputs: + size: 5Gi + +env: + HOST: 0.0.0.0 + PORT: 9621 + WEBUI_TITLE: Graph RAG Engine + WEBUI_DESCRIPTION: Simple and Fast Graph Based RAG System + LLM_BINDING: openai + LLM_MODEL: gpt-4o-mini + LLM_BINDING_HOST: + LLM_BINDING_API_KEY: + EMBEDDING_BINDING: openai + EMBEDDING_MODEL: text-embedding-ada-002 + EMBEDDING_DIM: 1536 + EMBEDDING_BINDING_API_KEY: + LIGHTRAG_KV_STORAGE: PGKVStorage + LIGHTRAG_VECTOR_STORAGE: PGVectorStorage +# LIGHTRAG_KV_STORAGE: RedisKVStorage +# LIGHTRAG_VECTOR_STORAGE: QdrantVectorDBStorage + LIGHTRAG_GRAPH_STORAGE: Neo4JStorage + LIGHTRAG_DOC_STATUS_STORAGE: PGDocStatusStorage + # Replace with your POSTGRES credentials + POSTGRES_HOST: pg-cluster-postgresql-postgresql + POSTGRES_PORT: 5432 + POSTGRES_USER: postgres + POSTGRES_PASSWORD: + POSTGRES_DATABASE: postgres + POSTGRES_WORKSPACE: default + # Replace with your NEO4J credentials + NEO4J_URI: neo4j://neo4j-cluster-neo4j:7687 + NEO4J_USERNAME: neo4j + NEO4J_PASSWORD: + # Replace with your Qdrant credentials + QDRANT_URL: http://qdrant-cluster-qdrant-qdrant:6333 + # REDIS_URI: redis://default:${REDIS_PASSWORD}@redis-cluster-redis-redis:6379 diff --git a/k8s-deploy/uninstall_lightrag.sh b/k8s-deploy/uninstall_lightrag.sh new file mode 100755 index 00000000..e4806b77 --- /dev/null +++ b/k8s-deploy/uninstall_lightrag.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +NAMESPACE=rag +helm uninstall lightrag --namespace $NAMESPACE diff --git a/k8s-deploy/uninstall_lightrag_dev.sh b/k8s-deploy/uninstall_lightrag_dev.sh new file mode 100755 index 00000000..cac01abd --- /dev/null +++ b/k8s-deploy/uninstall_lightrag_dev.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +NAMESPACE=rag +helm uninstall lightrag-dev --namespace $NAMESPACE diff --git a/lightrag/__init__.py b/lightrag/__init__.py index 2841c91e..392b3f60 100644 --- a/lightrag/__init__.py +++ b/lightrag/__init__.py @@ -1,5 +1,5 @@ from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam -__version__ = "1.3.7" +__version__ = "1.3.10" __author__ = "Zirui Guo" __url__ = "https://github.com/HKUDS/LightRAG" diff --git a/lightrag/api/README-zh.md b/lightrag/api/README-zh.md index 5709a572..1c33d835 100644 --- a/lightrag/api/README-zh.md +++ b/lightrag/api/README-zh.md @@ -41,7 +41,9 @@ LightRAG 需要同时集成 LLM（大型语言模型）和嵌入模型以有效 * openai 或 openai 兼容 * azure_openai -建议使用环境变量来配置 LightRAG 服务器。项目根目录中有一个名为 `env.example` 的示例环境变量文件。请将此文件复制到启动目录并重命名为 `.env`。之后，您可以在 `.env` 文件中修改与 LLM 和嵌入模型相关的参数。需要注意的是，LightRAG 服务器每次启动时都会将 `.env` 中的环境变量加载到系统环境变量中。由于 LightRAG 服务器会优先使用系统环境变量中的设置，如果您在通过命令行启动 LightRAG 服务器后修改了 `.env` 文件，则需要执行 `source .env` 使新设置生效。 +建议使用环境变量来配置 LightRAG 服务器。项目根目录中有一个名为 `env.example` 的示例环境变量文件。请将此文件复制到启动目录并重命名为 `.env`。之后，您可以在 `.env` 文件中修改与 LLM 和嵌入模型相关的参数。需要注意的是，LightRAG 服务器每次启动时都会将 `.env` 中的环境变量加载到系统环境变量中。**LightRAG 服务器会优先使用系统环境变量中的设置**。 + +> 由于安装了 Python 扩展的 VS Code 可能会在集成终端中自动加载 .env 文件，请在每次修改 .env 文件后打开新的终端会话。 以下是 LLM 和嵌入模型的一些常见设置示例： @@ -92,49 +94,93 @@ lightrag-server ``` lightrag-gunicorn --workers 4 ``` -`.env` 文件必须放在启动目录中。启动时，LightRAG 服务器将创建一个文档目录（默认为 `./inputs`）和一个数据目录（默认为 `./rag_storage`）。这允许您从不同目录启动多个 LightRAG 服务器实例，每个实例配置为监听不同的网络端口。 +启动LightRAG的时候，当前工作目录必须含有`.env`配置文件。**要求将.env文件置于启动目录中是经过特意设计的**。 这样做的目的是支持用户同时启动多个LightRAG实例，并为不同实例配置不同的.env文件。**修改.env文件后，您需要重新打开终端以使新设置生效**。 这是因为每次启动时，LightRAG Server会将.env文件中的环境变量加载至系统环境变量，且系统环境变量的设置具有更高优先级。 -以下是一些常用的启动参数： +启动时可以通过命令行参数覆盖`.env`文件中的配置。常用的命令行参数包括： - `--host`：服务器监听地址（默认：0.0.0.0） - `--port`：服务器监听端口（默认：9621） - `--timeout`：LLM 请求超时时间（默认：150 秒） - `--log-level`：日志级别（默认：INFO） -- --input-dir：指定要扫描文档的目录（默认：./input） +- `--working-dir`：数据库持久化目录（默认：./rag_storage） +- `--input-dir`：上传文件存放目录（默认：./inputs） +- `--workspace`: 工作空间名称，用于逻辑上隔离多个LightRAG实例之间的数据（默认：空） -> - **要求将.env文件置于启动目录中是经过特意设计的**。 这样做的目的是支持用户同时启动多个LightRAG实例，并为不同实例配置不同的.env文件。 -> - **修改.env文件后，您需要重新打开终端以使新设置生效**。 这是因为每次启动时，LightRAG Server会将.env文件中的环境变量加载至系统环境变量，且系统环境变量的设置具有更高优先级。 - -### 使用 Docker Compose 启动 LightRAG 服务器 - -* 克隆代码仓库： -``` -git clone https://github.com/HKUDS/LightRAG.git -cd LightRAG -``` +### 使用 Docker 启动 LightRAG 服务器 * 配置 .env 文件： - 通过复制 env.example 文件创建个性化的 .env 文件，并根据实际需求设置 LLM 及 Embedding 参数。 + 通过复制示例文件 [`env.example`](env.example) 创建个性化的 .env 文件，并根据实际需求设置 LLM 及 Embedding 参数。 +* 创建一个名为 docker-compose.yml 的文件： + +```yaml +services: + lightrag: + container_name: lightrag + image: ghcr.io/hkuds/lightrag:latest + ports: + - "${PORT:-9621}:9621" + volumes: + - ./data/rag_storage:/app/data/rag_storage + - ./data/inputs:/app/data/inputs + - ./config.ini:/app/config.ini + - ./.env:/app/.env + env_file: + - .env + restart: unless-stopped + extra_hosts: + - "host.docker.internal:host-gateway" +``` * 通过以下命令启动 LightRAG 服务器： -``` -docker compose up -# 如拉取了新版本，请添加 --build 重新构建 -docker compose up --build -``` -> 在此获取LightRAG docker镜像历史版本: [LightRAG Docker Images]( https://github.com/HKUDS/LightRAG/pkgs/container/lightrag) +```shell +docker compose up +# 如果希望启动后让程序退到后台运行，需要在命令的最后添加 -d 参数 +``` +> 可以通过以下链接获取官方的docker compose文件：[docker-compose.yml]( https://raw.githubusercontent.com/HKUDS/LightRAG/refs/heads/main/docker-compose.yml) 。如需获取LightRAG的历史版本镜像，可以访问以下链接: [LightRAG Docker Images]( https://github.com/HKUDS/LightRAG/pkgs/container/lightrag) ### 启动时自动扫描 -当使用 `--auto-scan-at-startup` 参数启动任何服务器时，系统将自动： +当使用 `--auto-scan-at-startup` 参数启动LightRAG Server时，系统将自动： 1. 扫描输入目录中的新文件 2. 为尚未在数据库中的新文档建立索引 3. 使所有内容立即可用于 RAG 查询 +这种工作模式给启动一个临时的RAG任务提供给了方便。 + > `--input-dir` 参数指定要扫描的输入目录。您可以从 webui 触发输入目录扫描。 +### 启动多个LightRAG实例 + +有两种方式可以启动多个LightRAG实例。第一种方式是为每个实例配置一个完全独立的工作环境。此时需要为每个实例创建一个独立的工作目录，然后在这个工作目录上放置一个当前实例专用的`.env`配置文件。不同实例的配置文件中的服务器监听端口不能重复，然后在工作目录上执行 lightrag-server 启动服务即可。 + +第二种方式是所有实例共享一套相同的`.env`配置文件，然后通过命令行参数来为每个实例指定不同的服务器监听端口和工作空间。你可以在同一个工作目录中通过不同的命令行参数启动多个LightRAG实例。例如： + +``` +# 启动实例1 +lightrag-server --port 9621 --workspace space1 + +# 启动实例2 +lightrag-server --port 9622 --workspace space2 +``` + +工作空间的作用是实现不同实例之间的数据隔离。因此不同实例之间的`workspace`参数必须不同，否则会导致数据混乱，数据将会被破坏。 + +### LightRAG实例间的数据隔离 + +每个实例配置一个独立的工作目录和专用`.env`配置文件通常能够保证内存数据库中的本地持久化文件保存在各自的工作目录，实现数据的相互隔离。LightRAG默认存储全部都是内存数据库，通过这种方式进行数据隔离是没有问题的。但是如果使用的是外部数据库，如果不同实例访问的是同一个数据库实例，就需要通过配置工作空间来实现数据隔离，否则不同实例的数据将会出现冲突并被破坏。 + +命令行的 workspace 参数和`.env`文件中的环境变量`WORKSPACE` 都可以用于指定当前实例的工作空间名字，命令行参数的优先级别更高。下面是不同类型的存储实现工作空间的方式： + +- **对于本地基于文件的数据库，数据隔离通过工作空间子目录实现：** JsonKVStorage, JsonDocStatusStorage, NetworkXStorage, NanoVectorDBStorage, FaissVectorDBStorage。 +- **对于将数据存储在集合（collection）中的数据库，通过在集合名称前添加工作空间前缀来实现：** RedisKVStorage, RedisDocStatusStorage, MilvusVectorDBStorage, QdrantVectorDBStorage, MongoKVStorage, MongoDocStatusStorage, MongoVectorDBStorage, MongoGraphStorage, PGGraphStorage。 +- **对于关系型数据库，数据隔离通过向表中添加 `workspace` 字段进行数据的逻辑隔离：** PGKVStorage, PGVectorStorage, PGDocStatusStorage。 + +* **对于Neo4j图数据库，通过label来实现数据的逻辑隔离**：Neo4JStorage + +为了保持对遗留数据的兼容，在未配置工作空间时PostgreSQL的默认工作空间为`default`，Neo4j的默认工作空间为`base`。对于所有的外部存储，系统都提供了专用的工作空间环境变量，用于覆盖公共的 `WORKSPACE`环境变量配置。这些适用于指定存储类型的工作空间环境变量为：`REDIS_WORKSPACE`, `MILVUS_WORKSPACE`, `QDRANT_WORKSPACE`, `MONGODB_WORKSPACE`, `POSTGRES_WORKSPACE`, `NEO4J_WORKSPACE`。 + ### Gunicorn + Uvicorn 的多工作进程 LightRAG 服务器可以在 `Gunicorn + Uvicorn` 预加载模式下运行。Gunicorn 的多工作进程（多进程）功能可以防止文档索引任务阻塞 RAG 查询。使用 CPU 密集型文档提取工具（如 docling）在纯 Uvicorn 模式下可能会导致整个系统被阻塞。 @@ -445,8 +491,6 @@ EMBEDDING_BINDING_HOST=http://localhost:11434 # WHITELIST_PATHS=/health,/api/* ``` - - #### 使用 ollama 默认本地服务器作为 llm 和嵌入后端运行 Lightrag 服务器 Ollama 是 llm 和嵌入的默认后端，因此默认情况下您可以不带参数运行 lightrag-server，将使用默认值。确保已安装 ollama 并且正在运行，且默认模型已安装在 ollama 上。 @@ -516,6 +560,23 @@ lightrag-server --help pip install lightrag-hku ``` +## 文档和块处理逻辑说明 + +LightRAG 中的文档处理流程有些复杂，分为两个主要阶段：提取阶段（实体和关系提取）和合并阶段（实体和关系合并）。有两个关键参数控制流程并发性：并行处理的最大文件数（`MAX_PARALLEL_INSERT`）和最大并发 LLM 请求数（`MAX_ASYNC`）。工作流程描述如下： + +1. `MAX_PARALLEL_INSERT` 控制提取阶段并行处理的文件数量。 +2. `MAX_ASYNC` 限制系统中并发 LLM 请求的总数，包括查询、提取和合并的请求。LLM 请求具有不同的优先级：查询操作优先级最高，其次是合并，然后是提取。 +3. 在单个文件中，来自不同文本块的实体和关系提取是并发处理的，并发度由 `MAX_ASYNC` 设置。只有在处理完 `MAX_ASYNC` 个文本块后，系统才会继续处理同一文件中的下一批文本块。 +4. 合并阶段仅在文件中所有文本块完成实体和关系提取后开始。当一个文件进入合并阶段时，流程允许下一个文件开始提取。 +5. 由于提取阶段通常比合并阶段快，因此实际并发处理的文件数可能会超过 `MAX_PARALLEL_INSERT`，因为此参数仅控制提取阶段的并行度。 +6. 为防止竞争条件，合并阶段不支持多个文件的并发处理；一次只能合并一个文件，其他文件必须在队列中等待。 +7. 每个文件在流程中被视为一个原子处理单元。只有当其所有文本块都完成提取和合并后，文件才会被标记为成功处理。如果在处理过程中发生任何错误，整个文件将被标记为失败，并且必须重新处理。 +8. 当由于错误而重新处理文件时，由于 LLM 缓存，先前处理的文本块可以快速跳过。尽管 LLM 缓存在合并阶段也会被利用，但合并顺序的不一致可能会限制其在此阶段的有效性。 +9. 如果在提取过程中发生错误，系统不会保留任何中间结果。如果在合并过程中发生错误，已合并的实体和关系可能会被保留；当重新处理同一文件时，重新提取的实体和关系将与现有实体和关系合并，而不会影响查询结果。 +10. 在合并阶段结束时，所有实体和关系数据都会在向量数据库中更新。如果此时发生错误，某些更新可能会被保留。但是，下一次处理尝试将覆盖先前结果，确保成功重新处理的文件不会影响未来查询结果的完整性。 + +大型文件应分割成较小的片段以启用增量处理。可以通过在 Web UI 上按“扫描”按钮来启动失败文件的重新处理。 + ## API 端点 所有服务器（LoLLMs、Ollama、OpenAI 和 Azure OpenAI）都为 RAG 功能提供相同的 REST API 端点。当 API 服务器运行时，访问： diff --git a/lightrag/api/README.md b/lightrag/api/README.md index 7b4771cf..915ad7f3 100644 --- a/lightrag/api/README.md +++ b/lightrag/api/README.md @@ -41,7 +41,9 @@ LightRAG necessitates the integration of both an LLM (Large Language Model) and * openai or openai compatible * azure_openai -It is recommended to use environment variables to configure the LightRAG Server. There is an example environment variable file named `env.example` in the root directory of the project. Please copy this file to the startup directory and rename it to `.env`. After that, you can modify the parameters related to the LLM and Embedding models in the `.env` file. It is important to note that the LightRAG Server will load the environment variables from `.env` into the system environment variables each time it starts. Since the LightRAG Server will prioritize the settings in the system environment variables, if you modify the `.env` file after starting the LightRAG Server via the command line, you need to execute `source .env` to make the new settings take effect. +It is recommended to use environment variables to configure the LightRAG Server. There is an example environment variable file named `env.example` in the root directory of the project. Please copy this file to the startup directory and rename it to `.env`. After that, you can modify the parameters related to the LLM and Embedding models in the `.env` file. It is important to note that the LightRAG Server will load the environment variables from `.env` into the system environment variables each time it starts. **LightRAG Server will prioritize the settings in the system environment variables to .env file**. + +> Since VS Code with the Python extension may automatically load the .env file in the integrated terminal, please open a new terminal session after each modification to the .env file. Here are some examples of common settings for LLM and Embedding models: @@ -92,51 +94,95 @@ lightrag-server ``` lightrag-gunicorn --workers 4 ``` -The `.env` file **must be placed in the startup directory**. -Upon launching, the LightRAG Server will create a documents directory (default is `./inputs`) and a data directory (default is `./rag_storage`). This allows you to initiate multiple instances of LightRAG Server from different directories, with each instance configured to listen on a distinct network port. +When starting LightRAG, the current working directory must contain the `.env` configuration file. **It is intentionally designed that the `.env` file must be placed in the startup directory**. The purpose of this is to allow users to launch multiple LightRAG instances simultaneously and configure different `.env` files for different instances. **After modifying the `.env` file, you need to reopen the terminal for the new settings to take effect.** This is because each time LightRAG Server starts, it loads the environment variables from the `.env` file into the system environment variables, and system environment variables have higher precedence. -Here are some commonly used startup parameters: +During startup, configurations in the `.env` file can be overridden by command-line parameters. Common command-line parameters include: - `--host`: Server listening address (default: 0.0.0.0) - `--port`: Server listening port (default: 9621) - `--timeout`: LLM request timeout (default: 150 seconds) -- `--log-level`: Logging level (default: INFO) -- `--input-dir`: Specifying the directory to scan for documents (default: ./inputs) +- `--log-level`: Log level (default: INFO) +- `--working-dir`: Database persistence directory (default: ./rag_storage) +- `--input-dir`: Directory for uploaded files (default: ./inputs) +- `--workspace`: Workspace name, used to logically isolate data between multiple LightRAG instances (default: empty) -> - The requirement for the .env file to be in the startup directory is intentionally designed this way. The purpose is to support users in launching multiple LightRAG instances simultaneously, allowing different .env files for different instances. -> - **After changing the .env file, you need to open a new terminal to make the new settings take effect.** This because the LightRAG Server will load the environment variables from .env into the system environment variables each time it starts, and LightRAG Server will prioritize the settings in the system environment variables. - -### Launching the LightRAG Server with Docker Compose - -* Clone the repository: -``` -git clone https://github.com/HKUDS/LightRAG.git -cd LightRAG -``` +### Launching LightRAG Server with Docker * Prepare the .env file: - Create a personalized .env file by duplicating env.example. Configure the LLM and embedding parameters according to your requirements. + Create a personalized .env file by copying the sample file [`env.example`](env.example). Configure the LLM and embedding parameters according to your requirements. -* Start the LightRAG Server using the following commands: +* Create a file named `docker-compose.yml`: + +```yaml +services: + lightrag: + container_name: lightrag + image: ghcr.io/hkuds/lightrag:latest + ports: + - "${PORT:-9621}:9621" + volumes: + - ./data/rag_storage:/app/data/rag_storage + - ./data/inputs:/app/data/inputs + - ./config.ini:/app/config.ini + - ./.env:/app/.env + env_file: + - .env + restart: unless-stopped + extra_hosts: + - "host.docker.internal:host-gateway" ``` + +* Start the LightRAG Server with the following command: + +```shell docker compose up -# Use --build if you have pulled a new version -docker compose up --build +# If you want the program to run in the background after startup, add the -d parameter at the end of the command. ``` -> Historical versions of LightRAG docker images can be found here: [LightRAG Docker Images]( https://github.com/HKUDS/LightRAG/pkgs/container/lightrag) +> You can get the official docker compose file from here: [docker-compose.yml](https://raw.githubusercontent.com/HKUDS/LightRAG/refs/heads/main/docker-compose.yml). For historical versions of LightRAG docker images, visit this link: [LightRAG Docker Images](https://github.com/HKUDS/LightRAG/pkgs/container/lightrag) ### Auto scan on startup -When starting any of the servers with the `--auto-scan-at-startup` parameter, the system will automatically: +When starting the LightRAG Server with the `--auto-scan-at-startup` parameter, the system will automatically: 1. Scan for new files in the input directory 2. Index new documents that aren't already in the database 3. Make all content immediately available for RAG queries +This offers an efficient method for deploying ad-hoc RAG processes. + > The `--input-dir` parameter specifies the input directory to scan. You can trigger the input directory scan from the Web UI. +### Starting Multiple LightRAG Instances + +There are two ways to start multiple LightRAG instances. The first way is to configure a completely independent working environment for each instance. This requires creating a separate working directory for each instance and placing a dedicated `.env` configuration file in that directory. The server listening ports in the configuration files of different instances cannot be the same. Then, you can start the service by running `lightrag-server` in the working directory. + +The second way is for all instances to share the same set of `.env` configuration files, and then use command-line arguments to specify different server listening ports and workspaces for each instance. You can start multiple LightRAG instances in the same working directory with different command-line arguments. For example: + +``` +# Start instance 1 +lightrag-server --port 9621 --workspace space1 + +# Start instance 2 +lightrag-server --port 9622 --workspace space2 +``` + +The purpose of a workspace is to achieve data isolation between different instances. Therefore, the `workspace` parameter must be different for different instances; otherwise, it will lead to data confusion and corruption. + +### Data Isolation Between LightRAG Instances + +Configuring an independent working directory and a dedicated `.env` configuration file for each instance can generally ensure that locally persisted files in the in-memory database are saved in their respective working directories, achieving data isolation. By default, LightRAG uses all in-memory databases, and this method of data isolation is sufficient. However, if you are using an external database, and different instances access the same database instance, you need to use workspaces to achieve data isolation; otherwise, the data of different instances will conflict and be destroyed. + +The command-line `workspace` argument and the `WORKSPACE` environment variable in the `.env` file can both be used to specify the workspace name for the current instance, with the command-line argument having higher priority. Here is how workspaces are implemented for different types of storage: + +- **For local file-based databases, data isolation is achieved through workspace subdirectories:** `JsonKVStorage`, `JsonDocStatusStorage`, `NetworkXStorage`, `NanoVectorDBStorage`, `FaissVectorDBStorage`. +- **For databases that store data in collections, it's done by adding a workspace prefix to the collection name:** `RedisKVStorage`, `RedisDocStatusStorage`, `MilvusVectorDBStorage`, `QdrantVectorDBStorage`, `MongoKVStorage`, `MongoDocStatusStorage`, `MongoVectorDBStorage`, `MongoGraphStorage`, `PGGraphStorage`. +- **For relational databases, data isolation is achieved by adding a `workspace` field to the tables for logical data separation:** `PGKVStorage`, `PGVectorStorage`, `PGDocStatusStorage`. +- **For the Neo4j graph database, logical data isolation is achieved through labels:** `Neo4JStorage` + +To maintain compatibility with legacy data, the default workspace for PostgreSQL is `default` and for Neo4j is `base` when no workspace is configured. For all external storages, the system provides dedicated workspace environment variables to override the common `WORKSPACE` environment variable configuration. These storage-specific workspace environment variables are: `REDIS_WORKSPACE`, `MILVUS_WORKSPACE`, `QDRANT_WORKSPACE`, `MONGODB_WORKSPACE`, `POSTGRES_WORKSPACE`, `NEO4J_WORKSPACE`. + ### Multiple workers for Gunicorn + Uvicorn The LightRAG Server can operate in the `Gunicorn + Uvicorn` preload mode. Gunicorn's multiple worker (multiprocess) capability prevents document indexing tasks from blocking RAG queries. Using CPU-exhaustive document extraction tools, such as docling, can lead to the entire system being blocked in pure Uvicorn mode. @@ -449,6 +495,22 @@ EMBEDDING_BINDING_HOST=http://localhost:11434 ``` +## Document and Chunk Processing Login Clarification + +The document processing pipeline in LightRAG is somewhat complex and is divided into two primary stages: the Extraction stage (entity and relationship extraction) and the Merging stage (entity and relationship merging). There are two key parameters that control pipeline concurrency: the maximum number of files processed in parallel (MAX_PARALLEL_INSERT) and the maximum number of concurrent LLM requests (MAX_ASYNC). The workflow is described as follows: + +1. MAX_PARALLEL_INSERT controls the number of files processed in parallel during the extraction stage. +2. MAX_ASYNC limits the total number of concurrent LLM requests in the system, including those for querying, extraction, and merging. LLM requests have different priorities: query operations have the highest priority, followed by merging, and then extraction. +3. Within a single file, entity and relationship extractions from different text blocks are processed concurrently, with the degree of concurrency set by MAX_ASYNC. Only after MAX_ASYNC text blocks are processed will the system proceed to the next batch within the same file. +4. The merging stage begins only after all text blocks in a file have completed entity and relationship extraction. When a file enters the merging stage, the pipeline allows the next file to begin extraction. +5. Since the extraction stage is generally faster than merging, the actual number of files being processed concurrently may exceed MAX_PARALLEL_INSERT, as this parameter only controls parallelism during the extraction stage. +6. To prevent race conditions, the merging stage does not support concurrent processing of multiple files; only one file can be merged at a time, while other files must wait in queue. +7. Each file is treated as an atomic processing unit in the pipeline. A file is marked as successfully processed only after all its text blocks have completed extraction and merging. If any error occurs during processing, the entire file is marked as failed and must be reprocessed. +8. When a file is reprocessed due to errors, previously processed text blocks can be quickly skipped thanks to LLM caching. Although LLM cache is also utilized during the merging stage, inconsistencies in merging order may limit its effectiveness in this stage. +9. If an error occurs during extraction, the system does not retain any intermediate results. If an error occurs during merging, already merged entities and relationships might be preserved; when the same file is reprocessed, re-extracted entities and relationships will be merged with the existing ones, without impacting the query results. +10. At the end of the merging stage, all entity and relationship data are updated in the vector database. Should an error occur at this point, some updates may be retained. However, the next processing attempt will overwrite previous results, ensuring that successfully reprocessed files do not affect the integrity of future query results. + +Large files should be divided into smaller segments to enable incremental processing. Reprocessing of failed files can be initiated by pressing the "Scan" button on the web UI. ## API Endpoints diff --git a/lightrag/api/__init__.py b/lightrag/api/__init__.py index ea0310c0..7a52ac82 100644 --- a/lightrag/api/__init__.py +++ b/lightrag/api/__init__.py @@ -1 +1 @@ -__api_version__ = "0170" +__api_version__ = "0180" diff --git a/lightrag/api/config.py b/lightrag/api/config.py index 006ab452..ad0e670b 100644 --- a/lightrag/api/config.py +++ b/lightrag/api/config.py @@ -184,10 +184,10 @@ def parse_args() -> argparse.Namespace: # Namespace parser.add_argument( - "--namespace-prefix", + "--workspace", type=str, - default=get_env_value("NAMESPACE_PREFIX", ""), - help="Prefix of the namespace", + default=get_env_value("WORKSPACE", ""), + help="Default workspace for all storage", ) parser.add_argument( @@ -244,6 +244,9 @@ def parse_args() -> argparse.Namespace: # Get MAX_PARALLEL_INSERT from environment args.max_parallel_insert = get_env_value("MAX_PARALLEL_INSERT", 2, int) + # Get MAX_GRAPH_NODES from environment + args.max_graph_nodes = get_env_value("MAX_GRAPH_NODES", 1000, int) + # Handle openai-ollama special case if args.llm_binding == "openai-ollama": args.llm_binding = "openai" diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py index f159bfc4..cd87af22 100644 --- a/lightrag/api/lightrag_server.py +++ b/lightrag/api/lightrag_server.py @@ -112,8 +112,8 @@ def create_app(args): # Check if API key is provided either through env var or args api_key = os.getenv("LIGHTRAG_API_KEY") or args.key - # Initialize document manager - doc_manager = DocumentManager(args.input_dir) + # Initialize document manager with workspace support for data isolation + doc_manager = DocumentManager(args.input_dir, workspace=args.workspace) @asynccontextmanager async def lifespan(app: FastAPI): @@ -295,6 +295,7 @@ def create_app(args): if args.llm_binding in ["lollms", "ollama", "openai"]: rag = LightRAG( working_dir=args.working_dir, + workspace=args.workspace, llm_model_func=lollms_model_complete if args.llm_binding == "lollms" else ollama_model_complete @@ -325,11 +326,13 @@ def create_app(args): enable_llm_cache=args.enable_llm_cache, auto_manage_storages_states=False, max_parallel_insert=args.max_parallel_insert, + max_graph_nodes=args.max_graph_nodes, addon_params={"language": args.summary_language}, ) else: # azure_openai rag = LightRAG( working_dir=args.working_dir, + workspace=args.workspace, llm_model_func=azure_openai_model_complete, chunk_token_size=int(args.chunk_size), chunk_overlap_token_size=int(args.chunk_overlap_size), @@ -351,11 +354,18 @@ def create_app(args): enable_llm_cache=args.enable_llm_cache, auto_manage_storages_states=False, max_parallel_insert=args.max_parallel_insert, + max_graph_nodes=args.max_graph_nodes, addon_params={"language": args.summary_language}, ) # Add routes - app.include_router(create_document_routes(rag, doc_manager, api_key)) + app.include_router( + create_document_routes( + rag, + doc_manager, + api_key, + ) + ) app.include_router(create_query_routes(rag, api_key, args.top_k)) app.include_router(create_graph_routes(rag, api_key)) @@ -466,6 +476,8 @@ def create_app(args): "vector_storage": args.vector_storage, "enable_llm_cache_for_extract": args.enable_llm_cache_for_extract, "enable_llm_cache": args.enable_llm_cache, + "workspace": args.workspace, + "max_graph_nodes": args.max_graph_nodes, }, "auth_mode": auth_mode, "pipeline_busy": pipeline_status.get("busy", False), @@ -478,16 +490,31 @@ def create_app(args): logger.error(f"Error getting health status: {str(e)}") raise HTTPException(status_code=500, detail=str(e)) - # Custom StaticFiles class to prevent caching of HTML files - class NoCacheStaticFiles(StaticFiles): + # Custom StaticFiles class for smart caching + class SmartStaticFiles(StaticFiles): # Renamed from NoCacheStaticFiles async def get_response(self, path: str, scope): response = await super().get_response(path, scope) + if path.endswith(".html"): response.headers["Cache-Control"] = ( "no-cache, no-store, must-revalidate" ) response.headers["Pragma"] = "no-cache" response.headers["Expires"] = "0" + elif ( + "/assets/" in path + ): # Assets (JS, CSS, images, fonts) generated by Vite with hash in filename + response.headers["Cache-Control"] = ( + "public, max-age=31536000, immutable" + ) + # Add other rules here if needed for non-HTML, non-asset files + + # Ensure correct Content-Type + if path.endswith(".js"): + response.headers["Content-Type"] = "application/javascript" + elif path.endswith(".css"): + response.headers["Content-Type"] = "text/css" + return response # Webui mount webui/index.html @@ -495,7 +522,9 @@ def create_app(args): static_dir.mkdir(exist_ok=True) app.mount( "/webui", - NoCacheStaticFiles(directory=static_dir, html=True, check_dir=True), + SmartStaticFiles( + directory=static_dir, html=True, check_dir=True + ), # Use SmartStaticFiles name="webui", ) diff --git a/lightrag/api/requirements.txt b/lightrag/api/requirements.txt index 80fb74cf..808f95b5 100644 --- a/lightrag/api/requirements.txt +++ b/lightrag/api/requirements.txt @@ -4,7 +4,6 @@ asyncpg distro dotenv fastapi -graspologic>=3.4.1 httpcore httpx jiter diff --git a/lightrag/api/routers/document_routes.py b/lightrag/api/routers/document_routes.py index aa5af7e5..288a66e1 100644 --- a/lightrag/api/routers/document_routes.py +++ b/lightrag/api/routers/document_routes.py @@ -12,11 +12,18 @@ import pipmaster as pm from datetime import datetime, timezone from pathlib import Path from typing import Dict, List, Optional, Any, Literal -from fastapi import APIRouter, BackgroundTasks, Depends, File, HTTPException, UploadFile +from fastapi import ( + APIRouter, + BackgroundTasks, + Depends, + File, + HTTPException, + UploadFile, +) from pydantic import BaseModel, Field, field_validator from lightrag import LightRAG -from lightrag.base import DocProcessingStatus, DocStatus +from lightrag.base import DeletionResult, DocProcessingStatus, DocStatus from lightrag.api.utils_api import get_combined_auth_dependency from ..config import global_args @@ -55,6 +62,51 @@ router = APIRouter( temp_prefix = "__tmp__" +def sanitize_filename(filename: str, input_dir: Path) -> str: + """ + Sanitize uploaded filename to prevent Path Traversal attacks. + + Args: + filename: The original filename from the upload + input_dir: The target input directory + + Returns: + str: Sanitized filename that is safe to use + + Raises: + HTTPException: If the filename is unsafe or invalid + """ + # Basic validation + if not filename or not filename.strip(): + raise HTTPException(status_code=400, detail="Filename cannot be empty") + + # Remove path separators and traversal sequences + clean_name = filename.replace("/", "").replace("\\", "") + clean_name = clean_name.replace("..", "") + + # Remove control characters and null bytes + clean_name = "".join(c for c in clean_name if ord(c) >= 32 and c != "\x7f") + + # Remove leading/trailing whitespace and dots + clean_name = clean_name.strip().strip(".") + + # Check if anything is left after sanitization + if not clean_name: + raise HTTPException( + status_code=400, detail="Invalid filename after sanitization" + ) + + # Verify the final path stays within the input directory + try: + final_path = (input_dir / clean_name).resolve() + if not final_path.is_relative_to(input_dir.resolve()): + raise HTTPException(status_code=400, detail="Unsafe filename detected") + except (OSError, ValueError): + raise HTTPException(status_code=400, detail="Invalid filename") + + return clean_name + + class ScanResponse(BaseModel): """Response model for document scanning operation @@ -84,22 +136,30 @@ class InsertTextRequest(BaseModel): Attributes: text: The text content to be inserted into the RAG system + file_source: Source of the text (optional) """ text: str = Field( min_length=1, description="The text to insert", ) + file_source: str = Field(default=None, min_length=0, description="File Source") @field_validator("text", mode="after") @classmethod - def strip_after(cls, text: str) -> str: + def strip_text_after(cls, text: str) -> str: return text.strip() + @field_validator("file_source", mode="after") + @classmethod + def strip_source_after(cls, file_source: str) -> str: + return file_source.strip() + class Config: json_schema_extra = { "example": { - "text": "This is a sample text to be inserted into the RAG system." + "text": "This is a sample text to be inserted into the RAG system.", + "file_source": "Source of the text (optional)", } } @@ -109,25 +169,37 @@ class InsertTextsRequest(BaseModel): Attributes: texts: List of text contents to be inserted into the RAG system + file_sources: Sources of the texts (optional) """ texts: list[str] = Field( min_length=1, description="The texts to insert", ) + file_sources: list[str] = Field( + default=None, min_length=0, description="Sources of the texts" + ) @field_validator("texts", mode="after") @classmethod - def strip_after(cls, texts: list[str]) -> list[str]: + def strip_texts_after(cls, texts: list[str]) -> list[str]: return [text.strip() for text in texts] + @field_validator("file_sources", mode="after") + @classmethod + def strip_sources_after(cls, file_sources: list[str]) -> list[str]: + return [file_source.strip() for file_source in file_sources] + class Config: json_schema_extra = { "example": { "texts": [ "This is the first text to be inserted.", "This is the second text to be inserted.", - ] + ], + "file_sources": [ + "First file source (optional)", + ], } } @@ -232,6 +304,55 @@ Attributes: """ +class DeleteDocRequest(BaseModel): + doc_ids: List[str] = Field(..., description="The IDs of the documents to delete.") + delete_file: bool = Field( + default=False, + description="Whether to delete the corresponding file in the upload directory.", + ) + + @field_validator("doc_ids", mode="after") + @classmethod + def validate_doc_ids(cls, doc_ids: List[str]) -> List[str]: + if not doc_ids: + raise ValueError("Document IDs list cannot be empty") + + validated_ids = [] + for doc_id in doc_ids: + if not doc_id or not doc_id.strip(): + raise ValueError("Document ID cannot be empty") + validated_ids.append(doc_id.strip()) + + # Check for duplicates + if len(validated_ids) != len(set(validated_ids)): + raise ValueError("Document IDs must be unique") + + return validated_ids + + +class DeleteEntityRequest(BaseModel): + entity_name: str = Field(..., description="The name of the entity to delete.") + + @field_validator("entity_name", mode="after") + @classmethod + def validate_entity_name(cls, entity_name: str) -> str: + if not entity_name or not entity_name.strip(): + raise ValueError("Entity name cannot be empty") + return entity_name.strip() + + +class DeleteRelationRequest(BaseModel): + source_entity: str = Field(..., description="The name of the source entity.") + target_entity: str = Field(..., description="The name of the target entity.") + + @field_validator("source_entity", "target_entity", mode="after") + @classmethod + def validate_entity_names(cls, entity_name: str) -> str: + if not entity_name or not entity_name.strip(): + raise ValueError("Entity name cannot be empty") + return entity_name.strip() + + class DocStatusResponse(BaseModel): id: str = Field(description="Document identifier") content_summary: str = Field(description="Summary of document content") @@ -354,6 +475,7 @@ class DocumentManager: def __init__( self, input_dir: str, + workspace: str = "", # New parameter for workspace isolation supported_extensions: tuple = ( ".txt", ".md", @@ -394,10 +516,19 @@ class DocumentManager: ".less", # LESS CSS ), ): - self.input_dir = Path(input_dir) + # Store the base input directory and workspace + self.base_input_dir = Path(input_dir) + self.workspace = workspace self.supported_extensions = supported_extensions self.indexed_files = set() + # Create workspace-specific input directory + # If workspace is provided, create a subdirectory for data isolation + if workspace: + self.input_dir = self.base_input_dir / workspace + else: + self.input_dir = self.base_input_dir + # Create input directory if it doesn't exist self.input_dir.mkdir(parents=True, exist_ok=True) @@ -593,6 +724,12 @@ async def pipeline_enqueue_file(rag: LightRAG, file_path: Path) -> bool: # Insert into the RAG queue if content: + # Check if content contains only whitespace characters + if not content.strip(): + logger.warning( + f"File contains only whitespace characters. file_paths={file_path.name}" + ) + await rag.apipeline_enqueue_documents(content, file_paths=file_path.name) logger.info(f"Successfully fetched and enqueued file: {file_path.name}") return True @@ -656,16 +793,25 @@ async def pipeline_index_files(rag: LightRAG, file_paths: List[Path]): logger.error(traceback.format_exc()) -async def pipeline_index_texts(rag: LightRAG, texts: List[str]): +async def pipeline_index_texts( + rag: LightRAG, texts: List[str], file_sources: List[str] = None +): """Index a list of texts Args: rag: LightRAG instance texts: The texts to index + file_sources: Sources of the texts """ if not texts: return - await rag.apipeline_enqueue_documents(texts) + if file_sources is not None: + if len(file_sources) != 0 and len(file_sources) != len(texts): + [ + file_sources.append("unknown_source") + for _ in range(len(file_sources), len(texts)) + ] + await rag.apipeline_enqueue_documents(input=texts, file_paths=file_sources) await rag.apipeline_process_enqueue_documents() @@ -698,7 +844,7 @@ async def run_scanning_process(rag: LightRAG, doc_manager: DocumentManager): try: new_files = doc_manager.scan_directory_for_new_files() total_files = len(new_files) - logger.info(f"Found {total_files} new files to index.") + logger.info(f"Found {total_files} files to index.") if not new_files: return @@ -712,6 +858,161 @@ async def run_scanning_process(rag: LightRAG, doc_manager: DocumentManager): logger.error(traceback.format_exc()) +async def background_delete_documents( + rag: LightRAG, + doc_manager: DocumentManager, + doc_ids: List[str], + delete_file: bool = False, +): + """Background task to delete multiple documents""" + from lightrag.kg.shared_storage import ( + get_namespace_data, + get_pipeline_status_lock, + ) + + pipeline_status = await get_namespace_data("pipeline_status") + pipeline_status_lock = get_pipeline_status_lock() + + total_docs = len(doc_ids) + successful_deletions = [] + failed_deletions = [] + + # Double-check pipeline status before proceeding + async with pipeline_status_lock: + if pipeline_status.get("busy", False): + logger.warning("Error: Unexpected pipeline busy state, aborting deletion.") + return # Abort deletion operation + + # Set pipeline status to busy for deletion + pipeline_status.update( + { + "busy": True, + "job_name": f"Deleting {total_docs} Documents", + "job_start": datetime.now().isoformat(), + "docs": total_docs, + "batchs": total_docs, + "cur_batch": 0, + "latest_message": "Starting document deletion process", + } + ) + # Use slice assignment to clear the list in place + pipeline_status["history_messages"][:] = ["Starting document deletion process"] + + try: + # Loop through each document ID and delete them one by one + for i, doc_id in enumerate(doc_ids, 1): + async with pipeline_status_lock: + start_msg = f"Deleting document {i}/{total_docs}: {doc_id}" + logger.info(start_msg) + pipeline_status["cur_batch"] = i + pipeline_status["latest_message"] = start_msg + pipeline_status["history_messages"].append(start_msg) + + file_path = "#" + try: + result = await rag.adelete_by_doc_id(doc_id) + file_path = ( + getattr(result, "file_path", "-") if "result" in locals() else "-" + ) + if result.status == "success": + successful_deletions.append(doc_id) + success_msg = ( + f"Deleted document {i}/{total_docs}: {doc_id}[{file_path}]" + ) + logger.info(success_msg) + async with pipeline_status_lock: + pipeline_status["history_messages"].append(success_msg) + + # Handle file deletion if requested and file_path is available + if ( + delete_file + and result.file_path + and result.file_path != "unknown_source" + ): + try: + file_path = doc_manager.input_dir / result.file_path + if file_path.exists(): + file_path.unlink() + file_delete_msg = ( + f"Successfully deleted file: {result.file_path}" + ) + logger.info(file_delete_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = file_delete_msg + pipeline_status["history_messages"].append( + file_delete_msg + ) + else: + file_not_found_msg = ( + f"File not found for deletion: {result.file_path}" + ) + logger.warning(file_not_found_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = ( + file_not_found_msg + ) + pipeline_status["history_messages"].append( + file_not_found_msg + ) + except Exception as file_error: + file_error_msg = f"Failed to delete file {result.file_path}: {str(file_error)}" + logger.error(file_error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = file_error_msg + pipeline_status["history_messages"].append( + file_error_msg + ) + elif delete_file: + no_file_msg = f"No valid file path found for document {doc_id}" + logger.warning(no_file_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = no_file_msg + pipeline_status["history_messages"].append(no_file_msg) + else: + failed_deletions.append(doc_id) + error_msg = f"Failed to delete {i}/{total_docs}: {doc_id}[{file_path}] - {result.message}" + logger.error(error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append(error_msg) + + except Exception as e: + failed_deletions.append(doc_id) + error_msg = f"Error deleting document {i}/{total_docs}: {doc_id}[{file_path}] - {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append(error_msg) + + except Exception as e: + error_msg = f"Critical error during batch deletion: {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + async with pipeline_status_lock: + pipeline_status["history_messages"].append(error_msg) + finally: + # Final summary and check for pending requests + async with pipeline_status_lock: + pipeline_status["busy"] = False + completion_msg = f"Deletion completed: {len(successful_deletions)} successful, {len(failed_deletions)} failed" + pipeline_status["latest_message"] = completion_msg + pipeline_status["history_messages"].append(completion_msg) + + # Check if there are pending document indexing requests + has_pending_request = pipeline_status.get("request_pending", False) + + # If there are pending requests, start document processing pipeline + if has_pending_request: + try: + logger.info( + "Processing pending document indexing requests after deletion" + ) + await rag.apipeline_process_enqueue_documents() + except Exception as e: + logger.error(f"Error processing pending documents after deletion: {e}") + + def create_document_routes( rag: LightRAG, doc_manager: DocumentManager, api_key: Optional[str] = None ): @@ -764,18 +1065,21 @@ def create_document_routes( HTTPException: If the file type is not supported (400) or other errors occur (500). """ try: - if not doc_manager.is_supported_file(file.filename): + # Sanitize filename to prevent Path Traversal attacks + safe_filename = sanitize_filename(file.filename, doc_manager.input_dir) + + if not doc_manager.is_supported_file(safe_filename): raise HTTPException( status_code=400, detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}", ) - file_path = doc_manager.input_dir / file.filename + file_path = doc_manager.input_dir / safe_filename # Check if file already exists if file_path.exists(): return InsertResponse( status="duplicated", - message=f"File '{file.filename}' already exists in the input directory.", + message=f"File '{safe_filename}' already exists in the input directory.", ) with open(file_path, "wb") as buffer: @@ -786,7 +1090,7 @@ def create_document_routes( return InsertResponse( status="success", - message=f"File '{file.filename}' uploaded successfully. Processing will continue in background.", + message=f"File '{safe_filename}' uploaded successfully. Processing will continue in background.", ) except Exception as e: logger.error(f"Error /documents/upload: {file.filename}: {str(e)}") @@ -816,7 +1120,12 @@ def create_document_routes( HTTPException: If an error occurs during text processing (500). """ try: - background_tasks.add_task(pipeline_index_texts, rag, [request.text]) + background_tasks.add_task( + pipeline_index_texts, + rag, + [request.text], + file_sources=[request.file_source], + ) return InsertResponse( status="success", message="Text successfully received. Processing will continue in background.", @@ -851,7 +1160,12 @@ def create_document_routes( HTTPException: If an error occurs during text processing (500). """ try: - background_tasks.add_task(pipeline_index_texts, rag, request.texts) + background_tasks.add_task( + pipeline_index_texts, + rag, + request.texts, + file_sources=request.file_sources, + ) return InsertResponse( status="success", message="Text successfully received. Processing will continue in background.", @@ -861,114 +1175,6 @@ def create_document_routes( logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) - # TODO: deprecated, use /upload instead - @router.post( - "/file", response_model=InsertResponse, dependencies=[Depends(combined_auth)] - ) - async def insert_file( - background_tasks: BackgroundTasks, file: UploadFile = File(...) - ): - """ - Insert a file directly into the RAG system. - - This endpoint accepts a file upload and processes it for inclusion in the RAG system. - The file is saved temporarily and processed in the background. - - Args: - background_tasks: FastAPI BackgroundTasks for async processing - file (UploadFile): The file to be processed - - Returns: - InsertResponse: A response object containing the status of the operation. - - Raises: - HTTPException: If the file type is not supported (400) or other errors occur (500). - """ - try: - if not doc_manager.is_supported_file(file.filename): - raise HTTPException( - status_code=400, - detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}", - ) - - temp_path = await save_temp_file(doc_manager.input_dir, file) - - # Add to background tasks - background_tasks.add_task(pipeline_index_file, rag, temp_path) - - return InsertResponse( - status="success", - message=f"File '{file.filename}' saved successfully. Processing will continue in background.", - ) - except Exception as e: - logger.error(f"Error /documents/file: {str(e)}") - logger.error(traceback.format_exc()) - raise HTTPException(status_code=500, detail=str(e)) - - # TODO: deprecated, use /upload instead - @router.post( - "/file_batch", - response_model=InsertResponse, - dependencies=[Depends(combined_auth)], - ) - async def insert_batch( - background_tasks: BackgroundTasks, files: List[UploadFile] = File(...) - ): - """ - Process multiple files in batch mode. - - This endpoint allows uploading and processing multiple files simultaneously. - It handles partial successes and provides detailed feedback about failed files. - - Args: - background_tasks: FastAPI BackgroundTasks for async processing - files (List[UploadFile]): List of files to process - - Returns: - InsertResponse: A response object containing: - - status: "success", "partial_success", or "failure" - - message: Detailed information about the operation results - - Raises: - HTTPException: If an error occurs during processing (500). - """ - try: - inserted_count = 0 - failed_files = [] - temp_files = [] - - for file in files: - if doc_manager.is_supported_file(file.filename): - # Create a temporary file to save the uploaded content - temp_files.append(await save_temp_file(doc_manager.input_dir, file)) - inserted_count += 1 - else: - failed_files.append(f"{file.filename} (unsupported type)") - - if temp_files: - background_tasks.add_task(pipeline_index_files, rag, temp_files) - - # Prepare status message - if inserted_count == len(files): - status = "success" - status_message = f"Successfully inserted all {inserted_count} documents" - elif inserted_count > 0: - status = "partial_success" - status_message = f"Successfully inserted {inserted_count} out of {len(files)} documents" - if failed_files: - status_message += f". Failed files: {', '.join(failed_files)}" - else: - status = "failure" - status_message = "No documents were successfully inserted" - if failed_files: - status_message += f". Failed files: {', '.join(failed_files)}" - - return InsertResponse(status=status, message=status_message) - except Exception as e: - logger.error(f"Error /documents/batch: {str(e)}") - logger.error(traceback.format_exc()) - raise HTTPException(status_code=500, detail=str(e)) - @router.delete( "", response_model=ClearDocumentsResponse, dependencies=[Depends(combined_auth)] ) @@ -1279,6 +1485,94 @@ def create_document_routes( logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) + class DeleteDocByIdResponse(BaseModel): + """Response model for single document deletion operation.""" + + status: Literal["deletion_started", "busy", "not_allowed"] = Field( + description="Status of the deletion operation" + ) + message: str = Field(description="Message describing the operation result") + doc_id: str = Field(description="The ID of the document to delete") + + @router.delete( + "/delete_document", + response_model=DeleteDocByIdResponse, + dependencies=[Depends(combined_auth)], + summary="Delete a document and all its associated data by its ID.", + ) + async def delete_document( + delete_request: DeleteDocRequest, + background_tasks: BackgroundTasks, + ) -> DeleteDocByIdResponse: + """ + Delete documents and all their associated data by their IDs using background processing. + + Deletes specific documents and all their associated data, including their status, + text chunks, vector embeddings, and any related graph data. + The deletion process runs in the background to avoid blocking the client connection. + It is disabled when llm cache for entity extraction is disabled. + + This operation is irreversible and will interact with the pipeline status. + + Args: + delete_request (DeleteDocRequest): The request containing the document IDs and delete_file options. + background_tasks: FastAPI BackgroundTasks for async processing + + Returns: + DeleteDocByIdResponse: The result of the deletion operation. + - status="deletion_started": The document deletion has been initiated in the background. + - status="busy": The pipeline is busy with another operation. + - status="not_allowed": Operation not allowed when LLM cache for entity extraction is disabled. + + Raises: + HTTPException: + - 500: If an unexpected internal error occurs during initialization. + """ + doc_ids = delete_request.doc_ids + + # The rag object is initialized from the server startup args, + # so we can access its properties here. + if not rag.enable_llm_cache_for_entity_extract: + return DeleteDocByIdResponse( + status="not_allowed", + message="Operation not allowed when LLM cache for entity extraction is disabled.", + doc_id=", ".join(delete_request.doc_ids), + ) + + try: + from lightrag.kg.shared_storage import get_namespace_data + + pipeline_status = await get_namespace_data("pipeline_status") + + # Check if pipeline is busy + if pipeline_status.get("busy", False): + return DeleteDocByIdResponse( + status="busy", + message="Cannot delete documents while pipeline is busy", + doc_id=", ".join(doc_ids), + ) + + # Add deletion task to background tasks + background_tasks.add_task( + background_delete_documents, + rag, + doc_manager, + doc_ids, + delete_request.delete_file, + ) + + return DeleteDocByIdResponse( + status="deletion_started", + message=f"Document deletion for {len(doc_ids)} documents has been initiated. Processing will continue in background.", + doc_id=", ".join(doc_ids), + ) + + except Exception as e: + error_msg = f"Error initiating document deletion for {delete_request.doc_ids}: {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=error_msg) + @router.post( "/clear_cache", response_model=ClearCacheResponse, @@ -1332,4 +1626,77 @@ def create_document_routes( logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) + @router.delete( + "/delete_entity", + response_model=DeletionResult, + dependencies=[Depends(combined_auth)], + ) + async def delete_entity(request: DeleteEntityRequest): + """ + Delete an entity and all its relationships from the knowledge graph. + + Args: + request (DeleteEntityRequest): The request body containing the entity name. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + + Raises: + HTTPException: If the entity is not found (404) or an error occurs (500). + """ + try: + result = await rag.adelete_by_entity(entity_name=request.entity_name) + if result.status == "not_found": + raise HTTPException(status_code=404, detail=result.message) + if result.status == "fail": + raise HTTPException(status_code=500, detail=result.message) + # Set doc_id to empty string since this is an entity operation, not document + result.doc_id = "" + return result + except HTTPException: + raise + except Exception as e: + error_msg = f"Error deleting entity '{request.entity_name}': {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=error_msg) + + @router.delete( + "/delete_relation", + response_model=DeletionResult, + dependencies=[Depends(combined_auth)], + ) + async def delete_relation(request: DeleteRelationRequest): + """ + Delete a relationship between two entities from the knowledge graph. + + Args: + request (DeleteRelationRequest): The request body containing the source and target entity names. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + + Raises: + HTTPException: If the relation is not found (404) or an error occurs (500). + """ + try: + result = await rag.adelete_by_relation( + source_entity=request.source_entity, + target_entity=request.target_entity, + ) + if result.status == "not_found": + raise HTTPException(status_code=404, detail=result.message) + if result.status == "fail": + raise HTTPException(status_code=500, detail=result.message) + # Set doc_id to empty string since this is a relation operation, not document + result.doc_id = "" + return result + except HTTPException: + raise + except Exception as e: + error_msg = f"Error deleting relation from '{request.source_entity}' to '{request.target_entity}': {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=error_msg) + return router diff --git a/lightrag/api/routers/ollama_api.py b/lightrag/api/routers/ollama_api.py index 93394fbb..64c36a05 100644 --- a/lightrag/api/routers/ollama_api.py +++ b/lightrag/api/routers/ollama_api.py @@ -1,7 +1,7 @@ from fastapi import APIRouter, HTTPException, Request from pydantic import BaseModel -from typing import List, Dict, Any, Optional -import logging +from typing import List, Dict, Any, Optional, Type +from lightrag.utils import logger import time import json import re @@ -95,6 +95,68 @@ class OllamaTagResponse(BaseModel): models: List[OllamaModel] +class OllamaRunningModelDetails(BaseModel): + parent_model: str + format: str + family: str + families: List[str] + parameter_size: str + quantization_level: str + + +class OllamaRunningModel(BaseModel): + name: str + model: str + size: int + digest: str + details: OllamaRunningModelDetails + expires_at: str + size_vram: int + + +class OllamaPsResponse(BaseModel): + models: List[OllamaRunningModel] + + +async def parse_request_body( + request: Request, model_class: Type[BaseModel] +) -> BaseModel: + """ + Parse request body based on Content-Type header. + Supports both application/json and application/octet-stream. + + Args: + request: The FastAPI Request object + model_class: The Pydantic model class to parse the request into + + Returns: + An instance of the provided model_class + """ + content_type = request.headers.get("content-type", "").lower() + + try: + if content_type.startswith("application/json"): + # FastAPI already handles JSON parsing for us + body = await request.json() + elif content_type.startswith("application/octet-stream"): + # Manually parse octet-stream as JSON + body_bytes = await request.body() + body = json.loads(body_bytes.decode("utf-8")) + else: + # Try to parse as JSON for any other content type + body_bytes = await request.body() + body = json.loads(body_bytes.decode("utf-8")) + + # Create an instance of the model + return model_class(**body) + except json.JSONDecodeError: + raise HTTPException(status_code=400, detail="Invalid JSON in request body") + except Exception as e: + raise HTTPException( + status_code=400, detail=f"Error parsing request body: {str(e)}" + ) + + def estimate_tokens(text: str) -> int: """Estimate the number of tokens in text using tiktoken""" tokens = TiktokenTokenizer().encode(text) @@ -172,7 +234,7 @@ class OllamaAPI: @self.router.get("/version", dependencies=[Depends(combined_auth)]) async def get_version(): """Get Ollama version information""" - return OllamaVersionResponse(version="0.5.4") + return OllamaVersionResponse(version="0.9.3") @self.router.get("/tags", dependencies=[Depends(combined_auth)]) async def get_tags(): @@ -182,9 +244,9 @@ class OllamaAPI: { "name": self.ollama_server_infos.LIGHTRAG_MODEL, "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "modified_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, "size": self.ollama_server_infos.LIGHTRAG_SIZE, "digest": self.ollama_server_infos.LIGHTRAG_DIGEST, - "modified_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, "details": { "parent_model": "", "format": "gguf", @@ -197,13 +259,43 @@ class OllamaAPI: ] ) - @self.router.post("/generate", dependencies=[Depends(combined_auth)]) - async def generate(raw_request: Request, request: OllamaGenerateRequest): + @self.router.get("/ps", dependencies=[Depends(combined_auth)]) + async def get_running_models(): + """List Running Models - returns currently running models""" + return OllamaPsResponse( + models=[ + { + "name": self.ollama_server_infos.LIGHTRAG_MODEL, + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "size": self.ollama_server_infos.LIGHTRAG_SIZE, + "digest": self.ollama_server_infos.LIGHTRAG_DIGEST, + "details": { + "parent_model": "", + "format": "gguf", + "family": "llama", + "families": ["llama"], + "parameter_size": "7.2B", + "quantization_level": "Q4_0", + }, + "expires_at": "2050-12-31T14:38:31.83753-07:00", + "size_vram": self.ollama_server_infos.LIGHTRAG_SIZE, + } + ] + ) + + @self.router.post( + "/generate", dependencies=[Depends(combined_auth)], include_in_schema=True + ) + async def generate(raw_request: Request): """Handle generate completion requests acting as an Ollama model For compatibility purpose, the request is not processed by LightRAG, and will be handled by underlying LLM model. + Supports both application/json and application/octet-stream Content-Types. """ try: + # Parse the request body manually + request = await parse_request_body(raw_request, OllamaGenerateRequest) + query = request.prompt start_time = time.time_ns() prompt_tokens = estimate_tokens(query) @@ -245,7 +337,10 @@ class OllamaAPI: data = { "model": self.ollama_server_infos.LIGHTRAG_MODEL, "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "response": "", "done": True, + "done_reason": "stop", + "context": [], "total_duration": total_time, "load_duration": 0, "prompt_eval_count": prompt_tokens, @@ -278,13 +373,14 @@ class OllamaAPI: else: error_msg = f"Provider error: {error_msg}" - logging.error(f"Stream error: {error_msg}") + logger.error(f"Stream error: {error_msg}") # Send error message to client error_data = { "model": self.ollama_server_infos.LIGHTRAG_MODEL, "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, "response": f"\n\nError: {error_msg}", + "error": f"\n\nError: {error_msg}", "done": False, } yield f"{json.dumps(error_data, ensure_ascii=False)}\n" @@ -293,6 +389,7 @@ class OllamaAPI: final_data = { "model": self.ollama_server_infos.LIGHTRAG_MODEL, "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "response": "", "done": True, } yield f"{json.dumps(final_data, ensure_ascii=False)}\n" @@ -307,7 +404,10 @@ class OllamaAPI: data = { "model": self.ollama_server_infos.LIGHTRAG_MODEL, "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "response": "", "done": True, + "done_reason": "stop", + "context": [], "total_duration": total_time, "load_duration": 0, "prompt_eval_count": prompt_tokens, @@ -352,6 +452,8 @@ class OllamaAPI: "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, "response": str(response_text), "done": True, + "done_reason": "stop", + "context": [], "total_duration": total_time, "load_duration": 0, "prompt_eval_count": prompt_tokens, @@ -363,13 +465,19 @@ class OllamaAPI: trace_exception(e) raise HTTPException(status_code=500, detail=str(e)) - @self.router.post("/chat", dependencies=[Depends(combined_auth)]) - async def chat(raw_request: Request, request: OllamaChatRequest): + @self.router.post( + "/chat", dependencies=[Depends(combined_auth)], include_in_schema=True + ) + async def chat(raw_request: Request): """Process chat completion requests acting as an Ollama model Routes user queries through LightRAG by selecting query mode based on prefix indicators. Detects and forwards OpenWebUI session-related requests (for meta data generation task) directly to LLM. + Supports both application/json and application/octet-stream Content-Types. """ try: + # Parse the request body manually + request = await parse_request_body(raw_request, OllamaChatRequest) + # Get all messages messages = request.messages if not messages: @@ -459,6 +567,12 @@ class OllamaAPI: data = { "model": self.ollama_server_infos.LIGHTRAG_MODEL, "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "message": { + "role": "assistant", + "content": "", + "images": None, + }, + "done_reason": "stop", "done": True, "total_duration": total_time, "load_duration": 0, @@ -496,7 +610,7 @@ class OllamaAPI: else: error_msg = f"Provider error: {error_msg}" - logging.error(f"Stream error: {error_msg}") + logger.error(f"Stream error: {error_msg}") # Send error message to client error_data = { @@ -507,6 +621,7 @@ class OllamaAPI: "content": f"\n\nError: {error_msg}", "images": None, }, + "error": f"\n\nError: {error_msg}", "done": False, } yield f"{json.dumps(error_data, ensure_ascii=False)}\n" @@ -515,6 +630,11 @@ class OllamaAPI: final_data = { "model": self.ollama_server_infos.LIGHTRAG_MODEL, "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "message": { + "role": "assistant", + "content": "", + "images": None, + }, "done": True, } yield f"{json.dumps(final_data, ensure_ascii=False)}\n" @@ -530,6 +650,12 @@ class OllamaAPI: data = { "model": self.ollama_server_infos.LIGHTRAG_MODEL, "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "message": { + "role": "assistant", + "content": "", + "images": None, + }, + "done_reason": "stop", "done": True, "total_duration": total_time, "load_duration": 0, @@ -594,6 +720,7 @@ class OllamaAPI: "content": str(response_text), "images": None, }, + "done_reason": "stop", "done": True, "total_duration": total_time, "load_duration": 0, diff --git a/lightrag/api/routers/query_routes.py b/lightrag/api/routers/query_routes.py index 6fe86f79..69aa32d8 100644 --- a/lightrag/api/routers/query_routes.py +++ b/lightrag/api/routers/query_routes.py @@ -78,6 +78,10 @@ class QueryRequest(BaseModel): description="Number of complete conversation turns (user-assistant pairs) to consider in the response context.", ) + ids: list[str] | None = Field( + default=None, description="List of ids to filter the results." + ) + user_prompt: Optional[str] = Field( default=None, description="User-provided prompt for the query. If provided, this will be used instead of the default value from prompt template.", @@ -179,6 +183,9 @@ def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60): if isinstance(response, str): # If it's a string, send it all at once yield f"{json.dumps({'response': response})}\n" + elif response is None: + # Handle None response (e.g., when only_need_context=True but no context found) + yield f"{json.dumps({'response': 'No relevant context found for the query.'})}\n" else: # If it's an async generator, send chunks one by one try: diff --git a/lightrag/api/utils_api.py b/lightrag/api/utils_api.py index 13ec4ddd..a724069d 100644 --- a/lightrag/api/utils_api.py +++ b/lightrag/api/utils_api.py @@ -175,12 +175,24 @@ def display_splash_screen(args: argparse.Namespace) -> None: args: Parsed command line arguments """ # Banner - ASCIIColors.cyan(f""" - ╔══════════════════════════════════════════════════════════════╗ - ║ 🚀 LightRAG Server v{core_version}/{api_version} ║ - ║ Fast, Lightweight RAG Server Implementation ║ - ╚══════════════════════════════════════════════════════════════╝ - """) + # Banner + top_border = "╔══════════════════════════════════════════════════════════════╗" + bottom_border = "╚══════════════════════════════════════════════════════════════╝" + width = len(top_border) - 4 # width inside the borders + + line1_text = f"LightRAG Server v{core_version}/{api_version}" + line2_text = "Fast, Lightweight RAG Server Implementation" + + line1 = f"║ {line1_text.center(width)} ║" + line2 = f"║ {line2_text.center(width)} ║" + + banner = f""" + {top_border} + {line1} + {line2} + {bottom_border} + """ + ASCIIColors.cyan(banner) # Server Configuration ASCIIColors.magenta("\n📡 Server Configuration:") @@ -284,8 +296,10 @@ def display_splash_screen(args: argparse.Namespace) -> None: ASCIIColors.yellow(f"{args.vector_storage}") ASCIIColors.white(" ├─ Graph Storage: ", end="") ASCIIColors.yellow(f"{args.graph_storage}") - ASCIIColors.white(" └─ Document Status Storage: ", end="") + ASCIIColors.white(" ├─ Document Status Storage: ", end="") ASCIIColors.yellow(f"{args.doc_status_storage}") + ASCIIColors.white(" └─ Workspace: ", end="") + ASCIIColors.yellow(f"{args.workspace if args.workspace else '-'}") # Server Status ASCIIColors.green("\n✨ Server starting up...\n") diff --git a/lightrag/api/webui/assets/KaTeX_AMS-Regular-BQhdFMY1.woff2 b/lightrag/api/webui/assets/KaTeX_AMS-Regular-BQhdFMY1.woff2 new file mode 100644 index 00000000..0acaaff0 Binary files /dev/null and b/lightrag/api/webui/assets/KaTeX_AMS-Regular-BQhdFMY1.woff2 differ diff --git a/lightrag/api/webui/assets/KaTeX_AMS-Regular-DMm9YOAa.woff b/lightrag/api/webui/assets/KaTeX_AMS-Regular-DMm9YOAa.woff new file mode 100644 index 00000000..b804d7b3 Binary files /dev/null and b/lightrag/api/webui/assets/KaTeX_AMS-Regular-DMm9YOAa.woff differ diff --git a/lightrag/api/webui/assets/KaTeX_AMS-Regular-DRggAlZN.ttf b/lightrag/api/webui/assets/KaTeX_AMS-Regular-DRggAlZN.ttf 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H(b);a=!1,f=En,u=new I}else u=r?[]:s;n:for(;++ts?(this.rect.x-=(this.labelWidth-s)/2,this.setWidth(this.labelWidth)):this.labelPosHorizontal=="right"&&this.setWidth(s+this.labelWidth)),this.labelHeight&&(this.labelPosVertical=="top"?(this.rect.y-=this.labelHeight,this.setHeight(o+this.labelHeight)):this.labelPosVertical=="center"&&this.labelHeight>o?(this.rect.y-=(this.labelHeight-o)/2,this.setHeight(this.labelHeight)):this.labelPosVertical=="bottom"&&this.setHeight(o+this.labelHeight))}}},i.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==l.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},i.prototype.transform=function(t){var s=this.rect.x;s>r.WORLD_BOUNDARY?s=r.WORLD_BOUNDARY:s<-r.WORLD_BOUNDARY&&(s=-r.WORLD_BOUNDARY);var o=this.rect.y;o>r.WORLD_BOUNDARY?o=r.WORLD_BOUNDARY:o<-r.WORLD_BOUNDARY&&(o=-r.WORLD_BOUNDARY);var c=new f(s,o),h=t.inverseTransformPoint(c);this.setLocation(h.x,h.y)},i.prototype.getLeft=function(){return this.rect.x},i.prototype.getRight=function(){return this.rect.x+this.rect.width},i.prototype.getTop=function(){return this.rect.y},i.prototype.getBottom=function(){return this.rect.y+this.rect.height},i.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},A.exports=i},function(A,G,L){var u=L(0);function l(){}for(var n in u)l[n]=u[n];l.MAX_ITERATIONS=2500,l.DEFAULT_EDGE_LENGTH=50,l.DEFAULT_SPRING_STRENGTH=.45,l.DEFAULT_REPULSION_STRENGTH=4500,l.DEFAULT_GRAVITY_STRENGTH=.4,l.DEFAULT_COMPOUND_GRAVITY_STRENGTH=1,l.DEFAULT_GRAVITY_RANGE_FACTOR=3.8,l.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=1.5,l.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION=!0,l.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION=!0,l.DEFAULT_COOLING_FACTOR_INCREMENTAL=.3,l.COOLING_ADAPTATION_FACTOR=.33,l.ADAPTATION_LOWER_NODE_LIMIT=1e3,l.ADAPTATION_UPPER_NODE_LIMIT=5e3,l.MAX_NODE_DISPLACEMENT_INCREMENTAL=100,l.MAX_NODE_DISPLACEMENT=l.MAX_NODE_DISPLACEMENT_INCREMENTAL*3,l.MIN_REPULSION_DIST=l.DEFAULT_EDGE_LENGTH/10,l.CONVERGENCE_CHECK_PERIOD=100,l.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=.1,l.MIN_EDGE_LENGTH=1,l.GRID_CALCULATION_CHECK_PERIOD=10,A.exports=l},function(A,G,L){function u(l,n){l==null&&n==null?(this.x=0,this.y=0):(this.x=l,this.y=n)}u.prototype.getX=function(){return this.x},u.prototype.getY=function(){return this.y},u.prototype.setX=function(l){this.x=l},u.prototype.setY=function(l){this.y=l},u.prototype.getDifference=function(l){return new DimensionD(this.x-l.x,this.y-l.y)},u.prototype.getCopy=function(){return new u(this.x,this.y)},u.prototype.translate=function(l){return this.x+=l.width,this.y+=l.height,this},A.exports=u},function(A,G,L){var u=L(2),l=L(10),n=L(0),r=L(7),e=L(3),f=L(1),i=L(13),g=L(12),t=L(11);function s(c,h,T){u.call(this,T),this.estimatedSize=l.MIN_VALUE,this.margin=n.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=c,h!=null&&h instanceof r?this.graphManager=h:h!=null&&h instanceof Layout&&(this.graphManager=h.graphManager)}s.prototype=Object.create(u.prototype);for(var o in u)s[o]=u[o];s.prototype.getNodes=function(){return this.nodes},s.prototype.getEdges=function(){return this.edges},s.prototype.getGraphManager=function(){return this.graphManager},s.prototype.getParent=function(){return this.parent},s.prototype.getLeft=function(){return this.left},s.prototype.getRight=function(){return this.right},s.prototype.getTop=function(){return this.top},s.prototype.getBottom=function(){return this.bottom},s.prototype.isConnected=function(){return this.isConnected},s.prototype.add=function(c,h,T){if(h==null&&T==null){var v=c;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(v)>-1)throw"Node already in graph!";return v.owner=this,this.getNodes().push(v),v}else{var d=c;if(!(this.getNodes().indexOf(h)>-1&&this.getNodes().indexOf(T)>-1))throw"Source or target not in graph!";if(!(h.owner==T.owner&&h.owner==this))throw"Both owners must be this graph!";return h.owner!=T.owner?null:(d.source=h,d.target=T,d.isInterGraph=!1,this.getEdges().push(d),h.edges.push(d),T!=h&&T.edges.push(d),d)}},s.prototype.remove=function(c){var h=c;if(c instanceof e){if(h==null)throw"Node is null!";if(!(h.owner!=null&&h.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var T=h.edges.slice(),v,d=T.length,N=0;N-1&&P>-1))throw"Source and/or target doesn't know this edge!";v.source.edges.splice(M,1),v.target!=v.source&&v.target.edges.splice(P,1);var S=v.source.owner.getEdges().indexOf(v);if(S==-1)throw"Not in owner's edge list!";v.source.owner.getEdges().splice(S,1)}},s.prototype.updateLeftTop=function(){for(var c=l.MAX_VALUE,h=l.MAX_VALUE,T,v,d,N=this.getNodes(),S=N.length,M=0;MT&&(c=T),h>v&&(h=v)}return c==l.MAX_VALUE?null:(N[0].getParent().paddingLeft!=null?d=N[0].getParent().paddingLeft:d=this.margin,this.left=h-d,this.top=c-d,new g(this.left,this.top))},s.prototype.updateBounds=function(c){for(var h=l.MAX_VALUE,T=-l.MAX_VALUE,v=l.MAX_VALUE,d=-l.MAX_VALUE,N,S,M,P,K,X=this.nodes,k=X.length,D=0;DN&&(h=N),TM&&(v=M),dN&&(h=N),TM&&(v=M),d=this.nodes.length){var k=0;T.forEach(function(D){D.owner==c&&k++}),k==this.nodes.length&&(this.isConnected=!0)}},A.exports=s},function(A,G,L){var u,l=L(1);function n(r){u=L(6),this.layout=r,this.graphs=[],this.edges=[]}n.prototype.addRoot=function(){var r=this.layout.newGraph(),e=this.layout.newNode(null),f=this.add(r,e);return this.setRootGraph(f),this.rootGraph},n.prototype.add=function(r,e,f,i,g){if(f==null&&i==null&&g==null){if(r==null)throw"Graph is null!";if(e==null)throw"Parent node is null!";if(this.graphs.indexOf(r)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(r),r.parent!=null)throw"Already has a parent!";if(e.child!=null)throw"Already has a child!";return r.parent=e,e.child=r,r}else{g=f,i=e,f=r;var t=i.getOwner(),s=g.getOwner();if(!(t!=null&&t.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(s!=null&&s.getGraphManager()==this))throw"Target not in this graph mgr!";if(t==s)return f.isInterGraph=!1,t.add(f,i,g);if(f.isInterGraph=!0,f.source=i,f.target=g,this.edges.indexOf(f)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(f),!(f.source!=null&&f.target!=null))throw"Edge source and/or target is null!";if(!(f.source.edges.indexOf(f)==-1&&f.target.edges.indexOf(f)==-1))throw"Edge already in source and/or target incidency list!";return f.source.edges.push(f),f.target.edges.push(f),f}},n.prototype.remove=function(r){if(r instanceof u){var e=r;if(e.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(e==this.rootGraph||e.parent!=null&&e.parent.graphManager==this))throw"Invalid parent node!";var f=[];f=f.concat(e.getEdges());for(var i,g=f.length,t=0;t=r.getRight()?e[0]+=Math.min(r.getX()-n.getX(),n.getRight()-r.getRight()):r.getX()<=n.getX()&&r.getRight()>=n.getRight()&&(e[0]+=Math.min(n.getX()-r.getX(),r.getRight()-n.getRight())),n.getY()<=r.getY()&&n.getBottom()>=r.getBottom()?e[1]+=Math.min(r.getY()-n.getY(),n.getBottom()-r.getBottom()):r.getY()<=n.getY()&&r.getBottom()>=n.getBottom()&&(e[1]+=Math.min(n.getY()-r.getY(),r.getBottom()-n.getBottom()));var g=Math.abs((r.getCenterY()-n.getCenterY())/(r.getCenterX()-n.getCenterX()));r.getCenterY()===n.getCenterY()&&r.getCenterX()===n.getCenterX()&&(g=1);var t=g*e[0],s=e[1]/g;e[0]t)return e[0]=f,e[1]=o,e[2]=g,e[3]=X,!1;if(ig)return e[0]=s,e[1]=i,e[2]=P,e[3]=t,!1;if(fg?(e[0]=h,e[1]=T,a=!0):(e[0]=c,e[1]=o,a=!0):p===y&&(f>g?(e[0]=s,e[1]=o,a=!0):(e[0]=v,e[1]=T,a=!0)),-E===y?g>f?(e[2]=K,e[3]=X,m=!0):(e[2]=P,e[3]=M,m=!0):E===y&&(g>f?(e[2]=S,e[3]=M,m=!0):(e[2]=k,e[3]=X,m=!0)),a&&m)return!1;if(f>g?i>t?(I=this.getCardinalDirection(p,y,4),w=this.getCardinalDirection(E,y,2)):(I=this.getCardinalDirection(-p,y,3),w=this.getCardinalDirection(-E,y,1)):i>t?(I=this.getCardinalDirection(-p,y,1),w=this.getCardinalDirection(-E,y,3)):(I=this.getCardinalDirection(p,y,2),w=this.getCardinalDirection(E,y,4)),!a)switch(I){case 1:W=o,R=f+-N/y,e[0]=R,e[1]=W;break;case 2:R=v,W=i+d*y,e[0]=R,e[1]=W;break;case 3:W=T,R=f+N/y,e[0]=R,e[1]=W;break;case 4:R=h,W=i+-d*y,e[0]=R,e[1]=W;break}if(!m)switch(w){case 1:q=M,x=g+-rt/y,e[2]=x,e[3]=q;break;case 2:x=k,q=t+D*y,e[2]=x,e[3]=q;break;case 3:q=X,x=g+rt/y,e[2]=x,e[3]=q;break;case 4:x=K,q=t+-D*y,e[2]=x,e[3]=q;break}}return!1},l.getCardinalDirection=function(n,r,e){return n>r?e:1+e%4},l.getIntersection=function(n,r,e,f){if(f==null)return this.getIntersection2(n,r,e);var i=n.x,g=n.y,t=r.x,s=r.y,o=e.x,c=e.y,h=f.x,T=f.y,v=void 0,d=void 0,N=void 0,S=void 0,M=void 0,P=void 0,K=void 0,X=void 0,k=void 0;return N=s-g,M=i-t,K=t*g-i*s,S=T-c,P=o-h,X=h*c-o*T,k=N*P-S*M,k===0?null:(v=(M*X-P*K)/k,d=(S*K-N*X)/k,new u(v,d))},l.angleOfVector=function(n,r,e,f){var i=void 0;return n!==e?(i=Math.atan((f-r)/(e-n)),e=0){var T=(-o+Math.sqrt(o*o-4*s*c))/(2*s),v=(-o-Math.sqrt(o*o-4*s*c))/(2*s),d=null;return T>=0&&T<=1?[T]:v>=0&&v<=1?[v]:d}else return null},l.HALF_PI=.5*Math.PI,l.ONE_AND_HALF_PI=1.5*Math.PI,l.TWO_PI=2*Math.PI,l.THREE_PI=3*Math.PI,A.exports=l},function(A,G,L){function u(){}u.sign=function(l){return l>0?1:l<0?-1:0},u.floor=function(l){return l<0?Math.ceil(l):Math.floor(l)},u.ceil=function(l){return l<0?Math.floor(l):Math.ceil(l)},A.exports=u},function(A,G,L){function u(){}u.MAX_VALUE=2147483647,u.MIN_VALUE=-2147483648,A.exports=u},function(A,G,L){var u=function(){function i(g,t){for(var s=0;s"u"?"undefined":u(n);return n==null||r!="object"&&r!="function"},A.exports=l},function(A,G,L){function u(o){if(Array.isArray(o)){for(var c=0,h=Array(o.length);c0&&c;){for(N.push(M[0]);N.length>0&&c;){var P=N[0];N.splice(0,1),d.add(P);for(var K=P.getEdges(),v=0;v-1&&M.splice(rt,1)}d=new Set,S=new Map}}return o},s.prototype.createDummyNodesForBendpoints=function(o){for(var c=[],h=o.source,T=this.graphManager.calcLowestCommonAncestor(o.source,o.target),v=0;v0){for(var T=this.edgeToDummyNodes.get(h),v=0;v=0&&c.splice(X,1);var k=S.getNeighborsList();k.forEach(function(a){if(h.indexOf(a)<0){var m=T.get(a),p=m-1;p==1&&P.push(a),T.set(a,p)}})}h=h.concat(P),(c.length==1||c.length==2)&&(v=!0,d=c[0])}return d},s.prototype.setGraphManager=function(o){this.graphManager=o},A.exports=s},function(A,G,L){function u(){}u.seed=1,u.x=0,u.nextDouble=function(){return u.x=Math.sin(u.seed++)*1e4,u.x-Math.floor(u.x)},A.exports=u},function(A,G,L){var u=L(5);function l(n,r){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}l.prototype.getWorldOrgX=function(){return this.lworldOrgX},l.prototype.setWorldOrgX=function(n){this.lworldOrgX=n},l.prototype.getWorldOrgY=function(){return this.lworldOrgY},l.prototype.setWorldOrgY=function(n){this.lworldOrgY=n},l.prototype.getWorldExtX=function(){return this.lworldExtX},l.prototype.setWorldExtX=function(n){this.lworldExtX=n},l.prototype.getWorldExtY=function(){return this.lworldExtY},l.prototype.setWorldExtY=function(n){this.lworldExtY=n},l.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},l.prototype.setDeviceOrgX=function(n){this.ldeviceOrgX=n},l.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},l.prototype.setDeviceOrgY=function(n){this.ldeviceOrgY=n},l.prototype.getDeviceExtX=function(){return this.ldeviceExtX},l.prototype.setDeviceExtX=function(n){this.ldeviceExtX=n},l.prototype.getDeviceExtY=function(){return this.ldeviceExtY},l.prototype.setDeviceExtY=function(n){this.ldeviceExtY=n},l.prototype.transformX=function(n){var r=0,e=this.lworldExtX;return e!=0&&(r=this.ldeviceOrgX+(n-this.lworldOrgX)*this.ldeviceExtX/e),r},l.prototype.transformY=function(n){var r=0,e=this.lworldExtY;return e!=0&&(r=this.ldeviceOrgY+(n-this.lworldOrgY)*this.ldeviceExtY/e),r},l.prototype.inverseTransformX=function(n){var r=0,e=this.ldeviceExtX;return e!=0&&(r=this.lworldOrgX+(n-this.ldeviceOrgX)*this.lworldExtX/e),r},l.prototype.inverseTransformY=function(n){var r=0,e=this.ldeviceExtY;return e!=0&&(r=this.lworldOrgY+(n-this.ldeviceOrgY)*this.lworldExtY/e),r},l.prototype.inverseTransformPoint=function(n){var r=new u(this.inverseTransformX(n.x),this.inverseTransformY(n.y));return r},A.exports=l},function(A,G,L){function u(t){if(Array.isArray(t)){for(var s=0,o=Array(t.length);sn.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*n.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(t-n.ADAPTATION_LOWER_NODE_LIMIT)/(n.ADAPTATION_UPPER_NODE_LIMIT-n.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-n.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=n.MAX_NODE_DISPLACEMENT_INCREMENTAL):(t>n.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(n.COOLING_ADAPTATION_FACTOR,1-(t-n.ADAPTATION_LOWER_NODE_LIMIT)/(n.ADAPTATION_UPPER_NODE_LIMIT-n.ADAPTATION_LOWER_NODE_LIMIT)*(1-n.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=n.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.displacementThresholdPerNode=3*n.DEFAULT_EDGE_LENGTH/100,this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},i.prototype.calcSpringForces=function(){for(var t=this.getAllEdges(),s,o=0;o0&&arguments[0]!==void 0?arguments[0]:!0,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,o,c,h,T,v=this.getAllNodes(),d;if(this.useFRGridVariant)for(this.totalIterations%n.GRID_CALCULATION_CHECK_PERIOD==1&&t&&this.updateGrid(),d=new Set,o=0;oN||d>N)&&(t.gravitationForceX=-this.gravityConstant*h,t.gravitationForceY=-this.gravityConstant*T)):(N=s.getEstimatedSize()*this.compoundGravityRangeFactor,(v>N||d>N)&&(t.gravitationForceX=-this.gravityConstant*h*this.compoundGravityConstant,t.gravitationForceY=-this.gravityConstant*T*this.compoundGravityConstant))},i.prototype.isConverged=function(){var t,s=!1;return this.totalIterations>this.maxIterations/3&&(s=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),t=this.totalDisplacement=v.length||N>=v[0].length)){for(var S=0;Si}}]),e}();A.exports=r},function(A,G,L){function u(){}u.svd=function(l){this.U=null,this.V=null,this.s=null,this.m=0,this.n=0,this.m=l.length,this.n=l[0].length;var n=Math.min(this.m,this.n);this.s=function(Nt){for(var Mt=[];Nt-- >0;)Mt.push(0);return Mt}(Math.min(this.m+1,this.n)),this.U=function(Nt){var Mt=function Zt(Gt){if(Gt.length==0)return 0;for(var $t=[],Ft=0;Ft0;)Mt.push(0);return Mt}(this.n),e=function(Nt){for(var Mt=[];Nt-- >0;)Mt.push(0);return Mt}(this.m),f=!0,i=Math.min(this.m-1,this.n),g=Math.max(0,Math.min(this.n-2,this.m)),t=0;t=0;E--)if(this.s[E]!==0){for(var y=E+1;y=0;V--){if(function(Nt,Mt){return Nt&&Mt}(V0;){var J=void 0,Rt=void 0;for(J=a-2;J>=-1&&J!==-1;J--)if(Math.abs(r[J])<=lt+_*(Math.abs(this.s[J])+Math.abs(this.s[J+1]))){r[J]=0;break}if(J===a-2)Rt=4;else{var Lt=void 0;for(Lt=a-1;Lt>=J&&Lt!==J;Lt--){var vt=(Lt!==a?Math.abs(r[Lt]):0)+(Lt!==J+1?Math.abs(r[Lt-1]):0);if(Math.abs(this.s[Lt])<=lt+_*vt){this.s[Lt]=0;break}}Lt===J?Rt=3:Lt===a-1?Rt=1:(Rt=2,J=Lt)}switch(J++,Rt){case 1:{var it=r[a-2];r[a-2]=0;for(var gt=a-2;gt>=J;gt--){var Tt=u.hypot(this.s[gt],it),At=this.s[gt]/Tt,Dt=it/Tt;this.s[gt]=Tt,gt!==J&&(it=-Dt*r[gt-1],r[gt-1]=At*r[gt-1]);for(var mt=0;mt=this.s[J+1]);){var Ct=this.s[J];if(this.s[J]=this.s[J+1],this.s[J+1]=Ct,JMath.abs(n)?(r=n/l,r=Math.abs(l)*Math.sqrt(1+r*r)):n!=0?(r=l/n,r=Math.abs(n)*Math.sqrt(1+r*r)):r=0,r},A.exports=u},function(A,G,L){var u=function(){function r(e,f){for(var i=0;i2&&arguments[2]!==void 0?arguments[2]:1,g=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,t=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;l(this,r),this.sequence1=e,this.sequence2=f,this.match_score=i,this.mismatch_penalty=g,this.gap_penalty=t,this.iMax=e.length+1,this.jMax=f.length+1,this.grid=new Array(this.iMax);for(var s=0;s=0;e--){var f=this.listeners[e];f.event===n&&f.callback===r&&this.listeners.splice(e,1)}},l.emit=function(n,r){for(var e=0;e{var G={45:(n,r,e)=>{var f={};f.layoutBase=e(551),f.CoSEConstants=e(806),f.CoSEEdge=e(767),f.CoSEGraph=e(880),f.CoSEGraphManager=e(578),f.CoSELayout=e(765),f.CoSENode=e(991),f.ConstraintHandler=e(902),n.exports=f},806:(n,r,e)=>{var f=e(551).FDLayoutConstants;function i(){}for(var g in f)i[g]=f[g];i.DEFAULT_USE_MULTI_LEVEL_SCALING=!1,i.DEFAULT_RADIAL_SEPARATION=f.DEFAULT_EDGE_LENGTH,i.DEFAULT_COMPONENT_SEPERATION=60,i.TILE=!0,i.TILING_PADDING_VERTICAL=10,i.TILING_PADDING_HORIZONTAL=10,i.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,i.ENFORCE_CONSTRAINTS=!0,i.APPLY_LAYOUT=!0,i.RELAX_MOVEMENT_ON_CONSTRAINTS=!0,i.TREE_REDUCTION_ON_INCREMENTAL=!0,i.PURE_INCREMENTAL=i.DEFAULT_INCREMENTAL,n.exports=i},767:(n,r,e)=>{var f=e(551).FDLayoutEdge;function i(t,s,o){f.call(this,t,s,o)}i.prototype=Object.create(f.prototype);for(var g in f)i[g]=f[g];n.exports=i},880:(n,r,e)=>{var f=e(551).LGraph;function i(t,s,o){f.call(this,t,s,o)}i.prototype=Object.create(f.prototype);for(var g in f)i[g]=f[g];n.exports=i},578:(n,r,e)=>{var f=e(551).LGraphManager;function i(t){f.call(this,t)}i.prototype=Object.create(f.prototype);for(var g in f)i[g]=f[g];n.exports=i},765:(n,r,e)=>{var f=e(551).FDLayout,i=e(578),g=e(880),t=e(991),s=e(767),o=e(806),c=e(902),h=e(551).FDLayoutConstants,T=e(551).LayoutConstants,v=e(551).Point,d=e(551).PointD,N=e(551).DimensionD,S=e(551).Layout,M=e(551).Integer,P=e(551).IGeometry,K=e(551).LGraph,X=e(551).Transform,k=e(551).LinkedList;function D(){f.call(this),this.toBeTiled={},this.constraints={}}D.prototype=Object.create(f.prototype);for(var rt in f)D[rt]=f[rt];D.prototype.newGraphManager=function(){var a=new i(this);return this.graphManager=a,a},D.prototype.newGraph=function(a){return new g(null,this.graphManager,a)},D.prototype.newNode=function(a){return new t(this.graphManager,a)},D.prototype.newEdge=function(a){return new s(null,null,a)},D.prototype.initParameters=function(){f.prototype.initParameters.call(this,arguments),this.isSubLayout||(o.DEFAULT_EDGE_LENGTH<10?this.idealEdgeLength=10:this.idealEdgeLength=o.DEFAULT_EDGE_LENGTH,this.useSmartIdealEdgeLengthCalculation=o.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.gravityConstant=h.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=h.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=h.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=h.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.prunedNodesAll=[],this.growTreeIterations=0,this.afterGrowthIterations=0,this.isTreeGrowing=!1,this.isGrowthFinished=!1)},D.prototype.initSpringEmbedder=function(){f.prototype.initSpringEmbedder.call(this),this.coolingCycle=0,this.maxCoolingCycle=this.maxIterations/h.CONVERGENCE_CHECK_PERIOD,this.finalTemperature=.04,this.coolingAdjuster=1},D.prototype.layout=function(){var a=T.DEFAULT_CREATE_BENDS_AS_NEEDED;return a&&(this.createBendpoints(),this.graphManager.resetAllEdges()),this.level=0,this.classicLayout()},D.prototype.classicLayout=function(){if(this.nodesWithGravity=this.calculateNodesToApplyGravitationTo(),this.graphManager.setAllNodesToApplyGravitation(this.nodesWithGravity),this.calcNoOfChildrenForAllNodes(),this.graphManager.calcLowestCommonAncestors(),this.graphManager.calcInclusionTreeDepths(),this.graphManager.getRoot().calcEstimatedSize(),this.calcIdealEdgeLengths(),this.incremental){if(o.TREE_REDUCTION_ON_INCREMENTAL){this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var m=new Set(this.getAllNodes()),p=this.nodesWithGravity.filter(function(I){return m.has(I)});this.graphManager.setAllNodesToApplyGravitation(p)}}else{var a=this.getFlatForest();if(a.length>0)this.positionNodesRadially(a);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var m=new Set(this.getAllNodes()),p=this.nodesWithGravity.filter(function(E){return m.has(E)});this.graphManager.setAllNodesToApplyGravitation(p),this.positionNodesRandomly()}}return Object.keys(this.constraints).length>0&&(c.handleConstraints(this),this.initConstraintVariables()),this.initSpringEmbedder(),o.APPLY_LAYOUT&&this.runSpringEmbedder(),!0},D.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%h.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var a=new Set(this.getAllNodes()),m=this.nodesWithGravity.filter(function(y){return a.has(y)});this.graphManager.setAllNodesToApplyGravitation(m),this.graphManager.updateBounds(),this.updateGrid(),o.PURE_INCREMENTAL?this.coolingFactor=h.DEFAULT_COOLING_FACTOR_INCREMENTAL/2:this.coolingFactor=h.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),o.PURE_INCREMENTAL?this.coolingFactor=h.DEFAULT_COOLING_FACTOR_INCREMENTAL/2*((100-this.afterGrowthIterations)/100):this.coolingFactor=h.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var p=!this.isTreeGrowing&&!this.isGrowthFinished,E=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(p,E),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},D.prototype.getPositionsData=function(){for(var a=this.graphManager.getAllNodes(),m={},p=0;p0&&this.updateDisplacements();for(var p=0;p0&&(E.fixedNodeWeight=I)}}if(this.constraints.relativePlacementConstraint){var w=new Map,R=new Map;if(this.dummyToNodeForVerticalAlignment=new Map,this.dummyToNodeForHorizontalAlignment=new Map,this.fixedNodesOnHorizontal=new Set,this.fixedNodesOnVertical=new Set,this.fixedNodeSet.forEach(function(O){a.fixedNodesOnHorizontal.add(O),a.fixedNodesOnVertical.add(O)}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var W=this.constraints.alignmentConstraint.vertical,p=0;p=2*O.length/3;_--)H=Math.floor(Math.random()*(_+1)),B=O[_],O[_]=O[H],O[H]=B;return O},this.nodesInRelativeHorizontal=[],this.nodesInRelativeVertical=[],this.nodeToRelativeConstraintMapHorizontal=new Map,this.nodeToRelativeConstraintMapVertical=new Map,this.nodeToTempPositionMapHorizontal=new Map,this.nodeToTempPositionMapVertical=new Map,this.constraints.relativePlacementConstraint.forEach(function(O){if(O.left){var H=w.has(O.left)?w.get(O.left):O.left,B=w.has(O.right)?w.get(O.right):O.right;a.nodesInRelativeHorizontal.includes(H)||(a.nodesInRelativeHorizontal.push(H),a.nodeToRelativeConstraintMapHorizontal.set(H,[]),a.dummyToNodeForVerticalAlignment.has(H)?a.nodeToTempPositionMapHorizontal.set(H,a.idToNodeMap.get(a.dummyToNodeForVerticalAlignment.get(H)[0]).getCenterX()):a.nodeToTempPositionMapHorizontal.set(H,a.idToNodeMap.get(H).getCenterX())),a.nodesInRelativeHorizontal.includes(B)||(a.nodesInRelativeHorizontal.push(B),a.nodeToRelativeConstraintMapHorizontal.set(B,[]),a.dummyToNodeForVerticalAlignment.has(B)?a.nodeToTempPositionMapHorizontal.set(B,a.idToNodeMap.get(a.dummyToNodeForVerticalAlignment.get(B)[0]).getCenterX()):a.nodeToTempPositionMapHorizontal.set(B,a.idToNodeMap.get(B).getCenterX())),a.nodeToRelativeConstraintMapHorizontal.get(H).push({right:B,gap:O.gap}),a.nodeToRelativeConstraintMapHorizontal.get(B).push({left:H,gap:O.gap})}else{var _=R.has(O.top)?R.get(O.top):O.top,lt=R.has(O.bottom)?R.get(O.bottom):O.bottom;a.nodesInRelativeVertical.includes(_)||(a.nodesInRelativeVertical.push(_),a.nodeToRelativeConstraintMapVertical.set(_,[]),a.dummyToNodeForHorizontalAlignment.has(_)?a.nodeToTempPositionMapVertical.set(_,a.idToNodeMap.get(a.dummyToNodeForHorizontalAlignment.get(_)[0]).getCenterY()):a.nodeToTempPositionMapVertical.set(_,a.idToNodeMap.get(_).getCenterY())),a.nodesInRelativeVertical.includes(lt)||(a.nodesInRelativeVertical.push(lt),a.nodeToRelativeConstraintMapVertical.set(lt,[]),a.dummyToNodeForHorizontalAlignment.has(lt)?a.nodeToTempPositionMapVertical.set(lt,a.idToNodeMap.get(a.dummyToNodeForHorizontalAlignment.get(lt)[0]).getCenterY()):a.nodeToTempPositionMapVertical.set(lt,a.idToNodeMap.get(lt).getCenterY())),a.nodeToRelativeConstraintMapVertical.get(_).push({bottom:lt,gap:O.gap}),a.nodeToRelativeConstraintMapVertical.get(lt).push({top:_,gap:O.gap})}});else{var q=new Map,V=new Map;this.constraints.relativePlacementConstraint.forEach(function(O){if(O.left){var H=w.has(O.left)?w.get(O.left):O.left,B=w.has(O.right)?w.get(O.right):O.right;q.has(H)?q.get(H).push(B):q.set(H,[B]),q.has(B)?q.get(B).push(H):q.set(B,[H])}else{var _=R.has(O.top)?R.get(O.top):O.top,lt=R.has(O.bottom)?R.get(O.bottom):O.bottom;V.has(_)?V.get(_).push(lt):V.set(_,[lt]),V.has(lt)?V.get(lt).push(_):V.set(lt,[_])}});var Y=function(H,B){var _=[],lt=[],J=new k,Rt=new Set,Lt=0;return H.forEach(function(vt,it){if(!Rt.has(it)){_[Lt]=[],lt[Lt]=!1;var gt=it;for(J.push(gt),Rt.add(gt),_[Lt].push(gt);J.length!=0;){gt=J.shift(),B.has(gt)&&(lt[Lt]=!0);var Tt=H.get(gt);Tt.forEach(function(At){Rt.has(At)||(J.push(At),Rt.add(At),_[Lt].push(At))})}Lt++}}),{components:_,isFixed:lt}},et=Y(q,a.fixedNodesOnHorizontal);this.componentsOnHorizontal=et.components,this.fixedComponentsOnHorizontal=et.isFixed;var z=Y(V,a.fixedNodesOnVertical);this.componentsOnVertical=z.components,this.fixedComponentsOnVertical=z.isFixed}}},D.prototype.updateDisplacements=function(){var a=this;if(this.constraints.fixedNodeConstraint&&this.constraints.fixedNodeConstraint.forEach(function(z){var O=a.idToNodeMap.get(z.nodeId);O.displacementX=0,O.displacementY=0}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var m=this.constraints.alignmentConstraint.vertical,p=0;p1){var R;for(R=0;RE&&(E=Math.floor(w.y)),I=Math.floor(w.x+o.DEFAULT_COMPONENT_SEPERATION)}this.transform(new d(T.WORLD_CENTER_X-w.x/2,T.WORLD_CENTER_Y-w.y/2))},D.radialLayout=function(a,m,p){var E=Math.max(this.maxDiagonalInTree(a),o.DEFAULT_RADIAL_SEPARATION);D.branchRadialLayout(m,null,0,359,0,E);var y=K.calculateBounds(a),I=new X;I.setDeviceOrgX(y.getMinX()),I.setDeviceOrgY(y.getMinY()),I.setWorldOrgX(p.x),I.setWorldOrgY(p.y);for(var w=0;w1;){var B=H[0];H.splice(0,1);var _=V.indexOf(B);_>=0&&V.splice(_,1),z--,Y--}m!=null?O=(V.indexOf(H[0])+1)%z:O=0;for(var lt=Math.abs(E-p)/Y,J=O;et!=Y;J=++J%z){var Rt=V[J].getOtherEnd(a);if(Rt!=m){var Lt=(p+et*lt)%360,vt=(Lt+lt)%360;D.branchRadialLayout(Rt,a,Lt,vt,y+I,I),et++}}},D.maxDiagonalInTree=function(a){for(var m=M.MIN_VALUE,p=0;pm&&(m=y)}return m},D.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},D.prototype.groupZeroDegreeMembers=function(){var a=this,m={};this.memberGroups={},this.idToDummyNode={};for(var p=[],E=this.graphManager.getAllNodes(),y=0;y"u"&&(m[R]=[]),m[R]=m[R].concat(I)}Object.keys(m).forEach(function(W){if(m[W].length>1){var x="DummyCompound_"+W;a.memberGroups[x]=m[W];var q=m[W][0].getParent(),V=new t(a.graphManager);V.id=x,V.paddingLeft=q.paddingLeft||0,V.paddingRight=q.paddingRight||0,V.paddingBottom=q.paddingBottom||0,V.paddingTop=q.paddingTop||0,a.idToDummyNode[x]=V;var Y=a.getGraphManager().add(a.newGraph(),V),et=q.getChild();et.add(V);for(var z=0;zy?(E.rect.x-=(E.labelWidth-y)/2,E.setWidth(E.labelWidth),E.labelMarginLeft=(E.labelWidth-y)/2):E.labelPosHorizontal=="right"&&E.setWidth(y+E.labelWidth)),E.labelHeight&&(E.labelPosVertical=="top"?(E.rect.y-=E.labelHeight,E.setHeight(I+E.labelHeight),E.labelMarginTop=E.labelHeight):E.labelPosVertical=="center"&&E.labelHeight>I?(E.rect.y-=(E.labelHeight-I)/2,E.setHeight(E.labelHeight),E.labelMarginTop=(E.labelHeight-I)/2):E.labelPosVertical=="bottom"&&E.setHeight(I+E.labelHeight))}})},D.prototype.repopulateCompounds=function(){for(var a=this.compoundOrder.length-1;a>=0;a--){var m=this.compoundOrder[a],p=m.id,E=m.paddingLeft,y=m.paddingTop,I=m.labelMarginLeft,w=m.labelMarginTop;this.adjustLocations(this.tiledMemberPack[p],m.rect.x,m.rect.y,E,y,I,w)}},D.prototype.repopulateZeroDegreeMembers=function(){var a=this,m=this.tiledZeroDegreePack;Object.keys(m).forEach(function(p){var E=a.idToDummyNode[p],y=E.paddingLeft,I=E.paddingTop,w=E.labelMarginLeft,R=E.labelMarginTop;a.adjustLocations(m[p],E.rect.x,E.rect.y,y,I,w,R)})},D.prototype.getToBeTiled=function(a){var m=a.id;if(this.toBeTiled[m]!=null)return this.toBeTiled[m];var p=a.getChild();if(p==null)return this.toBeTiled[m]=!1,!1;for(var E=p.getNodes(),y=0;y0)return this.toBeTiled[m]=!1,!1;if(I.getChild()==null){this.toBeTiled[I.id]=!1;continue}if(!this.getToBeTiled(I))return this.toBeTiled[m]=!1,!1}return this.toBeTiled[m]=!0,!0},D.prototype.getNodeDegree=function(a){a.id;for(var m=a.getEdges(),p=0,E=0;Eq&&(q=Y.rect.height)}p+=q+a.verticalPadding}},D.prototype.tileCompoundMembers=function(a,m){var p=this;this.tiledMemberPack=[],Object.keys(a).forEach(function(E){var y=m[E];if(p.tiledMemberPack[E]=p.tileNodes(a[E],y.paddingLeft+y.paddingRight),y.rect.width=p.tiledMemberPack[E].width,y.rect.height=p.tiledMemberPack[E].height,y.setCenter(p.tiledMemberPack[E].centerX,p.tiledMemberPack[E].centerY),y.labelMarginLeft=0,y.labelMarginTop=0,o.NODE_DIMENSIONS_INCLUDE_LABELS){var I=y.rect.width,w=y.rect.height;y.labelWidth&&(y.labelPosHorizontal=="left"?(y.rect.x-=y.labelWidth,y.setWidth(I+y.labelWidth),y.labelMarginLeft=y.labelWidth):y.labelPosHorizontal=="center"&&y.labelWidth>I?(y.rect.x-=(y.labelWidth-I)/2,y.setWidth(y.labelWidth),y.labelMarginLeft=(y.labelWidth-I)/2):y.labelPosHorizontal=="right"&&y.setWidth(I+y.labelWidth)),y.labelHeight&&(y.labelPosVertical=="top"?(y.rect.y-=y.labelHeight,y.setHeight(w+y.labelHeight),y.labelMarginTop=y.labelHeight):y.labelPosVertical=="center"&&y.labelHeight>w?(y.rect.y-=(y.labelHeight-w)/2,y.setHeight(y.labelHeight),y.labelMarginTop=(y.labelHeight-w)/2):y.labelPosVertical=="bottom"&&y.setHeight(w+y.labelHeight))}})},D.prototype.tileNodes=function(a,m){var p=this.tileNodesByFavoringDim(a,m,!0),E=this.tileNodesByFavoringDim(a,m,!1),y=this.getOrgRatio(p),I=this.getOrgRatio(E),w;return IR&&(R=z.getWidth())});var W=I/y,x=w/y,q=Math.pow(p-E,2)+4*(W+E)*(x+p)*y,V=(E-p+Math.sqrt(q))/(2*(W+E)),Y;m?(Y=Math.ceil(V),Y==V&&Y++):Y=Math.floor(V);var et=Y*(W+E)-E;return R>et&&(et=R),et+=E*2,et},D.prototype.tileNodesByFavoringDim=function(a,m,p){var E=o.TILING_PADDING_VERTICAL,y=o.TILING_PADDING_HORIZONTAL,I=o.TILING_COMPARE_BY,w={rows:[],rowWidth:[],rowHeight:[],width:0,height:m,verticalPadding:E,horizontalPadding:y,centerX:0,centerY:0};I&&(w.idealRowWidth=this.calcIdealRowWidth(a,p));var R=function(O){return O.rect.width*O.rect.height},W=function(O,H){return R(H)-R(O)};a.sort(function(z,O){var H=W;return w.idealRowWidth?(H=I,H(z.id,O.id)):H(z,O)});for(var x=0,q=0,V=0;V0&&(w+=a.horizontalPadding),a.rowWidth[p]=w,a.width0&&(R+=a.verticalPadding);var W=0;R>a.rowHeight[p]&&(W=a.rowHeight[p],a.rowHeight[p]=R,W=a.rowHeight[p]-W),a.height+=W,a.rows[p].push(m)},D.prototype.getShortestRowIndex=function(a){for(var m=-1,p=Number.MAX_VALUE,E=0;Ep&&(m=E,p=a.rowWidth[E]);return m},D.prototype.canAddHorizontal=function(a,m,p){if(a.idealRowWidth){var E=a.rows.length-1,y=a.rowWidth[E];return y+m+a.horizontalPadding<=a.idealRowWidth}var I=this.getShortestRowIndex(a);if(I<0)return!0;var w=a.rowWidth[I];if(w+a.horizontalPadding+m<=a.width)return!0;var R=0;a.rowHeight[I]0&&(R=p+a.verticalPadding-a.rowHeight[I]);var W;a.width-w>=m+a.horizontalPadding?W=(a.height+R)/(w+m+a.horizontalPadding):W=(a.height+R)/a.width,R=p+a.verticalPadding;var x;return a.widthI&&m!=p){E.splice(-1,1),a.rows[p].push(y),a.rowWidth[m]=a.rowWidth[m]-I,a.rowWidth[p]=a.rowWidth[p]+I,a.width=a.rowWidth[instance.getLongestRowIndex(a)];for(var w=Number.MIN_VALUE,R=0;Rw&&(w=E[R].height);m>0&&(w+=a.verticalPadding);var W=a.rowHeight[m]+a.rowHeight[p];a.rowHeight[m]=w,a.rowHeight[p]0)for(var et=y;et<=I;et++)Y[0]+=this.grid[et][w-1].length+this.grid[et][w].length-1;if(I0)for(var et=w;et<=R;et++)Y[3]+=this.grid[y-1][et].length+this.grid[y][et].length-1;for(var z=M.MAX_VALUE,O,H,B=0;B{var f=e(551).FDLayoutNode,i=e(551).IMath;function g(s,o,c,h){f.call(this,s,o,c,h)}g.prototype=Object.create(f.prototype);for(var t in f)g[t]=f[t];g.prototype.calculateDisplacement=function(){var s=this.graphManager.getLayout();this.getChild()!=null&&this.fixedNodeWeight?(this.displacementX+=s.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.fixedNodeWeight,this.displacementY+=s.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.fixedNodeWeight):(this.displacementX+=s.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.noOfChildren,this.displacementY+=s.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.noOfChildren),Math.abs(this.displacementX)>s.coolingFactor*s.maxNodeDisplacement&&(this.displacementX=s.coolingFactor*s.maxNodeDisplacement*i.sign(this.displacementX)),Math.abs(this.displacementY)>s.coolingFactor*s.maxNodeDisplacement&&(this.displacementY=s.coolingFactor*s.maxNodeDisplacement*i.sign(this.displacementY)),this.child&&this.child.getNodes().length>0&&this.propogateDisplacementToChildren(this.displacementX,this.displacementY)},g.prototype.propogateDisplacementToChildren=function(s,o){for(var c=this.getChild().getNodes(),h,T=0;T{function f(c){if(Array.isArray(c)){for(var h=0,T=Array(c.length);h0){var Ct=0;st.forEach(function(ht){$=="horizontal"?(tt.set(ht,v.has(ht)?d[v.get(ht)]:Z.get(ht)),Ct+=tt.get(ht)):(tt.set(ht,v.has(ht)?N[v.get(ht)]:Z.get(ht)),Ct+=tt.get(ht))}),Ct=Ct/st.length,ft.forEach(function(ht){Q.has(ht)||tt.set(ht,Ct)})}else{var ct=0;ft.forEach(function(ht){$=="horizontal"?ct+=v.has(ht)?d[v.get(ht)]:Z.get(ht):ct+=v.has(ht)?N[v.get(ht)]:Z.get(ht)}),ct=ct/ft.length,ft.forEach(function(ht){tt.set(ht,ct)})}});for(var wt=function(){var st=dt.shift(),Ct=b.get(st);Ct.forEach(function(ct){if(tt.get(ct.id)ht&&(ht=qt),_tWt&&(Wt=_t)}}catch(ie){Mt=!0,Zt=ie}finally{try{!Nt&&Gt.return&&Gt.return()}finally{if(Mt)throw Zt}}var de=(Ct+ht)/2-(ct+Wt)/2,Kt=!0,te=!1,ee=void 0;try{for(var jt=ft[Symbol.iterator](),se;!(Kt=(se=jt.next()).done);Kt=!0){var re=se.value;tt.set(re,tt.get(re)+de)}}catch(ie){te=!0,ee=ie}finally{try{!Kt&&jt.return&&jt.return()}finally{if(te)throw ee}}})}return tt},rt=function(b){var $=0,Q=0,Z=0,nt=0;if(b.forEach(function(j){j.left?d[v.get(j.left)]-d[v.get(j.right)]>=0?$++:Q++:N[v.get(j.top)]-N[v.get(j.bottom)]>=0?Z++:nt++}),$>Q&&Z>nt)for(var ut=0;utQ)for(var ot=0;otnt)for(var tt=0;tt1)h.fixedNodeConstraint.forEach(function(F,b){E[b]=[F.position.x,F.position.y],y[b]=[d[v.get(F.nodeId)],N[v.get(F.nodeId)]]}),I=!0;else if(h.alignmentConstraint)(function(){var F=0;if(h.alignmentConstraint.vertical){for(var b=h.alignmentConstraint.vertical,$=function(tt){var j=new Set;b[tt].forEach(function(yt){j.add(yt)});var dt=new Set([].concat(f(j)).filter(function(yt){return R.has(yt)})),wt=void 0;dt.size>0?wt=d[v.get(dt.values().next().value)]:wt=k(j).x,b[tt].forEach(function(yt){E[F]=[wt,N[v.get(yt)]],y[F]=[d[v.get(yt)],N[v.get(yt)]],F++})},Q=0;Q0?wt=d[v.get(dt.values().next().value)]:wt=k(j).y,Z[tt].forEach(function(yt){E[F]=[d[v.get(yt)],wt],y[F]=[d[v.get(yt)],N[v.get(yt)]],F++})},ut=0;utV&&(V=q[et].length,Y=et);if(V0){var mt={x:0,y:0};h.fixedNodeConstraint.forEach(function(F,b){var $={x:d[v.get(F.nodeId)],y:N[v.get(F.nodeId)]},Q=F.position,Z=X(Q,$);mt.x+=Z.x,mt.y+=Z.y}),mt.x/=h.fixedNodeConstraint.length,mt.y/=h.fixedNodeConstraint.length,d.forEach(function(F,b){d[b]+=mt.x}),N.forEach(function(F,b){N[b]+=mt.y}),h.fixedNodeConstraint.forEach(function(F){d[v.get(F.nodeId)]=F.position.x,N[v.get(F.nodeId)]=F.position.y})}if(h.alignmentConstraint){if(h.alignmentConstraint.vertical)for(var xt=h.alignmentConstraint.vertical,St=function(b){var $=new Set;xt[b].forEach(function(nt){$.add(nt)});var Q=new Set([].concat(f($)).filter(function(nt){return R.has(nt)})),Z=void 0;Q.size>0?Z=d[v.get(Q.values().next().value)]:Z=k($).x,$.forEach(function(nt){R.has(nt)||(d[v.get(nt)]=Z)})},Vt=0;Vt0?Z=N[v.get(Q.values().next().value)]:Z=k($).y,$.forEach(function(nt){R.has(nt)||(N[v.get(nt)]=Z)})},bt=0;bt{n.exports=A}},L={};function u(n){var r=L[n];if(r!==void 0)return r.exports;var e=L[n]={exports:{}};return G[n](e,e.exports,u),e.exports}var l=u(45);return l})()})}(fe)),fe.exports}var vr=le.exports,Re;function pr(){return Re||(Re=1,function(C,U){(function(G,L){C.exports=L(dr())})(vr,function(A){return(()=>{var G={658:n=>{n.exports=Object.assign!=null?Object.assign.bind(Object):function(r){for(var e=arguments.length,f=Array(e>1?e-1:0),i=1;i{var f=function(){function t(s,o){var c=[],h=!0,T=!1,v=void 0;try{for(var d=s[Symbol.iterator](),N;!(h=(N=d.next()).done)&&(c.push(N.value),!(o&&c.length===o));h=!0);}catch(S){T=!0,v=S}finally{try{!h&&d.return&&d.return()}finally{if(T)throw v}}return c}return function(s,o){if(Array.isArray(s))return s;if(Symbol.iterator in Object(s))return t(s,o);throw new TypeError("Invalid attempt to destructure non-iterable instance")}}(),i=e(140).layoutBase.LinkedList,g={};g.getTopMostNodes=function(t){for(var s={},o=0;o0&&I.merge(x)});for(var w=0;w1){N=v[0],S=N.connectedEdges().length,v.forEach(function(y){y.connectedEdges().length0&&c.set("dummy"+(c.size+1),K),X},g.relocateComponent=function(t,s,o){if(!o.fixedNodeConstraint){var c=Number.POSITIVE_INFINITY,h=Number.NEGATIVE_INFINITY,T=Number.POSITIVE_INFINITY,v=Number.NEGATIVE_INFINITY;if(o.quality=="draft"){var d=!0,N=!1,S=void 0;try{for(var M=s.nodeIndexes[Symbol.iterator](),P;!(d=(P=M.next()).done);d=!0){var K=P.value,X=f(K,2),k=X[0],D=X[1],rt=o.cy.getElementById(k);if(rt){var a=rt.boundingBox(),m=s.xCoords[D]-a.w/2,p=s.xCoords[D]+a.w/2,E=s.yCoords[D]-a.h/2,y=s.yCoords[D]+a.h/2;mh&&(h=p),Ev&&(v=y)}}}catch(x){N=!0,S=x}finally{try{!d&&M.return&&M.return()}finally{if(N)throw S}}var I=t.x-(h+c)/2,w=t.y-(v+T)/2;s.xCoords=s.xCoords.map(function(x){return x+I}),s.yCoords=s.yCoords.map(function(x){return x+w})}else{Object.keys(s).forEach(function(x){var q=s[x],V=q.getRect().x,Y=q.getRect().x+q.getRect().width,et=q.getRect().y,z=q.getRect().y+q.getRect().height;Vh&&(h=Y),etv&&(v=z)});var R=t.x-(h+c)/2,W=t.y-(v+T)/2;Object.keys(s).forEach(function(x){var q=s[x];q.setCenter(q.getCenterX()+R,q.getCenterY()+W)})}}},g.calcBoundingBox=function(t,s,o,c){for(var h=Number.MAX_SAFE_INTEGER,T=Number.MIN_SAFE_INTEGER,v=Number.MAX_SAFE_INTEGER,d=Number.MIN_SAFE_INTEGER,N=void 0,S=void 0,M=void 0,P=void 0,K=t.descendants().not(":parent"),X=K.length,k=0;kN&&(h=N),TM&&(v=M),d{var f=e(548),i=e(140).CoSELayout,g=e(140).CoSENode,t=e(140).layoutBase.PointD,s=e(140).layoutBase.DimensionD,o=e(140).layoutBase.LayoutConstants,c=e(140).layoutBase.FDLayoutConstants,h=e(140).CoSEConstants,T=function(d,N){var S=d.cy,M=d.eles,P=M.nodes(),K=M.edges(),X=void 0,k=void 0,D=void 0,rt={};d.randomize&&(X=N.nodeIndexes,k=N.xCoords,D=N.yCoords);var a=function(x){return typeof x=="function"},m=function(x,q){return a(x)?x(q):x},p=f.calcParentsWithoutChildren(S,M),E=function W(x,q,V,Y){for(var et=q.length,z=0;z0){var J=void 0;J=V.getGraphManager().add(V.newGraph(),B),W(J,H,V,Y)}}},y=function(x,q,V){for(var Y=0,et=0,z=0;z0?h.DEFAULT_EDGE_LENGTH=c.DEFAULT_EDGE_LENGTH=Y/et:a(d.idealEdgeLength)?h.DEFAULT_EDGE_LENGTH=c.DEFAULT_EDGE_LENGTH=50:h.DEFAULT_EDGE_LENGTH=c.DEFAULT_EDGE_LENGTH=d.idealEdgeLength,h.MIN_REPULSION_DIST=c.MIN_REPULSION_DIST=c.DEFAULT_EDGE_LENGTH/10,h.DEFAULT_RADIAL_SEPARATION=c.DEFAULT_EDGE_LENGTH)},I=function(x,q){q.fixedNodeConstraint&&(x.constraints.fixedNodeConstraint=q.fixedNodeConstraint),q.alignmentConstraint&&(x.constraints.alignmentConstraint=q.alignmentConstraint),q.relativePlacementConstraint&&(x.constraints.relativePlacementConstraint=q.relativePlacementConstraint)};d.nestingFactor!=null&&(h.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=c.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=d.nestingFactor),d.gravity!=null&&(h.DEFAULT_GRAVITY_STRENGTH=c.DEFAULT_GRAVITY_STRENGTH=d.gravity),d.numIter!=null&&(h.MAX_ITERATIONS=c.MAX_ITERATIONS=d.numIter),d.gravityRange!=null&&(h.DEFAULT_GRAVITY_RANGE_FACTOR=c.DEFAULT_GRAVITY_RANGE_FACTOR=d.gravityRange),d.gravityCompound!=null&&(h.DEFAULT_COMPOUND_GRAVITY_STRENGTH=c.DEFAULT_COMPOUND_GRAVITY_STRENGTH=d.gravityCompound),d.gravityRangeCompound!=null&&(h.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=c.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=d.gravityRangeCompound),d.initialEnergyOnIncremental!=null&&(h.DEFAULT_COOLING_FACTOR_INCREMENTAL=c.DEFAULT_COOLING_FACTOR_INCREMENTAL=d.initialEnergyOnIncremental),d.tilingCompareBy!=null&&(h.TILING_COMPARE_BY=d.tilingCompareBy),d.quality=="proof"?o.QUALITY=2:o.QUALITY=0,h.NODE_DIMENSIONS_INCLUDE_LABELS=c.NODE_DIMENSIONS_INCLUDE_LABELS=o.NODE_DIMENSIONS_INCLUDE_LABELS=d.nodeDimensionsIncludeLabels,h.DEFAULT_INCREMENTAL=c.DEFAULT_INCREMENTAL=o.DEFAULT_INCREMENTAL=!d.randomize,h.ANIMATE=c.ANIMATE=o.ANIMATE=d.animate,h.TILE=d.tile,h.TILING_PADDING_VERTICAL=typeof d.tilingPaddingVertical=="function"?d.tilingPaddingVertical.call():d.tilingPaddingVertical,h.TILING_PADDING_HORIZONTAL=typeof d.tilingPaddingHorizontal=="function"?d.tilingPaddingHorizontal.call():d.tilingPaddingHorizontal,h.DEFAULT_INCREMENTAL=c.DEFAULT_INCREMENTAL=o.DEFAULT_INCREMENTAL=!0,h.PURE_INCREMENTAL=!d.randomize,o.DEFAULT_UNIFORM_LEAF_NODE_SIZES=d.uniformNodeDimensions,d.step=="transformed"&&(h.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,h.ENFORCE_CONSTRAINTS=!1,h.APPLY_LAYOUT=!1),d.step=="enforced"&&(h.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,h.ENFORCE_CONSTRAINTS=!0,h.APPLY_LAYOUT=!1),d.step=="cose"&&(h.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,h.ENFORCE_CONSTRAINTS=!1,h.APPLY_LAYOUT=!0),d.step=="all"&&(d.randomize?h.TRANSFORM_ON_CONSTRAINT_HANDLING=!0:h.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,h.ENFORCE_CONSTRAINTS=!0,h.APPLY_LAYOUT=!0),d.fixedNodeConstraint||d.alignmentConstraint||d.relativePlacementConstraint?h.TREE_REDUCTION_ON_INCREMENTAL=!1:h.TREE_REDUCTION_ON_INCREMENTAL=!0;var w=new i,R=w.newGraphManager();return E(R.addRoot(),f.getTopMostNodes(P),w,d),y(w,R,K),I(w,d),w.runLayout(),rt};n.exports={coseLayout:T}},212:(n,r,e)=>{var f=function(){function d(N,S){for(var M=0;M0)if(p){var I=t.getTopMostNodes(M.eles.nodes());if(D=t.connectComponents(P,M.eles,I),D.forEach(function(vt){var it=vt.boundingBox();rt.push({x:it.x1+it.w/2,y:it.y1+it.h/2})}),M.randomize&&D.forEach(function(vt){M.eles=vt,X.push(o(M))}),M.quality=="default"||M.quality=="proof"){var w=P.collection();if(M.tile){var R=new Map,W=[],x=[],q=0,V={nodeIndexes:R,xCoords:W,yCoords:x},Y=[];if(D.forEach(function(vt,it){vt.edges().length==0&&(vt.nodes().forEach(function(gt,Tt){w.merge(vt.nodes()[Tt]),gt.isParent()||(V.nodeIndexes.set(vt.nodes()[Tt].id(),q++),V.xCoords.push(vt.nodes()[0].position().x),V.yCoords.push(vt.nodes()[0].position().y))}),Y.push(it))}),w.length>1){var et=w.boundingBox();rt.push({x:et.x1+et.w/2,y:et.y1+et.h/2}),D.push(w),X.push(V);for(var z=Y.length-1;z>=0;z--)D.splice(Y[z],1),X.splice(Y[z],1),rt.splice(Y[z],1)}}D.forEach(function(vt,it){M.eles=vt,k.push(h(M,X[it])),t.relocateComponent(rt[it],k[it],M)})}else D.forEach(function(vt,it){t.relocateComponent(rt[it],X[it],M)});var O=new Set;if(D.length>1){var H=[],B=K.filter(function(vt){return vt.css("display")=="none"});D.forEach(function(vt,it){var gt=void 0;if(M.quality=="draft"&&(gt=X[it].nodeIndexes),vt.nodes().not(B).length>0){var Tt={};Tt.edges=[],Tt.nodes=[];var At=void 0;vt.nodes().not(B).forEach(function(Dt){if(M.quality=="draft")if(!Dt.isParent())At=gt.get(Dt.id()),Tt.nodes.push({x:X[it].xCoords[At]-Dt.boundingbox().w/2,y:X[it].yCoords[At]-Dt.boundingbox().h/2,width:Dt.boundingbox().w,height:Dt.boundingbox().h});else{var mt=t.calcBoundingBox(Dt,X[it].xCoords,X[it].yCoords,gt);Tt.nodes.push({x:mt.topLeftX,y:mt.topLeftY,width:mt.width,height:mt.height})}else k[it][Dt.id()]&&Tt.nodes.push({x:k[it][Dt.id()].getLeft(),y:k[it][Dt.id()].getTop(),width:k[it][Dt.id()].getWidth(),height:k[it][Dt.id()].getHeight()})}),vt.edges().forEach(function(Dt){var mt=Dt.source(),xt=Dt.target();if(mt.css("display")!="none"&&xt.css("display")!="none")if(M.quality=="draft"){var St=gt.get(mt.id()),Vt=gt.get(xt.id()),Xt=[],Ut=[];if(mt.isParent()){var bt=t.calcBoundingBox(mt,X[it].xCoords,X[it].yCoords,gt);Xt.push(bt.topLeftX+bt.width/2),Xt.push(bt.topLeftY+bt.height/2)}else Xt.push(X[it].xCoords[St]),Xt.push(X[it].yCoords[St]);if(xt.isParent()){var Ht=t.calcBoundingBox(xt,X[it].xCoords,X[it].yCoords,gt);Ut.push(Ht.topLeftX+Ht.width/2),Ut.push(Ht.topLeftY+Ht.height/2)}else Ut.push(X[it].xCoords[Vt]),Ut.push(X[it].yCoords[Vt]);Tt.edges.push({startX:Xt[0],startY:Xt[1],endX:Ut[0],endY:Ut[1]})}else k[it][mt.id()]&&k[it][xt.id()]&&Tt.edges.push({startX:k[it][mt.id()].getCenterX(),startY:k[it][mt.id()].getCenterY(),endX:k[it][xt.id()].getCenterX(),endY:k[it][xt.id()].getCenterY()})}),Tt.nodes.length>0&&(H.push(Tt),O.add(it))}});var _=m.packComponents(H,M.randomize).shifts;if(M.quality=="draft")X.forEach(function(vt,it){var gt=vt.xCoords.map(function(At){return At+_[it].dx}),Tt=vt.yCoords.map(function(At){return At+_[it].dy});vt.xCoords=gt,vt.yCoords=Tt});else{var lt=0;O.forEach(function(vt){Object.keys(k[vt]).forEach(function(it){var gt=k[vt][it];gt.setCenter(gt.getCenterX()+_[lt].dx,gt.getCenterY()+_[lt].dy)}),lt++})}}}else{var E=M.eles.boundingBox();if(rt.push({x:E.x1+E.w/2,y:E.y1+E.h/2}),M.randomize){var y=o(M);X.push(y)}M.quality=="default"||M.quality=="proof"?(k.push(h(M,X[0])),t.relocateComponent(rt[0],k[0],M)):t.relocateComponent(rt[0],X[0],M)}var J=function(it,gt){if(M.quality=="default"||M.quality=="proof"){typeof it=="number"&&(it=gt);var Tt=void 0,At=void 0,Dt=it.data("id");return k.forEach(function(xt){Dt in xt&&(Tt={x:xt[Dt].getRect().getCenterX(),y:xt[Dt].getRect().getCenterY()},At=xt[Dt])}),M.nodeDimensionsIncludeLabels&&(At.labelWidth&&(At.labelPosHorizontal=="left"?Tt.x+=At.labelWidth/2:At.labelPosHorizontal=="right"&&(Tt.x-=At.labelWidth/2)),At.labelHeight&&(At.labelPosVertical=="top"?Tt.y+=At.labelHeight/2:At.labelPosVertical=="bottom"&&(Tt.y-=At.labelHeight/2))),Tt==null&&(Tt={x:it.position("x"),y:it.position("y")}),{x:Tt.x,y:Tt.y}}else{var mt=void 0;return X.forEach(function(xt){var St=xt.nodeIndexes.get(it.id());St!=null&&(mt={x:xt.xCoords[St],y:xt.yCoords[St]})}),mt==null&&(mt={x:it.position("x"),y:it.position("y")}),{x:mt.x,y:mt.y}}};if(M.quality=="default"||M.quality=="proof"||M.randomize){var Rt=t.calcParentsWithoutChildren(P,K),Lt=K.filter(function(vt){return vt.css("display")=="none"});M.eles=K.not(Lt),K.nodes().not(":parent").not(Lt).layoutPositions(S,M,J),Rt.length>0&&Rt.forEach(function(vt){vt.position(J(vt))})}else 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this.rect.x},i.prototype.getRight=function(){return this.rect.x+this.rect.width},i.prototype.getTop=function(){return this.rect.y},i.prototype.getBottom=function(){return this.rect.y+this.rect.height},i.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},A.exports=i},function(A,G,L){var u=L(0);function l(){}for(var n in u)l[n]=u[n];l.MAX_ITERATIONS=2500,l.DEFAULT_EDGE_LENGTH=50,l.DEFAULT_SPRING_STRENGTH=.45,l.DEFAULT_REPULSION_STRENGTH=4500,l.DEFAULT_GRAVITY_STRENGTH=.4,l.DEFAULT_COMPOUND_GRAVITY_STRENGTH=1,l.DEFAULT_GRAVITY_RANGE_FACTOR=3.8,l.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=1.5,l.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION=!0,l.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION=!0,l.DEFAULT_COOLING_FACTOR_INCREMENTAL=.3,l.COOLING_ADAPTATION_FACTOR=.33,l.ADAPTATION_LOWER_NODE_LIMIT=1e3,l.ADAPTATION_UPPER_NODE_LIMIT=5e3,l.MAX_NODE_DISPLACEMENT_INCREMENTAL=100,l.MAX_NODE_DISPLACEMENT=l.MAX_NODE_DISPLACEMENT_INCREMENTAL*3,l.MIN_REPULSION_DIST=l.DEFAULT_EDGE_LENGTH/10,l.CONVERGENCE_CHECK_PERIOD=100,l.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=.1,l.MIN_EDGE_LENGTH=1,l.GRID_CALCULATION_CHECK_PERIOD=10,A.exports=l},function(A,G,L){function u(l,n){l==null&&n==null?(this.x=0,this.y=0):(this.x=l,this.y=n)}u.prototype.getX=function(){return this.x},u.prototype.getY=function(){return this.y},u.prototype.setX=function(l){this.x=l},u.prototype.setY=function(l){this.y=l},u.prototype.getDifference=function(l){return new DimensionD(this.x-l.x,this.y-l.y)},u.prototype.getCopy=function(){return new u(this.x,this.y)},u.prototype.translate=function(l){return this.x+=l.width,this.y+=l.height,this},A.exports=u},function(A,G,L){var u=L(2),l=L(10),n=L(0),r=L(7),e=L(3),f=L(1),i=L(13),g=L(12),t=L(11);function s(c,h,T){u.call(this,T),this.estimatedSize=l.MIN_VALUE,this.margin=n.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=c,h!=null&&h instanceof r?this.graphManager=h:h!=null&&h instanceof Layout&&(this.graphManager=h.graphManager)}s.prototype=Object.create(u.prototype);for(var o in u)s[o]=u[o];s.prototype.getNodes=function(){return this.nodes},s.prototype.getEdges=function(){return this.edges},s.prototype.getGraphManager=function(){return this.graphManager},s.prototype.getParent=function(){return this.parent},s.prototype.getLeft=function(){return this.left},s.prototype.getRight=function(){return this.right},s.prototype.getTop=function(){return this.top},s.prototype.getBottom=function(){return this.bottom},s.prototype.isConnected=function(){return this.isConnected},s.prototype.add=function(c,h,T){if(h==null&&T==null){var v=c;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(v)>-1)throw"Node already in graph!";return v.owner=this,this.getNodes().push(v),v}else{var d=c;if(!(this.getNodes().indexOf(h)>-1&&this.getNodes().indexOf(T)>-1))throw"Source or target not in graph!";if(!(h.owner==T.owner&&h.owner==this))throw"Both owners must be this graph!";return h.owner!=T.owner?null:(d.source=h,d.target=T,d.isInterGraph=!1,this.getEdges().push(d),h.edges.push(d),T!=h&&T.edges.push(d),d)}},s.prototype.remove=function(c){var h=c;if(c instanceof e){if(h==null)throw"Node is null!";if(!(h.owner!=null&&h.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var T=h.edges.slice(),v,d=T.length,N=0;N-1&&P>-1))throw"Source and/or target doesn't know this edge!";v.source.edges.splice(M,1),v.target!=v.source&&v.target.edges.splice(P,1);var S=v.source.owner.getEdges().indexOf(v);if(S==-1)throw"Not in owner's edge list!";v.source.owner.getEdges().splice(S,1)}},s.prototype.updateLeftTop=function(){for(var c=l.MAX_VALUE,h=l.MAX_VALUE,T,v,d,N=this.getNodes(),S=N.length,M=0;MT&&(c=T),h>v&&(h=v)}return c==l.MAX_VALUE?null:(N[0].getParent().paddingLeft!=null?d=N[0].getParent().paddingLeft:d=this.margin,this.left=h-d,this.top=c-d,new g(this.left,this.top))},s.prototype.updateBounds=function(c){for(var h=l.MAX_VALUE,T=-l.MAX_VALUE,v=l.MAX_VALUE,d=-l.MAX_VALUE,N,S,M,P,K,X=this.nodes,k=X.length,D=0;DN&&(h=N),TM&&(v=M),dN&&(h=N),TM&&(v=M),d=this.nodes.length){var k=0;T.forEach(function(D){D.owner==c&&k++}),k==this.nodes.length&&(this.isConnected=!0)}},A.exports=s},function(A,G,L){var u,l=L(1);function n(r){u=L(6),this.layout=r,this.graphs=[],this.edges=[]}n.prototype.addRoot=function(){var r=this.layout.newGraph(),e=this.layout.newNode(null),f=this.add(r,e);return this.setRootGraph(f),this.rootGraph},n.prototype.add=function(r,e,f,i,g){if(f==null&&i==null&&g==null){if(r==null)throw"Graph is null!";if(e==null)throw"Parent node is null!";if(this.graphs.indexOf(r)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(r),r.parent!=null)throw"Already has a parent!";if(e.child!=null)throw"Already has a child!";return r.parent=e,e.child=r,r}else{g=f,i=e,f=r;var t=i.getOwner(),s=g.getOwner();if(!(t!=null&&t.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(s!=null&&s.getGraphManager()==this))throw"Target not in this graph mgr!";if(t==s)return f.isInterGraph=!1,t.add(f,i,g);if(f.isInterGraph=!0,f.source=i,f.target=g,this.edges.indexOf(f)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(f),!(f.source!=null&&f.target!=null))throw"Edge source and/or target is null!";if(!(f.source.edges.indexOf(f)==-1&&f.target.edges.indexOf(f)==-1))throw"Edge already in source and/or target incidency list!";return f.source.edges.push(f),f.target.edges.push(f),f}},n.prototype.remove=function(r){if(r instanceof u){var e=r;if(e.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(e==this.rootGraph||e.parent!=null&&e.parent.graphManager==this))throw"Invalid parent node!";var f=[];f=f.concat(e.getEdges());for(var i,g=f.length,t=0;t=r.getRight()?e[0]+=Math.min(r.getX()-n.getX(),n.getRight()-r.getRight()):r.getX()<=n.getX()&&r.getRight()>=n.getRight()&&(e[0]+=Math.min(n.getX()-r.getX(),r.getRight()-n.getRight())),n.getY()<=r.getY()&&n.getBottom()>=r.getBottom()?e[1]+=Math.min(r.getY()-n.getY(),n.getBottom()-r.getBottom()):r.getY()<=n.getY()&&r.getBottom()>=n.getBottom()&&(e[1]+=Math.min(n.getY()-r.getY(),r.getBottom()-n.getBottom()));var g=Math.abs((r.getCenterY()-n.getCenterY())/(r.getCenterX()-n.getCenterX()));r.getCenterY()===n.getCenterY()&&r.getCenterX()===n.getCenterX()&&(g=1);var t=g*e[0],s=e[1]/g;e[0]t)return e[0]=f,e[1]=o,e[2]=g,e[3]=X,!1;if(ig)return e[0]=s,e[1]=i,e[2]=P,e[3]=t,!1;if(fg?(e[0]=h,e[1]=T,a=!0):(e[0]=c,e[1]=o,a=!0):p===y&&(f>g?(e[0]=s,e[1]=o,a=!0):(e[0]=v,e[1]=T,a=!0)),-E===y?g>f?(e[2]=K,e[3]=X,m=!0):(e[2]=P,e[3]=M,m=!0):E===y&&(g>f?(e[2]=S,e[3]=M,m=!0):(e[2]=k,e[3]=X,m=!0)),a&&m)return!1;if(f>g?i>t?(I=this.getCardinalDirection(p,y,4),w=this.getCardinalDirection(E,y,2)):(I=this.getCardinalDirection(-p,y,3),w=this.getCardinalDirection(-E,y,1)):i>t?(I=this.getCardinalDirection(-p,y,1),w=this.getCardinalDirection(-E,y,3)):(I=this.getCardinalDirection(p,y,2),w=this.getCardinalDirection(E,y,4)),!a)switch(I){case 1:W=o,R=f+-N/y,e[0]=R,e[1]=W;break;case 2:R=v,W=i+d*y,e[0]=R,e[1]=W;break;case 3:W=T,R=f+N/y,e[0]=R,e[1]=W;break;case 4:R=h,W=i+-d*y,e[0]=R,e[1]=W;break}if(!m)switch(w){case 1:q=M,x=g+-rt/y,e[2]=x,e[3]=q;break;case 2:x=k,q=t+D*y,e[2]=x,e[3]=q;break;case 3:q=X,x=g+rt/y,e[2]=x,e[3]=q;break;case 4:x=K,q=t+-D*y,e[2]=x,e[3]=q;break}}return!1},l.getCardinalDirection=function(n,r,e){return n>r?e:1+e%4},l.getIntersection=function(n,r,e,f){if(f==null)return this.getIntersection2(n,r,e);var i=n.x,g=n.y,t=r.x,s=r.y,o=e.x,c=e.y,h=f.x,T=f.y,v=void 0,d=void 0,N=void 0,S=void 0,M=void 0,P=void 0,K=void 0,X=void 0,k=void 0;return N=s-g,M=i-t,K=t*g-i*s,S=T-c,P=o-h,X=h*c-o*T,k=N*P-S*M,k===0?null:(v=(M*X-P*K)/k,d=(S*K-N*X)/k,new u(v,d))},l.angleOfVector=function(n,r,e,f){var i=void 0;return n!==e?(i=Math.atan((f-r)/(e-n)),e=0){var T=(-o+Math.sqrt(o*o-4*s*c))/(2*s),v=(-o-Math.sqrt(o*o-4*s*c))/(2*s),d=null;return T>=0&&T<=1?[T]:v>=0&&v<=1?[v]:d}else return null},l.HALF_PI=.5*Math.PI,l.ONE_AND_HALF_PI=1.5*Math.PI,l.TWO_PI=2*Math.PI,l.THREE_PI=3*Math.PI,A.exports=l},function(A,G,L){function u(){}u.sign=function(l){return l>0?1:l<0?-1:0},u.floor=function(l){return l<0?Math.ceil(l):Math.floor(l)},u.ceil=function(l){return l<0?Math.floor(l):Math.ceil(l)},A.exports=u},function(A,G,L){function u(){}u.MAX_VALUE=2147483647,u.MIN_VALUE=-2147483648,A.exports=u},function(A,G,L){var u=function(){function i(g,t){for(var s=0;s"u"?"undefined":u(n);return n==null||r!="object"&&r!="function"},A.exports=l},function(A,G,L){function u(o){if(Array.isArray(o)){for(var c=0,h=Array(o.length);c0&&c;){for(N.push(M[0]);N.length>0&&c;){var P=N[0];N.splice(0,1),d.add(P);for(var K=P.getEdges(),v=0;v-1&&M.splice(rt,1)}d=new Set,S=new Map}}return o},s.prototype.createDummyNodesForBendpoints=function(o){for(var c=[],h=o.source,T=this.graphManager.calcLowestCommonAncestor(o.source,o.target),v=0;v0){for(var T=this.edgeToDummyNodes.get(h),v=0;v=0&&c.splice(X,1);var k=S.getNeighborsList();k.forEach(function(a){if(h.indexOf(a)<0){var m=T.get(a),p=m-1;p==1&&P.push(a),T.set(a,p)}})}h=h.concat(P),(c.length==1||c.length==2)&&(v=!0,d=c[0])}return d},s.prototype.setGraphManager=function(o){this.graphManager=o},A.exports=s},function(A,G,L){function u(){}u.seed=1,u.x=0,u.nextDouble=function(){return u.x=Math.sin(u.seed++)*1e4,u.x-Math.floor(u.x)},A.exports=u},function(A,G,L){var u=L(5);function l(n,r){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}l.prototype.getWorldOrgX=function(){return this.lworldOrgX},l.prototype.setWorldOrgX=function(n){this.lworldOrgX=n},l.prototype.getWorldOrgY=function(){return this.lworldOrgY},l.prototype.setWorldOrgY=function(n){this.lworldOrgY=n},l.prototype.getWorldExtX=function(){return this.lworldExtX},l.prototype.setWorldExtX=function(n){this.lworldExtX=n},l.prototype.getWorldExtY=function(){return this.lworldExtY},l.prototype.setWorldExtY=function(n){this.lworldExtY=n},l.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},l.prototype.setDeviceOrgX=function(n){this.ldeviceOrgX=n},l.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},l.prototype.setDeviceOrgY=function(n){this.ldeviceOrgY=n},l.prototype.getDeviceExtX=function(){return this.ldeviceExtX},l.prototype.setDeviceExtX=function(n){this.ldeviceExtX=n},l.prototype.getDeviceExtY=function(){return this.ldeviceExtY},l.prototype.setDeviceExtY=function(n){this.ldeviceExtY=n},l.prototype.transformX=function(n){var r=0,e=this.lworldExtX;return e!=0&&(r=this.ldeviceOrgX+(n-this.lworldOrgX)*this.ldeviceExtX/e),r},l.prototype.transformY=function(n){var r=0,e=this.lworldExtY;return e!=0&&(r=this.ldeviceOrgY+(n-this.lworldOrgY)*this.ldeviceExtY/e),r},l.prototype.inverseTransformX=function(n){var r=0,e=this.ldeviceExtX;return e!=0&&(r=this.lworldOrgX+(n-this.ldeviceOrgX)*this.lworldExtX/e),r},l.prototype.inverseTransformY=function(n){var r=0,e=this.ldeviceExtY;return e!=0&&(r=this.lworldOrgY+(n-this.ldeviceOrgY)*this.lworldExtY/e),r},l.prototype.inverseTransformPoint=function(n){var r=new u(this.inverseTransformX(n.x),this.inverseTransformY(n.y));return r},A.exports=l},function(A,G,L){function u(t){if(Array.isArray(t)){for(var s=0,o=Array(t.length);sn.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*n.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(t-n.ADAPTATION_LOWER_NODE_LIMIT)/(n.ADAPTATION_UPPER_NODE_LIMIT-n.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-n.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=n.MAX_NODE_DISPLACEMENT_INCREMENTAL):(t>n.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(n.COOLING_ADAPTATION_FACTOR,1-(t-n.ADAPTATION_LOWER_NODE_LIMIT)/(n.ADAPTATION_UPPER_NODE_LIMIT-n.ADAPTATION_LOWER_NODE_LIMIT)*(1-n.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=n.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.displacementThresholdPerNode=3*n.DEFAULT_EDGE_LENGTH/100,this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},i.prototype.calcSpringForces=function(){for(var t=this.getAllEdges(),s,o=0;o0&&arguments[0]!==void 0?arguments[0]:!0,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,o,c,h,T,v=this.getAllNodes(),d;if(this.useFRGridVariant)for(this.totalIterations%n.GRID_CALCULATION_CHECK_PERIOD==1&&t&&this.updateGrid(),d=new Set,o=0;oN||d>N)&&(t.gravitationForceX=-this.gravityConstant*h,t.gravitationForceY=-this.gravityConstant*T)):(N=s.getEstimatedSize()*this.compoundGravityRangeFactor,(v>N||d>N)&&(t.gravitationForceX=-this.gravityConstant*h*this.compoundGravityConstant,t.gravitationForceY=-this.gravityConstant*T*this.compoundGravityConstant))},i.prototype.isConverged=function(){var t,s=!1;return this.totalIterations>this.maxIterations/3&&(s=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),t=this.totalDisplacement=v.length||N>=v[0].length)){for(var S=0;Si}}]),e}();A.exports=r},function(A,G,L){function u(){}u.svd=function(l){this.U=null,this.V=null,this.s=null,this.m=0,this.n=0,this.m=l.length,this.n=l[0].length;var n=Math.min(this.m,this.n);this.s=function(Nt){for(var Mt=[];Nt-- >0;)Mt.push(0);return Mt}(Math.min(this.m+1,this.n)),this.U=function(Nt){var Mt=function Zt(Gt){if(Gt.length==0)return 0;for(var $t=[],Ft=0;Ft0;)Mt.push(0);return Mt}(this.n),e=function(Nt){for(var Mt=[];Nt-- >0;)Mt.push(0);return Mt}(this.m),f=!0,i=Math.min(this.m-1,this.n),g=Math.max(0,Math.min(this.n-2,this.m)),t=0;t=0;E--)if(this.s[E]!==0){for(var y=E+1;y=0;V--){if(function(Nt,Mt){return Nt&&Mt}(V0;){var J=void 0,Rt=void 0;for(J=a-2;J>=-1&&J!==-1;J--)if(Math.abs(r[J])<=lt+_*(Math.abs(this.s[J])+Math.abs(this.s[J+1]))){r[J]=0;break}if(J===a-2)Rt=4;else{var Lt=void 0;for(Lt=a-1;Lt>=J&&Lt!==J;Lt--){var vt=(Lt!==a?Math.abs(r[Lt]):0)+(Lt!==J+1?Math.abs(r[Lt-1]):0);if(Math.abs(this.s[Lt])<=lt+_*vt){this.s[Lt]=0;break}}Lt===J?Rt=3:Lt===a-1?Rt=1:(Rt=2,J=Lt)}switch(J++,Rt){case 1:{var it=r[a-2];r[a-2]=0;for(var gt=a-2;gt>=J;gt--){var Tt=u.hypot(this.s[gt],it),At=this.s[gt]/Tt,Dt=it/Tt;this.s[gt]=Tt,gt!==J&&(it=-Dt*r[gt-1],r[gt-1]=At*r[gt-1]);for(var mt=0;mt=this.s[J+1]);){var Ct=this.s[J];if(this.s[J]=this.s[J+1],this.s[J+1]=Ct,JMath.abs(n)?(r=n/l,r=Math.abs(l)*Math.sqrt(1+r*r)):n!=0?(r=l/n,r=Math.abs(n)*Math.sqrt(1+r*r)):r=0,r},A.exports=u},function(A,G,L){var u=function(){function r(e,f){for(var i=0;i2&&arguments[2]!==void 0?arguments[2]:1,g=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,t=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;l(this,r),this.sequence1=e,this.sequence2=f,this.match_score=i,this.mismatch_penalty=g,this.gap_penalty=t,this.iMax=e.length+1,this.jMax=f.length+1,this.grid=new Array(this.iMax);for(var s=0;s=0;e--){var f=this.listeners[e];f.event===n&&f.callback===r&&this.listeners.splice(e,1)}},l.emit=function(n,r){for(var e=0;e{var G={45:(n,r,e)=>{var f={};f.layoutBase=e(551),f.CoSEConstants=e(806),f.CoSEEdge=e(767),f.CoSEGraph=e(880),f.CoSEGraphManager=e(578),f.CoSELayout=e(765),f.CoSENode=e(991),f.ConstraintHandler=e(902),n.exports=f},806:(n,r,e)=>{var f=e(551).FDLayoutConstants;function i(){}for(var g in f)i[g]=f[g];i.DEFAULT_USE_MULTI_LEVEL_SCALING=!1,i.DEFAULT_RADIAL_SEPARATION=f.DEFAULT_EDGE_LENGTH,i.DEFAULT_COMPONENT_SEPERATION=60,i.TILE=!0,i.TILING_PADDING_VERTICAL=10,i.TILING_PADDING_HORIZONTAL=10,i.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,i.ENFORCE_CONSTRAINTS=!0,i.APPLY_LAYOUT=!0,i.RELAX_MOVEMENT_ON_CONSTRAINTS=!0,i.TREE_REDUCTION_ON_INCREMENTAL=!0,i.PURE_INCREMENTAL=i.DEFAULT_INCREMENTAL,n.exports=i},767:(n,r,e)=>{var f=e(551).FDLayoutEdge;function i(t,s,o){f.call(this,t,s,o)}i.prototype=Object.create(f.prototype);for(var g in f)i[g]=f[g];n.exports=i},880:(n,r,e)=>{var f=e(551).LGraph;function i(t,s,o){f.call(this,t,s,o)}i.prototype=Object.create(f.prototype);for(var g in f)i[g]=f[g];n.exports=i},578:(n,r,e)=>{var f=e(551).LGraphManager;function i(t){f.call(this,t)}i.prototype=Object.create(f.prototype);for(var g in f)i[g]=f[g];n.exports=i},765:(n,r,e)=>{var f=e(551).FDLayout,i=e(578),g=e(880),t=e(991),s=e(767),o=e(806),c=e(902),h=e(551).FDLayoutConstants,T=e(551).LayoutConstants,v=e(551).Point,d=e(551).PointD,N=e(551).DimensionD,S=e(551).Layout,M=e(551).Integer,P=e(551).IGeometry,K=e(551).LGraph,X=e(551).Transform,k=e(551).LinkedList;function D(){f.call(this),this.toBeTiled={},this.constraints={}}D.prototype=Object.create(f.prototype);for(var rt in f)D[rt]=f[rt];D.prototype.newGraphManager=function(){var a=new i(this);return this.graphManager=a,a},D.prototype.newGraph=function(a){return new g(null,this.graphManager,a)},D.prototype.newNode=function(a){return new t(this.graphManager,a)},D.prototype.newEdge=function(a){return new s(null,null,a)},D.prototype.initParameters=function(){f.prototype.initParameters.call(this,arguments),this.isSubLayout||(o.DEFAULT_EDGE_LENGTH<10?this.idealEdgeLength=10:this.idealEdgeLength=o.DEFAULT_EDGE_LENGTH,this.useSmartIdealEdgeLengthCalculation=o.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.gravityConstant=h.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=h.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=h.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=h.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.prunedNodesAll=[],this.growTreeIterations=0,this.afterGrowthIterations=0,this.isTreeGrowing=!1,this.isGrowthFinished=!1)},D.prototype.initSpringEmbedder=function(){f.prototype.initSpringEmbedder.call(this),this.coolingCycle=0,this.maxCoolingCycle=this.maxIterations/h.CONVERGENCE_CHECK_PERIOD,this.finalTemperature=.04,this.coolingAdjuster=1},D.prototype.layout=function(){var a=T.DEFAULT_CREATE_BENDS_AS_NEEDED;return a&&(this.createBendpoints(),this.graphManager.resetAllEdges()),this.level=0,this.classicLayout()},D.prototype.classicLayout=function(){if(this.nodesWithGravity=this.calculateNodesToApplyGravitationTo(),this.graphManager.setAllNodesToApplyGravitation(this.nodesWithGravity),this.calcNoOfChildrenForAllNodes(),this.graphManager.calcLowestCommonAncestors(),this.graphManager.calcInclusionTreeDepths(),this.graphManager.getRoot().calcEstimatedSize(),this.calcIdealEdgeLengths(),this.incremental){if(o.TREE_REDUCTION_ON_INCREMENTAL){this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var m=new Set(this.getAllNodes()),p=this.nodesWithGravity.filter(function(I){return m.has(I)});this.graphManager.setAllNodesToApplyGravitation(p)}}else{var a=this.getFlatForest();if(a.length>0)this.positionNodesRadially(a);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var m=new Set(this.getAllNodes()),p=this.nodesWithGravity.filter(function(E){return m.has(E)});this.graphManager.setAllNodesToApplyGravitation(p),this.positionNodesRandomly()}}return Object.keys(this.constraints).length>0&&(c.handleConstraints(this),this.initConstraintVariables()),this.initSpringEmbedder(),o.APPLY_LAYOUT&&this.runSpringEmbedder(),!0},D.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%h.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var a=new Set(this.getAllNodes()),m=this.nodesWithGravity.filter(function(y){return a.has(y)});this.graphManager.setAllNodesToApplyGravitation(m),this.graphManager.updateBounds(),this.updateGrid(),o.PURE_INCREMENTAL?this.coolingFactor=h.DEFAULT_COOLING_FACTOR_INCREMENTAL/2:this.coolingFactor=h.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),o.PURE_INCREMENTAL?this.coolingFactor=h.DEFAULT_COOLING_FACTOR_INCREMENTAL/2*((100-this.afterGrowthIterations)/100):this.coolingFactor=h.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var p=!this.isTreeGrowing&&!this.isGrowthFinished,E=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(p,E),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},D.prototype.getPositionsData=function(){for(var a=this.graphManager.getAllNodes(),m={},p=0;p0&&this.updateDisplacements();for(var p=0;p0&&(E.fixedNodeWeight=I)}}if(this.constraints.relativePlacementConstraint){var w=new Map,R=new Map;if(this.dummyToNodeForVerticalAlignment=new Map,this.dummyToNodeForHorizontalAlignment=new Map,this.fixedNodesOnHorizontal=new Set,this.fixedNodesOnVertical=new Set,this.fixedNodeSet.forEach(function(O){a.fixedNodesOnHorizontal.add(O),a.fixedNodesOnVertical.add(O)}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var W=this.constraints.alignmentConstraint.vertical,p=0;p=2*O.length/3;_--)H=Math.floor(Math.random()*(_+1)),B=O[_],O[_]=O[H],O[H]=B;return O},this.nodesInRelativeHorizontal=[],this.nodesInRelativeVertical=[],this.nodeToRelativeConstraintMapHorizontal=new Map,this.nodeToRelativeConstraintMapVertical=new Map,this.nodeToTempPositionMapHorizontal=new Map,this.nodeToTempPositionMapVertical=new Map,this.constraints.relativePlacementConstraint.forEach(function(O){if(O.left){var H=w.has(O.left)?w.get(O.left):O.left,B=w.has(O.right)?w.get(O.right):O.right;a.nodesInRelativeHorizontal.includes(H)||(a.nodesInRelativeHorizontal.push(H),a.nodeToRelativeConstraintMapHorizontal.set(H,[]),a.dummyToNodeForVerticalAlignment.has(H)?a.nodeToTempPositionMapHorizontal.set(H,a.idToNodeMap.get(a.dummyToNodeForVerticalAlignment.get(H)[0]).getCenterX()):a.nodeToTempPositionMapHorizontal.set(H,a.idToNodeMap.get(H).getCenterX())),a.nodesInRelativeHorizontal.includes(B)||(a.nodesInRelativeHorizontal.push(B),a.nodeToRelativeConstraintMapHorizontal.set(B,[]),a.dummyToNodeForVerticalAlignment.has(B)?a.nodeToTempPositionMapHorizontal.set(B,a.idToNodeMap.get(a.dummyToNodeForVerticalAlignment.get(B)[0]).getCenterX()):a.nodeToTempPositionMapHorizontal.set(B,a.idToNodeMap.get(B).getCenterX())),a.nodeToRelativeConstraintMapHorizontal.get(H).push({right:B,gap:O.gap}),a.nodeToRelativeConstraintMapHorizontal.get(B).push({left:H,gap:O.gap})}else{var _=R.has(O.top)?R.get(O.top):O.top,lt=R.has(O.bottom)?R.get(O.bottom):O.bottom;a.nodesInRelativeVertical.includes(_)||(a.nodesInRelativeVertical.push(_),a.nodeToRelativeConstraintMapVertical.set(_,[]),a.dummyToNodeForHorizontalAlignment.has(_)?a.nodeToTempPositionMapVertical.set(_,a.idToNodeMap.get(a.dummyToNodeForHorizontalAlignment.get(_)[0]).getCenterY()):a.nodeToTempPositionMapVertical.set(_,a.idToNodeMap.get(_).getCenterY())),a.nodesInRelativeVertical.includes(lt)||(a.nodesInRelativeVertical.push(lt),a.nodeToRelativeConstraintMapVertical.set(lt,[]),a.dummyToNodeForHorizontalAlignment.has(lt)?a.nodeToTempPositionMapVertical.set(lt,a.idToNodeMap.get(a.dummyToNodeForHorizontalAlignment.get(lt)[0]).getCenterY()):a.nodeToTempPositionMapVertical.set(lt,a.idToNodeMap.get(lt).getCenterY())),a.nodeToRelativeConstraintMapVertical.get(_).push({bottom:lt,gap:O.gap}),a.nodeToRelativeConstraintMapVertical.get(lt).push({top:_,gap:O.gap})}});else{var q=new Map,V=new Map;this.constraints.relativePlacementConstraint.forEach(function(O){if(O.left){var H=w.has(O.left)?w.get(O.left):O.left,B=w.has(O.right)?w.get(O.right):O.right;q.has(H)?q.get(H).push(B):q.set(H,[B]),q.has(B)?q.get(B).push(H):q.set(B,[H])}else{var _=R.has(O.top)?R.get(O.top):O.top,lt=R.has(O.bottom)?R.get(O.bottom):O.bottom;V.has(_)?V.get(_).push(lt):V.set(_,[lt]),V.has(lt)?V.get(lt).push(_):V.set(lt,[_])}});var Y=function(H,B){var _=[],lt=[],J=new k,Rt=new Set,Lt=0;return H.forEach(function(vt,it){if(!Rt.has(it)){_[Lt]=[],lt[Lt]=!1;var gt=it;for(J.push(gt),Rt.add(gt),_[Lt].push(gt);J.length!=0;){gt=J.shift(),B.has(gt)&&(lt[Lt]=!0);var Tt=H.get(gt);Tt.forEach(function(At){Rt.has(At)||(J.push(At),Rt.add(At),_[Lt].push(At))})}Lt++}}),{components:_,isFixed:lt}},et=Y(q,a.fixedNodesOnHorizontal);this.componentsOnHorizontal=et.components,this.fixedComponentsOnHorizontal=et.isFixed;var z=Y(V,a.fixedNodesOnVertical);this.componentsOnVertical=z.components,this.fixedComponentsOnVertical=z.isFixed}}},D.prototype.updateDisplacements=function(){var a=this;if(this.constraints.fixedNodeConstraint&&this.constraints.fixedNodeConstraint.forEach(function(z){var O=a.idToNodeMap.get(z.nodeId);O.displacementX=0,O.displacementY=0}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var m=this.constraints.alignmentConstraint.vertical,p=0;p1){var R;for(R=0;RE&&(E=Math.floor(w.y)),I=Math.floor(w.x+o.DEFAULT_COMPONENT_SEPERATION)}this.transform(new d(T.WORLD_CENTER_X-w.x/2,T.WORLD_CENTER_Y-w.y/2))},D.radialLayout=function(a,m,p){var E=Math.max(this.maxDiagonalInTree(a),o.DEFAULT_RADIAL_SEPARATION);D.branchRadialLayout(m,null,0,359,0,E);var y=K.calculateBounds(a),I=new X;I.setDeviceOrgX(y.getMinX()),I.setDeviceOrgY(y.getMinY()),I.setWorldOrgX(p.x),I.setWorldOrgY(p.y);for(var w=0;w1;){var B=H[0];H.splice(0,1);var _=V.indexOf(B);_>=0&&V.splice(_,1),z--,Y--}m!=null?O=(V.indexOf(H[0])+1)%z:O=0;for(var lt=Math.abs(E-p)/Y,J=O;et!=Y;J=++J%z){var Rt=V[J].getOtherEnd(a);if(Rt!=m){var Lt=(p+et*lt)%360,vt=(Lt+lt)%360;D.branchRadialLayout(Rt,a,Lt,vt,y+I,I),et++}}},D.maxDiagonalInTree=function(a){for(var m=M.MIN_VALUE,p=0;pm&&(m=y)}return m},D.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},D.prototype.groupZeroDegreeMembers=function(){var a=this,m={};this.memberGroups={},this.idToDummyNode={};for(var p=[],E=this.graphManager.getAllNodes(),y=0;y"u"&&(m[R]=[]),m[R]=m[R].concat(I)}Object.keys(m).forEach(function(W){if(m[W].length>1){var x="DummyCompound_"+W;a.memberGroups[x]=m[W];var q=m[W][0].getParent(),V=new t(a.graphManager);V.id=x,V.paddingLeft=q.paddingLeft||0,V.paddingRight=q.paddingRight||0,V.paddingBottom=q.paddingBottom||0,V.paddingTop=q.paddingTop||0,a.idToDummyNode[x]=V;var Y=a.getGraphManager().add(a.newGraph(),V),et=q.getChild();et.add(V);for(var z=0;zy?(E.rect.x-=(E.labelWidth-y)/2,E.setWidth(E.labelWidth),E.labelMarginLeft=(E.labelWidth-y)/2):E.labelPosHorizontal=="right"&&E.setWidth(y+E.labelWidth)),E.labelHeight&&(E.labelPosVertical=="top"?(E.rect.y-=E.labelHeight,E.setHeight(I+E.labelHeight),E.labelMarginTop=E.labelHeight):E.labelPosVertical=="center"&&E.labelHeight>I?(E.rect.y-=(E.labelHeight-I)/2,E.setHeight(E.labelHeight),E.labelMarginTop=(E.labelHeight-I)/2):E.labelPosVertical=="bottom"&&E.setHeight(I+E.labelHeight))}})},D.prototype.repopulateCompounds=function(){for(var a=this.compoundOrder.length-1;a>=0;a--){var m=this.compoundOrder[a],p=m.id,E=m.paddingLeft,y=m.paddingTop,I=m.labelMarginLeft,w=m.labelMarginTop;this.adjustLocations(this.tiledMemberPack[p],m.rect.x,m.rect.y,E,y,I,w)}},D.prototype.repopulateZeroDegreeMembers=function(){var a=this,m=this.tiledZeroDegreePack;Object.keys(m).forEach(function(p){var E=a.idToDummyNode[p],y=E.paddingLeft,I=E.paddingTop,w=E.labelMarginLeft,R=E.labelMarginTop;a.adjustLocations(m[p],E.rect.x,E.rect.y,y,I,w,R)})},D.prototype.getToBeTiled=function(a){var m=a.id;if(this.toBeTiled[m]!=null)return this.toBeTiled[m];var p=a.getChild();if(p==null)return this.toBeTiled[m]=!1,!1;for(var E=p.getNodes(),y=0;y0)return this.toBeTiled[m]=!1,!1;if(I.getChild()==null){this.toBeTiled[I.id]=!1;continue}if(!this.getToBeTiled(I))return this.toBeTiled[m]=!1,!1}return this.toBeTiled[m]=!0,!0},D.prototype.getNodeDegree=function(a){a.id;for(var m=a.getEdges(),p=0,E=0;Eq&&(q=Y.rect.height)}p+=q+a.verticalPadding}},D.prototype.tileCompoundMembers=function(a,m){var p=this;this.tiledMemberPack=[],Object.keys(a).forEach(function(E){var y=m[E];if(p.tiledMemberPack[E]=p.tileNodes(a[E],y.paddingLeft+y.paddingRight),y.rect.width=p.tiledMemberPack[E].width,y.rect.height=p.tiledMemberPack[E].height,y.setCenter(p.tiledMemberPack[E].centerX,p.tiledMemberPack[E].centerY),y.labelMarginLeft=0,y.labelMarginTop=0,o.NODE_DIMENSIONS_INCLUDE_LABELS){var I=y.rect.width,w=y.rect.height;y.labelWidth&&(y.labelPosHorizontal=="left"?(y.rect.x-=y.labelWidth,y.setWidth(I+y.labelWidth),y.labelMarginLeft=y.labelWidth):y.labelPosHorizontal=="center"&&y.labelWidth>I?(y.rect.x-=(y.labelWidth-I)/2,y.setWidth(y.labelWidth),y.labelMarginLeft=(y.labelWidth-I)/2):y.labelPosHorizontal=="right"&&y.setWidth(I+y.labelWidth)),y.labelHeight&&(y.labelPosVertical=="top"?(y.rect.y-=y.labelHeight,y.setHeight(w+y.labelHeight),y.labelMarginTop=y.labelHeight):y.labelPosVertical=="center"&&y.labelHeight>w?(y.rect.y-=(y.labelHeight-w)/2,y.setHeight(y.labelHeight),y.labelMarginTop=(y.labelHeight-w)/2):y.labelPosVertical=="bottom"&&y.setHeight(w+y.labelHeight))}})},D.prototype.tileNodes=function(a,m){var p=this.tileNodesByFavoringDim(a,m,!0),E=this.tileNodesByFavoringDim(a,m,!1),y=this.getOrgRatio(p),I=this.getOrgRatio(E),w;return IR&&(R=z.getWidth())});var W=I/y,x=w/y,q=Math.pow(p-E,2)+4*(W+E)*(x+p)*y,V=(E-p+Math.sqrt(q))/(2*(W+E)),Y;m?(Y=Math.ceil(V),Y==V&&Y++):Y=Math.floor(V);var et=Y*(W+E)-E;return R>et&&(et=R),et+=E*2,et},D.prototype.tileNodesByFavoringDim=function(a,m,p){var E=o.TILING_PADDING_VERTICAL,y=o.TILING_PADDING_HORIZONTAL,I=o.TILING_COMPARE_BY,w={rows:[],rowWidth:[],rowHeight:[],width:0,height:m,verticalPadding:E,horizontalPadding:y,centerX:0,centerY:0};I&&(w.idealRowWidth=this.calcIdealRowWidth(a,p));var R=function(O){return O.rect.width*O.rect.height},W=function(O,H){return R(H)-R(O)};a.sort(function(z,O){var H=W;return w.idealRowWidth?(H=I,H(z.id,O.id)):H(z,O)});for(var x=0,q=0,V=0;V0&&(w+=a.horizontalPadding),a.rowWidth[p]=w,a.width0&&(R+=a.verticalPadding);var W=0;R>a.rowHeight[p]&&(W=a.rowHeight[p],a.rowHeight[p]=R,W=a.rowHeight[p]-W),a.height+=W,a.rows[p].push(m)},D.prototype.getShortestRowIndex=function(a){for(var m=-1,p=Number.MAX_VALUE,E=0;Ep&&(m=E,p=a.rowWidth[E]);return m},D.prototype.canAddHorizontal=function(a,m,p){if(a.idealRowWidth){var E=a.rows.length-1,y=a.rowWidth[E];return y+m+a.horizontalPadding<=a.idealRowWidth}var I=this.getShortestRowIndex(a);if(I<0)return!0;var w=a.rowWidth[I];if(w+a.horizontalPadding+m<=a.width)return!0;var R=0;a.rowHeight[I]0&&(R=p+a.verticalPadding-a.rowHeight[I]);var W;a.width-w>=m+a.horizontalPadding?W=(a.height+R)/(w+m+a.horizontalPadding):W=(a.height+R)/a.width,R=p+a.verticalPadding;var x;return a.widthI&&m!=p){E.splice(-1,1),a.rows[p].push(y),a.rowWidth[m]=a.rowWidth[m]-I,a.rowWidth[p]=a.rowWidth[p]+I,a.width=a.rowWidth[instance.getLongestRowIndex(a)];for(var w=Number.MIN_VALUE,R=0;Rw&&(w=E[R].height);m>0&&(w+=a.verticalPadding);var W=a.rowHeight[m]+a.rowHeight[p];a.rowHeight[m]=w,a.rowHeight[p]0)for(var et=y;et<=I;et++)Y[0]+=this.grid[et][w-1].length+this.grid[et][w].length-1;if(I0)for(var et=w;et<=R;et++)Y[3]+=this.grid[y-1][et].length+this.grid[y][et].length-1;for(var z=M.MAX_VALUE,O,H,B=0;B{var f=e(551).FDLayoutNode,i=e(551).IMath;function g(s,o,c,h){f.call(this,s,o,c,h)}g.prototype=Object.create(f.prototype);for(var t in f)g[t]=f[t];g.prototype.calculateDisplacement=function(){var s=this.graphManager.getLayout();this.getChild()!=null&&this.fixedNodeWeight?(this.displacementX+=s.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.fixedNodeWeight,this.displacementY+=s.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.fixedNodeWeight):(this.displacementX+=s.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.noOfChildren,this.displacementY+=s.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.noOfChildren),Math.abs(this.displacementX)>s.coolingFactor*s.maxNodeDisplacement&&(this.displacementX=s.coolingFactor*s.maxNodeDisplacement*i.sign(this.displacementX)),Math.abs(this.displacementY)>s.coolingFactor*s.maxNodeDisplacement&&(this.displacementY=s.coolingFactor*s.maxNodeDisplacement*i.sign(this.displacementY)),this.child&&this.child.getNodes().length>0&&this.propogateDisplacementToChildren(this.displacementX,this.displacementY)},g.prototype.propogateDisplacementToChildren=function(s,o){for(var c=this.getChild().getNodes(),h,T=0;T{function f(c){if(Array.isArray(c)){for(var h=0,T=Array(c.length);h0){var Ct=0;st.forEach(function(ht){$=="horizontal"?(tt.set(ht,v.has(ht)?d[v.get(ht)]:Z.get(ht)),Ct+=tt.get(ht)):(tt.set(ht,v.has(ht)?N[v.get(ht)]:Z.get(ht)),Ct+=tt.get(ht))}),Ct=Ct/st.length,ft.forEach(function(ht){Q.has(ht)||tt.set(ht,Ct)})}else{var ct=0;ft.forEach(function(ht){$=="horizontal"?ct+=v.has(ht)?d[v.get(ht)]:Z.get(ht):ct+=v.has(ht)?N[v.get(ht)]:Z.get(ht)}),ct=ct/ft.length,ft.forEach(function(ht){tt.set(ht,ct)})}});for(var wt=function(){var st=dt.shift(),Ct=b.get(st);Ct.forEach(function(ct){if(tt.get(ct.id)ht&&(ht=qt),_tWt&&(Wt=_t)}}catch(ie){Mt=!0,Zt=ie}finally{try{!Nt&&Gt.return&&Gt.return()}finally{if(Mt)throw Zt}}var de=(Ct+ht)/2-(ct+Wt)/2,Kt=!0,te=!1,ee=void 0;try{for(var jt=ft[Symbol.iterator](),se;!(Kt=(se=jt.next()).done);Kt=!0){var re=se.value;tt.set(re,tt.get(re)+de)}}catch(ie){te=!0,ee=ie}finally{try{!Kt&&jt.return&&jt.return()}finally{if(te)throw ee}}})}return tt},rt=function(b){var $=0,Q=0,Z=0,nt=0;if(b.forEach(function(j){j.left?d[v.get(j.left)]-d[v.get(j.right)]>=0?$++:Q++:N[v.get(j.top)]-N[v.get(j.bottom)]>=0?Z++:nt++}),$>Q&&Z>nt)for(var ut=0;utQ)for(var ot=0;otnt)for(var tt=0;tt1)h.fixedNodeConstraint.forEach(function(F,b){E[b]=[F.position.x,F.position.y],y[b]=[d[v.get(F.nodeId)],N[v.get(F.nodeId)]]}),I=!0;else if(h.alignmentConstraint)(function(){var F=0;if(h.alignmentConstraint.vertical){for(var b=h.alignmentConstraint.vertical,$=function(tt){var j=new Set;b[tt].forEach(function(yt){j.add(yt)});var dt=new Set([].concat(f(j)).filter(function(yt){return R.has(yt)})),wt=void 0;dt.size>0?wt=d[v.get(dt.values().next().value)]:wt=k(j).x,b[tt].forEach(function(yt){E[F]=[wt,N[v.get(yt)]],y[F]=[d[v.get(yt)],N[v.get(yt)]],F++})},Q=0;Q0?wt=d[v.get(dt.values().next().value)]:wt=k(j).y,Z[tt].forEach(function(yt){E[F]=[d[v.get(yt)],wt],y[F]=[d[v.get(yt)],N[v.get(yt)]],F++})},ut=0;utV&&(V=q[et].length,Y=et);if(V0){var 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Y=0,et=0,z=0;z0?h.DEFAULT_EDGE_LENGTH=c.DEFAULT_EDGE_LENGTH=Y/et:a(d.idealEdgeLength)?h.DEFAULT_EDGE_LENGTH=c.DEFAULT_EDGE_LENGTH=50:h.DEFAULT_EDGE_LENGTH=c.DEFAULT_EDGE_LENGTH=d.idealEdgeLength,h.MIN_REPULSION_DIST=c.MIN_REPULSION_DIST=c.DEFAULT_EDGE_LENGTH/10,h.DEFAULT_RADIAL_SEPARATION=c.DEFAULT_EDGE_LENGTH)},I=function(x,q){q.fixedNodeConstraint&&(x.constraints.fixedNodeConstraint=q.fixedNodeConstraint),q.alignmentConstraint&&(x.constraints.alignmentConstraint=q.alignmentConstraint),q.relativePlacementConstraint&&(x.constraints.relativePlacementConstraint=q.relativePlacementConstraint)};d.nestingFactor!=null&&(h.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=c.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=d.nestingFactor),d.gravity!=null&&(h.DEFAULT_GRAVITY_STRENGTH=c.DEFAULT_GRAVITY_STRENGTH=d.gravity),d.numIter!=null&&(h.MAX_ITERATIONS=c.MAX_ITERATIONS=d.numIter),d.gravityRange!=null&&(h.DEFAULT_GRAVITY_RANGE_FACTOR=c.DEFAULT_GRAVITY_RANGE_FACTOR=d.gravityRange),d.gravityCompound!=null&&(h.DEFAULT_COMPOUND_GRAVITY_STRENGTH=c.DEFAULT_COMPOUND_GRAVITY_STRENGTH=d.gravityCompound),d.gravityRangeCompound!=null&&(h.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=c.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=d.gravityRangeCompound),d.initialEnergyOnIncremental!=null&&(h.DEFAULT_COOLING_FACTOR_INCREMENTAL=c.DEFAULT_COOLING_FACTOR_INCREMENTAL=d.initialEnergyOnIncremental),d.tilingCompareBy!=null&&(h.TILING_COMPARE_BY=d.tilingCompareBy),d.quality=="proof"?o.QUALITY=2:o.QUALITY=0,h.NODE_DIMENSIONS_INCLUDE_LABELS=c.NODE_DIMENSIONS_INCLUDE_LABELS=o.NODE_DIMENSIONS_INCLUDE_LABELS=d.nodeDimensionsIncludeLabels,h.DEFAULT_INCREMENTAL=c.DEFAULT_INCREMENTAL=o.DEFAULT_INCREMENTAL=!d.randomize,h.ANIMATE=c.ANIMATE=o.ANIMATE=d.animate,h.TILE=d.tile,h.TILING_PADDING_VERTICAL=typeof 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k[it][Dt.id()]&&Tt.nodes.push({x:k[it][Dt.id()].getLeft(),y:k[it][Dt.id()].getTop(),width:k[it][Dt.id()].getWidth(),height:k[it][Dt.id()].getHeight()})}),vt.edges().forEach(function(Dt){var mt=Dt.source(),xt=Dt.target();if(mt.css("display")!="none"&&xt.css("display")!="none")if(M.quality=="draft"){var St=gt.get(mt.id()),Vt=gt.get(xt.id()),Xt=[],Ut=[];if(mt.isParent()){var bt=t.calcBoundingBox(mt,X[it].xCoords,X[it].yCoords,gt);Xt.push(bt.topLeftX+bt.width/2),Xt.push(bt.topLeftY+bt.height/2)}else Xt.push(X[it].xCoords[St]),Xt.push(X[it].yCoords[St]);if(xt.isParent()){var Ht=t.calcBoundingBox(xt,X[it].xCoords,X[it].yCoords,gt);Ut.push(Ht.topLeftX+Ht.width/2),Ut.push(Ht.topLeftY+Ht.height/2)}else Ut.push(X[it].xCoords[Vt]),Ut.push(X[it].yCoords[Vt]);Tt.edges.push({startX:Xt[0],startY:Xt[1],endX:Ut[0],endY:Ut[1]})}else 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this.yy=m||this.yy||{},this._input=x,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:g(function(){var x=this._input[0];this.yytext+=x,this.yyleng++,this.offset++,this.match+=x,this.matched+=x;var m=x.match(/(?:\r\n?|\n).*/g);return m?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),x},"input"),unput:g(function(x){var m=x.length,v=x.split(/(?:\r\n?|\n)/g);this._input=x+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-m),this.offset-=m;var b=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),v.length-1&&(this.yylineno-=v.length-1);var R=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:v?(v.length===b.length?this.yylloc.first_column:0)+b[b.length-v.length].length-v[0].length:this.yylloc.first_column-m},this.options.ranges&&(this.yylloc.range=[R[0],R[0]+this.yyleng-m]),this.yyleng=this.yytext.length,this},"unput"),more:g(function(){return this._more=!0,this},"more"),reject:g(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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index df6fa18f..a74d0312 100644 --- a/lightrag/api/webui/assets/chunk-4BMEZGHF-Ct0jZH9M.js +++ b/lightrag/api/webui/assets/chunk-4BMEZGHF-BqribV_z.js @@ -1 +1 @@ -import{_ as l}from"./mermaid-vendor-d7rbry5E.js";function m(e,c){var i,t,o;e.accDescr&&((i=c.setAccDescription)==null||i.call(c,e.accDescr)),e.accTitle&&((t=c.setAccTitle)==null||t.call(c,e.accTitle)),e.title&&((o=c.setDiagramTitle)==null||o.call(c,e.title))}l(m,"populateCommonDb");export{m as p}; +import{_ as l}from"./mermaid-vendor-BVBgFwCv.js";function m(e,c){var i,t,o;e.accDescr&&((i=c.setAccDescription)==null||i.call(c,e.accDescr)),e.accTitle&&((t=c.setAccTitle)==null||t.call(c,e.accTitle)),e.title&&((o=c.setDiagramTitle)==null||o.call(c,e.title))}l(m,"populateCommonDb");export{m as p}; diff --git a/lightrag/api/webui/assets/chunk-A2AXSNBT-CvfQgLmG.js b/lightrag/api/webui/assets/chunk-A2AXSNBT-79sAOFxS.js similarity index 99% rename from lightrag/api/webui/assets/chunk-A2AXSNBT-CvfQgLmG.js rename to lightrag/api/webui/assets/chunk-A2AXSNBT-79sAOFxS.js index 08f764ac..d5c1d384 100644 --- a/lightrag/api/webui/assets/chunk-A2AXSNBT-CvfQgLmG.js +++ b/lightrag/api/webui/assets/chunk-A2AXSNBT-79sAOFxS.js @@ -1,4 +1,4 @@ -import{g as et,s as tt}from"./chunk-RZ5BOZE2-C762jHXr.js";import{_ as f,l as Oe,c as F,p as st,r as it,u as we,d as $,b as at,a as nt,s as rt,g as ut,q as lt,t as ct,k as v,z as ot,y as ht,i as dt,Y as R}from"./mermaid-vendor-d7rbry5E.js";var Ve=function(){var s=f(function(I,c,h,p){for(h=h||{},p=I.length;p--;h[I[p]]=c);return 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_=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:a?(a.length===T.length?this.yylloc.first_column:0)+T[T.length-a.length].length-a[0].length:this.yylloc.first_column-h},this.options.ranges&&(this.yylloc.range=[_[0],_[0]+this.yyleng-h]),this.yyleng=this.yytext.length,this},"unput"),more:p(function(){return this._more=!0,this},"more"),reject:p(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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index 7b5cfea3..95004ab2 100644 --- a/lightrag/api/webui/assets/chunk-D6G4REZN-CQqXYBrW.js +++ b/lightrag/api/webui/assets/chunk-D6G4REZN-DYSFhgH1.js @@ -1 +1 @@ -import{_ as n,a1 as x,j as l}from"./mermaid-vendor-d7rbry5E.js";var c=n((a,t)=>{const e=a.append("rect");if(e.attr("x",t.x),e.attr("y",t.y),e.attr("fill",t.fill),e.attr("stroke",t.stroke),e.attr("width",t.width),e.attr("height",t.height),t.name&&e.attr("name",t.name),t.rx&&e.attr("rx",t.rx),t.ry&&e.attr("ry",t.ry),t.attrs!==void 0)for(const r in t.attrs)e.attr(r,t.attrs[r]);return t.class&&e.attr("class",t.class),e},"drawRect"),d=n((a,t)=>{const e={x:t.startx,y:t.starty,width:t.stopx-t.startx,height:t.stopy-t.starty,fill:t.fill,stroke:t.stroke,class:"rect"};c(a,e).lower()},"drawBackgroundRect"),g=n((a,t)=>{const e=t.text.replace(x," "),r=a.append("text");r.attr("x",t.x),r.attr("y",t.y),r.attr("class","legend"),r.style("text-anchor",t.anchor),t.class&&r.attr("class",t.class);const s=r.append("tspan");return s.attr("x",t.x+t.textMargin*2),s.text(e),r},"drawText"),h=n((a,t,e,r)=>{const s=a.append("image");s.attr("x",t),s.attr("y",e);const i=l.sanitizeUrl(r);s.attr("xlink:href",i)},"drawImage"),m=n((a,t,e,r)=>{const s=a.append("use");s.attr("x",t),s.attr("y",e);const i=l.sanitizeUrl(r);s.attr("xlink:href",`#${i}`)},"drawEmbeddedImage"),y=n(()=>({x:0,y:0,width:100,height:100,fill:"#EDF2AE",stroke:"#666",anchor:"start",rx:0,ry:0}),"getNoteRect"),p=n(()=>({x:0,y:0,width:100,height:100,"text-anchor":"start",style:"#666",textMargin:0,rx:0,ry:0,tspan:!0}),"getTextObj");export{d as a,p as b,m as c,c as d,h as e,g as f,y as g}; +import{_ as n,a1 as x,j as l}from"./mermaid-vendor-BVBgFwCv.js";var c=n((a,t)=>{const e=a.append("rect");if(e.attr("x",t.x),e.attr("y",t.y),e.attr("fill",t.fill),e.attr("stroke",t.stroke),e.attr("width",t.width),e.attr("height",t.height),t.name&&e.attr("name",t.name),t.rx&&e.attr("rx",t.rx),t.ry&&e.attr("ry",t.ry),t.attrs!==void 0)for(const r in t.attrs)e.attr(r,t.attrs[r]);return t.class&&e.attr("class",t.class),e},"drawRect"),d=n((a,t)=>{const e={x:t.startx,y:t.starty,width:t.stopx-t.startx,height:t.stopy-t.starty,fill:t.fill,stroke:t.stroke,class:"rect"};c(a,e).lower()},"drawBackgroundRect"),g=n((a,t)=>{const e=t.text.replace(x," "),r=a.append("text");r.attr("x",t.x),r.attr("y",t.y),r.attr("class","legend"),r.style("text-anchor",t.anchor),t.class&&r.attr("class",t.class);const s=r.append("tspan");return s.attr("x",t.x+t.textMargin*2),s.text(e),r},"drawText"),h=n((a,t,e,r)=>{const s=a.append("image");s.attr("x",t),s.attr("y",e);const i=l.sanitizeUrl(r);s.attr("xlink:href",i)},"drawImage"),m=n((a,t,e,r)=>{const s=a.append("use");s.attr("x",t),s.attr("y",e);const i=l.sanitizeUrl(r);s.attr("xlink:href",`#${i}`)},"drawEmbeddedImage"),y=n(()=>({x:0,y:0,width:100,height:100,fill:"#EDF2AE",stroke:"#666",anchor:"start",rx:0,ry:0}),"getNoteRect"),p=n(()=>({x:0,y:0,width:100,height:100,"text-anchor":"start",style:"#666",textMargin:0,rx:0,ry:0,tspan:!0}),"getTextObj");export{d as a,p as b,m as c,c as d,h as e,g as f,y as g}; diff --git a/lightrag/api/webui/assets/chunk-RZ5BOZE2-C762jHXr.js b/lightrag/api/webui/assets/chunk-RZ5BOZE2-PbmQbmec.js similarity index 81% rename from lightrag/api/webui/assets/chunk-RZ5BOZE2-C762jHXr.js rename to lightrag/api/webui/assets/chunk-RZ5BOZE2-PbmQbmec.js index 2165a0f3..a9cd6817 100644 --- a/lightrag/api/webui/assets/chunk-RZ5BOZE2-C762jHXr.js +++ b/lightrag/api/webui/assets/chunk-RZ5BOZE2-PbmQbmec.js @@ -1 +1 @@ -import{_ as n,d as r,e as d,l as g}from"./mermaid-vendor-d7rbry5E.js";var u=n((e,t)=>{let o;return t==="sandbox"&&(o=r("#i"+e)),(t==="sandbox"?r(o.nodes()[0].contentDocument.body):r("body")).select(`[id="${e}"]`)},"getDiagramElement"),b=n((e,t,o,i)=>{e.attr("class",o);const{width:a,height:s,x:h,y:x}=l(e,t);d(e,s,a,i);const c=w(h,x,a,s,t);e.attr("viewBox",c),g.debug(`viewBox configured: ${c} with padding: ${t}`)},"setupViewPortForSVG"),l=n((e,t)=>{var i;const o=((i=e.node())==null?void 0:i.getBBox())||{width:0,height:0,x:0,y:0};return{width:o.width+t*2,height:o.height+t*2,x:o.x,y:o.y}},"calculateDimensionsWithPadding"),w=n((e,t,o,i,a)=>`${e-a} ${t-a} ${o} ${i}`,"createViewBox");export{u as g,b as s}; +import{_ as n,d as r,e as d,l as g}from"./mermaid-vendor-BVBgFwCv.js";var u=n((e,t)=>{let o;return t==="sandbox"&&(o=r("#i"+e)),(t==="sandbox"?r(o.nodes()[0].contentDocument.body):r("body")).select(`[id="${e}"]`)},"getDiagramElement"),b=n((e,t,o,i)=>{e.attr("class",o);const{width:a,height:s,x:h,y:x}=l(e,t);d(e,s,a,i);const c=w(h,x,a,s,t);e.attr("viewBox",c),g.debug(`viewBox configured: 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s}from"./mermaid-vendor-BVBgFwCv.js";var t,e=(t=class{constructor(i){this.init=i,this.records=this.init()}reset(){this.records=this.init()}},s(t,"ImperativeState"),t);export{e as I}; diff --git a/lightrag/api/webui/assets/classDiagram-GIVACNV2-Bx6IdAZy.js b/lightrag/api/webui/assets/classDiagram-GIVACNV2-CtQONuGk.js similarity index 61% rename from lightrag/api/webui/assets/classDiagram-GIVACNV2-Bx6IdAZy.js rename to lightrag/api/webui/assets/classDiagram-GIVACNV2-CtQONuGk.js index 671954b9..8c8238ea 100644 --- a/lightrag/api/webui/assets/classDiagram-GIVACNV2-Bx6IdAZy.js +++ b/lightrag/api/webui/assets/classDiagram-GIVACNV2-CtQONuGk.js @@ -1 +1 @@ -import{s as a,c as s,a as e,C as t}from"./chunk-A2AXSNBT-CvfQgLmG.js";import{_ as i}from"./mermaid-vendor-d7rbry5E.js";import"./chunk-RZ5BOZE2-C762jHXr.js";import"./feature-graph-DbHHHM9y.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var f={parser:e,get db(){return new t},renderer:s,styles:a,init:i(r=>{r.class||(r.class={}),r.class.arrowMarkerAbsolute=r.arrowMarkerAbsolute},"init")};export{f as diagram}; +import{s as a,c as s,a as e,C as t}from"./chunk-A2AXSNBT-79sAOFxS.js";import{_ as i}from"./mermaid-vendor-BVBgFwCv.js";import"./chunk-RZ5BOZE2-PbmQbmec.js";import"./feature-graph-D-mwOi0p.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var f={parser:e,get db(){return new t},renderer:s,styles:a,init:i(r=>{r.class||(r.class={}),r.class.arrowMarkerAbsolute=r.arrowMarkerAbsolute},"init")};export{f as diagram}; diff --git a/lightrag/api/webui/assets/classDiagram-v2-COTLJTTW-Bx6IdAZy.js b/lightrag/api/webui/assets/classDiagram-v2-COTLJTTW-CtQONuGk.js similarity index 61% rename from lightrag/api/webui/assets/classDiagram-v2-COTLJTTW-Bx6IdAZy.js rename to lightrag/api/webui/assets/classDiagram-v2-COTLJTTW-CtQONuGk.js index 671954b9..8c8238ea 100644 --- a/lightrag/api/webui/assets/classDiagram-v2-COTLJTTW-Bx6IdAZy.js +++ b/lightrag/api/webui/assets/classDiagram-v2-COTLJTTW-CtQONuGk.js @@ -1 +1 @@ -import{s as a,c as s,a as e,C as t}from"./chunk-A2AXSNBT-CvfQgLmG.js";import{_ as i}from"./mermaid-vendor-d7rbry5E.js";import"./chunk-RZ5BOZE2-C762jHXr.js";import"./feature-graph-DbHHHM9y.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var f={parser:e,get db(){return new t},renderer:s,styles:a,init:i(r=>{r.class||(r.class={}),r.class.arrowMarkerAbsolute=r.arrowMarkerAbsolute},"init")};export{f as diagram}; +import{s as a,c as s,a as e,C as t}from"./chunk-A2AXSNBT-79sAOFxS.js";import{_ as i}from"./mermaid-vendor-BVBgFwCv.js";import"./chunk-RZ5BOZE2-PbmQbmec.js";import"./feature-graph-D-mwOi0p.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var f={parser:e,get db(){return new t},renderer:s,styles:a,init:i(r=>{r.class||(r.class={}),r.class.arrowMarkerAbsolute=r.arrowMarkerAbsolute},"init")};export{f as diagram}; diff --git a/lightrag/api/webui/assets/clone-UTZGcTvC.js b/lightrag/api/webui/assets/clone-UTZGcTvC.js new file mode 100644 index 00000000..9e8db024 --- /dev/null +++ b/lightrag/api/webui/assets/clone-UTZGcTvC.js @@ -0,0 +1 @@ +import{b as r}from"./_baseUniq-CoKY6BVy.js";var e=4;function a(o){return r(o,e)}export{a as c}; diff --git a/lightrag/api/webui/assets/clone-vL6XIcCC.js b/lightrag/api/webui/assets/clone-vL6XIcCC.js deleted file mode 100644 index 140ee720..00000000 --- a/lightrag/api/webui/assets/clone-vL6XIcCC.js +++ /dev/null @@ -1 +0,0 @@ -import{b as 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A}from"./_basePickBy-Lz6agtdo.js";import"./feature-graph-DbHHHM9y.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";function E(e){var t={options:{directed:e.isDirected(),multigraph:e.isMultigraph(),compound:e.isCompound()},nodes:ee(e),edges:ne(e)};return x(e.graph())||(t.value=L(e.graph())),t}function ee(e){return A(e.nodes(),function(t){var n=e.node(t),o=e.parent(t),c={v:t};return x(n)||(c.value=n),x(o)||(c.parent=o),c})}function ne(e){return A(e.edges(),function(t){var n=e.edge(t),o={v:t.v,w:t.w};return x(t.name)||(o.name=t.name),x(n)||(o.value=n),o})}var f=new Map,b=new Map,J=new Map,te=p(()=>{b.clear(),J.clear(),f.clear()},"clear"),O=p((e,t)=>{const n=b.get(t)||[];return i.trace("In isDescendant",t," ",e," = ",n.includes(e)),n.includes(e)},"isDescendant"),se=p((e,t)=>{const n=b.get(t)||[];return i.info("Descendants of ",t," is ",n),i.info("Edge is 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");e.nodes().forEach(function(n){e.children(n).length>0&&(i.warn("Cluster identified",n," Replacement id in edges: ",D(n,e,n)),b.set(n,R(n,e)),f.set(n,{id:D(n,e,n),clusterData:e.node(n)}))}),e.nodes().forEach(function(n){const o=e.children(n),c=e.edges();o.length>0?(i.debug("Cluster identified",n,b),c.forEach(a=>{const r=O(a.v,n),u=O(a.w,n);r^u&&(i.warn("Edge: ",a," leaves cluster ",n),i.warn("Descendants of XXX ",n,": ",b.get(n)),f.get(n).externalConnections=!0)})):i.debug("Not a cluster ",n,b)});for(let n of f.keys()){const o=f.get(n).id,c=e.parent(o);c!==n&&f.has(c)&&!f.get(c).externalConnections&&(f.get(n).id=c)}e.edges().forEach(function(n){const o=e.edge(n);i.warn("Edge "+n.v+" -> "+n.w+": "+JSON.stringify(n)),i.warn("Edge "+n.v+" -> "+n.w+": "+JSON.stringify(e.edge(n)));let c=n.v,a=n.w;if(i.warn("Fix XXX",f,"ids:",n.v,n.w,"Translating: ",f.get(n.v)," --- ",f.get(n.w)),f.get(n.v)||f.get(n.w)){if(i.warn("Fixing and trying - removing XXX",n.v,n.w,n.name),c=k(n.v),a=k(n.w),e.removeEdge(n.v,n.w,n.name),c!==n.v){const r=e.parent(c);f.get(r).externalConnections=!0,o.fromCluster=n.v}if(a!==n.w){const r=e.parent(a);f.get(r).externalConnections=!0,o.toCluster=n.w}i.warn("Fix Replacing with XXX",c,a,n.name),e.setEdge(c,a,o,n.name)}}),i.warn("Adjusted Graph",E(e)),T(e,0),i.trace(f)},"adjustClustersAndEdges"),T=p((e,t)=>{var c,a;if(i.warn("extractor - ",t,E(e),e.children("D")),t>10){i.error("Bailing out");return}let n=e.nodes(),o=!1;for(const r of n){const u=e.children(r);o=o||u.length>0}if(!o){i.debug("Done, no node has children",e.nodes());return}i.debug("Nodes = ",n,t);for(const r of n)if(i.debug("Extracting node",r,f,f.has(r)&&!f.get(r).externalConnections,!e.parent(r),e.node(r),e.children("D")," Depth ",t),!f.has(r))i.debug("Not a cluster",r,t);else if(!f.get(r).externalConnections&&e.children(r)&&e.children(r).length>0){i.warn("Cluster without external connections, without a parent and with children",r,t);let l=e.graph().rankdir==="TB"?"LR":"TB";(a=(c=f.get(r))==null?void 0:c.clusterData)!=null&&a.dir&&(l=f.get(r).clusterData.dir,i.warn("Fixing dir",f.get(r).clusterData.dir,l));const v=new B({multigraph:!0,compound:!0}).setGraph({rankdir:l,nodesep:50,ranksep:50,marginx:8,marginy:8}).setDefaultEdgeLabel(function(){return{}});i.warn("Old graph before copy",E(e)),G(r,e,v,r),e.setNode(r,{clusterNode:!0,id:r,clusterData:f.get(r).clusterData,label:f.get(r).label,graph:v}),i.warn("New graph after copy node: (",r,")",E(v)),i.debug("Old graph after copy",E(e))}else i.warn("Cluster ** ",r," **not meeting the criteria !externalConnections:",!f.get(r).externalConnections," no parent: ",!e.parent(r)," children ",e.children(r)&&e.children(r).length>0,e.children("D"),t),i.debug(f);n=e.nodes(),i.warn("New list of nodes",n);for(const r of n){const u=e.node(r);i.warn(" Now next level",r,u),u!=null&&u.clusterNode&&T(u.graph,t+1)}},"extractor"),M=p((e,t)=>{if(t.length===0)return[];let n=Object.assign([],t);return t.forEach(o=>{const c=e.children(o),a=M(e,c);n=[...n,...a]}),n},"sorter"),oe=p(e=>M(e,e.children()),"sortNodesByHierarchy"),j=p(async(e,t,n,o,c,a)=>{i.warn("Graph in recursive render:XAX",E(t),c);const r=t.graph().rankdir;i.trace("Dir in recursive render - dir:",r);const u=e.insert("g").attr("class","root");t.nodes()?i.info("Recursive render XXX",t.nodes()):i.info("No nodes found for",t),t.edges().length>0&&i.info("Recursive edges",t.edge(t.edges()[0]));const l=u.insert("g").attr("class","clusters"),v=u.insert("g").attr("class","edgePaths"),C=u.insert("g").attr("class","edgeLabels"),g=u.insert("g").attr("class","nodes");await Promise.all(t.nodes().map(async function(d){const s=t.node(d);if(c!==void 0){const w=JSON.parse(JSON.stringify(c.clusterData));i.trace(`Setting data for parent cluster XXX +import{_ as p,ah as F,ai as Y,aj as _,ak as H,l as i,c as V,al as U,am as $,a8 as q,ad as z,a9 as P,a7 as K,an as Q,ao as W,ap as Z}from"./mermaid-vendor-BVBgFwCv.js";import{G as B}from"./graph-gg_UPtwE.js";import{l as I}from"./layout-DgVd6nly.js";import{i as x}from"./_baseUniq-CoKY6BVy.js";import{c as L}from"./clone-UTZGcTvC.js";import{m as A}from"./_basePickBy-BVZSZRdU.js";import"./feature-graph-D-mwOi0p.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";function E(e){var t={options:{directed:e.isDirected(),multigraph:e.isMultigraph(),compound:e.isCompound()},nodes:ee(e),edges:ne(e)};return x(e.graph())||(t.value=L(e.graph())),t}function ee(e){return A(e.nodes(),function(t){var n=e.node(t),o=e.parent(t),c={v:t};return x(n)||(c.value=n),x(o)||(c.parent=o),c})}function ne(e){return A(e.edges(),function(t){var n=e.edge(t),o={v:t.v,w:t.w};return x(t.name)||(o.name=t.name),x(n)||(o.value=n),o})}var f=new Map,b=new Map,J=new Map,te=p(()=>{b.clear(),J.clear(),f.clear()},"clear"),O=p((e,t)=>{const n=b.get(t)||[];return i.trace("In isDescendant",t," ",e," = ",n.includes(e)),n.includes(e)},"isDescendant"),se=p((e,t)=>{const n=b.get(t)||[];return i.info("Descendants of ",t," is ",n),i.info("Edge is ",e),e.v===t||e.w===t?!1:n?n.includes(e.v)||O(e.v,t)||O(e.w,t)||n.includes(e.w):(i.debug("Tilt, ",t,",not in descendants"),!1)},"edgeInCluster"),G=p((e,t,n,o)=>{i.warn("Copying children of ",e,"root",o,"data",t.node(e),o);const c=t.children(e)||[];e!==o&&c.push(e),i.warn("Copying (nodes) clusterId",e,"nodes",c),c.forEach(a=>{if(t.children(a).length>0)G(a,t,n,o);else{const r=t.node(a);i.info("cp ",a," to ",o," with parent ",e),n.setNode(a,r),o!==t.parent(a)&&(i.warn("Setting parent",a,t.parent(a)),n.setParent(a,t.parent(a))),e!==o&&a!==e?(i.debug("Setting parent",a,e),n.setParent(a,e)):(i.info("In copy ",e,"root",o,"data",t.node(e),o),i.debug("Not Setting parent for node=",a,"cluster!==rootId",e!==o,"node!==clusterId",a!==e));const u=t.edges(a);i.debug("Copying Edges",u),u.forEach(l=>{i.info("Edge",l);const v=t.edge(l.v,l.w,l.name);i.info("Edge data",v,o);try{se(l,o)?(i.info("Copying as ",l.v,l.w,v,l.name),n.setEdge(l.v,l.w,v,l.name),i.info("newGraph edges ",n.edges(),n.edge(n.edges()[0]))):i.info("Skipping copy of edge ",l.v,"-->",l.w," rootId: ",o," clusterId:",e)}catch(C){i.error(C)}})}i.debug("Removing node",a),t.removeNode(a)})},"copy"),R=p((e,t)=>{const n=t.children(e);let o=[...n];for(const c of n)J.set(c,e),o=[...o,...R(c,t)];return o},"extractDescendants"),ie=p((e,t,n)=>{const o=e.edges().filter(l=>l.v===t||l.w===t),c=e.edges().filter(l=>l.v===n||l.w===n),a=o.map(l=>({v:l.v===t?n:l.v,w:l.w===t?t:l.w})),r=c.map(l=>({v:l.v,w:l.w}));return a.filter(l=>r.some(v=>l.v===v.v&&l.w===v.w))},"findCommonEdges"),D=p((e,t,n)=>{const o=t.children(e);if(i.trace("Searching children of id ",e,o),o.length<1)return e;let c;for(const a of o){const r=D(a,t,n),u=ie(t,n,r);if(r)if(u.length>0)c=r;else return r}return c},"findNonClusterChild"),k=p(e=>!f.has(e)||!f.get(e).externalConnections?e:f.has(e)?f.get(e).id:e,"getAnchorId"),re=p((e,t)=>{if(!e||t>10){i.debug("Opting out, no graph ");return}else i.debug("Opting in, graph ");e.nodes().forEach(function(n){e.children(n).length>0&&(i.warn("Cluster identified",n," Replacement id in edges: ",D(n,e,n)),b.set(n,R(n,e)),f.set(n,{id:D(n,e,n),clusterData:e.node(n)}))}),e.nodes().forEach(function(n){const o=e.children(n),c=e.edges();o.length>0?(i.debug("Cluster identified",n,b),c.forEach(a=>{const r=O(a.v,n),u=O(a.w,n);r^u&&(i.warn("Edge: ",a," leaves cluster ",n),i.warn("Descendants of XXX ",n,": ",b.get(n)),f.get(n).externalConnections=!0)})):i.debug("Not a cluster ",n,b)});for(let n of f.keys()){const o=f.get(n).id,c=e.parent(o);c!==n&&f.has(c)&&!f.get(c).externalConnections&&(f.get(n).id=c)}e.edges().forEach(function(n){const o=e.edge(n);i.warn("Edge "+n.v+" -> "+n.w+": "+JSON.stringify(n)),i.warn("Edge "+n.v+" -> "+n.w+": "+JSON.stringify(e.edge(n)));let c=n.v,a=n.w;if(i.warn("Fix XXX",f,"ids:",n.v,n.w,"Translating: ",f.get(n.v)," --- ",f.get(n.w)),f.get(n.v)||f.get(n.w)){if(i.warn("Fixing and trying - removing XXX",n.v,n.w,n.name),c=k(n.v),a=k(n.w),e.removeEdge(n.v,n.w,n.name),c!==n.v){const r=e.parent(c);f.get(r).externalConnections=!0,o.fromCluster=n.v}if(a!==n.w){const r=e.parent(a);f.get(r).externalConnections=!0,o.toCluster=n.w}i.warn("Fix Replacing with XXX",c,a,n.name),e.setEdge(c,a,o,n.name)}}),i.warn("Adjusted Graph",E(e)),T(e,0),i.trace(f)},"adjustClustersAndEdges"),T=p((e,t)=>{var c,a;if(i.warn("extractor - ",t,E(e),e.children("D")),t>10){i.error("Bailing out");return}let n=e.nodes(),o=!1;for(const r of n){const u=e.children(r);o=o||u.length>0}if(!o){i.debug("Done, no node has children",e.nodes());return}i.debug("Nodes = ",n,t);for(const r of n)if(i.debug("Extracting node",r,f,f.has(r)&&!f.get(r).externalConnections,!e.parent(r),e.node(r),e.children("D")," Depth ",t),!f.has(r))i.debug("Not a cluster",r,t);else if(!f.get(r).externalConnections&&e.children(r)&&e.children(r).length>0){i.warn("Cluster without external connections, without a parent and with children",r,t);let l=e.graph().rankdir==="TB"?"LR":"TB";(a=(c=f.get(r))==null?void 0:c.clusterData)!=null&&a.dir&&(l=f.get(r).clusterData.dir,i.warn("Fixing dir",f.get(r).clusterData.dir,l));const v=new B({multigraph:!0,compound:!0}).setGraph({rankdir:l,nodesep:50,ranksep:50,marginx:8,marginy:8}).setDefaultEdgeLabel(function(){return{}});i.warn("Old graph before copy",E(e)),G(r,e,v,r),e.setNode(r,{clusterNode:!0,id:r,clusterData:f.get(r).clusterData,label:f.get(r).label,graph:v}),i.warn("New graph after copy node: (",r,")",E(v)),i.debug("Old graph after copy",E(e))}else i.warn("Cluster ** ",r," **not meeting the criteria !externalConnections:",!f.get(r).externalConnections," no parent: ",!e.parent(r)," children ",e.children(r)&&e.children(r).length>0,e.children("D"),t),i.debug(f);n=e.nodes(),i.warn("New list of nodes",n);for(const r of n){const u=e.node(r);i.warn(" Now next level",r,u),u!=null&&u.clusterNode&&T(u.graph,t+1)}},"extractor"),M=p((e,t)=>{if(t.length===0)return[];let n=Object.assign([],t);return t.forEach(o=>{const c=e.children(o),a=M(e,c);n=[...n,...a]}),n},"sorter"),oe=p(e=>M(e,e.children()),"sortNodesByHierarchy"),j=p(async(e,t,n,o,c,a)=>{i.warn("Graph in recursive render:XAX",E(t),c);const r=t.graph().rankdir;i.trace("Dir in recursive render - dir:",r);const u=e.insert("g").attr("class","root");t.nodes()?i.info("Recursive render XXX",t.nodes()):i.info("No nodes found for",t),t.edges().length>0&&i.info("Recursive edges",t.edge(t.edges()[0]));const l=u.insert("g").attr("class","clusters"),v=u.insert("g").attr("class","edgePaths"),C=u.insert("g").attr("class","edgeLabels"),g=u.insert("g").attr("class","nodes");await Promise.all(t.nodes().map(async function(d){const s=t.node(d);if(c!==void 0){const w=JSON.parse(JSON.stringify(c.clusterData));i.trace(`Setting data for parent cluster XXX Node.id = `,d,` data=`,w.height,` Parent cluster`,c.height),t.setNode(c.id,w),t.parent(d)||(i.trace("Setting parent",d,c.id),t.setParent(d,c.id,w))}if(i.info("(Insert) Node XXX"+d+": "+JSON.stringify(t.node(d))),s!=null&&s.clusterNode){i.info("Cluster identified XBX",d,s.width,t.node(d));const{ranksep:w,nodesep:m}=t.graph();s.graph.setGraph({...s.graph.graph(),ranksep:w+25,nodesep:m});const N=await j(g,s.graph,n,o,t.node(d),a),S=N.elem;U(s,S),s.diff=N.diff||0,i.info("New compound node after recursive render XAX",d,"width",s.width,"height",s.height),$(S,s)}else t.children(d).length>0?(i.trace("Cluster - the non recursive path XBX",d,s.id,s,s.width,"Graph:",t),i.trace(D(s.id,t)),f.set(s.id,{id:D(s.id,t),node:s})):(i.trace("Node - the non recursive path XAX",d,g,t.node(d),r),await q(g,t.node(d),{config:a,dir:r}))})),await p(async()=>{const d=t.edges().map(async function(s){const w=t.edge(s.v,s.w,s.name);i.info("Edge "+s.v+" -> "+s.w+": "+JSON.stringify(s)),i.info("Edge "+s.v+" -> "+s.w+": ",s," ",JSON.stringify(t.edge(s))),i.info("Fix",f,"ids:",s.v,s.w,"Translating: ",f.get(s.v),f.get(s.w)),await Z(C,w)});await Promise.all(d)},"processEdges")(),i.info("Graph before layout:",JSON.stringify(E(t))),i.info("############################################# XXX"),i.info("### Layout ### XXX"),i.info("############################################# XXX"),I(t),i.info("Graph after layout:",JSON.stringify(E(t)));let y=0,{subGraphTitleTotalMargin:X}=z(a);return await Promise.all(oe(t).map(async function(d){var w;const s=t.node(d);if(i.info("Position XBX => "+d+": ("+s.x,","+s.y,") width: ",s.width," height: ",s.height),s!=null&&s.clusterNode)s.y+=X,i.info("A tainted cluster node XBX1",d,s.id,s.width,s.height,s.x,s.y,t.parent(d)),f.get(s.id).node=s,P(s);else if(t.children(d).length>0){i.info("A pure cluster node XBX1",d,s.id,s.x,s.y,s.width,s.height,t.parent(d)),s.height+=X,t.node(s.parentId);const m=(s==null?void 0:s.padding)/2||0,N=((w=s==null?void 0:s.labelBBox)==null?void 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17:r.addEntity(t[e-2]),r.setClass([t[e-2]],t[e]);break;case 18:r.addEntity(t[e-6],t[e-4]),r.addAttributes(t[e-6],t[e-1]);break;case 19:r.addEntity(t[e-8],t[e-6]),r.addAttributes(t[e-8],t[e-1]),r.setClass([t[e-8]],t[e-3]);break;case 20:r.addEntity(t[e-5],t[e-3]);break;case 21:r.addEntity(t[e-7],t[e-5]),r.setClass([t[e-7]],t[e-2]);break;case 22:r.addEntity(t[e-3],t[e-1]);break;case 23:r.addEntity(t[e-5],t[e-3]),r.setClass([t[e-5]],t[e]);break;case 24:case 25:this.$=t[e].trim(),r.setAccTitle(this.$);break;case 26:case 27:this.$=t[e].trim(),r.setAccDescription(this.$);break;case 32:r.setDirection("TB");break;case 33:r.setDirection("BT");break;case 34:r.setDirection("RL");break;case 35:r.setDirection("LR");break;case 36:this.$=t[e-3],r.addClass(t[e-2],t[e-1]);break;case 37:case 38:case 56:case 64:this.$=[t[e]];break;case 39:case 40:this.$=t[e-2].concat([t[e]]);break;case 41:this.$=t[e-2],r.setClass(t[e-1],t[e]);break;case 42:this.$=t[e-3],r.addCssStyles(t[e-2],t[e-1]);break;case 43:this.$=[t[e]];break;case 44:t[e-2].push(t[e]),this.$=t[e-2];break;case 46:this.$=t[e-1]+t[e];break;case 54:case 76:case 77:this.$=t[e].replace(/"/g,"");break;case 55:case 78:this.$=t[e];break;case 57:t[e].push(t[e-1]),this.$=t[e];break;case 58:this.$={type:t[e-1],name:t[e]};break;case 59:this.$={type:t[e-2],name:t[e-1],keys:t[e]};break;case 60:this.$={type:t[e-2],name:t[e-1],comment:t[e]};break;case 61:this.$={type:t[e-3],name:t[e-2],keys:t[e-1],comment:t[e]};break;case 62:case 63:case 66:this.$=t[e];break;case 65:t[e-2].push(t[e]),this.$=t[e-2];break;case 67:this.$=t[e].replace(/"/g,"");break;case 68:this.$={cardA:t[e],relType:t[e-1],cardB:t[e-2]};break;case 69:this.$=r.Cardinality.ZERO_OR_ONE;break;case 70:this.$=r.Cardinality.ZERO_OR_MORE;break;case 71:this.$=r.Cardinality.ONE_OR_MORE;break;case 72:this.$=r.Cardinality.ONLY_ONE;break;case 73:this.$=r.Cardinality.MD_PARENT;break;case 74:this.$=r.Identification.NON_IDENTIFYING;break;case 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c=new Error(n);throw c.hash=a,c}},"parseError"),parse:u(function(n){var a=this,c=[0],r=[],p=[null],t=[],K=this.table,e="",H=0,St=0,It=2,Tt=1,xt=t.slice.call(arguments,1),y=Object.create(this.lexer),I={yy:{}};for(var lt in this.yy)Object.prototype.hasOwnProperty.call(this.yy,lt)&&(I.yy[lt]=this.yy[lt]);y.setInput(n,I.yy),I.yy.lexer=y,I.yy.parser=this,typeof y.yylloc>"u"&&(y.yylloc={});var ot=y.yylloc;t.push(ot);var vt=y.options&&y.options.ranges;typeof I.yy.parseError=="function"?this.parseError=I.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function Ct(b){c.length=c.length-2*b,p.length=p.length-b,t.length=t.length-b}u(Ct,"popStack");function Ot(){var b;return b=r.pop()||y.lex()||Tt,typeof b!="number"&&(b instanceof Array&&(r=b,b=r.pop()),b=a.symbols_[b]||b),b}u(Ot,"lex");for(var g,x,m,ht,C={},J,k,At,$;;){if(x=c[c.length-1],this.defaultActions[x]?m=this.defaultActions[x]:((g===null||typeof g>"u")&&(g=Ot()),m=K[x]&&K[x][g]),typeof m>"u"||!m.length||!m[0]){var ut="";$=[];for(J in K[x])this.terminals_[J]&&J>It&&$.push("'"+this.terminals_[J]+"'");y.showPosition?ut="Parse error on line "+(H+1)+`: `+y.showPosition()+` Expecting `+$.join(", ")+", got '"+(this.terminals_[g]||g)+"'":ut="Parse error on line "+(H+1)+": Unexpected "+(g==Tt?"end of input":"'"+(this.terminals_[g]||g)+"'"),this.parseError(ut,{text:y.match,token:this.terminals_[g]||g,line:y.yylineno,loc:ot,expected:$})}if(m[0]instanceof Array&&m.length>1)throw new Error("Parse Error: multiple actions possible at state: "+x+", token: "+g);switch(m[0]){case 1:c.push(g),p.push(y.yytext),t.push(y.yylloc),c.push(m[1]),g=null,St=y.yyleng,e=y.yytext,H=y.yylineno,ot=y.yylloc;break;case 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vec4 a_color; +attribute vec2 a_position; +attribute float a_size; + +uniform float u_sizeRatio; +uniform float u_pixelRatio; +uniform mat3 u_matrix; + +varying vec4 v_color; +varying float v_border; + +const float bias = 255.0 / 254.0; + +void main() { + gl_Position = vec4( + (u_matrix * vec3(a_position, 1)).xy, + 0, + 1 + ); + + // Multiply the point size twice: + // - x SCALING_RATIO to correct the canvas scaling + // - x 2 to correct the formulae + gl_PointSize = a_size / u_sizeRatio * u_pixelRatio * 2.0; + + v_border = (0.5 / a_size) * u_sizeRatio; + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,Fu=Gu,Za=WebGLRenderingContext,oo=Za.UNSIGNED_BYTE,ao=Za.FLOAT,Mu=["u_sizeRatio","u_pixelRatio","u_matrix"],$u=function(e){function t(){return la(this,t),ca(this,t,arguments)}return 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a_sourceRadiusCoef; +attribute float a_targetRadiusCoef; + +uniform mat3 u_matrix; +uniform float u_zoomRatio; +uniform float u_sizeRatio; +uniform float u_pixelRatio; +uniform float u_correctionRatio; +uniform float u_minEdgeThickness; +uniform float u_lengthToThicknessRatio; +uniform float u_feather; + +varying vec4 v_color; +varying vec2 v_normal; +varying float v_thickness; +varying float v_feather; + +const float bias = 255.0 / 254.0; + +void main() { + float minThickness = u_minEdgeThickness; + + vec2 normal = a_normal * a_normalCoef; + vec2 position = a_positionStart * (1.0 - a_positionCoef) + a_positionEnd * a_positionCoef; + + float normalLength = length(normal); + vec2 unitNormal = normal / normalLength; + + // These first computations are taken from edge.vert.glsl. Please read it to + // get better comments on what's happening: + float pixelsThickness = max(normalLength, minThickness * u_sizeRatio); + float webGLThickness = pixelsThickness * u_correctionRatio / u_sizeRatio; + + // Here, we move the point to leave space for the arrow heads: + // Source arrow head + float sourceRadius = a_sourceRadius * a_sourceRadiusCoef; + float sourceDirection = sign(sourceRadius); + float webGLSourceRadius = sourceDirection * sourceRadius * 2.0 * u_correctionRatio / u_sizeRatio; + float webGLSourceArrowHeadLength = webGLThickness * u_lengthToThicknessRatio * 2.0; + vec2 sourceCompensationVector = + vec2(-sourceDirection * unitNormal.y, sourceDirection * unitNormal.x) + * (webGLSourceRadius + webGLSourceArrowHeadLength); + + // Target arrow head + float targetRadius = a_targetRadius * a_targetRadiusCoef; + float targetDirection = sign(targetRadius); + float webGLTargetRadius = targetDirection * targetRadius * 2.0 * u_correctionRatio / u_sizeRatio; + float webGLTargetArrowHeadLength = webGLThickness * u_lengthToThicknessRatio * 2.0; + vec2 targetCompensationVector = + vec2(-targetDirection * unitNormal.y, targetDirection * unitNormal.x) + * (webGLTargetRadius + webGLTargetArrowHeadLength); + + // Here is the proper position of the vertex + gl_Position = vec4((u_matrix * vec3(position + unitNormal * webGLThickness + sourceCompensationVector + targetCompensationVector, 1)).xy, 0, 1); + + v_thickness = webGLThickness / u_zoomRatio; + + v_normal = unitNormal; + + v_feather = u_feather * u_correctionRatio / u_zoomRatio / u_pixelRatio * 2.0; + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,Bu=Hu,es=WebGLRenderingContext,so=es.UNSIGNED_BYTE,Ue=es.FLOAT,Vu=["u_matrix","u_zoomRatio","u_sizeRatio","u_correctionRatio","u_pixelRatio","u_feather","u_minEdgeThickness","u_lengthToThicknessRatio"],Uu={lengthToThicknessRatio:bn.lengthToThicknessRatio};function ts(e){var t=Ut(Ut({},Uu),{});return function(r){function n(){return la(this,n),ca(this,n,arguments)}return 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m=this.array;m[l++]=d,m[l++]=h,m[l++]=f,m[l++]=b,m[l++]=N,m[l++]=z,m[l++]=y,m[l++]=o,m[l++]=E,m[l++]=j}},{key:"setUniforms",value:function(o,l){var i=l.gl,s=l.uniformLocations,c=s.u_matrix,u=s.u_zoomRatio,d=s.u_feather,h=s.u_pixelRatio,f=s.u_correctionRatio,b=s.u_sizeRatio,y=s.u_minEdgeThickness,k=s.u_lengthToThicknessRatio;i.uniformMatrix3fv(c,!1,o.matrix),i.uniform1f(u,o.zoomRatio),i.uniform1f(b,o.sizeRatio),i.uniform1f(f,o.correctionRatio),i.uniform1f(h,o.pixelRatio),i.uniform1f(d,o.antiAliasingFeather),i.uniform1f(y,o.minEdgeThickness),i.uniform1f(k,t.lengthToThicknessRatio)}}])}(ua)}ts();function qu(e){return vi([ts(),Un(e),Un(Ut(Ut({},e),{},{extremity:"source"}))])}qu();function Wu(e){if(Array.isArray(e))return e}function Xu(e,t){var r=e==null?null:typeof Symbol<"u"&&e[Symbol.iterator]||e["@@iterator"];if(r!=null){var 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+`).concat(t.flatMap(function(a,o){var l=a.size;return"fill"in l?[" float borderSize_".concat(o+1," = fillBorderSize;")]:[]}).join(` +`),` + + // Finally, normalize all border sizes, to start from the full size and to end with the smallest: + float adjustedBorderSize_0 = v_radius; +`).concat(t.map(function(a,o){return" float adjustedBorderSize_".concat(o+1," = adjustedBorderSize_").concat(o," - borderSize_").concat(o+1,";")}).join(` +`),` + + // Colors: + vec4 borderColor_0 = transparent; +`).concat(t.map(function(a,o){var l=a.color,i=[];return"attribute"in l?i.push(" vec4 borderColor_".concat(o+1," = v_borderColor_").concat(o+1,";")):"transparent"in l?i.push(" vec4 borderColor_".concat(o+1," = vec4(0.0, 0.0, 0.0, 0.0);")):i.push(" vec4 borderColor_".concat(o+1," = u_borderColor_").concat(o+1,";")),i.push(" borderColor_".concat(o+1,".a *= bias;")),i.push(" if (borderSize_".concat(o+1," <= 1.0 * u_correctionRatio) { borderColor_").concat(o+1," = borderColor_").concat(o,"; }")),i.join(` +`)}).join(` +`),` + if (dist > adjustedBorderSize_0) { + gl_FragColor = borderColor_0; + } else `).concat(t.map(function(a,o){return"if (dist > adjustedBorderSize_".concat(o,` - aaBorder) { + gl_FragColor = mix(borderColor_`).concat(o+1,", borderColor_").concat(o,", (dist - adjustedBorderSize_").concat(o,` + aaBorder) / aaBorder); + } else if (dist > adjustedBorderSize_`).concat(o+1,`) { + gl_FragColor = borderColor_`).concat(o+1,`; + } else `)}).join(""),` { /* Nothing to add here */ } + #endif +} +`);return n}function dd(e){var t=e.borders,r=` +attribute vec2 a_position; +attribute float a_size; +attribute float a_angle; + +uniform mat3 u_matrix; +uniform float u_sizeRatio; +uniform float u_correctionRatio; + +varying vec2 v_diffVector; +varying float v_radius; + +#ifdef PICKING_MODE +attribute vec4 a_id; +varying vec4 v_color; +#else +`.concat(t.flatMap(function(n,a){var o=n.size;return"attribute"in o?["attribute float a_borderSize_".concat(a+1,";"),"varying float 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n.set(_e,r(n.get(_e))),this}fetch(t,r){if(typeof t!="string")throw new Error("key must be a string");this._size=void 0;const n=Br(this._tree,t);let a=n.get(_e);return a===void 0&&n.set(_e,a=r()),a}values(){return new Hr(this,Os)}[Symbol.iterator](){return this.entries()}static from(t){const r=new nt;for(const[n,a]of t)r.set(n,a);return r}static fromObject(t){return nt.from(Object.entries(t))}}const Qt=(e,t,r=[])=>{if(t.length===0||e==null)return[e,r];for(const n of e.keys())if(n!==_e&&t.startsWith(n))return r.push([e,n]),Qt(e.get(n),t.slice(n.length),r);return r.push([e,t]),Qt(void 0,"",r)},gn=(e,t)=>{if(t.length===0||e==null)return e;for(const r of e.keys())if(r!==_e&&t.startsWith(r))return gn(e.get(r),t.slice(r.length))},Br=(e,t)=>{const r=t.length;e:for(let n=0;e&&n{const[r,n]=Qt(e,t);if(r!==void 0){if(r.delete(_e),r.size===0)Fs(n);else if(r.size===1){const[a,o]=r.entries().next().value;Ms(n,a,o)}}},Fs=e=>{if(e.length===0)return;const[t,r]=On(e);if(t.delete(r),t.size===0)Fs(e.slice(0,-1));else if(t.size===1){const[n,a]=t.entries().next().value;n!==_e&&Ms(e.slice(0,-1),n,a)}},Ms=(e,t,r)=>{if(e.length===0)return;const[n,a]=On(e);n.set(a+t,r),n.delete(a)},On=e=>e[e.length-1],Gn="or",$s="and",hh="and_not";class at{constructor(t){if((t==null?void 0:t.fields)==null)throw new Error('MiniSearch: option "fields" must be provided');const r=t.autoVacuum==null||t.autoVacuum===!0?qr:t.autoVacuum;this._options={...Ur,...t,autoVacuum:r,searchOptions:{...Bo,...t.searchOptions||{}},autoSuggestOptions:{...yh,...t.autoSuggestOptions||{}}},this._index=new nt,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldIds={},this._fieldLength=new Map,this._avgFieldLength=[],this._nextId=0,this._storedFields=new Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=mn,this.addFields(this._options.fields)}add(t){const{extractField:r,tokenize:n,processTerm:a,fields:o,idField:l}=this._options,i=r(t,l);if(i==null)throw new Error(`MiniSearch: document does not have ID field "${l}"`);if(this._idToShortId.has(i))throw new Error(`MiniSearch: duplicate ID ${i}`);const s=this.addDocumentId(i);this.saveStoredFields(s,t);for(const c of o){const u=r(t,c);if(u==null)continue;const d=n(u.toString(),c),h=this._fieldIds[c],f=new Set(d).size;this.addFieldLength(s,h,this._documentCount-1,f);for(const b of d){const y=a(b,c);if(Array.isArray(y))for(const k of y)this.addTerm(h,s,k);else y&&this.addTerm(h,s,y)}}}addAll(t){for(const r of t)this.add(r)}addAllAsync(t,r={}){const{chunkSize:n=10}=r,a={chunk:[],promise:Promise.resolve()},{chunk:o,promise:l}=t.reduce(({chunk:i,promise:s},c,u)=>(i.push(c),(u+1)%n===0?{chunk:[],promise:s.then(()=>new Promise(d=>setTimeout(d,0))).then(()=>this.addAll(i))}:{chunk:i,promise:s}),a);return l.then(()=>this.addAll(o))}remove(t){const{tokenize:r,processTerm:n,extractField:a,fields:o,idField:l}=this._options,i=a(t,l);if(i==null)throw new Error(`MiniSearch: document does not have ID field "${l}"`);const s=this._idToShortId.get(i);if(s==null)throw new Error(`MiniSearch: cannot remove document with ID ${i}: it is not in the index`);for(const c of o){const u=a(t,c);if(u==null)continue;const d=r(u.toString(),c),h=this._fieldIds[c],f=new Set(d).size;this.removeFieldLength(s,h,this._documentCount,f);for(const b of d){const y=n(b,c);if(Array.isArray(y))for(const k of y)this.removeTerm(h,s,k);else y&&this.removeTerm(h,s,y)}}this._storedFields.delete(s),this._documentIds.delete(s),this._idToShortId.delete(i),this._fieldLength.delete(s),this._documentCount-=1}removeAll(t){if(t)for(const r of t)this.remove(r);else{if(arguments.length>0)throw new Error("Expected documents to be present. Omit the argument to remove all documents.");this._index=new nt,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldLength=new Map,this._avgFieldLength=[],this._storedFields=new Map,this._nextId=0}}discard(t){const r=this._idToShortId.get(t);if(r==null)throw new Error(`MiniSearch: cannot discard document with ID ${t}: it is not in the index`);this._idToShortId.delete(t),this._documentIds.delete(r),this._storedFields.delete(r),(this._fieldLength.get(r)||[]).forEach((n,a)=>{this.removeFieldLength(r,a,this._documentCount,n)}),this._fieldLength.delete(r),this._documentCount-=1,this._dirtCount+=1,this.maybeAutoVacuum()}maybeAutoVacuum(){if(this._options.autoVacuum===!1)return;const{minDirtFactor:t,minDirtCount:r,batchSize:n,batchWait:a}=this._options.autoVacuum;this.conditionalVacuum({batchSize:n,batchWait:a},{minDirtCount:r,minDirtFactor:t})}discardAll(t){const r=this._options.autoVacuum;try{this._options.autoVacuum=!1;for(const n of t)this.discard(n)}finally{this._options.autoVacuum=r}this.maybeAutoVacuum()}replace(t){const{idField:r,extractField:n}=this._options,a=n(t,r);this.discard(a),this.add(t)}vacuum(t={}){return this.conditionalVacuum(t)}conditionalVacuum(t,r){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&r,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const n=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=mn,this.performVacuuming(t,n)}),this._enqueuedVacuum)):this.vacuumConditionsMet(r)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(t),this._currentVacuum)}async performVacuuming(t,r){const n=this._dirtCount;if(this.vacuumConditionsMet(r)){const a=t.batchSize||pn.batchSize,o=t.batchWait||pn.batchWait;let l=1;for(const[i,s]of this._index){for(const[c,u]of s)for(const[d]of u)this._documentIds.has(d)||(u.size<=1?s.delete(c):u.delete(d));this._index.get(i).size===0&&this._index.delete(i),l%a===0&&await new Promise(c=>setTimeout(c,o)),l+=1}this._dirtCount-=n}await null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null}vacuumConditionsMet(t){if(t==null)return!0;let{minDirtCount:r,minDirtFactor:n}=t;return r=r||qr.minDirtCount,n=n||qr.minDirtFactor,this.dirtCount>=r&&this.dirtFactor>=n}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(t){return this._idToShortId.has(t)}getStoredFields(t){const r=this._idToShortId.get(t);if(r!=null)return this._storedFields.get(r)}search(t,r={}){const{searchOptions:n}=this._options,a={...n,...r},o=this.executeQuery(t,r),l=[];for(const[i,{score:s,terms:c,match:u}]of o){const d=c.length||1,h={id:this._documentIds.get(i),score:s*d,terms:Object.keys(u),queryTerms:c,match:u};Object.assign(h,this._storedFields.get(i)),(a.filter==null||a.filter(h))&&l.push(h)}return t===at.wildcard&&a.boostDocument==null||l.sort(Uo),l}autoSuggest(t,r={}){r={...this._options.autoSuggestOptions,...r};const n=new Map;for(const{score:o,terms:l}of this.search(t,r)){const i=l.join(" "),s=n.get(i);s!=null?(s.score+=o,s.count+=1):n.set(i,{score:o,terms:l,count:1})}const a=[];for(const[o,{score:l,terms:i,count:s}]of n)a.push({suggestion:o,terms:i,score:l/s});return a.sort(Uo),a}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(t,r){if(r==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(t),r)}static async loadJSONAsync(t,r){if(r==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJSAsync(JSON.parse(t),r)}static getDefault(t){if(Ur.hasOwnProperty(t))return Vr(Ur,t);throw new Error(`MiniSearch: unknown option "${t}"`)}static loadJS(t,r){const{index:n,documentIds:a,fieldLength:o,storedFields:l,serializationVersion:i}=t,s=this.instantiateMiniSearch(t,r);s._documentIds=Dt(a),s._fieldLength=Dt(o),s._storedFields=Dt(l);for(const[c,u]of s._documentIds)s._idToShortId.set(u,c);for(const[c,u]of n){const d=new Map;for(const h of Object.keys(u)){let f=u[h];i===1&&(f=f.ds),d.set(parseInt(h,10),Dt(f))}s._index.set(c,d)}return s}static async loadJSAsync(t,r){const{index:n,documentIds:a,fieldLength:o,storedFields:l,serializationVersion:i}=t,s=this.instantiateMiniSearch(t,r);s._documentIds=await Ot(a),s._fieldLength=await Ot(o),s._storedFields=await Ot(l);for(const[u,d]of s._documentIds)s._idToShortId.set(d,u);let c=0;for(const[u,d]of n){const h=new Map;for(const f of Object.keys(d)){let b=d[f];i===1&&(b=b.ds),h.set(parseInt(f,10),await Ot(b))}++c%1e3===0&&await Hs(0),s._index.set(u,h)}return s}static instantiateMiniSearch(t,r){const{documentCount:n,nextId:a,fieldIds:o,averageFieldLength:l,dirtCount:i,serializationVersion:s}=t;if(s!==1&&s!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new at(r);return c._documentCount=n,c._nextId=a,c._idToShortId=new Map,c._fieldIds=o,c._avgFieldLength=l,c._dirtCount=i||0,c._index=new nt,c}executeQuery(t,r={}){if(t===at.wildcard)return this.executeWildcardQuery(r);if(typeof t!="string"){const h={...r,...t,queries:void 0},f=t.queries.map(b=>this.executeQuery(b,h));return this.combineResults(f,h.combineWith)}const{tokenize:n,processTerm:a,searchOptions:o}=this._options,l={tokenize:n,processTerm:a,...o,...r},{tokenize:i,processTerm:s}=l,d=i(t).flatMap(h=>s(h)).filter(h=>!!h).map(vh(l)).map(h=>this.executeQuerySpec(h,l));return this.combineResults(d,l.combineWith)}executeQuerySpec(t,r){const n={...this._options.searchOptions,...r},a=(n.fields||this._options.fields).reduce((y,k)=>({...y,[k]:Vr(n.boost,k)||1}),{}),{boostDocument:o,weights:l,maxFuzzy:i,bm25:s}=n,{fuzzy:c,prefix:u}={...Bo.weights,...l},d=this._index.get(t.term),h=this.termResults(t.term,t.term,1,t.termBoost,d,a,o,s);let f,b;if(t.prefix&&(f=this._index.atPrefix(t.term)),t.fuzzy){const y=t.fuzzy===!0?.2:t.fuzzy,k=y<1?Math.min(i,Math.round(t.term.length*y)):y;k&&(b=this._index.fuzzyGet(t.term,k))}if(f)for(const[y,k]of f){const I=y.length-t.term.length;if(!I)continue;b==null||b.delete(y);const E=u*y.length/(y.length+.3*I);this.termResults(t.term,y,E,t.termBoost,k,a,o,s,h)}if(b)for(const y of b.keys()){const[k,I]=b.get(y);if(!I)continue;const E=c*y.length/(y.length+I);this.termResults(t.term,y,E,t.termBoost,k,a,o,s,h)}return h}executeWildcardQuery(t){const r=new Map,n={...this._options.searchOptions,...t};for(const[a,o]of this._documentIds){const l=n.boostDocument?n.boostDocument(o,"",this._storedFields.get(a)):1;r.set(a,{score:l,terms:[],match:{}})}return r}combineResults(t,r=Gn){if(t.length===0)return new Map;const n=r.toLowerCase(),a=gh[n];if(!a)throw new Error(`Invalid combination operator: ${r}`);return t.reduce(a)||new Map}toJSON(){const t=[];for(const[r,n]of this._index){const a={};for(const[o,l]of n)a[o]=Object.fromEntries(l);t.push([r,a])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:t,serializationVersion:2}}termResults(t,r,n,a,o,l,i,s,c=new Map){if(o==null)return c;for(const u of Object.keys(l)){const d=l[u],h=this._fieldIds[u],f=o.get(h);if(f==null)continue;let b=f.size;const y=this._avgFieldLength[h];for(const k of f.keys()){if(!this._documentIds.has(k)){this.removeTerm(h,k,r),b-=1;continue}const I=i?i(this._documentIds.get(k),r,this._storedFields.get(k)):1;if(!I)continue;const E=f.get(k),j=this._fieldLength.get(k)[h],A=mh(E,b,this._documentCount,j,y,s),N=n*a*d*I*A,z=c.get(k);if(z){z.score+=N,bh(z.terms,t);const m=Vr(z.match,r);m?m.push(u):z.match[r]=[u]}else c.set(k,{score:N,terms:[t],match:{[r]:[u]}})}}return c}addTerm(t,r,n){const a=this._index.fetch(n,qo);let o=a.get(t);if(o==null)o=new Map,o.set(r,1),a.set(t,o);else{const l=o.get(r);o.set(r,(l||0)+1)}}removeTerm(t,r,n){if(!this._index.has(n)){this.warnDocumentChanged(r,t,n);return}const a=this._index.fetch(n,qo),o=a.get(t);o==null||o.get(r)==null?this.warnDocumentChanged(r,t,n):o.get(r)<=1?o.size<=1?a.delete(t):o.delete(r):o.set(r,o.get(r)-1),this._index.get(n).size===0&&this._index.delete(n)}warnDocumentChanged(t,r,n){for(const a of Object.keys(this._fieldIds))if(this._fieldIds[a]===r){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(t)} has changed before removal: term "${n}" was not present in field "${a}". 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n,a,o,l,i=[],s=!0,c=!1;try{if(o=(r=r.call(e)).next,t!==0)for(;!(s=(n=o.call(r)).done)&&(i.push(n.value),i.length!==t);s=!0);}catch(u){c=!0,a=u}finally{try{if(!s&&r.return!=null&&(l=r.return(),Object(l)!==l))return}finally{if(c)throw a}}return i}}function sn(e,t){(t==null||t>e.length)&&(t=e.length);for(var r=0,n=Array(t);r v_radius) - gl_FragColor = transparent; - else { - gl_FragColor = v_color; - gl_FragColor.a *= bias; - } - #else - // Sizes: -`).concat(t.flatMap(function(a,o){var l=a.size;if("fill"in l)return[];l=l;var i="attribute"in l?"v_borderSize_".concat(o+1):qn(l.value),s=(l.mode||ad)==="pixels"?"u_correctionRatio":"v_radius";return[" float borderSize_".concat(o+1," = ").concat(s," * ").concat(i,";")]}).join(` -`),` - // Now, let's split the remaining space between "fill" borders: - float fillBorderSize = (v_radius - (`).concat(t.flatMap(function(a,o){var l=a.size;return"fill"in l?[]:["borderSize_".concat(o+1)]}).join(" + "),") ) / ").concat(r,`; 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n.set(Se,r(n.get(Se))),this}fetch(t,r){if(typeof t!="string")throw new Error("key must be a string");this._size=void 0;const n=Br(this._tree,t);let a=n.get(Se);return a===void 0&&n.set(Se,a=r()),a}values(){return new Hr(this,Os)}[Symbol.iterator](){return this.entries()}static from(t){const r=new nt;for(const[n,a]of t)r.set(n,a);return r}static fromObject(t){return nt.from(Object.entries(t))}}const Qt=(e,t,r=[])=>{if(t.length===0||e==null)return[e,r];for(const n of e.keys())if(n!==Se&&t.startsWith(n))return r.push([e,n]),Qt(e.get(n),t.slice(n.length),r);return r.push([e,t]),Qt(void 0,"",r)},gn=(e,t)=>{if(t.length===0||e==null)return e;for(const r of e.keys())if(r!==Se&&t.startsWith(r))return gn(e.get(r),t.slice(r.length))},Br=(e,t)=>{const r=t.length;e:for(let n=0;e&&n{const[r,n]=Qt(e,t);if(r!==void 0){if(r.delete(Se),r.size===0)Fs(n);else if(r.size===1){const[a,o]=r.entries().next().value;$s(n,a,o)}}},Fs=e=>{if(e.length===0)return;const[t,r]=On(e);if(t.delete(r),t.size===0)Fs(e.slice(0,-1));else if(t.size===1){const[n,a]=t.entries().next().value;n!==Se&&$s(e.slice(0,-1),n,a)}},$s=(e,t,r)=>{if(e.length===0)return;const[n,a]=On(e);n.set(a+t,r),n.delete(a)},On=e=>e[e.length-1],Gn="or",Ms="and",dh="and_not";class at{constructor(t){if((t==null?void 0:t.fields)==null)throw new Error('MiniSearch: option "fields" must be provided');const r=t.autoVacuum==null||t.autoVacuum===!0?qr:t.autoVacuum;this._options={...Ur,...t,autoVacuum:r,searchOptions:{...Bo,...t.searchOptions||{}},autoSuggestOptions:{...mh,...t.autoSuggestOptions||{}}},this._index=new nt,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldIds={},this._fieldLength=new Map,this._avgFieldLength=[],this._nextId=0,this._storedFields=new Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=mn,this.addFields(this._options.fields)}add(t){const{extractField:r,tokenize:n,processTerm:a,fields:o,idField:l}=this._options,i=r(t,l);if(i==null)throw new Error(`MiniSearch: document does not have ID field "${l}"`);if(this._idToShortId.has(i))throw new Error(`MiniSearch: duplicate ID ${i}`);const s=this.addDocumentId(i);this.saveStoredFields(s,t);for(const c of o){const u=r(t,c);if(u==null)continue;const d=n(u.toString(),c),h=this._fieldIds[c],f=new Set(d).size;this.addFieldLength(s,h,this._documentCount-1,f);for(const b of d){const y=a(b,c);if(Array.isArray(y))for(const T of y)this.addTerm(h,s,T);else y&&this.addTerm(h,s,y)}}}addAll(t){for(const r of t)this.add(r)}addAllAsync(t,r={}){const{chunkSize:n=10}=r,a={chunk:[],promise:Promise.resolve()},{chunk:o,promise:l}=t.reduce(({chunk:i,promise:s},c,u)=>(i.push(c),(u+1)%n===0?{chunk:[],promise:s.then(()=>new Promise(d=>setTimeout(d,0))).then(()=>this.addAll(i))}:{chunk:i,promise:s}),a);return l.then(()=>this.addAll(o))}remove(t){const{tokenize:r,processTerm:n,extractField:a,fields:o,idField:l}=this._options,i=a(t,l);if(i==null)throw new Error(`MiniSearch: document does not have ID field "${l}"`);const s=this._idToShortId.get(i);if(s==null)throw new Error(`MiniSearch: cannot remove document with ID ${i}: it is not in the index`);for(const c of o){const u=a(t,c);if(u==null)continue;const d=r(u.toString(),c),h=this._fieldIds[c],f=new Set(d).size;this.removeFieldLength(s,h,this._documentCount,f);for(const b of d){const y=n(b,c);if(Array.isArray(y))for(const T of y)this.removeTerm(h,s,T);else y&&this.removeTerm(h,s,y)}}this._storedFields.delete(s),this._documentIds.delete(s),this._idToShortId.delete(i),this._fieldLength.delete(s),this._documentCount-=1}removeAll(t){if(t)for(const r of t)this.remove(r);else{if(arguments.length>0)throw new Error("Expected documents to be present. Omit the argument to remove all documents.");this._index=new nt,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldLength=new Map,this._avgFieldLength=[],this._storedFields=new Map,this._nextId=0}}discard(t){const r=this._idToShortId.get(t);if(r==null)throw new Error(`MiniSearch: cannot discard document with ID ${t}: it is not in the index`);this._idToShortId.delete(t),this._documentIds.delete(r),this._storedFields.delete(r),(this._fieldLength.get(r)||[]).forEach((n,a)=>{this.removeFieldLength(r,a,this._documentCount,n)}),this._fieldLength.delete(r),this._documentCount-=1,this._dirtCount+=1,this.maybeAutoVacuum()}maybeAutoVacuum(){if(this._options.autoVacuum===!1)return;const{minDirtFactor:t,minDirtCount:r,batchSize:n,batchWait:a}=this._options.autoVacuum;this.conditionalVacuum({batchSize:n,batchWait:a},{minDirtCount:r,minDirtFactor:t})}discardAll(t){const r=this._options.autoVacuum;try{this._options.autoVacuum=!1;for(const n of t)this.discard(n)}finally{this._options.autoVacuum=r}this.maybeAutoVacuum()}replace(t){const{idField:r,extractField:n}=this._options,a=n(t,r);this.discard(a),this.add(t)}vacuum(t={}){return this.conditionalVacuum(t)}conditionalVacuum(t,r){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&r,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const n=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=mn,this.performVacuuming(t,n)}),this._enqueuedVacuum)):this.vacuumConditionsMet(r)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(t),this._currentVacuum)}async performVacuuming(t,r){const n=this._dirtCount;if(this.vacuumConditionsMet(r)){const a=t.batchSize||pn.batchSize,o=t.batchWait||pn.batchWait;let l=1;for(const[i,s]of this._index){for(const[c,u]of s)for(const[d]of u)this._documentIds.has(d)||(u.size<=1?s.delete(c):u.delete(d));this._index.get(i).size===0&&this._index.delete(i),l%a===0&&await new Promise(c=>setTimeout(c,o)),l+=1}this._dirtCount-=n}await null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null}vacuumConditionsMet(t){if(t==null)return!0;let{minDirtCount:r,minDirtFactor:n}=t;return r=r||qr.minDirtCount,n=n||qr.minDirtFactor,this.dirtCount>=r&&this.dirtFactor>=n}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(t){return this._idToShortId.has(t)}getStoredFields(t){const r=this._idToShortId.get(t);if(r!=null)return this._storedFields.get(r)}search(t,r={}){const{searchOptions:n}=this._options,a={...n,...r},o=this.executeQuery(t,r),l=[];for(const[i,{score:s,terms:c,match:u}]of o){const d=c.length||1,h={id:this._documentIds.get(i),score:s*d,terms:Object.keys(u),queryTerms:c,match:u};Object.assign(h,this._storedFields.get(i)),(a.filter==null||a.filter(h))&&l.push(h)}return t===at.wildcard&&a.boostDocument==null||l.sort(Uo),l}autoSuggest(t,r={}){r={...this._options.autoSuggestOptions,...r};const n=new Map;for(const{score:o,terms:l}of this.search(t,r)){const i=l.join(" "),s=n.get(i);s!=null?(s.score+=o,s.count+=1):n.set(i,{score:o,terms:l,count:1})}const a=[];for(const[o,{score:l,terms:i,count:s}]of n)a.push({suggestion:o,terms:i,score:l/s});return a.sort(Uo),a}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(t,r){if(r==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(t),r)}static async loadJSONAsync(t,r){if(r==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJSAsync(JSON.parse(t),r)}static getDefault(t){if(Ur.hasOwnProperty(t))return Vr(Ur,t);throw new Error(`MiniSearch: unknown option "${t}"`)}static loadJS(t,r){const{index:n,documentIds:a,fieldLength:o,storedFields:l,serializationVersion:i}=t,s=this.instantiateMiniSearch(t,r);s._documentIds=Dt(a),s._fieldLength=Dt(o),s._storedFields=Dt(l);for(const[c,u]of s._documentIds)s._idToShortId.set(u,c);for(const[c,u]of n){const d=new Map;for(const h of Object.keys(u)){let f=u[h];i===1&&(f=f.ds),d.set(parseInt(h,10),Dt(f))}s._index.set(c,d)}return s}static async loadJSAsync(t,r){const{index:n,documentIds:a,fieldLength:o,storedFields:l,serializationVersion:i}=t,s=this.instantiateMiniSearch(t,r);s._documentIds=await Ot(a),s._fieldLength=await Ot(o),s._storedFields=await Ot(l);for(const[u,d]of s._documentIds)s._idToShortId.set(d,u);let c=0;for(const[u,d]of n){const h=new Map;for(const f of Object.keys(d)){let b=d[f];i===1&&(b=b.ds),h.set(parseInt(f,10),await Ot(b))}++c%1e3===0&&await Hs(0),s._index.set(u,h)}return s}static instantiateMiniSearch(t,r){const{documentCount:n,nextId:a,fieldIds:o,averageFieldLength:l,dirtCount:i,serializationVersion:s}=t;if(s!==1&&s!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new at(r);return c._documentCount=n,c._nextId=a,c._idToShortId=new Map,c._fieldIds=o,c._avgFieldLength=l,c._dirtCount=i||0,c._index=new nt,c}executeQuery(t,r={}){if(t===at.wildcard)return this.executeWildcardQuery(r);if(typeof t!="string"){const h={...r,...t,queries:void 0},f=t.queries.map(b=>this.executeQuery(b,h));return this.combineResults(f,h.combineWith)}const{tokenize:n,processTerm:a,searchOptions:o}=this._options,l={tokenize:n,processTerm:a,...o,...r},{tokenize:i,processTerm:s}=l,d=i(t).flatMap(h=>s(h)).filter(h=>!!h).map(ph(l)).map(h=>this.executeQuerySpec(h,l));return this.combineResults(d,l.combineWith)}executeQuerySpec(t,r){const n={...this._options.searchOptions,...r},a=(n.fields||this._options.fields).reduce((y,T)=>({...y,[T]:Vr(n.boost,T)||1}),{}),{boostDocument:o,weights:l,maxFuzzy:i,bm25:s}=n,{fuzzy:c,prefix:u}={...Bo.weights,...l},d=this._index.get(t.term),h=this.termResults(t.term,t.term,1,t.termBoost,d,a,o,s);let f,b;if(t.prefix&&(f=this._index.atPrefix(t.term)),t.fuzzy){const y=t.fuzzy===!0?.2:t.fuzzy,T=y<1?Math.min(i,Math.round(t.term.length*y)):y;T&&(b=this._index.fuzzyGet(t.term,T))}if(f)for(const[y,T]of f){const L=y.length-t.term.length;if(!L)continue;b==null||b.delete(y);const _=u*y.length/(y.length+.3*L);this.termResults(t.term,y,_,t.termBoost,T,a,o,s,h)}if(b)for(const y of b.keys()){const[T,L]=b.get(y);if(!L)continue;const _=c*y.length/(y.length+L);this.termResults(t.term,y,_,t.termBoost,T,a,o,s,h)}return h}executeWildcardQuery(t){const r=new Map,n={...this._options.searchOptions,...t};for(const[a,o]of this._documentIds){const l=n.boostDocument?n.boostDocument(o,"",this._storedFields.get(a)):1;r.set(a,{score:l,terms:[],match:{}})}return r}combineResults(t,r=Gn){if(t.length===0)return new Map;const n=r.toLowerCase(),a=fh[n];if(!a)throw new Error(`Invalid combination operator: ${r}`);return t.reduce(a)||new Map}toJSON(){const t=[];for(const[r,n]of this._index){const a={};for(const[o,l]of n)a[o]=Object.fromEntries(l);t.push([r,a])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:t,serializationVersion:2}}termResults(t,r,n,a,o,l,i,s,c=new Map){if(o==null)return c;for(const u of Object.keys(l)){const d=l[u],h=this._fieldIds[u],f=o.get(h);if(f==null)continue;let b=f.size;const y=this._avgFieldLength[h];for(const T of f.keys()){if(!this._documentIds.has(T)){this.removeTerm(h,T,r),b-=1;continue}const L=i?i(this._documentIds.get(T),r,this._storedFields.get(T)):1;if(!L)continue;const _=f.get(T),A=this._fieldLength.get(T)[h],R=gh(_,b,this._documentCount,A,y,s),I=n*a*d*L*R,D=c.get(T);if(D){D.score+=I,vh(D.terms,t);const m=Vr(D.match,r);m?m.push(u):D.match[r]=[u]}else c.set(T,{score:I,terms:[t],match:{[r]:[u]}})}}return c}addTerm(t,r,n){const a=this._index.fetch(n,qo);let o=a.get(t);if(o==null)o=new Map,o.set(r,1),a.set(t,o);else{const l=o.get(r);o.set(r,(l||0)+1)}}removeTerm(t,r,n){if(!this._index.has(n)){this.warnDocumentChanged(r,t,n);return}const a=this._index.fetch(n,qo),o=a.get(t);o==null||o.get(r)==null?this.warnDocumentChanged(r,t,n):o.get(r)<=1?o.size<=1?a.delete(t):o.delete(r):o.set(r,o.get(r)-1),this._index.get(n).size===0&&this._index.delete(n)}warnDocumentChanged(t,r,n){for(const a of Object.keys(this._fieldIds))if(this._fieldIds[a]===r){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(t)} has changed before removal: term "${n}" was not present in field "${a}". Removing a document after it has changed can corrupt the index!`,"version_conflict");return}}addDocumentId(t){const r=this._nextId;return this._idToShortId.set(t,r),this._documentIds.set(r,t),this._documentCount+=1,this._nextId+=1,r}addFields(t){for(let r=0;rObject.prototype.hasOwnProperty.call(e,t)?e[t]:void 0,fh={[Gn]:(e,t)=>{for(const r of t.keys()){const n=e.get(r);if(n==null)e.set(r,t.get(r));else{const{score:a,terms:o,match:l}=t.get(r);n.score=n.score+a,n.match=Object.assign(n.match,l),Vo(n.terms,o)}}return e},[Ms]:(e,t)=>{const r=new Map;for(const n of t.keys()){const a=e.get(n);if(a==null)continue;const{score:o,terms:l,match:i}=t.get(n);Vo(a.terms,l),r.set(n,{score:a.score+o,terms:a.terms,match:Object.assign(a.match,i)})}return r},[dh]:(e,t)=>{for(const r of t.keys())e.delete(r);return e}},hh={k:1.2,b:.7,d:.5},gh=(e,t,r,n,a,o)=>{const{k:l,b:i,d:s}=o;return Math.log(1+(r-t+.5)/(t+.5))*(s+e*(l+1)/(e+l*(1-i+i*n/a)))},ph=e=>(t,r,n)=>{const a=typeof 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Object.prototype.hasOwnProperty.call(this._nodes,e)?(arguments.length>1&&(this._nodes[e]=t),this):(this._nodes[e]=arguments.length>1?t:this._defaultNodeLabelFn(e),this._isCompound&&(this._parent[e]=a,this._children[e]={},this._children[a][e]=!0),this._in[e]={},this._preds[e]={},this._out[e]={},this._sucs[e]={},++this._nodeCount,this)}node(e){return this._nodes[e]}hasNode(e){return Object.prototype.hasOwnProperty.call(this._nodes,e)}removeNode(e){if(Object.prototype.hasOwnProperty.call(this._nodes,e)){var t=s=>this.removeEdge(this._edgeObjs[s]);delete this._nodes[e],this._isCompound&&(this._removeFromParentsChildList(e),delete this._parent[e],d(this.children(e),s=>{this.setParent(s)}),delete this._children[e]),d(_(this._in[e]),t),delete this._in[e],delete this._preds[e],d(_(this._out[e]),t),delete this._out[e],delete this._sucs[e],--this._nodeCount}return this}setParent(e,t){if(!this._isCompound)throw new Error("Cannot set parent in a non-compound graph");if(l(t))t=a;else{t+="";for(var s=t;!l(s);s=this.parent(s))if(s===e)throw new Error("Setting "+t+" as parent of "+e+" would create a cycle");this.setNode(t)}return this.setNode(e),this._removeFromParentsChildList(e),this._parent[e]=t,this._children[t][e]=!0,this}_removeFromParentsChildList(e){delete this._children[this._parent[e]][e]}parent(e){if(this._isCompound){var t=this._parent[e];if(t!==a)return t}}children(e){if(l(e)&&(e=a),this._isCompound){var t=this._children[e];if(t)return _(t)}else{if(e===a)return this.nodes();if(this.hasNode(e))return[]}}predecessors(e){var t=this._preds[e];if(t)return _(t)}successors(e){var t=this._sucs[e];if(t)return _(t)}neighbors(e){var t=this.predecessors(e);if(t)return w(t,this.successors(e))}isLeaf(e){var t;return this.isDirected()?t=this.successors(e):t=this.neighbors(e),t.length===0}filterNodes(e){var t=new this.constructor({directed:this._isDirected,multigraph:this._isMultigraph,compound:this._isCompound});t.setGraph(this.graph());var 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E(e._out[t])})}setNodes(e,t){var s=arguments,i=this;return d(e,function(r){s.length>1?i.setNode(r,t):i.setNode(r)}),this}setNode(e,t){return Object.prototype.hasOwnProperty.call(this._nodes,e)?(arguments.length>1&&(this._nodes[e]=t),this):(this._nodes[e]=arguments.length>1?t:this._defaultNodeLabelFn(e),this._isCompound&&(this._parent[e]=a,this._children[e]={},this._children[a][e]=!0),this._in[e]={},this._preds[e]={},this._out[e]={},this._sucs[e]={},++this._nodeCount,this)}node(e){return this._nodes[e]}hasNode(e){return Object.prototype.hasOwnProperty.call(this._nodes,e)}removeNode(e){if(Object.prototype.hasOwnProperty.call(this._nodes,e)){var t=s=>this.removeEdge(this._edgeObjs[s]);delete this._nodes[e],this._isCompound&&(this._removeFromParentsChildList(e),delete this._parent[e],d(this.children(e),s=>{this.setParent(s)}),delete this._children[e]),d(_(this._in[e]),t),delete this._in[e],delete this._preds[e],d(_(this._out[e]),t),delete this._out[e],delete 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v40z`,shortbaraboveleftharpoon:`M7,435c-4,4,-6.3,8.7,-7,14c0,5.3,0.7,9,2,11 +c1.3,2,5.3,5.3,12,10c90.7,54,156,130,196,228c3.3,10.7,6.3,16.3,9,17c2,0.7,5,1,9, +1c0,0,5,0,5,0c10.7,0,16.7,-2,18,-6c2,-2.7,1,-9.7,-3,-21c-32,-87.3,-82.7,-157.7, +-152,-211c0,0,-3,-3,-3,-3l399907,0l0,-40c-399126,0,-399993,0,-399993,0z +M93 435 v40 H400000 v-40z M500 241 v40 H400000 v-40z M500 241 v40 H400000 v-40z`,shortrightharpoonabovebar:`M53,241l0,40c398570,0,399437,0,399437,0 +c4.7,-4.7,7,-9.3,7,-14c0,-9.3,-3.7,-15.3,-11,-18c-92.7,-56.7,-159,-133.7,-199, +-231c-3.3,-9.3,-6,-14.7,-8,-16c-2,-1.3,-7,-2,-15,-2c-10.7,0,-16.7,2,-18,6 +c-2,2.7,-1,9.7,3,21c15.3,42,36.7,81.8,64,119.5c27.3,37.7,58,69.2,92,94.5z +M500 241 v40 H399408 v-40z M500 435 v40 H400000 v-40z`},g1=function(e,t){switch(e){case"lbrack":return"M403 1759 V84 H666 V0 H319 V1759 v"+t+` v1759 h347 v-84 +H403z M403 1759 V0 H319 V1759 v`+t+" v1759 h84z";case"rbrack":return"M347 1759 V0 H0 V84 H263 V1759 v"+t+` v1759 H0 v84 H347z +M347 1759 V0 H263 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t=this.popToken(),a=t.text,n=t.noexpand?null:this._getExpansion(a);if(n==null||e&&n.unexpandable){if(e&&n==null&&a[0]==="\\"&&!this.isDefined(a))throw new A("Undefined control sequence: "+a);return this.pushToken(t),!1}this.countExpansion(1);var l=n.tokens,o=this.consumeArgs(n.numArgs,n.delimiters);if(n.numArgs){l=l.slice();for(var h=l.length-1;h>=0;--h){var c=l[h];if(c.text==="#"){if(h===0)throw new A("Incomplete placeholder at end of macro body",c);if(c=l[--h],c.text==="#")l.splice(h+1,1);else if(/^[1-9]$/.test(c.text))l.splice(h,2,...o[+c.text-1]);else throw new A("Not a valid argument number",c)}}}return this.pushTokens(l),l.length}expandAfterFuture(){return this.expandOnce(),this.future()}expandNextToken(){for(;;)if(this.expandOnce()===!1){var e=this.stack.pop();return e.treatAsRelax&&(e.text="\\relax"),e}throw new Error}expandMacro(e){return this.macros.has(e)?this.expandTokens([new x0(e)]):void 0}expandTokens(e){var t=[],a=this.stack.length;for(this.pushTokens(e);this.stack.length>a;)if(this.expandOnce(!0)===!1){var n=this.stack.pop();n.treatAsRelax&&(n.noexpand=!1,n.treatAsRelax=!1),t.push(n)}return this.countExpansion(t.length),t}expandMacroAsText(e){var t=this.expandMacro(e);return t&&t.map(a=>a.text).join("")}_getExpansion(e){var t=this.macros.get(e);if(t==null)return t;if(e.length===1){var a=this.lexer.catcodes[e];if(a!=null&&a!==13)return}var n=typeof t=="function"?t(this):t;if(typeof n=="string"){var l=0;if(n.indexOf("#")!==-1)for(var o=n.replace(/##/g,"");o.indexOf("#"+(l+1))!==-1;)++l;for(var h=new Pr(n,this.settings),c=[],p=h.lex();p.text!=="EOF";)c.push(p),p=h.lex();c.reverse();var g={tokens:c,numArgs:l};return g}return n}isDefined(e){return this.macros.has(e)||Y0.hasOwnProperty(e)||K.math.hasOwnProperty(e)||K.text.hasOwnProperty(e)||Za.hasOwnProperty(e)}isExpandable(e){var t=this.macros.get(e);return t!=null?typeof t=="string"||typeof t=="function"||!t.unexpandable:Y0.hasOwnProperty(e)&&!Y0[e].primitive}}var Ur=/^[₊₋₌₍₎₀₁₂₃₄₅₆₇₈₉ₐₑₕᵢⱼₖₗₘₙₒₚᵣₛₜᵤᵥₓᵦᵧᵨᵩᵪ]/,Oe=Object.freeze({"₊":"+","₋":"-","₌":"=","₍":"(","₎":")","₀":"0","₁":"1","₂":"2","₃":"3","₄":"4","₅":"5","₆":"6","₇":"7","₈":"8","₉":"9","ₐ":"a","ₑ":"e","ₕ":"h","ᵢ":"i","ⱼ":"j","ₖ":"k","ₗ":"l","ₘ":"m","ₙ":"n","ₒ":"o","ₚ":"p","ᵣ":"r","ₛ":"s","ₜ":"t","ᵤ":"u","ᵥ":"v","ₓ":"x","ᵦ":"β","ᵧ":"γ","ᵨ":"ρ","ᵩ":"ϕ","ᵪ":"χ","⁺":"+","⁻":"-","⁼":"=","⁽":"(","⁾":")","⁰":"0","¹":"1","²":"2","³":"3","⁴":"4","⁵":"5","⁶":"6","⁷":"7","⁸":"8","⁹":"9","ᴬ":"A","ᴮ":"B","ᴰ":"D","ᴱ":"E","ᴳ":"G","ᴴ":"H","ᴵ":"I","ᴶ":"J","ᴷ":"K","ᴸ":"L","ᴹ":"M","ᴺ":"N","ᴼ":"O","ᴾ":"P","ᴿ":"R","ᵀ":"T","ᵁ":"U","ⱽ":"V","ᵂ":"W","ᵃ":"a","ᵇ":"b","ᶜ":"c","ᵈ":"d","ᵉ":"e","ᶠ":"f","ᵍ":"g",ʰ:"h","ⁱ":"i",ʲ:"j","ᵏ":"k",ˡ:"l","ᵐ":"m",ⁿ:"n","ᵒ":"o","ᵖ":"p",ʳ:"r",ˢ:"s","ᵗ":"t","ᵘ":"u","ᵛ":"v",ʷ:"w",ˣ:"x",ʸ:"y","ᶻ":"z","ᵝ":"β","ᵞ":"γ","ᵟ":"δ","ᵠ":"ϕ","ᵡ":"χ","ᶿ":"θ"}),gt={"́":{text:"\\'",math:"\\acute"},"̀":{text:"\\`",math:"\\grave"},"̈":{text:'\\"',math:"\\ddot"},"̃":{text:"\\~",math:"\\tilde"},"̄":{text:"\\=",math:"\\bar"},"̆":{text:"\\u",math:"\\breve"},"̌":{text:"\\v",math:"\\check"},"̂":{text:"\\^",math:"\\hat"},"̇":{text:"\\.",math:"\\dot"},"̊":{text:"\\r",math:"\\mathring"},"̋":{text:"\\H"},"̧":{text:"\\c"}},Gr={á:"á",à:"à",ä:"ä",ǟ:"ǟ",ã:"ã",ā:"ā",ă:"ă",ắ:"ắ",ằ:"ằ",ẵ:"ẵ",ǎ:"ǎ",â:"â",ấ:"ấ",ầ:"ầ",ẫ:"ẫ",ȧ:"ȧ",ǡ:"ǡ",å:"å",ǻ:"ǻ",ḃ:"ḃ",ć:"ć",ḉ:"ḉ",č:"č",ĉ:"ĉ",ċ:"ċ",ç:"ç",ď:"ď",ḋ:"ḋ",ḑ:"ḑ",é:"é",è:"è",ë:"ë",ẽ:"ẽ",ē:"ē",ḗ:"ḗ",ḕ:"ḕ",ĕ:"ĕ",ḝ:"ḝ",ě:"ě",ê:"ê",ế:"ế",ề:"ề",ễ:"ễ",ė:"ė",ȩ:"ȩ",ḟ:"ḟ",ǵ:"ǵ",ḡ:"ḡ",ğ:"ğ",ǧ:"ǧ",ĝ:"ĝ",ġ:"ġ",ģ:"ģ",ḧ:"ḧ",ȟ:"ȟ",ĥ:"ĥ",ḣ:"ḣ",ḩ:"ḩ",í:"í",ì:"ì",ï:"ï",ḯ:"ḯ",ĩ:"ĩ",ī:"ī",ĭ:"ĭ",ǐ:"ǐ",î:"î",ǰ:"ǰ",ĵ:"ĵ",ḱ:"ḱ",ǩ:"ǩ",ķ:"ķ",ĺ:"ĺ",ľ:"ľ",ļ:"ļ",ḿ:"ḿ",ṁ:"ṁ",ń:"ń",ǹ:"ǹ",ñ:"ñ",ň:"ň",ṅ:"ṅ",ņ:"ņ",ó:"ó",ò:"ò",ö:"ö",ȫ:"ȫ",õ:"õ",ṍ:"ṍ",ṏ:"ṏ",ȭ:"ȭ",ō:"ō",ṓ:"ṓ",ṑ:"ṑ",ŏ:"ŏ",ǒ:"ǒ",ô:"ô",ố:"ố",ồ:"ồ",ỗ:"ỗ",ȯ:"ȯ",ȱ:"ȱ",ő:"ő",ṕ:"ṕ",ṗ:"ṗ",ŕ:"ŕ",ř:"ř",ṙ:"ṙ",ŗ:"ŗ",ś:"ś",ṥ:"ṥ",š:"š",ṧ:"ṧ",ŝ:"ŝ",ṡ:"ṡ",ş:"ş",ẗ:"ẗ",ť:"ť",ṫ:"ṫ",ţ:"ţ",ú:"ú",ù:"ù",ü:"ü",ǘ:"ǘ",ǜ:"ǜ",ǖ:"ǖ",ǚ:"ǚ",ũ:"ũ",ṹ:"ṹ",ū:"ū",ṻ:"ṻ",ŭ:"ŭ",ǔ:"ǔ",û:"û",ů:"ů",ű:"ű",ṽ:"ṽ",ẃ:"ẃ",ẁ:"ẁ",ẅ:"ẅ",ŵ:"ŵ",ẇ:"ẇ",ẘ:"ẘ",ẍ:"ẍ",ẋ:"ẋ",ý:"ý",ỳ:"ỳ",ÿ:"ÿ",ỹ:"ỹ",ȳ:"ȳ",ŷ:"ŷ",ẏ:"ẏ",ẙ:"ẙ",ź:"ź",ž:"ž",ẑ:"ẑ",ż:"ż",Á:"Á",À:"À",Ä:"Ä",Ǟ:"Ǟ",Ã:"Ã",Ā:"Ā",Ă:"Ă",Ắ:"Ắ",Ằ:"Ằ",Ẵ:"Ẵ",Ǎ:"Ǎ",Â:"Â",Ấ:"Ấ",Ầ:"Ầ",Ẫ:"Ẫ",Ȧ:"Ȧ",Ǡ:"Ǡ",Å:"Å",Ǻ:"Ǻ",Ḃ:"Ḃ",Ć:"Ć",Ḉ:"Ḉ",Č:"Č",Ĉ:"Ĉ",Ċ:"Ċ",Ç:"Ç",Ď:"Ď",Ḋ:"Ḋ",Ḑ:"Ḑ",É:"É",È:"È",Ë:"Ë",Ẽ:"Ẽ",Ē:"Ē",Ḗ:"Ḗ",Ḕ:"Ḕ",Ĕ:"Ĕ",Ḝ:"Ḝ",Ě:"Ě",Ê:"Ê",Ế:"Ế",Ề:"Ề",Ễ:"Ễ",Ė:"Ė",Ȩ:"Ȩ",Ḟ:"Ḟ",Ǵ:"Ǵ",Ḡ:"Ḡ",Ğ:"Ğ",Ǧ:"Ǧ",Ĝ:"Ĝ",Ġ:"Ġ",Ģ:"Ģ",Ḧ:"Ḧ",Ȟ:"Ȟ",Ĥ:"Ĥ",Ḣ:"Ḣ",Ḩ:"Ḩ",Í:"Í",Ì:"Ì",Ï:"Ï",Ḯ:"Ḯ",Ĩ:"Ĩ",Ī:"Ī",Ĭ:"Ĭ",Ǐ:"Ǐ",Î:"Î",İ:"İ",Ĵ:"Ĵ",Ḱ:"Ḱ",Ǩ:"Ǩ",Ķ:"Ķ",Ĺ:"Ĺ",Ľ:"Ľ",Ļ:"Ļ",Ḿ:"Ḿ",Ṁ:"Ṁ",Ń:"Ń",Ǹ:"Ǹ",Ñ:"Ñ",Ň:"Ň",Ṅ:"Ṅ",Ņ:"Ņ",Ó:"Ó",Ò:"Ò",Ö:"Ö",Ȫ:"Ȫ",Õ:"Õ",Ṍ:"Ṍ",Ṏ:"Ṏ",Ȭ:"Ȭ",Ō:"Ō",Ṓ:"Ṓ",Ṑ:"Ṑ",Ŏ:"Ŏ",Ǒ:"Ǒ",Ô:"Ô",Ố:"Ố",Ồ:"Ồ",Ỗ:"Ỗ",Ȯ:"Ȯ",Ȱ:"Ȱ",Ő:"Ő",Ṕ:"Ṕ",Ṗ:"Ṗ",Ŕ:"Ŕ",Ř:"Ř",Ṙ:"Ṙ",Ŗ:"Ŗ",Ś:"Ś",Ṥ:"Ṥ",Š:"Š",Ṧ:"Ṧ",Ŝ:"Ŝ",Ṡ:"Ṡ",Ş:"Ş",Ť:"Ť",Ṫ:"Ṫ",Ţ:"Ţ",Ú:"Ú",Ù:"Ù",Ü:"Ü",Ǘ:"Ǘ",Ǜ:"Ǜ",Ǖ:"Ǖ",Ǚ:"Ǚ",Ũ:"Ũ",Ṹ:"Ṹ",Ū:"Ū",Ṻ:"Ṻ",Ŭ:"Ŭ",Ǔ:"Ǔ",Û:"Û",Ů:"Ů",Ű:"Ű",Ṽ:"Ṽ",Ẃ:"Ẃ",Ẁ:"Ẁ",Ẅ:"Ẅ",Ŵ:"Ŵ",Ẇ:"Ẇ",Ẍ:"Ẍ",Ẋ:"Ẋ",Ý:"Ý",Ỳ:"Ỳ",Ÿ:"Ÿ",Ỹ:"Ỹ",Ȳ:"Ȳ",Ŷ:"Ŷ",Ẏ:"Ẏ",Ź:"Ź",Ž:"Ž",Ẑ:"Ẑ",Ż:"Ż",ά:"ά",ὰ:"ὰ",ᾱ:"ᾱ",ᾰ:"ᾰ",έ:"έ",ὲ:"ὲ",ή:"ή",ὴ:"ὴ",ί:"ί",ὶ:"ὶ",ϊ:"ϊ",ΐ:"ΐ",ῒ:"ῒ",ῑ:"ῑ",ῐ:"ῐ",ό:"ό",ὸ:"ὸ",ύ:"ύ",ὺ:"ὺ",ϋ:"ϋ",ΰ:"ΰ",ῢ:"ῢ",ῡ:"ῡ",ῠ:"ῠ",ώ:"ώ",ὼ:"ὼ",Ύ:"Ύ",Ὺ:"Ὺ",Ϋ:"Ϋ",Ῡ:"Ῡ",Ῠ:"Ῠ",Ώ:"Ώ",Ὼ:"Ὼ"};class Ke{constructor(e,t){this.mode=void 0,this.gullet=void 0,this.settings=void 0,this.leftrightDepth=void 0,this.nextToken=void 0,this.mode="math",this.gullet=new Fl(e,t,this.mode),this.settings=t,this.leftrightDepth=0}expect(e,t){if(t===void 0&&(t=!0),this.fetch().text!==e)throw new A("Expected '"+e+"', got '"+this.fetch().text+"'",this.fetch());t&&this.consume()}consume(){this.nextToken=null}fetch(){return this.nextToken==null&&(this.nextToken=this.gullet.expandNextToken()),this.nextToken}switchMode(e){this.mode=e,this.gullet.switchMode(e)}parse(){this.settings.globalGroup||this.gullet.beginGroup(),this.settings.colorIsTextColor&&this.gullet.macros.set("\\color","\\textcolor");try{var e=this.parseExpression(!1);return this.expect("EOF"),this.settings.globalGroup||this.gullet.endGroup(),e}finally{this.gullet.endGroups()}}subparse(e){var t=this.nextToken;this.consume(),this.gullet.pushToken(new x0("}")),this.gullet.pushTokens(e);var a=this.parseExpression(!1);return this.expect("}"),this.nextToken=t,a}parseExpression(e,t){for(var a=[];;){this.mode==="math"&&this.consumeSpaces();var n=this.fetch();if(Ke.endOfExpression.indexOf(n.text)!==-1||t&&n.text===t||e&&Y0[n.text]&&Y0[n.text].infix)break;var l=this.parseAtom(t);if(l){if(l.type==="internal")continue}else break;a.push(l)}return this.mode==="text"&&this.formLigatures(a),this.handleInfixNodes(a)}handleInfixNodes(e){for(var t=-1,a,n=0;n=0&&this.settings.reportNonstrict("unicodeTextInMathMode",'Latin-1/Unicode text character "'+t[0]+'" used in math mode',e);var h=K[this.mode][t].group,c=p0.range(e),p;if(z1.hasOwnProperty(h)){var g=h;p={type:"atom",mode:this.mode,family:g,loc:c,text:t}}else p={type:h,mode:this.mode,loc:c,text:t};o=p}else if(t.charCodeAt(0)>=128)this.settings.strict&&(ia(t.charCodeAt(0))?this.mode==="math"&&this.settings.reportNonstrict("unicodeTextInMathMode",'Unicode text character "'+t[0]+'" used in math mode',e):this.settings.reportNonstrict("unknownSymbol",'Unrecognized Unicode character "'+t[0]+'"'+(" ("+t.charCodeAt(0)+")"),e)),o={type:"textord",mode:"text",loc:p0.range(e),text:t};else return null;if(this.consume(),l)for(var b=0;b=40rem){.\!container{max-width:40rem!important}}@media (width>=48rem){.\!container{max-width:48rem!important}}@media (width>=64rem){.\!container{max-width:64rem!important}}@media (width>=80rem){.\!container{max-width:80rem!important}}@media (width>=96rem){.\!container{max-width:96rem!important}}.container{width:100%}@media (width>=40rem){.container{max-width:40rem}}@media (width>=48rem){.container{max-width:48rem}}@media (width>=64rem){.container{max-width:64rem}}@media (width>=80rem){.container{max-width:80rem}}@media (width>=96rem){.container{max-width:96rem}}.\!m-0{margin:calc(var(--spacing)*0)!important}.m-0{margin:calc(var(--spacing)*0)}.\!mx-4{margin-inline:calc(var(--spacing)*4)!important}.-mx-1{margin-inline:calc(var(--spacing)*-1)}.mx-1{margin-inline:calc(var(--spacing)*1)}.mx-4{margin-inline:calc(var(--spacing)*4)}.mx-auto{margin-inline:auto}.my-1{margin-block:calc(var(--spacing)*1)}.my-2{margin-block:calc(var(--spacing)*2)}.my-4{margin-block:calc(var(--spacing)*4)}.mt-1{margin-top:calc(var(--spacing)*1)}.mt-2{margin-top:calc(var(--spacing)*2)}.mt-3{margin-top:calc(var(--spacing)*3)}.mt-4{margin-top:calc(var(--spacing)*4)}.mr-1{margin-right:calc(var(--spacing)*1)}.mr-2{margin-right:calc(var(--spacing)*2)}.mr-4{margin-right:calc(var(--spacing)*4)}.mr-8{margin-right:calc(var(--spacing)*8)}.mb-1{margin-bottom:calc(var(--spacing)*1)}.mb-2{margin-bottom:calc(var(--spacing)*2)}.mb-4{margin-bottom:calc(var(--spacing)*4)}.ml-1{margin-left:calc(var(--spacing)*1)}.ml-2{margin-left:calc(var(--spacing)*2)}.ml-auto{margin-left:auto}.line-clamp-1{-webkit-line-clamp:1;-webkit-box-orient:vertical;display:-webkit-box;overflow:hidden}.\!inline{display:inline!important}.block{display:block}.flex{display:flex}.grid{display:grid}.hidden{display:none}.inline{display:inline}.inline-flex{display:inline-flex}.list-item{display:list-item}.table{display:table}.aspect-square{aspect-ratio:1}.\!size-full{width:100%!important;height:100%!important}.size-4{width:calc(var(--spacing)*4);height:calc(var(--spacing)*4)}.size-6{width:calc(var(--spacing)*6);height:calc(var(--spacing)*6)}.size-7{width:calc(var(--spacing)*7);height:calc(var(--spacing)*7)}.size-8{width:calc(var(--spacing)*8);height:calc(var(--spacing)*8)}.size-10{width:calc(var(--spacing)*10);height:calc(var(--spacing)*10)}.size-full{width:100%;height:100%}.h-1\/2{height:50%}.h-2{height:calc(var(--spacing)*2)}.h-2\.5{height:calc(var(--spacing)*2.5)}.h-3{height:calc(var(--spacing)*3)}.h-3\.5{height:calc(var(--spacing)*3.5)}.h-4{height:calc(var(--spacing)*4)}.h-5{height:calc(var(--spacing)*5)}.h-6{height:calc(var(--spacing)*6)}.h-7{height:calc(var(--spacing)*7)}.h-8{height:calc(var(--spacing)*8)}.h-9{height:calc(var(--spacing)*9)}.h-10{height:calc(var(--spacing)*10)}.h-11{height:calc(var(--spacing)*11)}.h-12{height:calc(var(--spacing)*12)}.h-24{height:calc(var(--spacing)*24)}.h-52{height:calc(var(--spacing)*52)}.h-\[1px\]{height:1px}.h-\[var\(--radix-select-trigger-height\)\]{height:var(--radix-select-trigger-height)}.h-fit{height:fit-content}.h-full{height:100%}.h-px{height:1px}.h-screen{height:100vh}.max-h-8{max-height:calc(var(--spacing)*8)}.max-h-48{max-height:calc(var(--spacing)*48)}.max-h-80{max-height:calc(var(--spacing)*80)}.max-h-96{max-height:calc(var(--spacing)*96)}.max-h-\[40vh\]{max-height:40vh}.max-h-\[50vh\]{max-height:50vh}.max-h-\[60vh\]{max-height:60vh}.max-h-\[300px\]{max-height:300px}.max-h-full{max-height:100%}.min-h-0{min-height:calc(var(--spacing)*0)}.min-h-\[7\.5em\]{min-height:7.5em}.min-h-\[10em\]{min-height:10em}.w-2{width:calc(var(--spacing)*2)}.w-2\.5{width:calc(var(--spacing)*2.5)}.w-3{width:calc(var(--spacing)*3)}.w-3\.5{width:calc(var(--spacing)*3.5)}.w-4{width:calc(var(--spacing)*4)}.w-5{width:calc(var(--spacing)*5)}.w-6{width:calc(var(--spacing)*6)}.w-7{width:calc(var(--spacing)*7)}.w-8{width:calc(var(--spacing)*8)}.w-9{width:calc(var(--spacing)*9)}.w-12{width:calc(var(--spacing)*12)}.w-16{width:calc(var(--spacing)*16)}.w-24{width:calc(var(--spacing)*24)}.w-56{width:calc(var(--spacing)*56)}.w-\[1px\]{width:1px}.w-\[95\%\]{width:95%}.w-\[200px\]{width:200px}.w-auto{width:auto}.w-full{width:100%}.w-screen{width:100vw}.max-w-80{max-width:calc(var(--spacing)*80)}.max-w-\[80\%\]{max-width:80%}.max-w-\[200px\]{max-width:200px}.max-w-\[250px\]{max-width:250px}.max-w-\[480px\]{max-width:480px}.max-w-lg{max-width:var(--container-lg)}.max-w-none{max-width:none}.max-w-xs{max-width:var(--container-xs)}.min-w-0{min-width:calc(var(--spacing)*0)}.min-w-45{min-width:calc(var(--spacing)*45)}.min-w-\[8rem\]{min-width:8rem}.min-w-\[200px\]{min-width:200px}.min-w-\[220px\]{min-width:220px}.min-w-\[300px\]{min-width:300px}.min-w-\[var\(--radix-select-trigger-width\)\]{min-width:var(--radix-select-trigger-width)}.min-w-auto{min-width:auto}.flex-1{flex:1}.flex-none{flex:none}.flex-shrink-0,.shrink-0{flex-shrink:0}.grow{flex-grow:1}.caption-bottom{caption-side:bottom}.\!-translate-x-1\/2{--tw-translate-x: -50% ;translate:var(--tw-translate-x)var(--tw-translate-y)!important}.\!translate-x-\[-50\%\]{--tw-translate-x:-50%;translate:var(--tw-translate-x)var(--tw-translate-y)!important}.-translate-x-13{--tw-translate-x:calc(var(--spacing)*-13);translate:var(--tw-translate-x)var(--tw-translate-y)}.-translate-x-15{--tw-translate-x:calc(var(--spacing)*-15);translate:var(--tw-translate-x)var(--tw-translate-y)}.translate-x-\[-50\%\]{--tw-translate-x:-50%;translate:var(--tw-translate-x)var(--tw-translate-y)}.-translate-y-1\/2{--tw-translate-y: -50% 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--tw-backdrop-brightness{syntax:"*";inherits:false}@property --tw-backdrop-contrast{syntax:"*";inherits:false}@property --tw-backdrop-grayscale{syntax:"*";inherits:false}@property --tw-backdrop-hue-rotate{syntax:"*";inherits:false}@property --tw-backdrop-invert{syntax:"*";inherits:false}@property --tw-backdrop-opacity{syntax:"*";inherits:false}@property --tw-backdrop-saturate{syntax:"*";inherits:false}@property --tw-backdrop-sepia{syntax:"*";inherits:false}@property --tw-duration{syntax:"*";inherits:false}@property --tw-ease{syntax:"*";inherits:false} diff --git a/lightrag/api/webui/assets/index-Bj1VgJta.js b/lightrag/api/webui/assets/index-CZQXgxUO.js similarity index 71% rename from lightrag/api/webui/assets/index-Bj1VgJta.js rename to lightrag/api/webui/assets/index-CZQXgxUO.js index 461032dd..9ebb2e9c 100644 --- a/lightrag/api/webui/assets/index-Bj1VgJta.js +++ b/lightrag/api/webui/assets/index-CZQXgxUO.js @@ -1,4 +1,4 @@ -import{j as o,Y as td,O as fg,k as dg,u as ad,Z as mg,c as 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-{{error}}`}}},Ny={dataIsTruncated:"圖資料已截斷至最大回傳節點數",statusDialog:{title:"LightRAG 伺服器設定",description:"查看目前系統狀態和連線資訊"},legend:"圖例",nodeTypes:{person:"人物角色",category:"分類",geo:"地理名稱",location:"位置",organization:"組織機構",event:"事件",equipment:"設備",weapon:"武器",animal:"動物",unknown:"未知",object:"物品",group:"群組",technology:"技術"},sideBar:{settings:{settings:"設定",healthCheck:"健康檢查",showPropertyPanel:"顯示屬性面板",showSearchBar:"顯示搜尋列",showNodeLabel:"顯示節點標籤",nodeDraggable:"節點可拖曳",showEdgeLabel:"顯示 Edge 標籤",hideUnselectedEdges:"隱藏未選取的 Edge",edgeEvents:"Edge 事件",maxQueryDepth:"最大查詢深度",maxNodes:"最大回傳節點數",maxLayoutIterations:"最大版面配置迭代次數",resetToDefault:"重設為預設值",edgeSizeRange:"Edge 粗細範圍",depth:"深度",max:"最大值",degree:"鄰邊",apiKey:"API key",enterYourAPIkey:"輸入您的 API 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事件",maxQueryDepth:"最大查詢深度",maxNodes:"最大回傳節點數",maxLayoutIterations:"最大版面配置迭代次數",resetToDefault:"重設為預設值",edgeSizeRange:"Edge 粗細範圍",depth:"深度",max:"最大值",degree:"鄰邊",apiKey:"API key",enterYourAPIkey:"輸入您的 API key",save:"儲存",refreshLayout:"重新整理版面配置"},zoomControl:{zoomIn:"放大",zoomOut:"縮小",resetZoom:"重設縮放",rotateCamera:"順時針旋轉圖形",rotateCameraCounterClockwise:"逆時針旋轉圖形"},layoutsControl:{startAnimation:"繼續版面配置動畫",stopAnimation:"停止版面配置動畫",layoutGraph:"圖形版面配置",layouts:{Circular:"環形",Circlepack:"圓形打包",Random:"隨機",Noverlaps:"無重疊","Force Directed":"力導向","Force Atlas":"力圖"}},fullScreenControl:{fullScreen:"全螢幕",windowed:"視窗"},legendControl:{toggleLegend:"切換圖例顯示"}},statusIndicator:{connected:"已連線",disconnected:"未連線"},statusCard:{unavailable:"狀態資訊不可用",storageInfo:"儲存資訊",workingDirectory:"工作目錄",inputDirectory:"輸入目錄",llmConfig:"LLM 設定",llmBinding:"LLM 綁定",llmBindingHost:"LLM 綁定主機",llmModel:"LLM 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個"},graphLabels:{selectTooltip:"選擇查詢標籤",noLabels:"未找到標籤",label:"標籤",placeholder:"搜尋標籤...",andOthers:"還有 {count} 個",refreshTooltip:"重載圖形數據(新增檔案後需重載)"},emptyGraph:"無數據(請重載圖形數據)"},Ey={chatMessage:{copyTooltip:"複製到剪貼簿",copyError:"複製文字到剪貼簿失敗"},retrieval:{startPrompt:"輸入查詢開始檢索",clear:"清空",send:"送出",placeholder:"輸入查詢內容 (支援模式前綴：/)",error:"錯誤：取得回應失敗",queryModeError:"僅支援以下查詢模式：{{modes}}",queryModePrefixInvalid:"無效的查詢模式前綴。請使用：/<模式> [空格] 查詢內容"},querySettings:{parametersTitle:"參數",parametersDescription:"設定查詢參數",queryMode:"查詢模式",queryModeTooltip:`選擇檢索策略： • Naive：基礎搜尋，無進階技術 • Local：上下文相關資訊檢索 • Global：利用全域知識庫 @@ -138,4 +148,4 @@ For more information, see https://radix-ui.com/primitives/docs/components/alert- • Bypass：直接傳遞查詢到LLM，不進行檢索`,queryModeOptions:{naive:"Naive",local:"Local",global:"Global",hybrid:"Hybrid",mix:"Mix",bypass:"Bypass"},responseFormat:"回應格式",responseFormatTooltip:`定義回應格式。例如： • 多段落 • 單段落 -• 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K結果",topKTooltip:"檢索的前幾項結果數。在'local'模式下表示實體，在'global'模式下表示關係",topKPlaceholder:"結果數量",maxTokensTextUnit:"文字單元最大權杖數",maxTokensTextUnitTooltip:"每個檢索文字區塊允許的最大權杖數",maxTokensGlobalContext:"全域上下文最大權杖數",maxTokensGlobalContextTooltip:"全域檢索中關係描述的最大權杖數",maxTokensLocalContext:"本地上下文最大權杖數",maxTokensLocalContextTooltip:"本地檢索中實體描述的最大權杖數",historyTurns:"歷史輪次",historyTurnsTooltip:"回應上下文中考慮的完整對話輪次（使用者-助手對）數量",historyTurnsPlaceholder:"歷史輪次數",onlyNeedContext:"僅需上下文",onlyNeedContextTooltip:"如果為True，僅回傳檢索到的上下文而不產生回應",onlyNeedPrompt:"僅需提示",onlyNeedPromptTooltip:"如果為True，僅回傳產生的提示而不產生回應",streamResponse:"串流回應",streamResponseTooltip:"如果為True，啟用即時串流輸出回應",userPrompt:"用戶提示詞",userPromptTooltip:"向LLM提供額外的響應要求（與查詢內容無關，僅用於處理輸出）。",userPromptPlaceholder:"輸入自定義提示詞（可選）"}},My={loading:"正在載入 API 文件..."},zy={title:"需要 API key",description:"請輸入您的 API key 以存取服務",placeholder:"請輸入 API key",save:"儲存"},Cy={settings:Sy,header:Ty,login:xy,common:Ay,documentPanel:Dy,graphPanel:Ny,retrievePanel:Ey,apiSite:My,apiKeyAlert:zy},Oy=()=>{var g;try{const v=localStorage.getItem("settings-storage");if(v)return((g=JSON.parse(v).state)==null?void 0:g.language)||"en"}catch(v){console.error("Failed to get stored language:",v)}return"en"};cs.use($g).init({resources:{en:{translation:Vp},zh:{translation:Ip},fr:{translation:oy},ar:{translation:by},zh_TW:{translation:Cy}},lng:Oy(),fallbackLng:"en",interpolation:{escapeValue:!1},returnEmptyString:!1,returnNull:!1});we.subscribe(g=>{const v=g.language;cs.language!==v&&cs.changeLanguage(v)});ap.createRoot(document.getElementById("root")).render(o.jsx(E.StrictMode,{children:o.jsx(Up,{})})); diff --git a/lightrag/api/webui/assets/infoDiagram-PH2N3AL5-Bexk_QKW.js b/lightrag/api/webui/assets/infoDiagram-PH2N3AL5-DKUJQA1J.js similarity index 61% rename from lightrag/api/webui/assets/infoDiagram-PH2N3AL5-Bexk_QKW.js rename to lightrag/api/webui/assets/infoDiagram-PH2N3AL5-DKUJQA1J.js index 643269e7..1c1c8eab 100644 --- a/lightrag/api/webui/assets/infoDiagram-PH2N3AL5-Bexk_QKW.js +++ b/lightrag/api/webui/assets/infoDiagram-PH2N3AL5-DKUJQA1J.js @@ -1,2 +1,2 @@ -import{_ as e,l as o,K as i,e as n,L as p}from"./mermaid-vendor-d7rbry5E.js";import{p as m}from"./radar-MK3ICKWK-zkXzSXFe.js";import"./feature-graph-DbHHHM9y.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-OtJ11HbN.js";import"./_basePickBy-Lz6agtdo.js";import"./clone-vL6XIcCC.js";var g={parse:e(async r=>{const a=await m("info",r);o.debug(a)},"parse")},v={version:p.version},d=e(()=>v.version,"getVersion"),c={getVersion:d},l=e((r,a,s)=>{o.debug(`rendering info diagram +import{_ as e,l as o,K as i,e as n,L as p}from"./mermaid-vendor-BVBgFwCv.js";import{p as m}from"./radar-MK3ICKWK-B0N6XiM2.js";import"./feature-graph-D-mwOi0p.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-CoKY6BVy.js";import"./_basePickBy-BVZSZRdU.js";import"./clone-UTZGcTvC.js";var g={parse:e(async r=>{const a=await m("info",r);o.debug(a)},"parse")},v={version:p.version},d=e(()=>v.version,"getVersion"),c={getVersion:d},l=e((r,a,s)=>{o.debug(`rendering info diagram `+r);const t=i(a);n(t,100,400,!0),t.append("g").append("text").attr("x",100).attr("y",40).attr("class","version").attr("font-size",32).style("text-anchor","middle").text(`v${s}`)},"draw"),f={draw:l},L={parser:g,db:c,renderer:f};export{L as diagram}; diff --git a/lightrag/api/webui/assets/journeyDiagram-U35MCT3I-Drj8e4jk.js b/lightrag/api/webui/assets/journeyDiagram-U35MCT3I-C9ZzbUpH.js similarity index 99% rename from lightrag/api/webui/assets/journeyDiagram-U35MCT3I-Drj8e4jk.js rename to lightrag/api/webui/assets/journeyDiagram-U35MCT3I-C9ZzbUpH.js index 0cd855b2..8d5cbd3b 100644 --- a/lightrag/api/webui/assets/journeyDiagram-U35MCT3I-Drj8e4jk.js +++ b/lightrag/api/webui/assets/journeyDiagram-U35MCT3I-C9ZzbUpH.js @@ -1,4 +1,4 @@ -import{a as pt,g as at,f as gt,d as mt}from"./chunk-D6G4REZN-CQqXYBrW.js";import{_ as s,g as xt,s as kt,a as _t,b as bt,t as vt,q as wt,c as A,d as W,e as Tt,z as St,N as tt}from"./mermaid-vendor-d7rbry5E.js";import"./feature-graph-DbHHHM9y.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var H=function(){var t=s(function(g,r,a,l){for(a=a||{},l=g.length;l--;a[g[l]]=r);return 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this.yy)Object.prototype.hasOwnProperty.call(this.yy,O)&&(E.yy[O]=this.yy[O]);k.setInput(r,E.yy),E.yy.lexer=k,E.yy.parser=this,typeof k.yylloc>"u"&&(k.yylloc={});var Y=k.yylloc;o.push(Y);var dt=k.options&&k.options.ranges;typeof E.yy.parseError=="function"?this.parseError=E.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function ft(v){l.length=l.length-2*v,p.length=p.length-v,o.length=o.length-v}s(ft,"popStack");function Q(){var v;return v=y.pop()||k.lex()||K,typeof v!="number"&&(v instanceof Array&&(y=v,v=y.pop()),v=a.symbols_[v]||v),v}s(Q,"lex");for(var b,P,w,q,C={},N,$,D,j;;){if(P=l[l.length-1],this.defaultActions[P]?w=this.defaultActions[P]:((b===null||typeof b>"u")&&(b=Q()),w=S[P]&&S[P][b]),typeof w>"u"||!w.length||!w[0]){var G="";j=[];for(N in S[P])this.terminals_[N]&&N>ut&&j.push("'"+this.terminals_[N]+"'");k.showPosition?G="Parse error on line "+(B+1)+`: `+k.showPosition()+` Expecting `+j.join(", ")+", got '"+(this.terminals_[b]||b)+"'":G="Parse error on 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a=r.length,l=r.split(/(?:\r\n?|\n)/g);this._input=r+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-a),this.offset-=a;var y=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),l.length-1&&(this.yylineno-=l.length-1);var p=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:l?(l.length===y.length?this.yylloc.first_column:0)+y[y.length-l.length].length-l[0].length:this.yylloc.first_column-a},this.options.ranges&&(this.yylloc.range=[p[0],p[0]+this.yyleng-a]),this.yyleng=this.yytext.length,this},"unput"),more:s(function(){return this._more=!0,this},"more"),reject:s(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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l=e.length-1;switch(u){case 6:case 7:return o;case 8:o.getLogger().trace("Stop NL ");break;case 9:o.getLogger().trace("Stop EOF ");break;case 11:o.getLogger().trace("Stop NL2 ");break;case 12:o.getLogger().trace("Stop EOF2 ");break;case 15:o.getLogger().info("Node: ",e[l-1].id),o.addNode(e[l-2].length,e[l-1].id,e[l-1].descr,e[l-1].type,e[l]);break;case 16:o.getLogger().info("Node: ",e[l].id),o.addNode(e[l-1].length,e[l].id,e[l].descr,e[l].type);break;case 17:o.getLogger().trace("Icon: ",e[l]),o.decorateNode({icon:e[l]});break;case 18:case 23:o.decorateNode({class:e[l]});break;case 19:o.getLogger().trace("SPACELIST");break;case 20:o.getLogger().trace("Node: ",e[l-1].id),o.addNode(0,e[l-1].id,e[l-1].descr,e[l-1].type,e[l]);break;case 21:o.getLogger().trace("Node: ",e[l].id),o.addNode(0,e[l].id,e[l].descr,e[l].type);break;case 22:o.decorateNode({icon:e[l]});break;case 27:o.getLogger().trace("node found ..",e[l-2]),this.$={id:e[l-1],descr:e[l-1],type:o.getType(e[l-2],e[l])};break;case 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v40z`,shortbaraboveleftharpoon:`M7,435c-4,4,-6.3,8.7,-7,14c0,5.3,0.7,9,2,11 +c1.3,2,5.3,5.3,12,10c90.7,54,156,130,196,228c3.3,10.7,6.3,16.3,9,17c2,0.7,5,1,9, +1c0,0,5,0,5,0c10.7,0,16.7,-2,18,-6c2,-2.7,1,-9.7,-3,-21c-32,-87.3,-82.7,-157.7, +-152,-211c0,0,-3,-3,-3,-3l399907,0l0,-40c-399126,0,-399993,0,-399993,0z +M93 435 v40 H400000 v-40z M500 241 v40 H400000 v-40z M500 241 v40 H400000 v-40z`,shortrightharpoonabovebar:`M53,241l0,40c398570,0,399437,0,399437,0 +c4.7,-4.7,7,-9.3,7,-14c0,-9.3,-3.7,-15.3,-11,-18c-92.7,-56.7,-159,-133.7,-199, +-231c-3.3,-9.3,-6,-14.7,-8,-16c-2,-1.3,-7,-2,-15,-2c-10.7,0,-16.7,2,-18,6 +c-2,2.7,-1,9.7,3,21c15.3,42,36.7,81.8,64,119.5c27.3,37.7,58,69.2,92,94.5z +M500 241 v40 H399408 v-40z M500 435 v40 H400000 v-40z`},Ga=function(e,t){switch(e){case"lbrack":return"M403 1759 V84 H666 V0 H319 V1759 v"+t+` v1759 h347 v-84 +H403z M403 1759 V0 H319 V1759 v`+t+" v1759 h84z";case"rbrack":return"M347 1759 V0 H0 V84 H263 V1759 v"+t+` v1759 H0 v84 H347z +M347 1759 V0 H263 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e(t){(function(r){var n="(?:"+[/[a-zA-Z_\x80-\uFFFF][\w\x80-\uFFFF]*/.source,/-?(?:\.\d+|\d+(?:\.\d*)?)/.source,/"[^"\\]*(?:\\[\s\S][^"\\]*)*"/.source,/<(?:[^<>]|(?!)*>/.source].join("|")+")",a={markup:{pattern:/(^<)[\s\S]+(?=>$)/,lookbehind:!0,alias:["language-markup","language-html","language-xml"],inside:r.languages.markup}};function i(o,s){return RegExp(o.replace(//g,function(){return n}),s)}r.languages.dot={comment:{pattern:/\/\/.*|\/\*[\s\S]*?\*\/|^#.*/m,greedy:!0},"graph-name":{pattern:i(/(\b(?:digraph|graph|subgraph)[ \t\r\n]+)/.source,"i"),lookbehind:!0,greedy:!0,alias:"class-name",inside:a},"attr-value":{pattern:i(/(=[ \t\r\n]*)/.source),lookbehind:!0,greedy:!0,inside:a},"attr-name":{pattern:i(/([\[;, \t\r\n])(?=[ \t\r\n]*=)/.source),lookbehind:!0,greedy:!0,inside:a},keyword:/\b(?:digraph|edge|graph|node|strict|subgraph)\b/i,"compass-point":{pattern:/(:[ 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po}var mo,Hm;function Bx(){if(Hm)return mo;Hm=1,mo=e,e.displayName="elm",e.aliases=[];function e(t){t.languages.elm={comment:/--.*|\{-[\s\S]*?-\}/,char:{pattern:/'(?:[^\\'\r\n]|\\(?:[abfnrtv\\']|\d+|x[0-9a-fA-F]+|u\{[0-9a-fA-F]+\}))'/,greedy:!0},string:[{pattern:/"""[\s\S]*?"""/,greedy:!0},{pattern:/"(?:[^\\"\r\n]|\\.)*"/,greedy:!0}],"import-statement":{pattern:/(^[\t 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r(n){n.register(e),n.register(t),function(a){a.languages.erb={delimiter:{pattern:/^(\s*)<%=?|%>(?=\s*$)/,lookbehind:!0,alias:"punctuation"},ruby:{pattern:/\s*\S[\s\S]*/,alias:"language-ruby",inside:a.languages.ruby}},a.hooks.add("before-tokenize",function(i){var o=/<%=?(?:[^\r\n]|[\r\n](?!=begin)|[\r\n]=begin\s(?:[^\r\n]|[\r\n](?!=end))*[\r\n]=end)+?%>/g;a.languages["markup-templating"].buildPlaceholders(i,"erb",o)}),a.hooks.add("after-tokenize",function(i){a.languages["markup-templating"].tokenizePlaceholders(i,"erb")})}(n)}return fo}var ho,jm;function Ux(){if(jm)return ho;jm=1,ho=e,e.displayName="erlang",e.aliases=[];function e(t){t.languages.erlang={comment:/%.+/,string:{pattern:/"(?:\\.|[^\\"\r\n])*"/,greedy:!0},"quoted-function":{pattern:/'(?:\\.|[^\\'\r\n])+'(?=\()/,alias:"function"},"quoted-atom":{pattern:/'(?:\\.|[^\\'\r\n])+'/,alias:"atom"},boolean:/\b(?:false|true)\b/,keyword:/\b(?:after|case|catch|end|fun|if|of|receive|try|when)\b/,number:[/\$\\?./,/\b\d+#[a-z0-9]+/i,/(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:e[+-]?\d+)?/i],function:/\b[a-z][\w@]*(?=\()/,variable:{pattern:/(^|[^@])(?:\b|\?)[A-Z_][\w@]*/,lookbehind:!0},operator:[/[=\/<>:]=|=[:\/]=|\+\+?|--?|[=*\/!]|\b(?:and|andalso|band|bnot|bor|bsl|bsr|bxor|div|not|or|orelse|rem|xor)\b/,{pattern:/(^|[^<])<(?!<)/,lookbehind:!0},{pattern:/(^|[^>])>(?!>)/,lookbehind:!0}],atom:/\b[a-z][\w@]*/,punctuation:/[()[\]{}:;,.#|]|<<|>>/}}return ho}var go,Wm;function Jy(){if(Wm)return go;Wm=1,go=e,e.displayName="lua",e.aliases=[];function 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e(t){(function(r){r.languages.kotlin=r.languages.extend("clike",{keyword:{pattern:/(^|[^.])\b(?:abstract|actual|annotation|as|break|by|catch|class|companion|const|constructor|continue|crossinline|data|do|dynamic|else|enum|expect|external|final|finally|for|fun|get|if|import|in|infix|init|inline|inner|interface|internal|is|lateinit|noinline|null|object|open|operator|out|override|package|private|protected|public|reified|return|sealed|set|super|suspend|tailrec|this|throw|to|try|typealias|val|var|vararg|when|where|while)\b/,lookbehind:!0},function:[{pattern:/(?:`[^\r\n`]+`|\b\w+)(?=\s*\()/,greedy:!0},{pattern:/(\.)(?:`[^\r\n`]+`|\w+)(?=\s*\{)/,lookbehind:!0,greedy:!0}],number:/\b(?:0[xX][\da-fA-F]+(?:_[\da-fA-F]+)*|0[bB][01]+(?:_[01]+)*|\d+(?:_\d+)*(?:\.\d+(?:_\d+)*)?(?:[eE][+-]?\d+(?:_\d+)*)?[fFL]?)\b/,operator:/\+[+=]?|-[-=>]?|==?=?|!(?:!|==?)?|[\/*%<>]=?|[?:]:?|\.\.|&&|\|\||\b(?:and|inv|or|shl|shr|ushr|xor)\b/}),delete r.languages.kotlin["class-name"];var n={"interpolation-punctuation":{pattern:/^\$\{?|\}$/,alias:"punctuation"},expression:{pattern:/[\s\S]+/,inside:r.languages.kotlin}};r.languages.insertBefore("kotlin","string",{"string-literal":[{pattern:/"""(?:[^$]|\$(?:(?!\{)|\{[^{}]*\}))*?"""/,alias:"multiline",inside:{interpolation:{pattern:/\$(?:[a-z_]\w*|\{[^{}]*\})/i,inside:n},string:/[\s\S]+/}},{pattern:/"(?:[^"\\\r\n$]|\\.|\$(?:(?!\{)|\{[^{}]*\}))*"/,alias:"singleline",inside:{interpolation:{pattern:/((?:^|[^\\])(?:\\{2})*)\$(?:[a-z_]\w*|\{[^{}]*\})/i,lookbehind:!0,inside:n},string:/[\s\S]+/}}],char:{pattern:/'(?:[^'\\\r\n]|\\(?:.|u[a-fA-F0-9]{0,4}))'/,greedy:!0}}),delete r.languages.kotlin.string,r.languages.insertBefore("kotlin","keyword",{annotation:{pattern:/\B@(?:\w+:)?(?:[A-Z]\w*|\[[^\]]+\])/,alias:"builtin"}}),r.languages.insertBefore("kotlin","function",{label:{pattern:/\b\w+@|@\w+\b/,alias:"symbol"}}),r.languages.kt=r.languages.kotlin,r.languages.kts=r.languages.kotlin})(t)}return Ts}var As,nh;function G7(){if(nh)return As;nh=1,As=e,e.displayName="kumir",e.aliases=["kum"];function e(t){(function(r){var n=/\s\x00-\x1f\x22-\x2f\x3a-\x3f\x5b-\x5e\x60\x7b-\x7e/.source;function a(i,o){return 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n=/\\(?:[^a-z()[\]]|[a-z*]+)/i,a={"equation-command":{pattern:n,alias:"regex"}};r.languages.latex={comment:/%.*/,cdata:{pattern:/(\\begin\{((?:lstlisting|verbatim)\*?)\})[\s\S]*?(?=\\end\{\2\})/,lookbehind:!0},equation:[{pattern:/\$\$(?:\\[\s\S]|[^\\$])+\$\$|\$(?:\\[\s\S]|[^\\$])+\$|\\\([\s\S]*?\\\)|\\\[[\s\S]*?\\\]/,inside:a,alias:"string"},{pattern:/(\\begin\{((?:align|eqnarray|equation|gather|math|multline)\*?)\})[\s\S]*?(?=\\end\{\2\})/,lookbehind:!0,inside:a,alias:"string"}],keyword:{pattern:/(\\(?:begin|cite|documentclass|end|label|ref|usepackage)(?:\[[^\]]+\])?\{)[^}]+(?=\})/,lookbehind:!0},url:{pattern:/(\\url\{)[^}]+(?=\})/,lookbehind:!0},headline:{pattern:/(\\(?:chapter|frametitle|paragraph|part|section|subparagraph|subsection|subsubparagraph|subsubsection|subsubsubparagraph)\*?(?:\[[^\]]+\])?\{)[^}]+(?=\})/,lookbehind:!0,alias:"class-name"},function:{pattern:n,alias:"selector"},punctuation:/[[\]{}&]/},r.languages.tex=r.languages.latex,r.languages.context=r.languages.latex})(t)}return Rs}var Is,oh;function Fn(){if(oh)return Is;oh=1;var e=et();Is=t,t.displayName="php",t.aliases=[];function t(r){r.register(e),function(n){var 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u={pattern:/\{\$(?:\{(?:\{[^{}]+\}|[^{}]+)\}|[^{}])+\}|(^|[^\\{])\$+(?:\w+(?:\[[^\r\n\[\]]+\]|->\w+)?)/,lookbehind:!0,inside:n.languages.php},c=[{pattern:/<<<'([^']+)'[\r\n](?:.*[\r\n])*?\1;/,alias:"nowdoc-string",greedy:!0,inside:{delimiter:{pattern:/^<<<'[^']+'|[a-z_]\w*;$/i,alias:"symbol",inside:{punctuation:/^<<<'?|[';]$/}}}},{pattern:/<<<(?:"([^"]+)"[\r\n](?:.*[\r\n])*?\1;|([a-z_]\w*)[\r\n](?:.*[\r\n])*?\2;)/i,alias:"heredoc-string",greedy:!0,inside:{delimiter:{pattern:/^<<<(?:"[^"]+"|[a-z_]\w*)|[a-z_]\w*;$/i,alias:"symbol",inside:{punctuation:/^<<<"?|[";]$/}},interpolation:u}},{pattern:/`(?:\\[\s\S]|[^\\`])*`/,alias:"backtick-quoted-string",greedy:!0},{pattern:/'(?:\\[\s\S]|[^\\'])*'/,alias:"single-quoted-string",greedy:!0},{pattern:/"(?:\\[\s\S]|[^\\"])*"/,alias:"double-quoted-string",greedy:!0,inside:{interpolation:u}}];n.languages.insertBefore("php","variable",{string:c,attribute:{pattern:/#\[(?:[^"'\/#]|\/(?![*/])|\/\/.*$|#(?!\[).*$|\/\*(?:[^*]|\*(?!\/))*\*\/|"(?:\\[\s\S]|[^\\"])*"|'(?:\\[\s\S]|[^\\'])*')+\](?=\s*[a-z$#])/im,greedy:!0,inside:{"attribute-content":{pattern:/^(#\[)[\s\S]+(?=\]$)/,lookbehind:!0,inside:{comment:a,string:c,"attribute-class-name":[{pattern:/([^:]|^)\b[a-z_]\w*(?!\\)\b/i,alias:"class-name",greedy:!0,lookbehind:!0},{pattern:/([^:]|^)(?:\\?\b[a-z_]\w*)+/i,alias:["class-name","class-name-fully-qualified"],greedy:!0,lookbehind:!0,inside:{punctuation:/\\/}}],constant:i,number:o,operator:s,punctuation:l}},delimiter:{pattern:/^#\[|\]$/,alias:"punctuation"}}}}),n.hooks.add("before-tokenize",function(d){if(/<\?/.test(d.code)){var h=/<\?(?:[^"'/#]|\/(?![*/])|("|')(?:\\[\s\S]|(?!\1)[^\\])*\1|(?:\/\/|#(?!\[))(?:[^?\n\r]|\?(?!>))*(?=$|\?>|[\r\n])|#\[|\/\*(?:[^*]|\*(?!\/))*(?:\*\/|$))*?(?:\?>|$)/g;n.languages["markup-templating"].buildPlaceholders(d,"php",h)}}),n.hooks.add("after-tokenize",function(d){n.languages["markup-templating"].tokenizePlaceholders(d,"php")})}(r)}return Is}var Ns,sh;function V7(){if(sh)return Ns;sh=1;var e=et(),t=Fn();Ns=r,r.displayName="latte",r.aliases=[];function r(n){n.register(e),n.register(t),function(a){a.languages.latte={comment:/^\{\*[\s\S]*/,"latte-tag":{pattern:/(^\{(?:\/(?=[a-z]))?)(?:[=_]|[a-z]\w*\b(?!\())/i,lookbehind:!0,alias:"important"},delimiter:{pattern:/^\{\/?|\}$/,alias:"punctuation"},php:{pattern:/\S(?:[\s\S]*\S)?/,alias:"language-php",inside:a.languages.php}};var 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a=[/"(?:\\[\s\S]|\$\([^)]+\)|\$(?!\()|`[^`]+`|[^"\\`$])*"/.source,/'[^']*'/.source,/\$'(?:[^'\\]|\\[\s\S])*'/.source,/<<-?\s*(["']?)(\w+)\1\s[\s\S]*?[\r\n]\2/.source].join("|");n.languages["shell-session"]={command:{pattern:RegExp(/^/.source+"(?:"+(/[^\s@:$#%*!/\\]+@[^\r\n@:$#%*!/\\]+(?::[^\0-\x1F$#%*?"<>:;|]+)?/.source+"|"+/[/~.][^\0-\x1F$#%*?"<>@:;|]*/.source)+")?"+/[$#%](?=\s)/.source+/(?:[^\\\r\n \t'"<$]|[ \t](?:(?!#)|#.*$)|\\(?:[^\r]|\r\n?)|\$(?!')|<(?!<)|<>)+/.source.replace(/<>/g,function(){return a}),"m"),greedy:!0,inside:{info:{pattern:/^[^#$%]+/,alias:"punctuation",inside:{user:/^[^\s@:$#%*!/\\]+@[^\r\n@:$#%*!/\\]+/,punctuation:/:/,path:/[\s\S]+/}},bash:{pattern:/(^[$#%]\s*)\S[\s\S]*/,lookbehind:!0,alias:"language-bash",inside:n.languages.bash},"shell-symbol":{pattern:/^[$#%]/,alias:"important"}}},output:/.(?:.*(?:[\r\n]|.$))*/},n.languages["sh-session"]=n.languages.shellsession=n.languages["shell-session"]}(r)}return tu}var ru,Dg;function bI(){if(Dg)return ru;Dg=1,ru=e,e.displayName="smali",e.aliases=[];function e(t){t.languages.smali={comment:/#.*/,string:{pattern:/"(?:[^\r\n\\"]|\\.)*"|'(?:[^\r\n\\']|\\(?:.|u[\da-fA-F]{4}))'/,greedy:!0},"class-name":{pattern:/(^|[^L])L(?:(?:\w+|`[^`\r\n]*`)\/)*(?:[\w$]+|`[^`\r\n]*`)(?=\s*;)/,lookbehind:!0,inside:{"class-name":{pattern:/(^L|\/)(?:[\w$]+|`[^`\r\n]*`)$/,lookbehind:!0},namespace:{pattern:/^(L)(?:(?:\w+|`[^`\r\n]*`)\/)+/,lookbehind:!0,inside:{punctuation:/\//}},builtin:/^L/}},builtin:[{pattern:/([();\[])[BCDFIJSVZ]+/,lookbehind:!0},{pattern:/([\w$>]:)[BCDFIJSVZ]/,lookbehind:!0}],keyword:[{pattern:/(\.end\s+)[\w-]+/,lookbehind:!0},{pattern:/(^|[^\w.-])\.(?!\d)[\w-]+/,lookbehind:!0},{pattern:/(^|[^\w.-])(?:abstract|annotation|bridge|constructor|enum|final|interface|private|protected|public|runtime|static|synthetic|system|transient)(?![\w.-])/,lookbehind:!0}],function:{pattern:/(^|[^\w.-])(?:\w+|<[\w$-]+>)(?=\()/,lookbehind:!0},field:{pattern:/[\w$]+(?=:)/,alias:"variable"},register:{pattern:/(^|[^\w.-])[vp]\d(?![\w.-])/,lookbehind:!0,alias:"variable"},boolean:{pattern:/(^|[^\w.-])(?:false|true)(?![\w.-])/,lookbehind:!0},number:{pattern:/(^|[^/\w.-])-?(?:NAN|INFINITY|0x(?:[\dA-F]+(?:\.[\dA-F]*)?|\.[\dA-F]+)(?:p[+-]?[\dA-F]+)?|(?:\d+(?:\.\d*)?|\.\d+)(?:e[+-]?\d+)?)[dflst]?(?![\w.-])/i,lookbehind:!0},label:{pattern:/(:)\w+/,lookbehind:!0,alias:"property"},operator:/->|\.\.|[\[=]/,punctuation:/[{}(),;:]/}}return ru}var nu,Mg;function yI(){if(Mg)return nu;Mg=1,nu=e,e.displayName="smalltalk",e.aliases=[];function e(t){t.languages.smalltalk={comment:{pattern:/"(?:""|[^"])*"/,greedy:!0},char:{pattern:/\$./,greedy:!0},string:{pattern:/'(?:''|[^'])*'/,greedy:!0},symbol:/#[\da-z]+|#(?:-|([+\/\\*~<>=@%|&?!])\1?)|#(?=\()/i,"block-arguments":{pattern:/(\[\s*):[^\[|]*\|/,lookbehind:!0,inside:{variable:/:[\da-z]+/i,punctuation:/\|/}},"temporary-variables":{pattern:/\|[^|]+\|/,inside:{variable:/[\da-z]+/i,punctuation:/\|/}},keyword:/\b(?:new|nil|self|super)\b/,boolean:/\b(?:false|true)\b/,number:[/\d+r-?[\dA-Z]+(?:\.[\dA-Z]+)?(?:e-?\d+)?/,/\b\d+(?:\.\d+)?(?:e-?\d+)?/],operator:/[<=]=?|:=|~[~=]|\/\/?|\\\\|>[>=]?|[!^+\-*&|,@]/,punctuation:/[.;:?\[\](){}]/}}return nu}var au,Fg;function vI(){if(Fg)return au;Fg=1;var e=et();au=t,t.displayName="smarty",t.aliases=[];function t(r){r.register(e),function(n){n.languages.smarty={comment:{pattern:/^\{\*[\s\S]*?\*\}/,greedy:!0},"embedded-php":{pattern:/^\{php\}[\s\S]*?\{\/php\}/,greedy:!0,inside:{smarty:{pattern:/^\{php\}|\{\/php\}$/,inside:null},php:{pattern:/[\s\S]+/,alias:"language-php",inside:n.languages.php}}},string:[{pattern:/"(?:\\.|[^"\\\r\n])*"/,greedy:!0,inside:{interpolation:{pattern:/\{[^{}]*\}|`[^`]*`/,inside:{"interpolation-punctuation":{pattern:/^[{`]|[`}]$/,alias:"punctuation"},expression:{pattern:/[\s\S]+/,inside:null}}},variable:/\$\w+/}},{pattern:/'(?:\\.|[^'\\\r\n])*'/,greedy:!0}],keyword:{pattern:/(^\{\/?)[a-z_]\w*\b(?!\()/i,lookbehind:!0,greedy:!0},delimiter:{pattern:/^\{\/?|\}$/,greedy:!0,alias:"punctuation"},number:/\b0x[\dA-Fa-f]+|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:[Ee][-+]?\d+)?/,variable:[/\$(?!\d)\w+/,/#(?!\d)\w+#/,{pattern:/(\.|->|\w\s*=)(?!\d)\w+\b(?!\()/,lookbehind:!0},{pattern:/(\[)(?!\d)\w+(?=\])/,lookbehind:!0}],function:{pattern:/(\|\s*)@?[a-z_]\w*|\b[a-z_]\w*(?=\()/i,lookbehind:!0},"attr-name":/\b[a-z_]\w*(?=\s*=)/i,boolean:/\b(?:false|no|off|on|true|yes)\b/,punctuation:/[\[\](){}.,:`]|->/,operator:[/[+\-*\/%]|==?=?|[!<>]=?|&&|\|\|?/,/\bis\s+(?:not\s+)?(?:div|even|odd)(?:\s+by)?\b/,/\b(?:and|eq|gt?e|gt|lt?e|lt|mod|neq?|not|or)\b/]},n.languages.smarty["embedded-php"].inside.smarty.inside=n.languages.smarty,n.languages.smarty.string[0].inside.interpolation.inside.expression.inside=n.languages.smarty;var a=/"(?:\\.|[^"\\\r\n])*"|'(?:\\.|[^'\\\r\n])*'/,i=RegExp(/\{\*[\s\S]*?\*\}/.source+"|"+/\{php\}[\s\S]*?\{\/php\}/.source+"|"+/\{(?:[^{}"']||\{(?:[^{}"']||\{(?:[^{}"']|)*\})*\})*\}/.source.replace(//g,function(){return a.source}),"g");n.hooks.add("before-tokenize",function(o){var s="{literal}",l="{/literal}",u=!1;n.languages["markup-templating"].buildPlaceholders(o,"smarty",i,function(c){return c===l&&(u=!1),u?!1:(c===s&&(u=!0),!0)})}),n.hooks.add("after-tokenize",function(o){n.languages["markup-templating"].tokenizePlaceholders(o,"smarty")})}(r)}return au}var iu,Bg;function EI(){if(Bg)return iu;Bg=1,iu=e,e.displayName="sml",e.aliases=["smlnj"];function e(t){(function(r){var n=/\b(?:abstype|and|andalso|as|case|datatype|do|else|end|eqtype|exception|fn|fun|functor|handle|if|in|include|infix|infixr|let|local|nonfix|of|op|open|orelse|raise|rec|sharing|sig|signature|struct|structure|then|type|val|where|while|with|withtype)\b/i;r.languages.sml={comment:/\(\*(?:[^*(]|\*(?!\))|\((?!\*)|\(\*(?:[^*(]|\*(?!\))|\((?!\*))*\*\))*\*\)/,string:{pattern:/#?"(?:[^"\\]|\\.)*"/,greedy:!0},"class-name":[{pattern:RegExp(/((?:^|[^:]):\s*)(?:\s*(?:(?:\*|->)\s*|,\s*(?:(?=)|(?!)\s+)))*/.source.replace(//g,function(){return/\s*(?:[*,]|->)/.source}).replace(//g,function(){return/(?:'[\w']*||\((?:[^()]|\([^()]*\))*\)|\{(?:[^{}]|\{[^{}]*\})*\})(?:\s+)*/.source}).replace(//g,function(){return/(?!)[a-z\d_][\w'.]*/.source}).replace(//g,function(){return n.source}),"i"),lookbehind:!0,greedy:!0,inside:null},{pattern:/((?:^|[^\w'])(?:datatype|exception|functor|signature|structure|type)\s+)[a-z_][\w'.]*/i,lookbehind:!0}],function:{pattern:/((?:^|[^\w'])fun\s+)[a-z_][\w'.]*/i,lookbehind:!0},keyword:n,variable:{pattern:/(^|[^\w'])'[\w']*/,lookbehind:!0},number:/~?\b(?:\d+(?:\.\d+)?(?:e~?\d+)?|0x[\da-f]+)\b/i,word:{pattern:/\b0w(?:\d+|x[\da-f]+)\b/i,alias:"constant"},boolean:/\b(?:false|true)\b/i,operator:/\.\.\.|:[>=:]|=>?|->|[<>]=?|[!+\-*/^#|@~]/,punctuation:/[(){}\[\].:,;]/},r.languages.sml["class-name"][0].inside=r.languages.sml,r.languages.smlnj=r.languages.sml})(t)}return iu}var ou,Pg;function SI(){if(Pg)return ou;Pg=1,ou=e,e.displayName="solidity",e.aliases=["sol"];function e(t){t.languages.solidity=t.languages.extend("clike",{"class-name":{pattern:/(\b(?:contract|enum|interface|library|new|struct|using)\s+)(?!\d)[\w$]+/,lookbehind:!0},keyword:/\b(?:_|anonymous|as|assembly|assert|break|calldata|case|constant|constructor|continue|contract|default|delete|do|else|emit|enum|event|external|for|from|function|if|import|indexed|inherited|interface|internal|is|let|library|mapping|memory|modifier|new|payable|pragma|private|public|pure|require|returns?|revert|selfdestruct|solidity|storage|struct|suicide|switch|this|throw|using|var|view|while)\b/,operator:/=>|->|:=|=:|\*\*|\+\+|--|\|\||&&|<<=?|>>=?|[-+*/%^&|<>!=]=?|[~?]/}),t.languages.insertBefore("solidity","keyword",{builtin:/\b(?:address|bool|byte|u?int(?:8|16|24|32|40|48|56|64|72|80|88|96|104|112|120|128|136|144|152|160|168|176|184|192|200|208|216|224|232|240|248|256)?|string|bytes(?:[1-9]|[12]\d|3[0-2])?)\b/}),t.languages.insertBefore("solidity","number",{version:{pattern:/([<>]=?|\^)\d+\.\d+\.\d+\b/,lookbehind:!0,alias:"number"}}),t.languages.sol=t.languages.solidity}return ou}var su,Ug;function wI(){if(Ug)return su;Ug=1,su=e,e.displayName="solutionFile",e.aliases=[];function e(t){(function(r){var n={pattern:/\{[\da-f]{8}-[\da-f]{4}-[\da-f]{4}-[\da-f]{4}-[\da-f]{12}\}/i,alias:"constant",inside:{punctuation:/[{}]/}};r.languages["solution-file"]={comment:{pattern:/#.*/,greedy:!0},string:{pattern:/"[^"\r\n]*"|'[^'\r\n]*'/,greedy:!0,inside:{guid:n}},object:{pattern:/^([ \t]*)(?:([A-Z]\w*)\b(?=.*(?:\r\n?|\n)(?:\1[ \t].*(?:\r\n?|\n))*\1End\2(?=[ \t]*$))|End[A-Z]\w*(?=[ \t]*$))/m,lookbehind:!0,greedy:!0,alias:"keyword"},property:{pattern:/^([ \t]*)(?!\s)[^\r\n"#=()]*[^\s"#=()](?=\s*=)/m,lookbehind:!0,inside:{guid:n}},guid:n,number:/\b\d+(?:\.\d+)*\b/,boolean:/\b(?:FALSE|TRUE)\b/,operator:/=/,punctuation:/[(),]/},r.languages.sln=r.languages["solution-file"]})(t)}return su}var lu,zg;function kI(){if(zg)return lu;zg=1;var e=et();lu=t,t.displayName="soy",t.aliases=[];function t(r){r.register(e),function(n){var a=/(["'])(?:\\(?:\r\n|[\s\S])|(?!\1)[^\\\r\n])*\1/,i=/\b\d+(?:\.\d+)?(?:[eE][+-]?\d+)?\b|\b0x[\dA-F]+\b/;n.languages.soy={comment:[/\/\*[\s\S]*?\*\//,{pattern:/(\s)\/\/.*/,lookbehind:!0,greedy:!0}],"command-arg":{pattern:/(\{+\/?\s*(?:alias|call|delcall|delpackage|deltemplate|namespace|template)\s+)\.?[\w.]+/,lookbehind:!0,alias:"string",inside:{punctuation:/\./}},parameter:{pattern:/(\{+\/?\s*@?param\??\s+)\.?[\w.]+/,lookbehind:!0,alias:"variable"},keyword:[{pattern:/(\{+\/?[^\S\r\n]*)(?:\\[nrt]|alias|call|case|css|default|delcall|delpackage|deltemplate|else(?:if)?|fallbackmsg|for(?:each)?|if(?:empty)?|lb|let|literal|msg|namespace|nil|@?param\??|rb|sp|switch|template|xid)/,lookbehind:!0},/\b(?:any|as|attributes|bool|css|float|html|in|int|js|list|map|null|number|string|uri)\b/],delimiter:{pattern:/^\{+\/?|\/?\}+$/,alias:"punctuation"},property:/\w+(?==)/,variable:{pattern:/\$[^\W\d]\w*(?:\??(?:\.\w+|\[[^\]]+\]))*/,inside:{string:{pattern:a,greedy:!0},number:i,punctuation:/[\[\].?]/}},string:{pattern:a,greedy:!0},function:[/\w+(?=\()/,{pattern:/(\|[^\S\r\n]*)\w+/,lookbehind:!0}],boolean:/\b(?:false|true)\b/,number:i,operator:/\?:?|<=?|>=?|==?|!=|[+*/%-]|\b(?:and|not|or)\b/,punctuation:/[{}()\[\]|.,:]/},n.hooks.add("before-tokenize",function(o){var s=/\{\{.+?\}\}|\{.+?\}|\s\/\/.*|\/\*[\s\S]*?\*\//g,l="{literal}",u="{/literal}",c=!1;n.languages["markup-templating"].buildPlaceholders(o,"soy",s,function(d){return d===u&&(c=!1),c?!1:(d===l&&(c=!0),!0)})}),n.hooks.add("after-tokenize",function(o){n.languages["markup-templating"].tokenizePlaceholders(o,"soy")})}(r)}return lu}var uu,qg;function tv(){if(qg)return uu;qg=1,uu=e,e.displayName="turtle",e.aliases=[];function e(t){t.languages.turtle={comment:{pattern:/#.*/,greedy:!0},"multiline-string":{pattern:/"""(?:(?:""?)?(?:[^"\\]|\\.))*"""|'''(?:(?:''?)?(?:[^'\\]|\\.))*'''/,greedy:!0,alias:"string",inside:{comment:/#.*/}},string:{pattern:/"(?:[^\\"\r\n]|\\.)*"|'(?:[^\\'\r\n]|\\.)*'/,greedy:!0},url:{pattern:/<(?:[^\x00-\x20<>"{}|^`\\]|\\(?:u[\da-fA-F]{4}|U[\da-fA-F]{8}))*>/,greedy:!0,inside:{punctuation:/[<>]/}},function:{pattern:/(?:(?![-.\d\xB7])[-.\w\xB7\xC0-\uFFFD]+)?:(?:(?![-.])(?:[-.:\w\xC0-\uFFFD]|%[\da-f]{2}|\\.)+)?/i,inside:{"local-name":{pattern:/([^:]*:)[\s\S]+/,lookbehind:!0},prefix:{pattern:/[\s\S]+/,inside:{punctuation:/:/}}}},number:/[+-]?\b\d+(?:\.\d*)?(?:e[+-]?\d+)?/i,punctuation:/[{}.,;()[\]]|\^\^/,boolean:/\b(?:false|true)\b/,keyword:[/(?:\ba|@prefix|@base)\b|=/,/\b(?:base|graph|prefix)\b/i],tag:{pattern:/@[a-z]+(?:-[a-z\d]+)*/i,inside:{punctuation:/@/}}},t.languages.trig=t.languages.turtle}return uu}var cu,Gg;function TI(){if(Gg)return cu;Gg=1;var e=tv();cu=t,t.displayName="sparql",t.aliases=["rq"];function t(r){r.register(e),r.languages.sparql=r.languages.extend("turtle",{boolean:/\b(?:false|true)\b/i,variable:{pattern:/[?$]\w+/,greedy:!0}}),r.languages.insertBefore("sparql","punctuation",{keyword:[/\b(?:A|ADD|ALL|AS|ASC|ASK|BNODE|BY|CLEAR|CONSTRUCT|COPY|CREATE|DATA|DEFAULT|DELETE|DESC|DESCRIBE|DISTINCT|DROP|EXISTS|FILTER|FROM|GROUP|HAVING|INSERT|INTO|LIMIT|LOAD|MINUS|MOVE|NAMED|NOT|NOW|OFFSET|OPTIONAL|ORDER|RAND|REDUCED|SELECT|SEPARATOR|SERVICE|SILENT|STRUUID|UNION|USING|UUID|VALUES|WHERE)\b/i,/\b(?:ABS|AVG|BIND|BOUND|CEIL|COALESCE|CONCAT|CONTAINS|COUNT|DATATYPE|DAY|ENCODE_FOR_URI|FLOOR|GROUP_CONCAT|HOURS|IF|IRI|isBLANK|isIRI|isLITERAL|isNUMERIC|isURI|LANG|LANGMATCHES|LCASE|MAX|MD5|MIN|MINUTES|MONTH|REGEX|REPLACE|ROUND|sameTerm|SAMPLE|SECONDS|SHA1|SHA256|SHA384|SHA512|STR|STRAFTER|STRBEFORE|STRDT|STRENDS|STRLANG|STRLEN|STRSTARTS|SUBSTR|SUM|TIMEZONE|TZ|UCASE|URI|YEAR)\b(?=\s*\()/i,/\b(?:BASE|GRAPH|PREFIX)\b/i]}),r.languages.rq=r.languages.sparql}return cu}var du,$g;function AI(){if($g)return du;$g=1,du=e,e.displayName="splunkSpl",e.aliases=[];function 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Or}var Lr,nd;function n_(){if(nd)return Lr;nd=1,Lr=e,e.displayName="coq",e.aliases=[];function e(t){(function(n){for(var r=/\(\*(?:[^(*]|\((?!\*)|\*(?!\))|)*\*\)/.source,a=0;a<2;a++)r=r.replace(//g,function(){return r});r=r.replace(//g,"[]"),n.languages.coq={comment:RegExp(r),string:{pattern:/"(?:[^"]|"")*"(?!")/,greedy:!0},attribute:[{pattern:RegExp(/#\[(?:[^\[\]("]|"(?:[^"]|"")*"(?!")|\((?!\*)|)*\]/.source.replace(//g,function(){return 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0}),n.languages.insertBefore("dart","class-name",{metadata:{pattern:/@\w+/,alias:"function"}}),n.languages.insertBefore("dart","class-name",{generics:{pattern:/<(?:[\w\s,.&?]|<(?:[\w\s,.&?]|<(?:[\w\s,.&?]|<[\w\s,.&?]*>)*>)*>)*>/,inside:{"class-name":i,keyword:r,punctuation:/[<>(),.:]/,operator:/[?&|]/}}})})(t)}return Gr}var zr,pd;function d_(){if(pd)return zr;pd=1,zr=e,e.displayName="dataweave",e.aliases=[];function e(t){(function(n){n.languages.dataweave={url:/\b[A-Za-z]+:\/\/[\w/:.?=&-]+|\burn:[\w:.?=&-]+/,property:{pattern:/(?:\b\w+#)?(?:"(?:\\.|[^\\"\r\n])*"|\b\w+)(?=\s*[:@])/,greedy:!0},string:{pattern:/(["'`])(?:\\[\s\S]|(?!\1)[^\\])*\1/,greedy:!0},"mime-type":/\b(?:application|audio|image|multipart|text|video)\/[\w+-]+/,date:{pattern:/\|[\w:+-]+\|/,greedy:!0},comment:[{pattern:/(^|[^\\])\/\*[\s\S]*?(?:\*\/|$)/,lookbehind:!0,greedy:!0},{pattern:/(^|[^\\:])\/\/.*/,lookbehind:!0,greedy:!0}],regex:{pattern:/\/(?:[^\\\/\r\n]|\\[^\r\n])+\//,greedy:!0},keyword:/\b(?:and|as|at|case|do|else|fun|if|input|is|match|not|ns|null|or|output|type|unless|update|using|var)\b/,function:/\b[A-Z_]\w*(?=\s*\()/i,number:/-?\b\d+(?:\.\d+)?(?:e[+-]?\d+)?\b/i,punctuation:/[{}[\];(),.:@]/,operator:/<<|>>|->|[<>~=]=?|!=|--?-?|\+\+?|!|\?/,boolean:/\b(?:false|true)\b/}})(t)}return zr}var Hr,gd;function p_(){if(gd)return Hr;gd=1,Hr=e,e.displayName="dax",e.aliases=[];function 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Hr}var jr,fd;function g_(){if(fd)return jr;fd=1,jr=e,e.displayName="dhall",e.aliases=[];function e(t){t.languages.dhall={comment:/--.*|\{-(?:[^-{]|-(?!\})|\{(?!-)|\{-(?:[^-{]|-(?!\})|\{(?!-))*-\})*-\}/,string:{pattern:/"(?:[^"\\]|\\.)*"|''(?:[^']|'(?!')|'''|''\$\{)*''(?!'|\$)/,greedy:!0,inside:{interpolation:{pattern:/\$\{[^{}]*\}/,inside:{expression:{pattern:/(^\$\{)[\s\S]+(?=\}$)/,lookbehind:!0,alias:"language-dhall",inside:null},punctuation:/\$\{|\}/}}}},label:{pattern:/`[^`]*`/,greedy:!0},url:{pattern:/\bhttps?:\/\/[\w.:%!$&'*+;=@~-]+(?:\/[\w.:%!$&'*+;=@~-]*)*(?:\?[/?\w.:%!$&'*+;=@~-]*)?/,greedy:!0},env:{pattern:/\benv:(?:(?!\d)\w+|"(?:[^"\\=]|\\.)*")/,greedy:!0,inside:{function:/^env/,operator:/^:/,variable:/[\s\S]+/}},hash:{pattern:/\bsha256:[\da-fA-F]{64}\b/,inside:{function:/sha256/,operator:/:/,number:/[\da-fA-F]{64}/}},keyword:/\b(?:as|assert|else|forall|if|in|let|merge|missing|then|toMap|using|with)\b|\u2200/,builtin:/\b(?:None|Some)\b/,boolean:/\b(?:False|True)\b/,number:/\bNaN\b|-?\bInfinity\b|[+-]?\b(?:0x[\da-fA-F]+|\d+(?:\.\d+)?(?:e[+-]?\d+)?)\b/,operator:/\/\\|\/\/\\\\|&&|\|\||===|[!=]=|\/\/|->|\+\+|::|[+*#@=:?<>|\\\u2227\u2a53\u2261\u2afd\u03bb\u2192]/,punctuation:/\.\.|[{}\[\](),./]/,"class-name":/\b[A-Z]\w*\b/},t.languages.dhall.string.inside.interpolation.inside.expression.inside=t.languages.dhall}return jr}var Vr,md;function f_(){if(md)return Vr;md=1,Vr=e,e.displayName="diff",e.aliases=[];function e(t){(function(n){n.languages.diff={coord:[/^(?:\*{3}|-{3}|\+{3}).*$/m,/^@@.*@@$/m,/^\d.*$/m]};var r={"deleted-sign":"-","deleted-arrow":"<","inserted-sign":"+","inserted-arrow":">",unchanged:" ",diff:"!"};Object.keys(r).forEach(function(a){var i=r[a],o=[];/^\w+$/.test(a)||o.push(/\w+/.exec(a)[0]),a==="diff"&&o.push("bold"),n.languages.diff[a]={pattern:RegExp("^(?:["+i+`].*(?:\r +?| +|(?![\\s\\S])))+`,"m"),alias:o,inside:{line:{pattern:/(.)(?=[\s\S]).*(?:\r\n?|\n)?/,lookbehind:!0},prefix:{pattern:/[\s\S]/,alias:/\w+/.exec(a)[0]}}}}),Object.defineProperty(n.languages.diff,"PREFIXES",{value:r})})(t)}return Vr}var Wr,bd;function he(){if(bd)return Wr;bd=1,Wr=e,e.displayName="markupTemplating",e.aliases=[];function e(t){(function(n){function r(a,i){return"___"+a.toUpperCase()+i+"___"}Object.defineProperties(n.languages["markup-templating"]={},{buildPlaceholders:{value:function(a,i,o,s){if(a.language===i){var l=a.tokenStack=[];a.code=a.code.replace(o,function(u){if(typeof s=="function"&&!s(u))return u;for(var d=l.length,c;a.code.indexOf(c=r(i,d))!==-1;)++d;return l[d]=u,c}),a.grammar=n.languages.markup}}},tokenizePlaceholders:{value:function(a,i){if(a.language!==i||!a.tokenStack)return;a.grammar=n.languages[i];var o=0,s=Object.keys(a.tokenStack);function l(u){for(var d=0;d=s.length);d++){var c=u[d];if(typeof c=="string"||c.content&&typeof c.content=="string"){var g=s[o],p=a.tokenStack[g],m=typeof c=="string"?c:c.content,b=r(i,g),T=m.indexOf(b);if(T>-1){++o;var S=m.substring(0,T),h=new n.Token(i,n.tokenize(p,a.grammar),"language-"+i,p),E=m.substring(T+b.length),k=[];S&&k.push.apply(k,l([S])),k.push(h),E&&k.push.apply(k,l([E])),typeof c=="string"?u.splice.apply(u,[d,1].concat(k)):c.content=k}}else 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a=/\{\{[\s\S]*?\}\}|\{%[\s\S]*?%\}|\{#[\s\S]*?#\}/g,i=r.languages["markup-templating"];r.hooks.add("before-tokenize",function(o){i.buildPlaceholders(o,"django",a)}),r.hooks.add("after-tokenize",function(o){i.tokenizePlaceholders(o,"django")}),r.languages.jinja2=r.languages.django,r.hooks.add("before-tokenize",function(o){i.buildPlaceholders(o,"jinja2",a)}),r.hooks.add("after-tokenize",function(o){i.tokenizePlaceholders(o,"jinja2")})}(n)}return Yr}var Kr,Ed;function b_(){if(Ed)return Kr;Ed=1,Kr=e,e.displayName="dnsZoneFile",e.aliases=[];function e(t){t.languages["dns-zone-file"]={comment:/;.*/,string:{pattern:/"(?:\\.|[^"\\\r\n])*"/,greedy:!0},variable:[{pattern:/(^\$ORIGIN[ 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off|Fono|Forgatókönyv|Forgatókönyv vázlat|Fundo|Geçmiş|Grundlage|Hannergrond|ghantoH|Háttér|Heave to|Istorik|Juhtumid|Keadaan|Khung kịch bản|Khung tình huống|Kịch bản|Koncept|Konsep skenario|Kontèks|Kontekst|Kontekstas|Konteksts|Kontext|Konturo de la scenaro|Latar Belakang|lut chovnatlh|lut|lutmey|Lýsing Atburðarásar|Lýsing Dæma|MISHUN SRSLY|MISHUN|Menggariskan Senario|mo'|Náčrt Scenára|Náčrt Scénáře|Náčrt Scenáru|Oris scenarija|Örnekler|Osnova|Osnova Scenára|Osnova scénáře|Osnutek|Ozadje|Paraugs|Pavyzdžiai|Példák|Piemēri|Plan du scénario|Plan du Scénario|Plan Senaryo|Plan senaryo|Plang vum Szenario|Pozadí|Pozadie|Pozadina|Príklady|Příklady|Primer|Primeri|Primjeri|Przykłady|Raamstsenaarium|Reckon it's like|Rerefons|Scenár|Scénář|Scenarie|Scenarij|Scenarijai|Scenarijaus šablonas|Scenariji|Scenārijs|Scenārijs pēc parauga|Scenarijus|Scenario|Scénario|Scenario Amlinellol|Scenario Outline|Scenario Template|Scenariomal|Scenariomall|Scenarios|Scenariu|Scenariusz|Scenaro|Schema dello scenario|Se ðe|Se the|Se þe|Senario|Senaryo Deskripsyon|Senaryo deskripsyon|Senaryo|Senaryo taslağı|Shiver me timbers|Situācija|Situai|Situasie Uiteensetting|Situasie|Skenario konsep|Skenario|Skica|Structura scenariu|Structură scenariu|Struktura scenarija|Stsenaarium|Swa hwaer swa|Swa|Swa hwær swa|Szablon scenariusza|Szenario|Szenariogrundriss|Tapaukset|Tapaus|Tapausaihio|Taust|Tausta|Template Keadaan|Template Senario|Template Situai|The thing of it is|Tình huống|Variantai|Voorbeelde|Voorbeelden|Wharrimean is|Yo-ho-ho|You'll wanna|Założenia|Παραδείγματα|Περιγραφή Σεναρίου|Σενάρια|Σενάριο|Υπόβαθρο|Кереш|Контекст|Концепт|Мисаллар|Мисоллар|Основа|Передумова|Позадина|Предистория|Предыстория|Приклади|Пример|Примери|Примеры|Рамка на сценарий|Скица|Структура сценарија|Структура сценария|Структура сценарію|Сценарий|Сценарий структураси|Сценарийның төзелеше|Сценарији|Сценарио|Сценарій|Тарих|Үрнәкләр|דוגמאות|רקע|תבנית תרחיש|תרחיש|الخلفية|الگوی سناریو|امثلة|پس منظر|زمینه|سناریو|سيناريو|سيناريو 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a|Lè|Logo|Lorsqu'<|Lorsque|mä|Maar|Mais|Mając|Ma|Majd|Maka|Manawa|Mas|Men|Menawa|Mutta|Nalika|Nalikaning|Nanging|Når|När|Nato|Nhưng|Niin|Njuk|O zaman|Och|Og|Oletetaan|Ond|Onda|Oraz|Pak|Pero|Però|Podano|Pokiaľ|Pokud|Potem|Potom|Privzeto|Pryd|Quan|Quand|Quando|qaSDI'|Så|Sed|Se|Siis|Sipoze ke|Sipoze Ke|Sipoze|Si|Şi|Și|Soit|Stel|Tada|Tad|Takrat|Tak|Tapi|Ter|Tetapi|Tha the|Tha|Then y'all|Then|Thì|Thurh|Toda|Too right|Un|Und|ugeholl|Và|vaj|Vendar|Ve|wann|Wanneer|WEN|Wenn|When y'all|When|Wtedy|Wun|Y'know|Yeah nah|Yna|Youse know like when|Youse know when youse got|Y|Za predpokladu|Za předpokladu|Zadan|Zadani|Zadano|Zadate|Zadato|Zakładając|Zaradi|Zatati|Þa þe|Þa|Þá|Þegar|Þurh|Αλλά|Δεδομένου|Και|Όταν|Τότε|А також|Агар|Але|Али|Аммо|А|Әгәр|Әйтик|Әмма|Бирок|Ва|Вә|Дадено|Дано|Допустим|Если|Задате|Задати|Задато|И|І|К тому же|Када|Кад|Когато|Когда|Коли|Ләкин|Лекин|Нәтиҗәдә|Нехай|Но|Онда|Припустимо, 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e(t){(function(n){n.languages.kotlin=n.languages.extend("clike",{keyword:{pattern:/(^|[^.])\b(?:abstract|actual|annotation|as|break|by|catch|class|companion|const|constructor|continue|crossinline|data|do|dynamic|else|enum|expect|external|final|finally|for|fun|get|if|import|in|infix|init|inline|inner|interface|internal|is|lateinit|noinline|null|object|open|operator|out|override|package|private|protected|public|reified|return|sealed|set|super|suspend|tailrec|this|throw|to|try|typealias|val|var|vararg|when|where|while)\b/,lookbehind:!0},function:[{pattern:/(?:`[^\r\n`]+`|\b\w+)(?=\s*\()/,greedy:!0},{pattern:/(\.)(?:`[^\r\n`]+`|\w+)(?=\s*\{)/,lookbehind:!0,greedy:!0}],number:/\b(?:0[xX][\da-fA-F]+(?:_[\da-fA-F]+)*|0[bB][01]+(?:_[01]+)*|\d+(?:_\d+)*(?:\.\d+(?:_\d+)*)?(?:[eE][+-]?\d+(?:_\d+)*)?[fFL]?)\b/,operator:/\+[+=]?|-[-=>]?|==?=?|!(?:!|==?)?|[\/*%<>]=?|[?:]:?|\.\.|&&|\|\||\b(?:and|inv|or|shl|shr|ushr|xor)\b/}),delete n.languages.kotlin["class-name"];var r={"interpolation-punctuation":{pattern:/^\$\{?|\}$/,alias:"punctuation"},expression:{pattern:/[\s\S]+/,inside:n.languages.kotlin}};n.languages.insertBefore("kotlin","string",{"string-literal":[{pattern:/"""(?:[^$]|\$(?:(?!\{)|\{[^{}]*\}))*?"""/,alias:"multiline",inside:{interpolation:{pattern:/\$(?:[a-z_]\w*|\{[^{}]*\})/i,inside:r},string:/[\s\S]+/}},{pattern:/"(?:[^"\\\r\n$]|\\.|\$(?:(?!\{)|\{[^{}]*\}))*"/,alias:"singleline",inside:{interpolation:{pattern:/((?:^|[^\\])(?:\\{2})*)\$(?:[a-z_]\w*|\{[^{}]*\})/i,lookbehind:!0,inside:r},string:/[\s\S]+/}}],char:{pattern:/'(?:[^'\\\r\n]|\\(?:.|u[a-fA-F0-9]{0,4}))'/,greedy:!0}}),delete n.languages.kotlin.string,n.languages.insertBefore("kotlin","keyword",{annotation:{pattern:/\B@(?:\w+:)?(?:[A-Z]\w*|\[[^\]]+\])/,alias:"builtin"}}),n.languages.insertBefore("kotlin","function",{label:{pattern:/\b\w+@|@\w+\b/,alias:"symbol"}}),n.languages.kt=n.languages.kotlin,n.languages.kts=n.languages.kotlin})(t)}return fi}var mi,Up;function NI(){if(Up)return mi;Up=1,mi=e,e.displayName="kumir",e.aliases=["kum"];function e(t){(function(n){var r=/\s\x00-\x1f\x22-\x2f\x3a-\x3f\x5b-\x5e\x60\x7b-\x7e/.source;function a(i,o){return 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mi}var bi,Bp;function CI(){if(Bp)return bi;Bp=1,bi=e,e.displayName="kusto",e.aliases=[];function 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r=/\\(?:[^a-z()[\]]|[a-z*]+)/i,a={"equation-command":{pattern:r,alias:"regex"}};n.languages.latex={comment:/%.*/,cdata:{pattern:/(\\begin\{((?:lstlisting|verbatim)\*?)\})[\s\S]*?(?=\\end\{\2\})/,lookbehind:!0},equation:[{pattern:/\$\$(?:\\[\s\S]|[^\\$])+\$\$|\$(?:\\[\s\S]|[^\\$])+\$|\\\([\s\S]*?\\\)|\\\[[\s\S]*?\\\]/,inside:a,alias:"string"},{pattern:/(\\begin\{((?:align|eqnarray|equation|gather|math|multline)\*?)\})[\s\S]*?(?=\\end\{\2\})/,lookbehind:!0,inside:a,alias:"string"}],keyword:{pattern:/(\\(?:begin|cite|documentclass|end|label|ref|usepackage)(?:\[[^\]]+\])?\{)[^}]+(?=\})/,lookbehind:!0},url:{pattern:/(\\url\{)[^}]+(?=\})/,lookbehind:!0},headline:{pattern:/(\\(?:chapter|frametitle|paragraph|part|section|subparagraph|subsection|subsubparagraph|subsubsection|subsubsubparagraph)\*?(?:\[[^\]]+\])?\{)[^}]+(?=\})/,lookbehind:!0,alias:"class-name"},function:{pattern:r,alias:"selector"},punctuation:/[[\]{}&]/},n.languages.tex=n.languages.latex,n.languages.context=n.languages.latex})(t)}return hi}var Ei,$p;function qt(){if($p)return Ei;$p=1;var e=he();Ei=t,t.displayName="php",t.aliases=[];function t(n){n.register(e),function(r){var a=/\/\*[\s\S]*?\*\/|\/\/.*|#(?!\[).*/,i=[{pattern:/\b(?:false|true)\b/i,alias:"boolean"},{pattern:/(::\s*)\b[a-z_]\w*\b(?!\s*\()/i,greedy:!0,lookbehind:!0},{pattern:/(\b(?:case|const)\s+)\b[a-z_]\w*(?=\s*[;=])/i,greedy:!0,lookbehind:!0},/\b(?:null)\b/i,/\b[A-Z_][A-Z0-9_]*\b(?!\s*\()/],o=/\b0b[01]+(?:_[01]+)*\b|\b0o[0-7]+(?:_[0-7]+)*\b|\b0x[\da-f]+(?:_[\da-f]+)*\b|(?:\b\d+(?:_\d+)*\.?(?:\d+(?:_\d+)*)?|\B\.\d+)(?:e[+-]?\d+)?/i,s=/|\?\?=?|\.{3}|\??->|[!=]=?=?|::|\*\*=?|--|\+\+|&&|\|\||<<|>>|[?~]|[/^|%*&<>.+-]=?/,l=/[{}\[\](),:;]/;r.languages.php={delimiter:{pattern:/\?>$|^<\?(?:php(?=\s)|=)?/i,alias:"important"},comment:a,variable:/\$+(?:\w+\b|(?=\{))/,package:{pattern:/(namespace\s+|use\s+(?:function\s+)?)(?:\\?\b[a-z_]\w*)+\b(?!\\)/i,lookbehind:!0,inside:{punctuation:/\\/}},"class-name-definition":{pattern:/(\b(?:class|enum|interface|trait)\s+)\b[a-z_]\w*(?!\\)\b/i,lookbehind:!0,alias:"class-name"},"function-definition":{pattern:/(\bfunction\s+)[a-z_]\w*(?=\s*\()/i,lookbehind:!0,alias:"function"},keyword:[{pattern:/(\(\s*)\b(?:array|bool|boolean|float|int|integer|object|string)\b(?=\s*\))/i,alias:"type-casting",greedy:!0,lookbehind:!0},{pattern:/([(,?]\s*)\b(?:array(?!\s*\()|bool|callable|(?:false|null)(?=\s*\|)|float|int|iterable|mixed|object|self|static|string)\b(?=\s*\$)/i,alias:"type-hint",greedy:!0,lookbehind:!0},{pattern:/(\)\s*:\s*(?:\?\s*)?)\b(?:array(?!\s*\()|bool|callable|(?:false|null)(?=\s*\|)|float|int|iterable|mixed|object|self|static|string|void)\b/i,alias:"return-type",greedy:!0,lookbehind:!0},{pattern:/\b(?:array(?!\s*\()|bool|float|int|iterable|mixed|object|string|void)\b/i,alias:"type-declaration",greedy:!0},{pattern:/(\|\s*)(?:false|null)\b|\b(?:false|null)(?=\s*\|)/i,alias:"type-declaration",greedy:!0,lookbehind:!0},{pattern:/\b(?:parent|self|static)(?=\s*::)/i,alias:"static-context",greedy:!0},{pattern:/(\byield\s+)from\b/i,lookbehind:!0},/\bclass\b/i,{pattern:/((?:^|[^\s>:]|(?:^|[^-])>|(?:^|[^:]):)\s*)\b(?:abstract|and|array|as|break|callable|case|catch|clone|const|continue|declare|default|die|do|echo|else|elseif|empty|enddeclare|endfor|endforeach|endif|endswitch|endwhile|enum|eval|exit|extends|final|finally|fn|for|foreach|function|global|goto|if|implements|include|include_once|instanceof|insteadof|interface|isset|list|match|namespace|new|or|parent|print|private|protected|public|require|require_once|return|self|static|switch|throw|trait|try|unset|use|var|while|xor|yield|__halt_compiler)\b/i,lookbehind:!0}],"argument-name":{pattern:/([(,]\s+)\b[a-z_]\w*(?=\s*:(?!:))/i,lookbehind:!0},"class-name":[{pattern:/(\b(?:extends|implements|instanceof|new(?!\s+self|\s+static))\s+|\bcatch\s*\()\b[a-z_]\w*(?!\\)\b/i,greedy:!0,lookbehind:!0},{pattern:/(\|\s*)\b[a-z_]\w*(?!\\)\b/i,greedy:!0,lookbehind:!0},{pattern:/\b[a-z_]\w*(?!\\)\b(?=\s*\|)/i,greedy:!0},{pattern:/(\|\s*)(?:\\?\b[a-z_]\w*)+\b/i,alias:"class-name-fully-qualified",greedy:!0,lookbehind:!0,inside:{punctuation:/\\/}},{pattern:/(?:\\?\b[a-z_]\w*)+\b(?=\s*\|)/i,alias:"class-name-fully-qualified",greedy:!0,inside:{punctuation:/\\/}},{pattern:/(\b(?:extends|implements|instanceof|new(?!\s+self\b|\s+static\b))\s+|\bcatch\s*\()(?:\\?\b[a-z_]\w*)+\b(?!\\)/i,alias:"class-name-fully-qualified",greedy:!0,lookbehind:!0,inside:{punctuation:/\\/}},{pattern:/\b[a-z_]\w*(?=\s*\$)/i,alias:"type-declaration",greedy:!0},{pattern:/(?:\\?\b[a-z_]\w*)+(?=\s*\$)/i,alias:["class-name-fully-qualified","type-declaration"],greedy:!0,inside:{punctuation:/\\/}},{pattern:/\b[a-z_]\w*(?=\s*::)/i,alias:"static-context",greedy:!0},{pattern:/(?:\\?\b[a-z_]\w*)+(?=\s*::)/i,alias:["class-name-fully-qualified","static-context"],greedy:!0,inside:{punctuation:/\\/}},{pattern:/([(,?]\s*)[a-z_]\w*(?=\s*\$)/i,alias:"type-hint",greedy:!0,lookbehind:!0},{pattern:/([(,?]\s*)(?:\\?\b[a-z_]\w*)+(?=\s*\$)/i,alias:["class-name-fully-qualified","type-hint"],greedy:!0,lookbehind:!0,inside:{punctuation:/\\/}},{pattern:/(\)\s*:\s*(?:\?\s*)?)\b[a-z_]\w*(?!\\)\b/i,alias:"return-type",greedy:!0,lookbehind:!0},{pattern:/(\)\s*:\s*(?:\?\s*)?)(?:\\?\b[a-z_]\w*)+\b(?!\\)/i,alias:["class-name-fully-qualified","return-type"],greedy:!0,lookbehind:!0,inside:{punctuation:/\\/}}],constant:i,function:{pattern:/(^|[^\\\w])\\?[a-z_](?:[\w\\]*\w)?(?=\s*\()/i,lookbehind:!0,inside:{punctuation:/\\/}},property:{pattern:/(->\s*)\w+/,lookbehind:!0},number:o,operator:s,punctuation:l};var u={pattern:/\{\$(?:\{(?:\{[^{}]+\}|[^{}]+)\}|[^{}])+\}|(^|[^\\{])\$+(?:\w+(?:\[[^\r\n\[\]]+\]|->\w+)?)/,lookbehind:!0,inside:r.languages.php},d=[{pattern:/<<<'([^']+)'[\r\n](?:.*[\r\n])*?\1;/,alias:"nowdoc-string",greedy:!0,inside:{delimiter:{pattern:/^<<<'[^']+'|[a-z_]\w*;$/i,alias:"symbol",inside:{punctuation:/^<<<'?|[';]$/}}}},{pattern:/<<<(?:"([^"]+)"[\r\n](?:.*[\r\n])*?\1;|([a-z_]\w*)[\r\n](?:.*[\r\n])*?\2;)/i,alias:"heredoc-string",greedy:!0,inside:{delimiter:{pattern:/^<<<(?:"[^"]+"|[a-z_]\w*)|[a-z_]\w*;$/i,alias:"symbol",inside:{punctuation:/^<<<"?|[";]$/}},interpolation:u}},{pattern:/`(?:\\[\s\S]|[^\\`])*`/,alias:"backtick-quoted-string",greedy:!0},{pattern:/'(?:\\[\s\S]|[^\\'])*'/,alias:"single-quoted-string",greedy:!0},{pattern:/"(?:\\[\s\S]|[^\\"])*"/,alias:"double-quoted-string",greedy:!0,inside:{interpolation:u}}];r.languages.insertBefore("php","variable",{string:d,attribute:{pattern:/#\[(?:[^"'\/#]|\/(?![*/])|\/\/.*$|#(?!\[).*$|\/\*(?:[^*]|\*(?!\/))*\*\/|"(?:\\[\s\S]|[^\\"])*"|'(?:\\[\s\S]|[^\\'])*')+\](?=\s*[a-z$#])/im,greedy:!0,inside:{"attribute-content":{pattern:/^(#\[)[\s\S]+(?=\]$)/,lookbehind:!0,inside:{comment:a,string:d,"attribute-class-name":[{pattern:/([^:]|^)\b[a-z_]\w*(?!\\)\b/i,alias:"class-name",greedy:!0,lookbehind:!0},{pattern:/([^:]|^)(?:\\?\b[a-z_]\w*)+/i,alias:["class-name","class-name-fully-qualified"],greedy:!0,lookbehind:!0,inside:{punctuation:/\\/}}],constant:i,number:o,operator:s,punctuation:l}},delimiter:{pattern:/^#\[|\]$/,alias:"punctuation"}}}}),r.hooks.add("before-tokenize",function(c){if(/<\?/.test(c.code)){var g=/<\?(?:[^"'/#]|\/(?![*/])|("|')(?:\\[\s\S]|(?!\1)[^\\])*\1|(?:\/\/|#(?!\[))(?:[^?\n\r]|\?(?!>))*(?=$|\?>|[\r\n])|#\[|\/\*(?:[^*]|\*(?!\/))*(?:\*\/|$))*?(?:\?>|$)/g;r.languages["markup-templating"].buildPlaceholders(c,"php",g)}}),r.hooks.add("after-tokenize",function(c){r.languages["markup-templating"].tokenizePlaceholders(c,"php")})}(n)}return Ei}var yi,Gp;function OI(){if(Gp)return yi;Gp=1;var e=he(),t=qt();yi=n,n.displayName="latte",n.aliases=[];function n(r){r.register(e),r.register(t),function(a){a.languages.latte={comment:/^\{\*[\s\S]*/,"latte-tag":{pattern:/(^\{(?:\/(?=[a-z]))?)(?:[=_]|[a-z]\w*\b(?!\())/i,lookbehind:!0,alias:"important"},delimiter:{pattern:/^\{\/?|\}$/,alias:"punctuation"},php:{pattern:/\S(?:[\s\S]*\S)?/,alias:"language-php",inside:a.languages.php}};var 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a=[/"(?:\\[\s\S]|\$\([^)]+\)|\$(?!\()|`[^`]+`|[^"\\`$])*"/.source,/'[^']*'/.source,/\$'(?:[^'\\]|\\[\s\S])*'/.source,/<<-?\s*(["']?)(\w+)\1\s[\s\S]*?[\r\n]\2/.source].join("|");r.languages["shell-session"]={command:{pattern:RegExp(/^/.source+"(?:"+(/[^\s@:$#%*!/\\]+@[^\r\n@:$#%*!/\\]+(?::[^\0-\x1F$#%*?"<>:;|]+)?/.source+"|"+/[/~.][^\0-\x1F$#%*?"<>@:;|]*/.source)+")?"+/[$#%](?=\s)/.source+/(?:[^\\\r\n \t'"<$]|[ \t](?:(?!#)|#.*$)|\\(?:[^\r]|\r\n?)|\$(?!')|<(?!<)|<>)+/.source.replace(/<>/g,function(){return a}),"m"),greedy:!0,inside:{info:{pattern:/^[^#$%]+/,alias:"punctuation",inside:{user:/^[^\s@:$#%*!/\\]+@[^\r\n@:$#%*!/\\]+/,punctuation:/:/,path:/[\s\S]+/}},bash:{pattern:/(^[$#%]\s*)\S[\s\S]*/,lookbehind:!0,alias:"language-bash",inside:r.languages.bash},"shell-symbol":{pattern:/^[$#%]/,alias:"important"}}},output:/.(?:.*(?:[\r\n]|.$))*/},r.languages["sh-session"]=r.languages.shellsession=r.languages["shell-session"]}(n)}return Vo}var Wo,bf;function aC(){if(bf)return Wo;bf=1,Wo=e,e.displayName="smali",e.aliases=[];function e(t){t.languages.smali={comment:/#.*/,string:{pattern:/"(?:[^\r\n\\"]|\\.)*"|'(?:[^\r\n\\']|\\(?:.|u[\da-fA-F]{4}))'/,greedy:!0},"class-name":{pattern:/(^|[^L])L(?:(?:\w+|`[^`\r\n]*`)\/)*(?:[\w$]+|`[^`\r\n]*`)(?=\s*;)/,lookbehind:!0,inside:{"class-name":{pattern:/(^L|\/)(?:[\w$]+|`[^`\r\n]*`)$/,lookbehind:!0},namespace:{pattern:/^(L)(?:(?:\w+|`[^`\r\n]*`)\/)+/,lookbehind:!0,inside:{punctuation:/\//}},builtin:/^L/}},builtin:[{pattern:/([();\[])[BCDFIJSVZ]+/,lookbehind:!0},{pattern:/([\w$>]:)[BCDFIJSVZ]/,lookbehind:!0}],keyword:[{pattern:/(\.end\s+)[\w-]+/,lookbehind:!0},{pattern:/(^|[^\w.-])\.(?!\d)[\w-]+/,lookbehind:!0},{pattern:/(^|[^\w.-])(?:abstract|annotation|bridge|constructor|enum|final|interface|private|protected|public|runtime|static|synthetic|system|transient)(?![\w.-])/,lookbehind:!0}],function:{pattern:/(^|[^\w.-])(?:\w+|<[\w$-]+>)(?=\()/,lookbehind:!0},field:{pattern:/[\w$]+(?=:)/,alias:"variable"},register:{pattern:/(^|[^\w.-])[vp]\d(?![\w.-])/,lookbehind:!0,alias:"variable"},boolean:{pattern:/(^|[^\w.-])(?:false|true)(?![\w.-])/,lookbehind:!0},number:{pattern:/(^|[^/\w.-])-?(?:NAN|INFINITY|0x(?:[\dA-F]+(?:\.[\dA-F]*)?|\.[\dA-F]+)(?:p[+-]?[\dA-F]+)?|(?:\d+(?:\.\d*)?|\.\d+)(?:e[+-]?\d+)?)[dflst]?(?![\w.-])/i,lookbehind:!0},label:{pattern:/(:)\w+/,lookbehind:!0,alias:"property"},operator:/->|\.\.|[\[=]/,punctuation:/[{}(),;:]/}}return Wo}var Yo,hf;function iC(){if(hf)return Yo;hf=1,Yo=e,e.displayName="smalltalk",e.aliases=[];function e(t){t.languages.smalltalk={comment:{pattern:/"(?:""|[^"])*"/,greedy:!0},char:{pattern:/\$./,greedy:!0},string:{pattern:/'(?:''|[^'])*'/,greedy:!0},symbol:/#[\da-z]+|#(?:-|([+\/\\*~<>=@%|&?!])\1?)|#(?=\()/i,"block-arguments":{pattern:/(\[\s*):[^\[|]*\|/,lookbehind:!0,inside:{variable:/:[\da-z]+/i,punctuation:/\|/}},"temporary-variables":{pattern:/\|[^|]+\|/,inside:{variable:/[\da-z]+/i,punctuation:/\|/}},keyword:/\b(?:new|nil|self|super)\b/,boolean:/\b(?:false|true)\b/,number:[/\d+r-?[\dA-Z]+(?:\.[\dA-Z]+)?(?:e-?\d+)?/,/\b\d+(?:\.\d+)?(?:e-?\d+)?/],operator:/[<=]=?|:=|~[~=]|\/\/?|\\\\|>[>=]?|[!^+\-*&|,@]/,punctuation:/[.;:?\[\](){}]/}}return Yo}var Ko,Ef;function oC(){if(Ef)return Ko;Ef=1;var e=he();Ko=t,t.displayName="smarty",t.aliases=[];function t(n){n.register(e),function(r){r.languages.smarty={comment:{pattern:/^\{\*[\s\S]*?\*\}/,greedy:!0},"embedded-php":{pattern:/^\{php\}[\s\S]*?\{\/php\}/,greedy:!0,inside:{smarty:{pattern:/^\{php\}|\{\/php\}$/,inside:null},php:{pattern:/[\s\S]+/,alias:"language-php",inside:r.languages.php}}},string:[{pattern:/"(?:\\.|[^"\\\r\n])*"/,greedy:!0,inside:{interpolation:{pattern:/\{[^{}]*\}|`[^`]*`/,inside:{"interpolation-punctuation":{pattern:/^[{`]|[`}]$/,alias:"punctuation"},expression:{pattern:/[\s\S]+/,inside:null}}},variable:/\$\w+/}},{pattern:/'(?:\\.|[^'\\\r\n])*'/,greedy:!0}],keyword:{pattern:/(^\{\/?)[a-z_]\w*\b(?!\()/i,lookbehind:!0,greedy:!0},delimiter:{pattern:/^\{\/?|\}$/,greedy:!0,alias:"punctuation"},number:/\b0x[\dA-Fa-f]+|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:[Ee][-+]?\d+)?/,variable:[/\$(?!\d)\w+/,/#(?!\d)\w+#/,{pattern:/(\.|->|\w\s*=)(?!\d)\w+\b(?!\()/,lookbehind:!0},{pattern:/(\[)(?!\d)\w+(?=\])/,lookbehind:!0}],function:{pattern:/(\|\s*)@?[a-z_]\w*|\b[a-z_]\w*(?=\()/i,lookbehind:!0},"attr-name":/\b[a-z_]\w*(?=\s*=)/i,boolean:/\b(?:false|no|off|on|true|yes)\b/,punctuation:/[\[\](){}.,:`]|->/,operator:[/[+\-*\/%]|==?=?|[!<>]=?|&&|\|\|?/,/\bis\s+(?:not\s+)?(?:div|even|odd)(?:\s+by)?\b/,/\b(?:and|eq|gt?e|gt|lt?e|lt|mod|neq?|not|or)\b/]},r.languages.smarty["embedded-php"].inside.smarty.inside=r.languages.smarty,r.languages.smarty.string[0].inside.interpolation.inside.expression.inside=r.languages.smarty;var a=/"(?:\\.|[^"\\\r\n])*"|'(?:\\.|[^'\\\r\n])*'/,i=RegExp(/\{\*[\s\S]*?\*\}/.source+"|"+/\{php\}[\s\S]*?\{\/php\}/.source+"|"+/\{(?:[^{}"']||\{(?:[^{}"']||\{(?:[^{}"']|)*\})*\})*\}/.source.replace(//g,function(){return a.source}),"g");r.hooks.add("before-tokenize",function(o){var s="{literal}",l="{/literal}",u=!1;r.languages["markup-templating"].buildPlaceholders(o,"smarty",i,function(d){return d===l&&(u=!1),u?!1:(d===s&&(u=!0),!0)})}),r.hooks.add("after-tokenize",function(o){r.languages["markup-templating"].tokenizePlaceholders(o,"smarty")})}(n)}return Ko}var Xo,yf;function sC(){if(yf)return Xo;yf=1,Xo=e,e.displayName="sml",e.aliases=["smlnj"];function e(t){(function(n){var r=/\b(?:abstype|and|andalso|as|case|datatype|do|else|end|eqtype|exception|fn|fun|functor|handle|if|in|include|infix|infixr|let|local|nonfix|of|op|open|orelse|raise|rec|sharing|sig|signature|struct|structure|then|type|val|where|while|with|withtype)\b/i;n.languages.sml={comment:/\(\*(?:[^*(]|\*(?!\))|\((?!\*)|\(\*(?:[^*(]|\*(?!\))|\((?!\*))*\*\))*\*\)/,string:{pattern:/#?"(?:[^"\\]|\\.)*"/,greedy:!0},"class-name":[{pattern:RegExp(/((?:^|[^:]):\s*)(?:\s*(?:(?:\*|->)\s*|,\s*(?:(?=)|(?!)\s+)))*/.source.replace(//g,function(){return/\s*(?:[*,]|->)/.source}).replace(//g,function(){return/(?:'[\w']*||\((?:[^()]|\([^()]*\))*\)|\{(?:[^{}]|\{[^{}]*\})*\})(?:\s+)*/.source}).replace(//g,function(){return/(?!)[a-z\d_][\w'.]*/.source}).replace(//g,function(){return r.source}),"i"),lookbehind:!0,greedy:!0,inside:null},{pattern:/((?:^|[^\w'])(?:datatype|exception|functor|signature|structure|type)\s+)[a-z_][\w'.]*/i,lookbehind:!0}],function:{pattern:/((?:^|[^\w'])fun\s+)[a-z_][\w'.]*/i,lookbehind:!0},keyword:r,variable:{pattern:/(^|[^\w'])'[\w']*/,lookbehind:!0},number:/~?\b(?:\d+(?:\.\d+)?(?:e~?\d+)?|0x[\da-f]+)\b/i,word:{pattern:/\b0w(?:\d+|x[\da-f]+)\b/i,alias:"constant"},boolean:/\b(?:false|true)\b/i,operator:/\.\.\.|:[>=:]|=>?|->|[<>]=?|[!+\-*/^#|@~]/,punctuation:/[(){}\[\].:,;]/},n.languages.sml["class-name"][0].inside=n.languages.sml,n.languages.smlnj=n.languages.sml})(t)}return Xo}var Zo,Sf;function lC(){if(Sf)return Zo;Sf=1,Zo=e,e.displayName="solidity",e.aliases=["sol"];function e(t){t.languages.solidity=t.languages.extend("clike",{"class-name":{pattern:/(\b(?:contract|enum|interface|library|new|struct|using)\s+)(?!\d)[\w$]+/,lookbehind:!0},keyword:/\b(?:_|anonymous|as|assembly|assert|break|calldata|case|constant|constructor|continue|contract|default|delete|do|else|emit|enum|event|external|for|from|function|if|import|indexed|inherited|interface|internal|is|let|library|mapping|memory|modifier|new|payable|pragma|private|public|pure|require|returns?|revert|selfdestruct|solidity|storage|struct|suicide|switch|this|throw|using|var|view|while)\b/,operator:/=>|->|:=|=:|\*\*|\+\+|--|\|\||&&|<<=?|>>=?|[-+*/%^&|<>!=]=?|[~?]/}),t.languages.insertBefore("solidity","keyword",{builtin:/\b(?:address|bool|byte|u?int(?:8|16|24|32|40|48|56|64|72|80|88|96|104|112|120|128|136|144|152|160|168|176|184|192|200|208|216|224|232|240|248|256)?|string|bytes(?:[1-9]|[12]\d|3[0-2])?)\b/}),t.languages.insertBefore("solidity","number",{version:{pattern:/([<>]=?|\^)\d+\.\d+\.\d+\b/,lookbehind:!0,alias:"number"}}),t.languages.sol=t.languages.solidity}return Zo}var Qo,vf;function uC(){if(vf)return Qo;vf=1,Qo=e,e.displayName="solutionFile",e.aliases=[];function e(t){(function(n){var r={pattern:/\{[\da-f]{8}-[\da-f]{4}-[\da-f]{4}-[\da-f]{4}-[\da-f]{12}\}/i,alias:"constant",inside:{punctuation:/[{}]/}};n.languages["solution-file"]={comment:{pattern:/#.*/,greedy:!0},string:{pattern:/"[^"\r\n]*"|'[^'\r\n]*'/,greedy:!0,inside:{guid:r}},object:{pattern:/^([ \t]*)(?:([A-Z]\w*)\b(?=.*(?:\r\n?|\n)(?:\1[ \t].*(?:\r\n?|\n))*\1End\2(?=[ \t]*$))|End[A-Z]\w*(?=[ \t]*$))/m,lookbehind:!0,greedy:!0,alias:"keyword"},property:{pattern:/^([ \t]*)(?!\s)[^\r\n"#=()]*[^\s"#=()](?=\s*=)/m,lookbehind:!0,inside:{guid:r}},guid:r,number:/\b\d+(?:\.\d+)*\b/,boolean:/\b(?:FALSE|TRUE)\b/,operator:/=/,punctuation:/[(),]/},n.languages.sln=n.languages["solution-file"]})(t)}return Qo}var Jo,Tf;function cC(){if(Tf)return Jo;Tf=1;var e=he();Jo=t,t.displayName="soy",t.aliases=[];function t(n){n.register(e),function(r){var a=/(["'])(?:\\(?:\r\n|[\s\S])|(?!\1)[^\\\r\n])*\1/,i=/\b\d+(?:\.\d+)?(?:[eE][+-]?\d+)?\b|\b0x[\dA-F]+\b/;r.languages.soy={comment:[/\/\*[\s\S]*?\*\//,{pattern:/(\s)\/\/.*/,lookbehind:!0,greedy:!0}],"command-arg":{pattern:/(\{+\/?\s*(?:alias|call|delcall|delpackage|deltemplate|namespace|template)\s+)\.?[\w.]+/,lookbehind:!0,alias:"string",inside:{punctuation:/\./}},parameter:{pattern:/(\{+\/?\s*@?param\??\s+)\.?[\w.]+/,lookbehind:!0,alias:"variable"},keyword:[{pattern:/(\{+\/?[^\S\r\n]*)(?:\\[nrt]|alias|call|case|css|default|delcall|delpackage|deltemplate|else(?:if)?|fallbackmsg|for(?:each)?|if(?:empty)?|lb|let|literal|msg|namespace|nil|@?param\??|rb|sp|switch|template|xid)/,lookbehind:!0},/\b(?:any|as|attributes|bool|css|float|html|in|int|js|list|map|null|number|string|uri)\b/],delimiter:{pattern:/^\{+\/?|\/?\}+$/,alias:"punctuation"},property:/\w+(?==)/,variable:{pattern:/\$[^\W\d]\w*(?:\??(?:\.\w+|\[[^\]]+\]))*/,inside:{string:{pattern:a,greedy:!0},number:i,punctuation:/[\[\].?]/}},string:{pattern:a,greedy:!0},function:[/\w+(?=\()/,{pattern:/(\|[^\S\r\n]*)\w+/,lookbehind:!0}],boolean:/\b(?:false|true)\b/,number:i,operator:/\?:?|<=?|>=?|==?|!=|[+*/%-]|\b(?:and|not|or)\b/,punctuation:/[{}()\[\]|.,:]/},r.hooks.add("before-tokenize",function(o){var s=/\{\{.+?\}\}|\{.+?\}|\s\/\/.*|\/\*[\s\S]*?\*\//g,l="{literal}",u="{/literal}",d=!1;r.languages["markup-templating"].buildPlaceholders(o,"soy",s,function(c){return c===u&&(d=!1),d?!1:(c===l&&(d=!0),!0)})}),r.hooks.add("after-tokenize",function(o){r.languages["markup-templating"].tokenizePlaceholders(o,"soy")})}(n)}return Jo}var es,Af;function Pb(){if(Af)return es;Af=1,es=e,e.displayName="turtle",e.aliases=[];function e(t){t.languages.turtle={comment:{pattern:/#.*/,greedy:!0},"multiline-string":{pattern:/"""(?:(?:""?)?(?:[^"\\]|\\.))*"""|'''(?:(?:''?)?(?:[^'\\]|\\.))*'''/,greedy:!0,alias:"string",inside:{comment:/#.*/}},string:{pattern:/"(?:[^\\"\r\n]|\\.)*"|'(?:[^\\'\r\n]|\\.)*'/,greedy:!0},url:{pattern:/<(?:[^\x00-\x20<>"{}|^`\\]|\\(?:u[\da-fA-F]{4}|U[\da-fA-F]{8}))*>/,greedy:!0,inside:{punctuation:/[<>]/}},function:{pattern:/(?:(?![-.\d\xB7])[-.\w\xB7\xC0-\uFFFD]+)?:(?:(?![-.])(?:[-.:\w\xC0-\uFFFD]|%[\da-f]{2}|\\.)+)?/i,inside:{"local-name":{pattern:/([^:]*:)[\s\S]+/,lookbehind:!0},prefix:{pattern:/[\s\S]+/,inside:{punctuation:/:/}}}},number:/[+-]?\b\d+(?:\.\d*)?(?:e[+-]?\d+)?/i,punctuation:/[{}.,;()[\]]|\^\^/,boolean:/\b(?:false|true)\b/,keyword:[/(?:\ba|@prefix|@base)\b|=/,/\b(?:base|graph|prefix)\b/i],tag:{pattern:/@[a-z]+(?:-[a-z\d]+)*/i,inside:{punctuation:/@/}}},t.languages.trig=t.languages.turtle}return es}var ts,kf;function dC(){if(kf)return ts;kf=1;var e=Pb();ts=t,t.displayName="sparql",t.aliases=["rq"];function t(n){n.register(e),n.languages.sparql=n.languages.extend("turtle",{boolean:/\b(?:false|true)\b/i,variable:{pattern:/[?$]\w+/,greedy:!0}}),n.languages.insertBefore("sparql","punctuation",{keyword:[/\b(?:A|ADD|ALL|AS|ASC|ASK|BNODE|BY|CLEAR|CONSTRUCT|COPY|CREATE|DATA|DEFAULT|DELETE|DESC|DESCRIBE|DISTINCT|DROP|EXISTS|FILTER|FROM|GROUP|HAVING|INSERT|INTO|LIMIT|LOAD|MINUS|MOVE|NAMED|NOT|NOW|OFFSET|OPTIONAL|ORDER|RAND|REDUCED|SELECT|SEPARATOR|SERVICE|SILENT|STRUUID|UNION|USING|UUID|VALUES|WHERE)\b/i,/\b(?:ABS|AVG|BIND|BOUND|CEIL|COALESCE|CONCAT|CONTAINS|COUNT|DATATYPE|DAY|ENCODE_FOR_URI|FLOOR|GROUP_CONCAT|HOURS|IF|IRI|isBLANK|isIRI|isLITERAL|isNUMERIC|isURI|LANG|LANGMATCHES|LCASE|MAX|MD5|MIN|MINUTES|MONTH|REGEX|REPLACE|ROUND|sameTerm|SAMPLE|SECONDS|SHA1|SHA256|SHA384|SHA512|STR|STRAFTER|STRBEFORE|STRDT|STRENDS|STRLANG|STRLEN|STRSTARTS|SUBSTR|SUM|TIMEZONE|TZ|UCASE|URI|YEAR)\b(?=\s*\()/i,/\b(?:BASE|GRAPH|PREFIX)\b/i]}),n.languages.rq=n.languages.sparql}return ts}var ns,Rf;function pC(){if(Rf)return ns;Rf=1,ns=e,e.displayName="splunkSpl",e.aliases=[];function 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w=0;w{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},g(oi,"Theme"),oi),Mx=g(e=>{const t=new Tx;return t.calculate(e),t},"getThemeVariables"),li,Ax=(li=class{constructor(){this.background="#333",this.primaryColor="#1f2020",this.secondaryColor=Y(this.primaryColor,16),this.tertiaryColor=M(this.primaryColor,{h:-160}),this.primaryBorderColor=H(this.background),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=H(this.primaryColor),this.secondaryTextColor=H(this.secondaryColor),this.tertiaryTextColor=H(this.tertiaryColor),this.lineColor=H(this.background),this.textColor=H(this.background),this.mainBkg="#1f2020",this.secondBkg="calculated",this.mainContrastColor="lightgrey",this.darkTextColor=Y(H("#323D47"),10),this.lineColor="calculated",this.border1="#ccc",this.border2=ln(255,255,255,.25),this.arrowheadColor="calculated",this.fontFamily='"trebuchet ms", verdana, arial, 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t,r,i,n,a,o,s,c,l,h,u,f,d,p,m,y,x,b,C,v,k;this.secondBkg=Y(this.mainBkg,16),this.lineColor=this.mainContrastColor,this.arrowheadColor=this.mainContrastColor,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.edgeLabelBackground=Y(this.labelBackground,25),this.actorBorder=this.border1,this.actorBkg=this.mainBkg,this.actorTextColor=this.mainContrastColor,this.actorLineColor=this.actorBorder,this.signalColor=this.mainContrastColor,this.signalTextColor=this.mainContrastColor,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.mainContrastColor,this.loopTextColor=this.mainContrastColor,this.noteBorderColor=this.secondaryBorderColor,this.noteBkgColor=this.secondBkg,this.noteTextColor=this.secondaryTextColor,this.activationBorderColor=this.border1,this.activationBkgColor=this.secondBkg,this.altSectionBkgColor=this.background,this.taskBkgColor=Y(this.mainBkg,23),this.taskTextColor=this.darkTextColor,this.taskTextLightColor=this.mainContrastColor,this.taskTextOutsideColor=this.taskTextLightColor,this.gridColor=this.mainContrastColor,this.doneTaskBkgColor=this.mainContrastColor,this.taskTextDarkColor=this.darkTextColor,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#555",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.primaryBorderColor,this.specialStateColor="#f4f4f4",this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=M(this.primaryColor,{h:64}),this.fillType3=M(this.secondaryColor,{h:64}),this.fillType4=M(this.primaryColor,{h:-64}),this.fillType5=M(this.secondaryColor,{h:-64}),this.fillType6=M(this.primaryColor,{h:128}),this.fillType7=M(this.secondaryColor,{h:128}),this.cScale1=this.cScale1||"#0b0000",this.cScale2=this.cScale2||"#4d1037",this.cScale3=this.cScale3||"#3f5258",this.cScale4=this.cScale4||"#4f2f1b",this.cScale5=this.cScale5||"#6e0a0a",this.cScale6=this.cScale6||"#3b0048",this.cScale7=this.cScale7||"#995a01",this.cScale8=this.cScale8||"#154706",this.cScale9=this.cScale9||"#161722",this.cScale10=this.cScale10||"#00296f",this.cScale11=this.cScale11||"#01629c",this.cScale12=this.cScale12||"#010029",this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||M(this.primaryColor,{h:30}),this.cScale4=this.cScale4||M(this.primaryColor,{h:60}),this.cScale5=this.cScale5||M(this.primaryColor,{h:90}),this.cScale6=this.cScale6||M(this.primaryColor,{h:120}),this.cScale7=this.cScale7||M(this.primaryColor,{h:150}),this.cScale8=this.cScale8||M(this.primaryColor,{h:210}),this.cScale9=this.cScale9||M(this.primaryColor,{h:270}),this.cScale10=this.cScale10||M(this.primaryColor,{h:300}),this.cScale11=this.cScale11||M(this.primaryColor,{h:330});for(let _=0;_{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},g(li,"Theme"),li),Lx=g(e=>{const t=new Ax;return t.calculate(e),t},"getThemeVariables"),ci,Bx=(ci=class{constructor(){this.background="#f4f4f4",this.primaryColor="#ECECFF",this.secondaryColor=M(this.primaryColor,{h:120}),this.secondaryColor="#ffffde",this.tertiaryColor=M(this.primaryColor,{h:-160}),this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=H(this.primaryColor),this.secondaryTextColor=H(this.secondaryColor),this.tertiaryTextColor=H(this.tertiaryColor),this.lineColor=H(this.background),this.textColor=H(this.background),this.background="white",this.mainBkg="#ECECFF",this.secondBkg="#ffffde",this.lineColor="#333333",this.border1="#9370DB",this.border2="#aaaa33",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="rgba(232,232,232, 0.8)",this.textColor="#333",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="calculated",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="calculated",this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor="calculated",this.taskTextOutsideColor=this.taskTextDarkColor,this.taskTextClickableColor="calculated",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBorderColor="calculated",this.critBkgColor="calculated",this.todayLineColor="calculated",this.sectionBkgColor=ln(102,102,255,.49),this.altSectionBkgColor="white",this.sectionBkgColor2="#fff400",this.taskBorderColor="#534fbc",this.taskBkgColor="#8a90dd",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="#534fbc",this.activeTaskBkgColor="#bfc7ff",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.rowOdd="calculated",this.rowEven="calculated",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222",this.updateColors()}updateColors(){var t,r,i,n,a,o,s,c,l,h,u,f,d,p,m,y,x,b,C,v,k;this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||M(this.primaryColor,{h:30}),this.cScale4=this.cScale4||M(this.primaryColor,{h:60}),this.cScale5=this.cScale5||M(this.primaryColor,{h:90}),this.cScale6=this.cScale6||M(this.primaryColor,{h:120}),this.cScale7=this.cScale7||M(this.primaryColor,{h:150}),this.cScale8=this.cScale8||M(this.primaryColor,{h:210}),this.cScale9=this.cScale9||M(this.primaryColor,{h:270}),this.cScale10=this.cScale10||M(this.primaryColor,{h:300}),this.cScale11=this.cScale11||M(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||at(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||at(this.tertiaryColor,40);for(let _=0;_{this[i]==="calculated"&&(this[i]=void 0)}),typeof t!="object"){this.updateColors();return}const r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},g(ci,"Theme"),ci),Ex=g(e=>{const t=new Bx;return t.calculate(e),t},"getThemeVariables"),hi,Fx=(hi=class{constructor(){this.background="#f4f4f4",this.primaryColor="#cde498",this.secondaryColor="#cdffb2",this.background="white",this.mainBkg="#cde498",this.secondBkg="#cdffb2",this.lineColor="green",this.border1="#13540c",this.border2="#6eaa49",this.arrowheadColor="green",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.tertiaryColor=Y("#cde498",10),this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=H(this.primaryColor),this.secondaryTextColor=H(this.secondaryColor),this.tertiaryTextColor=H(this.primaryColor),this.lineColor=H(this.background),this.textColor=H(this.background),this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#333",this.edgeLabelBackground="#e8e8e8",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="#333",this.signalTextColor="#333",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="#326932",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="#6eaa49",this.altSectionBkgColor="white",this.sectionBkgColor2="#6eaa49",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="#487e3a",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){var t,r,i,n,a,o,s,c,l,h,u,f,d,p,m,y,x,b,C,v,k;this.actorBorder=at(this.mainBkg,20),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.actorLineColor=this.actorBorder,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||M(this.primaryColor,{h:30}),this.cScale4=this.cScale4||M(this.primaryColor,{h:60}),this.cScale5=this.cScale5||M(this.primaryColor,{h:90}),this.cScale6=this.cScale6||M(this.primaryColor,{h:120}),this.cScale7=this.cScale7||M(this.primaryColor,{h:150}),this.cScale8=this.cScale8||M(this.primaryColor,{h:210}),this.cScale9=this.cScale9||M(this.primaryColor,{h:270}),this.cScale10=this.cScale10||M(this.primaryColor,{h:300}),this.cScale11=this.cScale11||M(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||at(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||at(this.tertiaryColor,40);for(let _=0;_{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},g(hi,"Theme"),hi),$x=g(e=>{const t=new Fx;return t.calculate(e),t},"getThemeVariables"),ui,Dx=(ui=class{constructor(){this.primaryColor="#eee",this.contrast="#707070",this.secondaryColor=Y(this.contrast,55),this.background="#ffffff",this.tertiaryColor=M(this.primaryColor,{h:-160}),this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=H(this.primaryColor),this.secondaryTextColor=H(this.secondaryColor),this.tertiaryTextColor=H(this.tertiaryColor),this.lineColor=H(this.background),this.textColor=H(this.background),this.mainBkg="#eee",this.secondBkg="calculated",this.lineColor="#666",this.border1="#999",this.border2="calculated",this.note="#ffa",this.text="#333",this.critical="#d42",this.done="#bbb",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="white",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor=this.actorBorder,this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="calculated",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="white",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBkgColor="calculated",this.critBorderColor="calculated",this.todayLineColor="calculated",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.rowOdd=this.rowOdd||Y(this.mainBkg,75)||"#ffffff",this.rowEven=this.rowEven||"#f4f4f4",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){var t,r,i,n,a,o,s,c,l,h,u,f,d,p,m,y,x,b,C,v,k;this.secondBkg=Y(this.contrast,55),this.border2=this.contrast,this.actorBorder=Y(this.border1,23),this.actorBkg=this.mainBkg,this.actorTextColor=this.text,this.actorLineColor=this.actorBorder,this.signalColor=this.text,this.signalTextColor=this.text,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.text,this.loopTextColor=this.text,this.noteBorderColor="#999",this.noteBkgColor="#666",this.noteTextColor="#fff",this.cScale0=this.cScale0||"#555",this.cScale1=this.cScale1||"#F4F4F4",this.cScale2=this.cScale2||"#555",this.cScale3=this.cScale3||"#BBB",this.cScale4=this.cScale4||"#777",this.cScale5=this.cScale5||"#999",this.cScale6=this.cScale6||"#DDD",this.cScale7=this.cScale7||"#FFF",this.cScale8=this.cScale8||"#DDD",this.cScale9=this.cScale9||"#BBB",this.cScale10=this.cScale10||"#999",this.cScale11=this.cScale11||"#777";for(let _=0;_{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},g(ui,"Theme"),ui),Ox=g(e=>{const t=new Dx;return t.calculate(e),t},"getThemeVariables"),Ze={base:{getThemeVariables:Mx},dark:{getThemeVariables:Lx},default:{getThemeVariables:Ex},forest:{getThemeVariables:$x},neutral:{getThemeVariables:Ox}},He={flowchart:{useMaxWidth:!0,titleTopMargin:25,subGraphTitleMargin:{top:0,bottom:0},diagramPadding:8,htmlLabels:!0,nodeSpacing:50,rankSpacing:50,curve:"basis",padding:15,defaultRenderer:"dagre-wrapper",wrappingWidth:200},sequence:{useMaxWidth:!0,hideUnusedParticipants:!1,activationWidth:10,diagramMarginX:50,diagramMarginY:10,actorMargin:50,width:150,height:65,boxMargin:10,boxTextMargin:5,noteMargin:10,messageMargin:35,messageAlign:"center",mirrorActors:!0,forceMenus:!1,bottomMarginAdj:1,rightAngles:!1,showSequenceNumbers:!1,actorFontSize:14,actorFontFamily:'"Open Sans", sans-serif',actorFontWeight:400,noteFontSize:14,noteFontFamily:'"trebuchet ms", verdana, arial, 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t[a]!="object")&&(e[a]=t[a])}),e)},"assignWithDepth"),Ht=Lo,ds="#ffffff",ps="#f2f2f2",Jt=g((e,t)=>t?M(e,{s:-40,l:10}):M(e,{s:-40,l:-10}),"mkBorder"),oi,Tx=(oi=class{constructor(){this.background="#f4f4f4",this.primaryColor="#fff4dd",this.noteBkgColor="#fff5ad",this.noteTextColor="#333",this.THEME_COLOR_LIMIT=12,this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px"}updateColors(){var r,i,n,a,o,s,c,l,h,u,f,d,p,m,y,x,b,C,v,k,_;if(this.primaryTextColor=this.primaryTextColor||(this.darkMode?"#eee":"#333"),this.secondaryColor=this.secondaryColor||M(this.primaryColor,{h:-120}),this.tertiaryColor=this.tertiaryColor||M(this.primaryColor,{h:180,l:5}),this.primaryBorderColor=this.primaryBorderColor||Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=this.secondaryBorderColor||Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=this.tertiaryBorderColor||Jt(this.tertiaryColor,this.darkMode),this.noteBorderColor=this.noteBorderColor||Jt(this.noteBkgColor,this.darkMode),this.noteBkgColor=this.noteBkgColor||"#fff5ad",this.noteTextColor=this.noteTextColor||"#333",this.secondaryTextColor=this.secondaryTextColor||H(this.secondaryColor),this.tertiaryTextColor=this.tertiaryTextColor||H(this.tertiaryColor),this.lineColor=this.lineColor||H(this.background),this.arrowheadColor=this.arrowheadColor||H(this.background),this.textColor=this.textColor||this.primaryTextColor,this.border2=this.border2||this.tertiaryBorderColor,this.nodeBkg=this.nodeBkg||this.primaryColor,this.mainBkg=this.mainBkg||this.primaryColor,this.nodeBorder=this.nodeBorder||this.primaryBorderColor,this.clusterBkg=this.clusterBkg||this.tertiaryColor,this.clusterBorder=this.clusterBorder||this.tertiaryBorderColor,this.defaultLinkColor=this.defaultLinkColor||this.lineColor,this.titleColor=this.titleColor||this.tertiaryTextColor,this.edgeLabelBackground=this.edgeLabelBackground||(this.darkMode?at(this.secondaryColor,30):this.secondaryColor),this.nodeTextColor=this.nodeTextColor||this.primaryTextColor,this.actorBorder=this.actorBorder||this.primaryBorderColor,this.actorBkg=this.actorBkg||this.mainBkg,this.actorTextColor=this.actorTextColor||this.primaryTextColor,this.actorLineColor=this.actorLineColor||this.actorBorder,this.labelBoxBkgColor=this.labelBoxBkgColor||this.actorBkg,this.signalColor=this.signalColor||this.textColor,this.signalTextColor=this.signalTextColor||this.textColor,this.labelBoxBorderColor=this.labelBoxBorderColor||this.actorBorder,this.labelTextColor=this.labelTextColor||this.actorTextColor,this.loopTextColor=this.loopTextColor||this.actorTextColor,this.activationBorderColor=this.activationBorderColor||at(this.secondaryColor,10),this.activationBkgColor=this.activationBkgColor||this.secondaryColor,this.sequenceNumberColor=this.sequenceNumberColor||H(this.lineColor),this.sectionBkgColor=this.sectionBkgColor||this.tertiaryColor,this.altSectionBkgColor=this.altSectionBkgColor||"white",this.sectionBkgColor=this.sectionBkgColor||this.secondaryColor,this.sectionBkgColor2=this.sectionBkgColor2||this.primaryColor,this.excludeBkgColor=this.excludeBkgColor||"#eeeeee",this.taskBorderColor=this.taskBorderColor||this.primaryBorderColor,this.taskBkgColor=this.taskBkgColor||this.primaryColor,this.activeTaskBorderColor=this.activeTaskBorderColor||this.primaryColor,this.activeTaskBkgColor=this.activeTaskBkgColor||Y(this.primaryColor,23),this.gridColor=this.gridColor||"lightgrey",this.doneTaskBkgColor=this.doneTaskBkgColor||"lightgrey",this.doneTaskBorderColor=this.doneTaskBorderColor||"grey",this.critBorderColor=this.critBorderColor||"#ff8888",this.critBkgColor=this.critBkgColor||"red",this.todayLineColor=this.todayLineColor||"red",this.taskTextColor=this.taskTextColor||this.textColor,this.taskTextOutsideColor=this.taskTextOutsideColor||this.textColor,this.taskTextLightColor=this.taskTextLightColor||this.textColor,this.taskTextColor=this.taskTextColor||this.primaryTextColor,this.taskTextDarkColor=this.taskTextDarkColor||this.textColor,this.taskTextClickableColor=this.taskTextClickableColor||"#003163",this.personBorder=this.personBorder||this.primaryBorderColor,this.personBkg=this.personBkg||this.mainBkg,this.darkMode?(this.rowOdd=this.rowOdd||at(this.mainBkg,5)||"#ffffff",this.rowEven=this.rowEven||at(this.mainBkg,10)):(this.rowOdd=this.rowOdd||Y(this.mainBkg,75)||"#ffffff",this.rowEven=this.rowEven||Y(this.mainBkg,5)),this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||this.tertiaryColor,this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.specialStateColor=this.lineColor,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||M(this.primaryColor,{h:30}),this.cScale4=this.cScale4||M(this.primaryColor,{h:60}),this.cScale5=this.cScale5||M(this.primaryColor,{h:90}),this.cScale6=this.cScale6||M(this.primaryColor,{h:120}),this.cScale7=this.cScale7||M(this.primaryColor,{h:150}),this.cScale8=this.cScale8||M(this.primaryColor,{h:210,l:150}),this.cScale9=this.cScale9||M(this.primaryColor,{h:270}),this.cScale10=this.cScale10||M(this.primaryColor,{h:300}),this.cScale11=this.cScale11||M(this.primaryColor,{h:330}),this.darkMode)for(let w=0;w{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},g(oi,"Theme"),oi),Mx=g(e=>{const t=new Tx;return t.calculate(e),t},"getThemeVariables"),li,Ax=(li=class{constructor(){this.background="#333",this.primaryColor="#1f2020",this.secondaryColor=Y(this.primaryColor,16),this.tertiaryColor=M(this.primaryColor,{h:-160}),this.primaryBorderColor=H(this.background),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=H(this.primaryColor),this.secondaryTextColor=H(this.secondaryColor),this.tertiaryTextColor=H(this.tertiaryColor),this.lineColor=H(this.background),this.textColor=H(this.background),this.mainBkg="#1f2020",this.secondBkg="calculated",this.mainContrastColor="lightgrey",this.darkTextColor=Y(H("#323D47"),10),this.lineColor="calculated",this.border1="#ccc",this.border2=ln(255,255,255,.25),this.arrowheadColor="calculated",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="#181818",this.textColor="#ccc",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#F9FFFE",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="calculated",this.activationBkgColor="calculated",this.sequenceNumberColor="black",this.sectionBkgColor=at("#EAE8D9",30),this.altSectionBkgColor="calculated",this.sectionBkgColor2="#EAE8D9",this.excludeBkgColor=at(this.sectionBkgColor,10),this.taskBorderColor=ln(255,255,255,70),this.taskBkgColor="calculated",this.taskTextColor="calculated",this.taskTextLightColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor=ln(255,255,255,50),this.activeTaskBkgColor="#81B1DB",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="grey",this.critBorderColor="#E83737",this.critBkgColor="#E83737",this.taskTextDarkColor="calculated",this.todayLineColor="#DB5757",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.rowOdd=this.rowOdd||Y(this.mainBkg,5)||"#ffffff",this.rowEven=this.rowEven||at(this.mainBkg,10),this.labelColor="calculated",this.errorBkgColor="#a44141",this.errorTextColor="#ddd"}updateColors(){var t,r,i,n,a,o,s,c,l,h,u,f,d,p,m,y,x,b,C,v,k;this.secondBkg=Y(this.mainBkg,16),this.lineColor=this.mainContrastColor,this.arrowheadColor=this.mainContrastColor,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.edgeLabelBackground=Y(this.labelBackground,25),this.actorBorder=this.border1,this.actorBkg=this.mainBkg,this.actorTextColor=this.mainContrastColor,this.actorLineColor=this.actorBorder,this.signalColor=this.mainContrastColor,this.signalTextColor=this.mainContrastColor,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.mainContrastColor,this.loopTextColor=this.mainContrastColor,this.noteBorderColor=this.secondaryBorderColor,this.noteBkgColor=this.secondBkg,this.noteTextColor=this.secondaryTextColor,this.activationBorderColor=this.border1,this.activationBkgColor=this.secondBkg,this.altSectionBkgColor=this.background,this.taskBkgColor=Y(this.mainBkg,23),this.taskTextColor=this.darkTextColor,this.taskTextLightColor=this.mainContrastColor,this.taskTextOutsideColor=this.taskTextLightColor,this.gridColor=this.mainContrastColor,this.doneTaskBkgColor=this.mainContrastColor,this.taskTextDarkColor=this.darkTextColor,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#555",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.primaryBorderColor,this.specialStateColor="#f4f4f4",this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=M(this.primaryColor,{h:64}),this.fillType3=M(this.secondaryColor,{h:64}),this.fillType4=M(this.primaryColor,{h:-64}),this.fillType5=M(this.secondaryColor,{h:-64}),this.fillType6=M(this.primaryColor,{h:128}),this.fillType7=M(this.secondaryColor,{h:128}),this.cScale1=this.cScale1||"#0b0000",this.cScale2=this.cScale2||"#4d1037",this.cScale3=this.cScale3||"#3f5258",this.cScale4=this.cScale4||"#4f2f1b",this.cScale5=this.cScale5||"#6e0a0a",this.cScale6=this.cScale6||"#3b0048",this.cScale7=this.cScale7||"#995a01",this.cScale8=this.cScale8||"#154706",this.cScale9=this.cScale9||"#161722",this.cScale10=this.cScale10||"#00296f",this.cScale11=this.cScale11||"#01629c",this.cScale12=this.cScale12||"#010029",this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||M(this.primaryColor,{h:30}),this.cScale4=this.cScale4||M(this.primaryColor,{h:60}),this.cScale5=this.cScale5||M(this.primaryColor,{h:90}),this.cScale6=this.cScale6||M(this.primaryColor,{h:120}),this.cScale7=this.cScale7||M(this.primaryColor,{h:150}),this.cScale8=this.cScale8||M(this.primaryColor,{h:210}),this.cScale9=this.cScale9||M(this.primaryColor,{h:270}),this.cScale10=this.cScale10||M(this.primaryColor,{h:300}),this.cScale11=this.cScale11||M(this.primaryColor,{h:330});for(let _=0;_{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},g(li,"Theme"),li),Lx=g(e=>{const t=new Ax;return t.calculate(e),t},"getThemeVariables"),ci,Bx=(ci=class{constructor(){this.background="#f4f4f4",this.primaryColor="#ECECFF",this.secondaryColor=M(this.primaryColor,{h:120}),this.secondaryColor="#ffffde",this.tertiaryColor=M(this.primaryColor,{h:-160}),this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=H(this.primaryColor),this.secondaryTextColor=H(this.secondaryColor),this.tertiaryTextColor=H(this.tertiaryColor),this.lineColor=H(this.background),this.textColor=H(this.background),this.background="white",this.mainBkg="#ECECFF",this.secondBkg="#ffffde",this.lineColor="#333333",this.border1="#9370DB",this.border2="#aaaa33",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="rgba(232,232,232, 0.8)",this.textColor="#333",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="calculated",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="calculated",this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor="calculated",this.taskTextOutsideColor=this.taskTextDarkColor,this.taskTextClickableColor="calculated",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBorderColor="calculated",this.critBkgColor="calculated",this.todayLineColor="calculated",this.sectionBkgColor=ln(102,102,255,.49),this.altSectionBkgColor="white",this.sectionBkgColor2="#fff400",this.taskBorderColor="#534fbc",this.taskBkgColor="#8a90dd",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="#534fbc",this.activeTaskBkgColor="#bfc7ff",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.rowOdd="calculated",this.rowEven="calculated",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222",this.updateColors()}updateColors(){var t,r,i,n,a,o,s,c,l,h,u,f,d,p,m,y,x,b,C,v,k;this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||M(this.primaryColor,{h:30}),this.cScale4=this.cScale4||M(this.primaryColor,{h:60}),this.cScale5=this.cScale5||M(this.primaryColor,{h:90}),this.cScale6=this.cScale6||M(this.primaryColor,{h:120}),this.cScale7=this.cScale7||M(this.primaryColor,{h:150}),this.cScale8=this.cScale8||M(this.primaryColor,{h:210}),this.cScale9=this.cScale9||M(this.primaryColor,{h:270}),this.cScale10=this.cScale10||M(this.primaryColor,{h:300}),this.cScale11=this.cScale11||M(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||at(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||at(this.tertiaryColor,40);for(let _=0;_{this[i]==="calculated"&&(this[i]=void 0)}),typeof t!="object"){this.updateColors();return}const r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},g(ci,"Theme"),ci),Ex=g(e=>{const t=new Bx;return t.calculate(e),t},"getThemeVariables"),hi,Fx=(hi=class{constructor(){this.background="#f4f4f4",this.primaryColor="#cde498",this.secondaryColor="#cdffb2",this.background="white",this.mainBkg="#cde498",this.secondBkg="#cdffb2",this.lineColor="green",this.border1="#13540c",this.border2="#6eaa49",this.arrowheadColor="green",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.tertiaryColor=Y("#cde498",10),this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=H(this.primaryColor),this.secondaryTextColor=H(this.secondaryColor),this.tertiaryTextColor=H(this.primaryColor),this.lineColor=H(this.background),this.textColor=H(this.background),this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#333",this.edgeLabelBackground="#e8e8e8",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="#333",this.signalTextColor="#333",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="#326932",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="#6eaa49",this.altSectionBkgColor="white",this.sectionBkgColor2="#6eaa49",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="#487e3a",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){var t,r,i,n,a,o,s,c,l,h,u,f,d,p,m,y,x,b,C,v,k;this.actorBorder=at(this.mainBkg,20),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.actorLineColor=this.actorBorder,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||M(this.primaryColor,{h:30}),this.cScale4=this.cScale4||M(this.primaryColor,{h:60}),this.cScale5=this.cScale5||M(this.primaryColor,{h:90}),this.cScale6=this.cScale6||M(this.primaryColor,{h:120}),this.cScale7=this.cScale7||M(this.primaryColor,{h:150}),this.cScale8=this.cScale8||M(this.primaryColor,{h:210}),this.cScale9=this.cScale9||M(this.primaryColor,{h:270}),this.cScale10=this.cScale10||M(this.primaryColor,{h:300}),this.cScale11=this.cScale11||M(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||at(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||at(this.tertiaryColor,40);for(let 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l.edges.forEach(function(E){if(E.target==n){if(E.source!=l)throw"Incorrect edge source!";c.push(E)}}),c},a.prototype.getEdgesBetween=function(n){var c=[],l=this;return l.edges.forEach(function(E){if(!(E.source==l||E.target==l))throw"Incorrect edge source and/or target";(E.target==n||E.source==n)&&c.push(E)}),c},a.prototype.getNeighborsList=function(){var n=new Set,c=this;return c.edges.forEach(function(l){if(l.source==c)n.add(l.target);else{if(l.target!=c)throw"Incorrect incidency!";n.add(l.source)}}),n},a.prototype.withChildren=function(){var n=new Set,c,l;if(n.add(this),this.child!=null)for(var E=this.child.getNodes(),T=0;Tc&&(this.rect.x-=(this.labelWidth-c)/2,this.setWidth(this.labelWidth)),this.labelHeight>l&&(this.labelPos=="center"?this.rect.y-=(this.labelHeight-l)/2:this.labelPos=="top"&&(this.rect.y-=this.labelHeight-l),this.setHeight(this.labelHeight))}}},a.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==t.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},a.prototype.transform=function(n){var c=this.rect.x;c>i.WORLD_BOUNDARY?c=i.WORLD_BOUNDARY:c<-i.WORLD_BOUNDARY&&(c=-i.WORLD_BOUNDARY);var l=this.rect.y;l>i.WORLD_BOUNDARY?l=i.WORLD_BOUNDARY:l<-i.WORLD_BOUNDARY&&(l=-i.WORLD_BOUNDARY);var E=new g(c,l),T=n.inverseTransformPoint(E);this.setLocation(T.x,T.y)},a.prototype.getLeft=function(){return this.rect.x},a.prototype.getRight=function(){return this.rect.x+this.rect.width},a.prototype.getTop=function(){return this.rect.y},a.prototype.getBottom=function(){return this.rect.y+this.rect.height},a.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},f.exports=a},function(f,D,y){function r(t,e){t==null&&e==null?(this.x=0,this.y=0):(this.x=t,this.y=e)}r.prototype.getX=function(){return this.x},r.prototype.getY=function(){return this.y},r.prototype.setX=function(t){this.x=t},r.prototype.setY=function(t){this.y=t},r.prototype.getDifference=function(t){return new DimensionD(this.x-t.x,this.y-t.y)},r.prototype.getCopy=function(){return new r(this.x,this.y)},r.prototype.translate=function(t){return this.x+=t.width,this.y+=t.height,this},f.exports=r},function(f,D,y){var r=y(2),t=y(10),e=y(0),i=y(6),o=y(3),g=y(1),a=y(13),v=y(12),n=y(11);function c(E,T,m){r.call(this,m),this.estimatedSize=t.MIN_VALUE,this.margin=e.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=E,T!=null&&T instanceof i?this.graphManager=T:T!=null&&T instanceof Layout&&(this.graphManager=T.graphManager)}c.prototype=Object.create(r.prototype);for(var l in r)c[l]=r[l];c.prototype.getNodes=function(){return this.nodes},c.prototype.getEdges=function(){return this.edges},c.prototype.getGraphManager=function(){return this.graphManager},c.prototype.getParent=function(){return this.parent},c.prototype.getLeft=function(){return this.left},c.prototype.getRight=function(){return this.right},c.prototype.getTop=function(){return this.top},c.prototype.getBottom=function(){return this.bottom},c.prototype.isConnected=function(){return this.isConnected},c.prototype.add=function(E,T,m){if(T==null&&m==null){var L=E;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(L)>-1)throw"Node already in graph!";return L.owner=this,this.getNodes().push(L),L}else{var O=E;if(!(this.getNodes().indexOf(T)>-1&&this.getNodes().indexOf(m)>-1))throw"Source or target not in graph!";if(!(T.owner==m.owner&&T.owner==this))throw"Both owners must be this graph!";return T.owner!=m.owner?null:(O.source=T,O.target=m,O.isInterGraph=!1,this.getEdges().push(O),T.edges.push(O),m!=T&&m.edges.push(O),O)}},c.prototype.remove=function(E){var T=E;if(E instanceof o){if(T==null)throw"Node is null!";if(!(T.owner!=null&&T.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var m=T.edges.slice(),L,O=m.length,d=0;d-1&&h>-1))throw"Source and/or target doesn't know this edge!";L.source.edges.splice(s,1),L.target!=L.source&&L.target.edges.splice(h,1);var N=L.source.owner.getEdges().indexOf(L);if(N==-1)throw"Not in owner's edge list!";L.source.owner.getEdges().splice(N,1)}},c.prototype.updateLeftTop=function(){for(var E=t.MAX_VALUE,T=t.MAX_VALUE,m,L,O,d=this.getNodes(),N=d.length,s=0;sm&&(E=m),T>L&&(T=L)}return E==t.MAX_VALUE?null:(d[0].getParent().paddingLeft!=null?O=d[0].getParent().paddingLeft:O=this.margin,this.left=T-O,this.top=E-O,new v(this.left,this.top))},c.prototype.updateBounds=function(E){for(var T=t.MAX_VALUE,m=-t.MAX_VALUE,L=t.MAX_VALUE,O=-t.MAX_VALUE,d,N,s,h,u,p=this.nodes,A=p.length,C=0;Cd&&(T=d),ms&&(L=s),Od&&(T=d),ms&&(L=s),O=this.nodes.length){var A=0;m.forEach(function(C){C.owner==E&&A++}),A==this.nodes.length&&(this.isConnected=!0)}},f.exports=c},function(f,D,y){var r,t=y(1);function e(i){r=y(5),this.layout=i,this.graphs=[],this.edges=[]}e.prototype.addRoot=function(){var i=this.layout.newGraph(),o=this.layout.newNode(null),g=this.add(i,o);return this.setRootGraph(g),this.rootGraph},e.prototype.add=function(i,o,g,a,v){if(g==null&&a==null&&v==null){if(i==null)throw"Graph is null!";if(o==null)throw"Parent node is null!";if(this.graphs.indexOf(i)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(i),i.parent!=null)throw"Already has a parent!";if(o.child!=null)throw"Already has a child!";return i.parent=o,o.child=i,i}else{v=g,a=o,g=i;var n=a.getOwner(),c=v.getOwner();if(!(n!=null&&n.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(c!=null&&c.getGraphManager()==this))throw"Target not in this graph mgr!";if(n==c)return g.isInterGraph=!1,n.add(g,a,v);if(g.isInterGraph=!0,g.source=a,g.target=v,this.edges.indexOf(g)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(g),!(g.source!=null&&g.target!=null))throw"Edge source and/or target is null!";if(!(g.source.edges.indexOf(g)==-1&&g.target.edges.indexOf(g)==-1))throw"Edge already in source and/or target incidency list!";return g.source.edges.push(g),g.target.edges.push(g),g}},e.prototype.remove=function(i){if(i instanceof r){var o=i;if(o.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(o==this.rootGraph||o.parent!=null&&o.parent.graphManager==this))throw"Invalid parent node!";var g=[];g=g.concat(o.getEdges());for(var a,v=g.length,n=0;n=i.getRight()?o[0]+=Math.min(i.getX()-e.getX(),e.getRight()-i.getRight()):i.getX()<=e.getX()&&i.getRight()>=e.getRight()&&(o[0]+=Math.min(e.getX()-i.getX(),i.getRight()-e.getRight())),e.getY()<=i.getY()&&e.getBottom()>=i.getBottom()?o[1]+=Math.min(i.getY()-e.getY(),e.getBottom()-i.getBottom()):i.getY()<=e.getY()&&i.getBottom()>=e.getBottom()&&(o[1]+=Math.min(e.getY()-i.getY(),i.getBottom()-e.getBottom()));var v=Math.abs((i.getCenterY()-e.getCenterY())/(i.getCenterX()-e.getCenterX()));i.getCenterY()===e.getCenterY()&&i.getCenterX()===e.getCenterX()&&(v=1);var n=v*o[0],c=o[1]/v;o[0]n)return o[0]=g,o[1]=l,o[2]=v,o[3]=p,!1;if(av)return o[0]=c,o[1]=a,o[2]=h,o[3]=n,!1;if(gv?(o[0]=T,o[1]=m,x=!0):(o[0]=E,o[1]=l,x=!0):U===M&&(g>v?(o[0]=c,o[1]=l,x=!0):(o[0]=L,o[1]=m,x=!0)),-X===M?v>g?(o[2]=u,o[3]=p,_=!0):(o[2]=h,o[3]=s,_=!0):X===M&&(v>g?(o[2]=N,o[3]=s,_=!0):(o[2]=A,o[3]=p,_=!0)),x&&_)return!1;if(g>v?a>n?(G=this.getCardinalDirection(U,M,4),F=this.getCardinalDirection(X,M,2)):(G=this.getCardinalDirection(-U,M,3),F=this.getCardinalDirection(-X,M,1)):a>n?(G=this.getCardinalDirection(-U,M,1),F=this.getCardinalDirection(-X,M,3)):(G=this.getCardinalDirection(U,M,2),F=this.getCardinalDirection(X,M,4)),!x)switch(G){case 1:Y=l,b=g+-d/M,o[0]=b,o[1]=Y;break;case 2:b=L,Y=a+O*M,o[0]=b,o[1]=Y;break;case 3:Y=m,b=g+d/M,o[0]=b,o[1]=Y;break;case 4:b=T,Y=a+-O*M,o[0]=b,o[1]=Y;break}if(!_)switch(F){case 1:H=s,k=v+-R/M,o[2]=k,o[3]=H;break;case 2:k=A,H=n+C*M,o[2]=k,o[3]=H;break;case 3:H=p,k=v+R/M,o[2]=k,o[3]=H;break;case 4:k=u,H=n+-C*M,o[2]=k,o[3]=H;break}}return!1},t.getCardinalDirection=function(e,i,o){return e>i?o:1+o%4},t.getIntersection=function(e,i,o,g){if(g==null)return this.getIntersection2(e,i,o);var a=e.x,v=e.y,n=i.x,c=i.y,l=o.x,E=o.y,T=g.x,m=g.y,L=void 0,O=void 0,d=void 0,N=void 0,s=void 0,h=void 0,u=void 0,p=void 0,A=void 0;return d=c-v,s=a-n,u=n*v-a*c,N=m-E,h=l-T,p=T*E-l*m,A=d*h-N*s,A===0?null:(L=(s*p-h*u)/A,O=(N*u-d*p)/A,new r(L,O))},t.angleOfVector=function(e,i,o,g){var a=void 0;return e!==o?(a=Math.atan((g-i)/(o-e)),o0?1:t<0?-1:0},r.floor=function(t){return t<0?Math.ceil(t):Math.floor(t)},r.ceil=function(t){return t<0?Math.floor(t):Math.ceil(t)},f.exports=r},function(f,D,y){function r(){}r.MAX_VALUE=2147483647,r.MIN_VALUE=-2147483648,f.exports=r},function(f,D,y){var r=function(){function a(v,n){for(var c=0;c"u"?"undefined":r(e);return e==null||i!="object"&&i!="function"},f.exports=t},function(f,D,y){function r(l){if(Array.isArray(l)){for(var E=0,T=Array(l.length);E0&&E;){for(d.push(s[0]);d.length>0&&E;){var h=d[0];d.splice(0,1),O.add(h);for(var u=h.getEdges(),L=0;L-1&&s.splice(R,1)}O=new Set,N=new Map}}return l},c.prototype.createDummyNodesForBendpoints=function(l){for(var E=[],T=l.source,m=this.graphManager.calcLowestCommonAncestor(l.source,l.target),L=0;L0){for(var m=this.edgeToDummyNodes.get(T),L=0;L=0&&E.splice(p,1);var A=N.getNeighborsList();A.forEach(function(x){if(T.indexOf(x)<0){var _=m.get(x),U=_-1;U==1&&h.push(x),m.set(x,U)}})}T=T.concat(h),(E.length==1||E.length==2)&&(L=!0,O=E[0])}return O},c.prototype.setGraphManager=function(l){this.graphManager=l},f.exports=c},function(f,D,y){function r(){}r.seed=1,r.x=0,r.nextDouble=function(){return r.x=Math.sin(r.seed++)*1e4,r.x-Math.floor(r.x)},f.exports=r},function(f,D,y){var r=y(4);function t(e,i){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}t.prototype.getWorldOrgX=function(){return this.lworldOrgX},t.prototype.setWorldOrgX=function(e){this.lworldOrgX=e},t.prototype.getWorldOrgY=function(){return this.lworldOrgY},t.prototype.setWorldOrgY=function(e){this.lworldOrgY=e},t.prototype.getWorldExtX=function(){return this.lworldExtX},t.prototype.setWorldExtX=function(e){this.lworldExtX=e},t.prototype.getWorldExtY=function(){return this.lworldExtY},t.prototype.setWorldExtY=function(e){this.lworldExtY=e},t.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},t.prototype.setDeviceOrgX=function(e){this.ldeviceOrgX=e},t.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},t.prototype.setDeviceOrgY=function(e){this.ldeviceOrgY=e},t.prototype.getDeviceExtX=function(){return this.ldeviceExtX},t.prototype.setDeviceExtX=function(e){this.ldeviceExtX=e},t.prototype.getDeviceExtY=function(){return this.ldeviceExtY},t.prototype.setDeviceExtY=function(e){this.ldeviceExtY=e},t.prototype.transformX=function(e){var i=0,o=this.lworldExtX;return o!=0&&(i=this.ldeviceOrgX+(e-this.lworldOrgX)*this.ldeviceExtX/o),i},t.prototype.transformY=function(e){var i=0,o=this.lworldExtY;return o!=0&&(i=this.ldeviceOrgY+(e-this.lworldOrgY)*this.ldeviceExtY/o),i},t.prototype.inverseTransformX=function(e){var i=0,o=this.ldeviceExtX;return o!=0&&(i=this.lworldOrgX+(e-this.ldeviceOrgX)*this.lworldExtX/o),i},t.prototype.inverseTransformY=function(e){var i=0,o=this.ldeviceExtY;return o!=0&&(i=this.lworldOrgY+(e-this.ldeviceOrgY)*this.lworldExtY/o),i},t.prototype.inverseTransformPoint=function(e){var i=new r(this.inverseTransformX(e.x),this.inverseTransformY(e.y));return i},f.exports=t},function(f,D,y){function r(n){if(Array.isArray(n)){for(var c=0,l=Array(n.length);ce.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*e.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(n-e.ADAPTATION_LOWER_NODE_LIMIT)/(e.ADAPTATION_UPPER_NODE_LIMIT-e.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-e.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=e.MAX_NODE_DISPLACEMENT_INCREMENTAL):(n>e.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(e.COOLING_ADAPTATION_FACTOR,1-(n-e.ADAPTATION_LOWER_NODE_LIMIT)/(e.ADAPTATION_UPPER_NODE_LIMIT-e.ADAPTATION_LOWER_NODE_LIMIT)*(1-e.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=e.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},a.prototype.calcSpringForces=function(){for(var n=this.getAllEdges(),c,l=0;l0&&arguments[0]!==void 0?arguments[0]:!0,c=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,l,E,T,m,L=this.getAllNodes(),O;if(this.useFRGridVariant)for(this.totalIterations%e.GRID_CALCULATION_CHECK_PERIOD==1&&n&&this.updateGrid(),O=new Set,l=0;ld||O>d)&&(n.gravitationForceX=-this.gravityConstant*T,n.gravitationForceY=-this.gravityConstant*m)):(d=c.getEstimatedSize()*this.compoundGravityRangeFactor,(L>d||O>d)&&(n.gravitationForceX=-this.gravityConstant*T*this.compoundGravityConstant,n.gravitationForceY=-this.gravityConstant*m*this.compoundGravityConstant))},a.prototype.isConverged=function(){var n,c=!1;return this.totalIterations>this.maxIterations/3&&(c=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),n=this.totalDisplacement=L.length||d>=L[0].length)){for(var N=0;Na}}]),o}();f.exports=i},function(f,D,y){var r=function(){function i(o,g){for(var a=0;a2&&arguments[2]!==void 0?arguments[2]:1,v=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,n=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;t(this,i),this.sequence1=o,this.sequence2=g,this.match_score=a,this.mismatch_penalty=v,this.gap_penalty=n,this.iMax=o.length+1,this.jMax=g.length+1,this.grid=new Array(this.iMax);for(var c=0;c=0;o--){var g=this.listeners[o];g.event===e&&g.callback===i&&this.listeners.splice(o,1)}},t.emit=function(e,i){for(var o=0;og.coolingFactor*g.maxNodeDisplacement&&(this.displacementX=g.coolingFactor*g.maxNodeDisplacement*e.sign(this.displacementX)),Math.abs(this.displacementY)>g.coolingFactor*g.maxNodeDisplacement&&(this.displacementY=g.coolingFactor*g.maxNodeDisplacement*e.sign(this.displacementY)),this.child==null?this.moveBy(this.displacementX,this.displacementY):this.child.getNodes().length==0?this.moveBy(this.displacementX,this.displacementY):this.propogateDisplacementToChildren(this.displacementX,this.displacementY),g.totalDisplacement+=Math.abs(this.displacementX)+Math.abs(this.displacementY),this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0},i.prototype.propogateDisplacementToChildren=function(g,a){for(var v=this.getChild().getNodes(),n,c=0;c0)this.positionNodesRadially(s);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var h=new Set(this.getAllNodes()),u=this.nodesWithGravity.filter(function(p){return h.has(p)});this.graphManager.setAllNodesToApplyGravitation(u),this.positionNodesRandomly()}}return this.initSpringEmbedder(),this.runSpringEmbedder(),!0},d.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%v.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var s=new Set(this.getAllNodes()),h=this.nodesWithGravity.filter(function(A){return s.has(A)});this.graphManager.setAllNodesToApplyGravitation(h),this.graphManager.updateBounds(),this.updateGrid(),this.coolingFactor=v.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),this.coolingFactor=v.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var u=!this.isTreeGrowing&&!this.isGrowthFinished,p=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(u,p),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},d.prototype.getPositionsData=function(){for(var s=this.graphManager.getAllNodes(),h={},u=0;u1){var x;for(x=0;xp&&(p=Math.floor(R.y)),C=Math.floor(R.x+a.DEFAULT_COMPONENT_SEPERATION)}this.transform(new l(n.WORLD_CENTER_X-R.x/2,n.WORLD_CENTER_Y-R.y/2))},d.radialLayout=function(s,h,u){var 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c=this.rect.x;c>i.WORLD_BOUNDARY?c=i.WORLD_BOUNDARY:c<-i.WORLD_BOUNDARY&&(c=-i.WORLD_BOUNDARY);var l=this.rect.y;l>i.WORLD_BOUNDARY?l=i.WORLD_BOUNDARY:l<-i.WORLD_BOUNDARY&&(l=-i.WORLD_BOUNDARY);var E=new g(c,l),T=n.inverseTransformPoint(E);this.setLocation(T.x,T.y)},a.prototype.getLeft=function(){return this.rect.x},a.prototype.getRight=function(){return this.rect.x+this.rect.width},a.prototype.getTop=function(){return this.rect.y},a.prototype.getBottom=function(){return this.rect.y+this.rect.height},a.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},f.exports=a},function(f,D,y){function r(t,e){t==null&&e==null?(this.x=0,this.y=0):(this.x=t,this.y=e)}r.prototype.getX=function(){return this.x},r.prototype.getY=function(){return this.y},r.prototype.setX=function(t){this.x=t},r.prototype.setY=function(t){this.y=t},r.prototype.getDifference=function(t){return new DimensionD(this.x-t.x,this.y-t.y)},r.prototype.getCopy=function(){return new r(this.x,this.y)},r.prototype.translate=function(t){return this.x+=t.width,this.y+=t.height,this},f.exports=r},function(f,D,y){var r=y(2),t=y(10),e=y(0),i=y(6),o=y(3),g=y(1),a=y(13),v=y(12),n=y(11);function c(E,T,m){r.call(this,m),this.estimatedSize=t.MIN_VALUE,this.margin=e.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=E,T!=null&&T instanceof i?this.graphManager=T:T!=null&&T instanceof Layout&&(this.graphManager=T.graphManager)}c.prototype=Object.create(r.prototype);for(var l in r)c[l]=r[l];c.prototype.getNodes=function(){return this.nodes},c.prototype.getEdges=function(){return this.edges},c.prototype.getGraphManager=function(){return this.graphManager},c.prototype.getParent=function(){return this.parent},c.prototype.getLeft=function(){return this.left},c.prototype.getRight=function(){return this.right},c.prototype.getTop=function(){return this.top},c.prototype.getBottom=function(){return this.bottom},c.prototype.isConnected=function(){return this.isConnected},c.prototype.add=function(E,T,m){if(T==null&&m==null){var L=E;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(L)>-1)throw"Node already in graph!";return L.owner=this,this.getNodes().push(L),L}else{var O=E;if(!(this.getNodes().indexOf(T)>-1&&this.getNodes().indexOf(m)>-1))throw"Source or target not in graph!";if(!(T.owner==m.owner&&T.owner==this))throw"Both owners must be this graph!";return T.owner!=m.owner?null:(O.source=T,O.target=m,O.isInterGraph=!1,this.getEdges().push(O),T.edges.push(O),m!=T&&m.edges.push(O),O)}},c.prototype.remove=function(E){var T=E;if(E instanceof o){if(T==null)throw"Node is null!";if(!(T.owner!=null&&T.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var m=T.edges.slice(),L,O=m.length,d=0;d-1&&h>-1))throw"Source and/or target doesn't know this edge!";L.source.edges.splice(s,1),L.target!=L.source&&L.target.edges.splice(h,1);var N=L.source.owner.getEdges().indexOf(L);if(N==-1)throw"Not in owner's edge list!";L.source.owner.getEdges().splice(N,1)}},c.prototype.updateLeftTop=function(){for(var E=t.MAX_VALUE,T=t.MAX_VALUE,m,L,O,d=this.getNodes(),N=d.length,s=0;sm&&(E=m),T>L&&(T=L)}return E==t.MAX_VALUE?null:(d[0].getParent().paddingLeft!=null?O=d[0].getParent().paddingLeft:O=this.margin,this.left=T-O,this.top=E-O,new v(this.left,this.top))},c.prototype.updateBounds=function(E){for(var T=t.MAX_VALUE,m=-t.MAX_VALUE,L=t.MAX_VALUE,O=-t.MAX_VALUE,d,N,s,h,u,p=this.nodes,A=p.length,C=0;Cd&&(T=d),ms&&(L=s),Od&&(T=d),ms&&(L=s),O=this.nodes.length){var A=0;m.forEach(function(C){C.owner==E&&A++}),A==this.nodes.length&&(this.isConnected=!0)}},f.exports=c},function(f,D,y){var r,t=y(1);function e(i){r=y(5),this.layout=i,this.graphs=[],this.edges=[]}e.prototype.addRoot=function(){var i=this.layout.newGraph(),o=this.layout.newNode(null),g=this.add(i,o);return this.setRootGraph(g),this.rootGraph},e.prototype.add=function(i,o,g,a,v){if(g==null&&a==null&&v==null){if(i==null)throw"Graph is null!";if(o==null)throw"Parent node is null!";if(this.graphs.indexOf(i)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(i),i.parent!=null)throw"Already has a parent!";if(o.child!=null)throw"Already has a child!";return i.parent=o,o.child=i,i}else{v=g,a=o,g=i;var n=a.getOwner(),c=v.getOwner();if(!(n!=null&&n.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(c!=null&&c.getGraphManager()==this))throw"Target not in this graph mgr!";if(n==c)return g.isInterGraph=!1,n.add(g,a,v);if(g.isInterGraph=!0,g.source=a,g.target=v,this.edges.indexOf(g)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(g),!(g.source!=null&&g.target!=null))throw"Edge source and/or target is null!";if(!(g.source.edges.indexOf(g)==-1&&g.target.edges.indexOf(g)==-1))throw"Edge already in source and/or target incidency list!";return g.source.edges.push(g),g.target.edges.push(g),g}},e.prototype.remove=function(i){if(i instanceof r){var o=i;if(o.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(o==this.rootGraph||o.parent!=null&&o.parent.graphManager==this))throw"Invalid parent node!";var g=[];g=g.concat(o.getEdges());for(var a,v=g.length,n=0;n=i.getRight()?o[0]+=Math.min(i.getX()-e.getX(),e.getRight()-i.getRight()):i.getX()<=e.getX()&&i.getRight()>=e.getRight()&&(o[0]+=Math.min(e.getX()-i.getX(),i.getRight()-e.getRight())),e.getY()<=i.getY()&&e.getBottom()>=i.getBottom()?o[1]+=Math.min(i.getY()-e.getY(),e.getBottom()-i.getBottom()):i.getY()<=e.getY()&&i.getBottom()>=e.getBottom()&&(o[1]+=Math.min(e.getY()-i.getY(),i.getBottom()-e.getBottom()));var v=Math.abs((i.getCenterY()-e.getCenterY())/(i.getCenterX()-e.getCenterX()));i.getCenterY()===e.getCenterY()&&i.getCenterX()===e.getCenterX()&&(v=1);var n=v*o[0],c=o[1]/v;o[0]n)return o[0]=g,o[1]=l,o[2]=v,o[3]=p,!1;if(av)return o[0]=c,o[1]=a,o[2]=h,o[3]=n,!1;if(gv?(o[0]=T,o[1]=m,x=!0):(o[0]=E,o[1]=l,x=!0):U===M&&(g>v?(o[0]=c,o[1]=l,x=!0):(o[0]=L,o[1]=m,x=!0)),-X===M?v>g?(o[2]=u,o[3]=p,_=!0):(o[2]=h,o[3]=s,_=!0):X===M&&(v>g?(o[2]=N,o[3]=s,_=!0):(o[2]=A,o[3]=p,_=!0)),x&&_)return!1;if(g>v?a>n?(G=this.getCardinalDirection(U,M,4),F=this.getCardinalDirection(X,M,2)):(G=this.getCardinalDirection(-U,M,3),F=this.getCardinalDirection(-X,M,1)):a>n?(G=this.getCardinalDirection(-U,M,1),F=this.getCardinalDirection(-X,M,3)):(G=this.getCardinalDirection(U,M,2),F=this.getCardinalDirection(X,M,4)),!x)switch(G){case 1:Y=l,b=g+-d/M,o[0]=b,o[1]=Y;break;case 2:b=L,Y=a+O*M,o[0]=b,o[1]=Y;break;case 3:Y=m,b=g+d/M,o[0]=b,o[1]=Y;break;case 4:b=T,Y=a+-O*M,o[0]=b,o[1]=Y;break}if(!_)switch(F){case 1:H=s,k=v+-R/M,o[2]=k,o[3]=H;break;case 2:k=A,H=n+C*M,o[2]=k,o[3]=H;break;case 3:H=p,k=v+R/M,o[2]=k,o[3]=H;break;case 4:k=u,H=n+-C*M,o[2]=k,o[3]=H;break}}return!1},t.getCardinalDirection=function(e,i,o){return e>i?o:1+o%4},t.getIntersection=function(e,i,o,g){if(g==null)return this.getIntersection2(e,i,o);var a=e.x,v=e.y,n=i.x,c=i.y,l=o.x,E=o.y,T=g.x,m=g.y,L=void 0,O=void 0,d=void 0,N=void 0,s=void 0,h=void 0,u=void 0,p=void 0,A=void 0;return d=c-v,s=a-n,u=n*v-a*c,N=m-E,h=l-T,p=T*E-l*m,A=d*h-N*s,A===0?null:(L=(s*p-h*u)/A,O=(N*u-d*p)/A,new r(L,O))},t.angleOfVector=function(e,i,o,g){var a=void 0;return e!==o?(a=Math.atan((g-i)/(o-e)),o0?1:t<0?-1:0},r.floor=function(t){return t<0?Math.ceil(t):Math.floor(t)},r.ceil=function(t){return t<0?Math.floor(t):Math.ceil(t)},f.exports=r},function(f,D,y){function r(){}r.MAX_VALUE=2147483647,r.MIN_VALUE=-2147483648,f.exports=r},function(f,D,y){var r=function(){function a(v,n){for(var c=0;c"u"?"undefined":r(e);return e==null||i!="object"&&i!="function"},f.exports=t},function(f,D,y){function r(l){if(Array.isArray(l)){for(var E=0,T=Array(l.length);E0&&E;){for(d.push(s[0]);d.length>0&&E;){var h=d[0];d.splice(0,1),O.add(h);for(var u=h.getEdges(),L=0;L-1&&s.splice(R,1)}O=new Set,N=new Map}}return l},c.prototype.createDummyNodesForBendpoints=function(l){for(var E=[],T=l.source,m=this.graphManager.calcLowestCommonAncestor(l.source,l.target),L=0;L0){for(var m=this.edgeToDummyNodes.get(T),L=0;L=0&&E.splice(p,1);var A=N.getNeighborsList();A.forEach(function(x){if(T.indexOf(x)<0){var _=m.get(x),U=_-1;U==1&&h.push(x),m.set(x,U)}})}T=T.concat(h),(E.length==1||E.length==2)&&(L=!0,O=E[0])}return O},c.prototype.setGraphManager=function(l){this.graphManager=l},f.exports=c},function(f,D,y){function r(){}r.seed=1,r.x=0,r.nextDouble=function(){return r.x=Math.sin(r.seed++)*1e4,r.x-Math.floor(r.x)},f.exports=r},function(f,D,y){var r=y(4);function t(e,i){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}t.prototype.getWorldOrgX=function(){return this.lworldOrgX},t.prototype.setWorldOrgX=function(e){this.lworldOrgX=e},t.prototype.getWorldOrgY=function(){return this.lworldOrgY},t.prototype.setWorldOrgY=function(e){this.lworldOrgY=e},t.prototype.getWorldExtX=function(){return this.lworldExtX},t.prototype.setWorldExtX=function(e){this.lworldExtX=e},t.prototype.getWorldExtY=function(){return this.lworldExtY},t.prototype.setWorldExtY=function(e){this.lworldExtY=e},t.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},t.prototype.setDeviceOrgX=function(e){this.ldeviceOrgX=e},t.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},t.prototype.setDeviceOrgY=function(e){this.ldeviceOrgY=e},t.prototype.getDeviceExtX=function(){return this.ldeviceExtX},t.prototype.setDeviceExtX=function(e){this.ldeviceExtX=e},t.prototype.getDeviceExtY=function(){return this.ldeviceExtY},t.prototype.setDeviceExtY=function(e){this.ldeviceExtY=e},t.prototype.transformX=function(e){var i=0,o=this.lworldExtX;return o!=0&&(i=this.ldeviceOrgX+(e-this.lworldOrgX)*this.ldeviceExtX/o),i},t.prototype.transformY=function(e){var i=0,o=this.lworldExtY;return o!=0&&(i=this.ldeviceOrgY+(e-this.lworldOrgY)*this.ldeviceExtY/o),i},t.prototype.inverseTransformX=function(e){var i=0,o=this.ldeviceExtX;return o!=0&&(i=this.lworldOrgX+(e-this.ldeviceOrgX)*this.lworldExtX/o),i},t.prototype.inverseTransformY=function(e){var i=0,o=this.ldeviceExtY;return o!=0&&(i=this.lworldOrgY+(e-this.ldeviceOrgY)*this.lworldExtY/o),i},t.prototype.inverseTransformPoint=function(e){var i=new r(this.inverseTransformX(e.x),this.inverseTransformY(e.y));return i},f.exports=t},function(f,D,y){function r(n){if(Array.isArray(n)){for(var c=0,l=Array(n.length);ce.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*e.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(n-e.ADAPTATION_LOWER_NODE_LIMIT)/(e.ADAPTATION_UPPER_NODE_LIMIT-e.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-e.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=e.MAX_NODE_DISPLACEMENT_INCREMENTAL):(n>e.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(e.COOLING_ADAPTATION_FACTOR,1-(n-e.ADAPTATION_LOWER_NODE_LIMIT)/(e.ADAPTATION_UPPER_NODE_LIMIT-e.ADAPTATION_LOWER_NODE_LIMIT)*(1-e.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=e.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},a.prototype.calcSpringForces=function(){for(var n=this.getAllEdges(),c,l=0;l0&&arguments[0]!==void 0?arguments[0]:!0,c=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,l,E,T,m,L=this.getAllNodes(),O;if(this.useFRGridVariant)for(this.totalIterations%e.GRID_CALCULATION_CHECK_PERIOD==1&&n&&this.updateGrid(),O=new Set,l=0;ld||O>d)&&(n.gravitationForceX=-this.gravityConstant*T,n.gravitationForceY=-this.gravityConstant*m)):(d=c.getEstimatedSize()*this.compoundGravityRangeFactor,(L>d||O>d)&&(n.gravitationForceX=-this.gravityConstant*T*this.compoundGravityConstant,n.gravitationForceY=-this.gravityConstant*m*this.compoundGravityConstant))},a.prototype.isConverged=function(){var n,c=!1;return this.totalIterations>this.maxIterations/3&&(c=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),n=this.totalDisplacement=L.length||d>=L[0].length)){for(var N=0;Na}}]),o}();f.exports=i},function(f,D,y){var r=function(){function i(o,g){for(var a=0;a2&&arguments[2]!==void 0?arguments[2]:1,v=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,n=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;t(this,i),this.sequence1=o,this.sequence2=g,this.match_score=a,this.mismatch_penalty=v,this.gap_penalty=n,this.iMax=o.length+1,this.jMax=g.length+1,this.grid=new Array(this.iMax);for(var c=0;c=0;o--){var g=this.listeners[o];g.event===e&&g.callback===i&&this.listeners.splice(o,1)}},t.emit=function(e,i){for(var o=0;og.coolingFactor*g.maxNodeDisplacement&&(this.displacementX=g.coolingFactor*g.maxNodeDisplacement*e.sign(this.displacementX)),Math.abs(this.displacementY)>g.coolingFactor*g.maxNodeDisplacement&&(this.displacementY=g.coolingFactor*g.maxNodeDisplacement*e.sign(this.displacementY)),this.child==null?this.moveBy(this.displacementX,this.displacementY):this.child.getNodes().length==0?this.moveBy(this.displacementX,this.displacementY):this.propogateDisplacementToChildren(this.displacementX,this.displacementY),g.totalDisplacement+=Math.abs(this.displacementX)+Math.abs(this.displacementY),this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0},i.prototype.propogateDisplacementToChildren=function(g,a){for(var v=this.getChild().getNodes(),n,c=0;c0)this.positionNodesRadially(s);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var h=new Set(this.getAllNodes()),u=this.nodesWithGravity.filter(function(p){return h.has(p)});this.graphManager.setAllNodesToApplyGravitation(u),this.positionNodesRandomly()}}return this.initSpringEmbedder(),this.runSpringEmbedder(),!0},d.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%v.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var s=new Set(this.getAllNodes()),h=this.nodesWithGravity.filter(function(A){return s.has(A)});this.graphManager.setAllNodesToApplyGravitation(h),this.graphManager.updateBounds(),this.updateGrid(),this.coolingFactor=v.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),this.coolingFactor=v.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var u=!this.isTreeGrowing&&!this.isGrowthFinished,p=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(u,p),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},d.prototype.getPositionsData=function(){for(var s=this.graphManager.getAllNodes(),h={},u=0;u1){var x;for(x=0;xp&&(p=Math.floor(R.y)),C=Math.floor(R.x+a.DEFAULT_COMPONENT_SEPERATION)}this.transform(new l(n.WORLD_CENTER_X-R.x/2,n.WORLD_CENTER_Y-R.y/2))},d.radialLayout=function(s,h,u){var p=Math.max(this.maxDiagonalInTree(s),a.DEFAULT_RADIAL_SEPARATION);d.branchRadialLayout(h,null,0,359,0,p);var A=L.calculateBounds(s),C=new O;C.setDeviceOrgX(A.getMinX()),C.setDeviceOrgY(A.getMinY()),C.setWorldOrgX(u.x),C.setWorldOrgY(u.y);for(var R=0;R1;){var H=k[0];k.splice(0,1);var P=M.indexOf(H);P>=0&&M.splice(P,1),b--,G--}h!=null?Y=(M.indexOf(k[0])+1)%b:Y=0;for(var B=Math.abs(p-u)/G,$=Y;F!=G;$=++$%b){var K=M[$].getOtherEnd(s);if(K!=h){var Q=(u+F*B)%360,q=(Q+B)%360;d.branchRadialLayout(K,s,Q,q,A+C,C),F++}}},d.maxDiagonalInTree=function(s){for(var h=T.MIN_VALUE,u=0;uh&&(h=A)}return h},d.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},d.prototype.groupZeroDegreeMembers=function(){var s=this,h={};this.memberGroups={},this.idToDummyNode={};for(var u=[],p=this.graphManager.getAllNodes(),A=0;A"u"&&(h[x]=[]),h[x]=h[x].concat(C)}Object.keys(h).forEach(function(_){if(h[_].length>1){var U="DummyCompound_"+_;s.memberGroups[U]=h[_];var X=h[_][0].getParent(),M=new o(s.graphManager);M.id=U,M.paddingLeft=X.paddingLeft||0,M.paddingRight=X.paddingRight||0,M.paddingBottom=X.paddingBottom||0,M.paddingTop=X.paddingTop||0,s.idToDummyNode[U]=M;var G=s.getGraphManager().add(s.newGraph(),M),F=X.getChild();F.add(M);for(var b=0;b=0;s--){var h=this.compoundOrder[s],u=h.id,p=h.paddingLeft,A=h.paddingTop;this.adjustLocations(this.tiledMemberPack[u],h.rect.x,h.rect.y,p,A)}},d.prototype.repopulateZeroDegreeMembers=function(){var s=this,h=this.tiledZeroDegreePack;Object.keys(h).forEach(function(u){var p=s.idToDummyNode[u],A=p.paddingLeft,C=p.paddingTop;s.adjustLocations(h[u],p.rect.x,p.rect.y,A,C)})},d.prototype.getToBeTiled=function(s){var h=s.id;if(this.toBeTiled[h]!=null)return this.toBeTiled[h];var u=s.getChild();if(u==null)return this.toBeTiled[h]=!1,!1;for(var p=u.getNodes(),A=0;A0)return this.toBeTiled[h]=!1,!1;if(C.getChild()==null){this.toBeTiled[C.id]=!1;continue}if(!this.getToBeTiled(C))return this.toBeTiled[h]=!1,!1}return this.toBeTiled[h]=!0,!0},d.prototype.getNodeDegree=function(s){s.id;for(var h=s.getEdges(),u=0,p=0;p_&&(_=X.rect.height)}u+=_+s.verticalPadding}},d.prototype.tileCompoundMembers=function(s,h){var u=this;this.tiledMemberPack=[],Object.keys(s).forEach(function(p){var A=h[p];u.tiledMemberPack[p]=u.tileNodes(s[p],A.paddingLeft+A.paddingRight),A.rect.width=u.tiledMemberPack[p].width,A.rect.height=u.tiledMemberPack[p].height})},d.prototype.tileNodes=function(s,h){var u=a.TILING_PADDING_VERTICAL,p=a.TILING_PADDING_HORIZONTAL,A={rows:[],rowWidth:[],rowHeight:[],width:0,height:h,verticalPadding:u,horizontalPadding:p};s.sort(function(x,_){return x.rect.width*x.rect.height>_.rect.width*_.rect.height?-1:x.rect.width*x.rect.height<_.rect.width*_.rect.height?1:0});for(var C=0;C0&&(R+=s.horizontalPadding),s.rowWidth[u]=R,s.width0&&(x+=s.verticalPadding);var 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F=R;F<=x;F++)G[3]+=this.grid[A-1][F].length+this.grid[A][F].length-1;for(var b=T.MAX_VALUE,Y,k,H=0;H0){var x;x=O.getGraphManager().add(O.newGraph(),u),this.processChildrenList(x,h,O)}}},l.prototype.stop=function(){return this.stopped=!0,this};var T=function(L){L("layout","cose-bilkent",l)};typeof cytoscape<"u"&&T(cytoscape),D.exports=T}])})}(tt)),tt.exports}var bt=Ft();const Ut=xt(bt);var at=function(){var I=S(function(O,d,N,s){for(N=N||{},s=O.length;s--;N[O[s]]=d);return 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this.yy=N||this.yy||{},this._input=d,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:S(function(){var d=this._input[0];this.yytext+=d,this.yyleng++,this.offset++,this.match+=d,this.matched+=d;var N=d.match(/(?:\r\n?|\n).*/g);return N?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),d},"input"),unput:S(function(d){var N=d.length,s=d.split(/(?:\r\n?|\n)/g);this._input=d+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-N),this.offset-=N;var h=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),s.length-1&&(this.yylineno-=s.length-1);var u=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:s?(s.length===h.length?this.yylloc.first_column:0)+h[h.length-s.length].length-s[0].length:this.yylloc.first_column-N},this.options.ranges&&(this.yylloc.range=[u[0],u[0]+this.yyleng-N]),this.yyleng=this.yytext.length,this},"unput"),more:S(function(){return this._more=!0,this},"more"),reject:S(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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lightrag/api/webui/assets/pieDiagram-IB7DONF6-DDY8pHxZ.js index cddb507b..f2712162 100644 --- a/lightrag/api/webui/assets/pieDiagram-IB7DONF6-DrMdtRDI.js +++ b/lightrag/api/webui/assets/pieDiagram-IB7DONF6-DDY8pHxZ.js @@ -1,4 +1,4 @@ -import{p as N}from"./chunk-4BMEZGHF-Ct0jZH9M.js";import{_ as i,g as B,s as U,a as q,b as H,t as K,q as V,l as C,c as Z,F as j,K as J,M as Q,N as z,O as X,e as Y,z as tt,P as et,H as at}from"./mermaid-vendor-d7rbry5E.js";import{p as rt}from"./radar-MK3ICKWK-zkXzSXFe.js";import"./feature-graph-DbHHHM9y.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-OtJ11HbN.js";import"./_basePickBy-Lz6agtdo.js";import"./clone-vL6XIcCC.js";var it=at.pie,D={sections:new Map,showData:!1},f=D.sections,w=D.showData,st=structuredClone(it),ot=i(()=>structuredClone(st),"getConfig"),nt=i(()=>{f=new 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rt}from"./radar-MK3ICKWK-B0N6XiM2.js";import"./feature-graph-D-mwOi0p.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-CoKY6BVy.js";import"./_basePickBy-BVZSZRdU.js";import"./clone-UTZGcTvC.js";var it=at.pie,D={sections:new Map,showData:!1},f=D.sections,w=D.showData,st=structuredClone(it),ot=i(()=>structuredClone(st),"getConfig"),nt=i(()=>{f=new Map,w=D.showData,tt()},"clear"),lt=i(({label:t,value:a})=>{f.has(t)||(f.set(t,a),C.debug(`added new section: ${t}, with value: ${a}`))},"addSection"),ct=i(()=>f,"getSections"),pt=i(t=>{w=t},"setShowData"),dt=i(()=>w,"getShowData"),F={getConfig:ot,clear:nt,setDiagramTitle:V,getDiagramTitle:K,setAccTitle:H,getAccTitle:q,setAccDescription:U,getAccDescription:B,addSection:lt,getSections:ct,setShowData:pt,getShowData:dt},gt=i((t,a)=>{N(t,a),a.setShowData(t.showData),t.sections.map(a.addSection)},"populateDb"),ut={parse:i(async t=>{const 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41:l[u-1].unshift({type:"breakStart",breakText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.BREAK_START}),l[u-1].push({type:"breakEnd",optText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.BREAK_END}),this.$=l[u-1];break;case 43:this.$=l[u-3].concat([{type:"option",optionText:b.parseMessage(l[u-1]),signalType:b.LINETYPE.CRITICAL_OPTION},l[u]]);break;case 45:this.$=l[u-3].concat([{type:"and",parText:b.parseMessage(l[u-1]),signalType:b.LINETYPE.PAR_AND},l[u]]);break;case 47:this.$=l[u-3].concat([{type:"else",altText:b.parseMessage(l[u-1]),signalType:b.LINETYPE.ALT_ELSE},l[u]]);break;case 48:l[u-3].draw="participant",l[u-3].type="addParticipant",l[u-3].description=b.parseMessage(l[u-1]),this.$=l[u-3];break;case 49:l[u-1].draw="participant",l[u-1].type="addParticipant",this.$=l[u-1];break;case 50:l[u-3].draw="actor",l[u-3].type="addParticipant",l[u-3].description=b.parseMessage(l[u-1]),this.$=l[u-3];break;case 51:l[u-1].draw="actor",l[u-1].type="addParticipant",this.$=l[u-1];break;case 52:l[u-1].type="destroyParticipant",this.$=l[u-1];break;case 53:this.$=[l[u-1],{type:"addNote",placement:l[u-2],actor:l[u-1].actor,text:l[u]}];break;case 54:l[u-2]=[].concat(l[u-1],l[u-1]).slice(0,2),l[u-2][0]=l[u-2][0].actor,l[u-2][1]=l[u-2][1].actor,this.$=[l[u-1],{type:"addNote",placement:b.PLACEMENT.OVER,actor:l[u-2].slice(0,2),text:l[u]}];break;case 55:this.$=[l[u-1],{type:"addLinks",actor:l[u-1].actor,text:l[u]}];break;case 56:this.$=[l[u-1],{type:"addALink",actor:l[u-1].actor,text:l[u]}];break;case 57:this.$=[l[u-1],{type:"addProperties",actor:l[u-1].actor,text:l[u]}];break;case 58:this.$=[l[u-1],{type:"addDetails",actor:l[u-1].actor,text:l[u]}];break;case 61:this.$=[l[u-2],l[u]];break;case 62:this.$=l[u];break;case 63:this.$=b.PLACEMENT.LEFTOF;break;case 64:this.$=b.PLACEMENT.RIGHTOF;break;case 65:this.$=[l[u-4],l[u-1],{type:"addMessage",from:l[u-4].actor,to:l[u-1].actor,signalType:l[u-3],msg:l[u],activate:!0},{type:"activeStart",signalType:b.LINETYPE.ACTIVE_START,actor:l[u-1].actor}];break;case 66:this.$=[l[u-4],l[u-1],{type:"addMessage",from:l[u-4].actor,to:l[u-1].actor,signalType:l[u-3],msg:l[u]},{type:"activeEnd",signalType:b.LINETYPE.ACTIVE_END,actor:l[u-4].actor}];break;case 67:this.$=[l[u-3],l[u-1],{type:"addMessage",from:l[u-3].actor,to:l[u-1].actor,signalType:l[u-2],msg:l[u]}];break;case 68:this.$={type:"addParticipant",actor:l[u]};break;case 69:this.$=b.LINETYPE.SOLID_OPEN;break;case 70:this.$=b.LINETYPE.DOTTED_OPEN;break;case 71:this.$=b.LINETYPE.SOLID;break;case 72:this.$=b.LINETYPE.BIDIRECTIONAL_SOLID;break;case 73:this.$=b.LINETYPE.DOTTED;break;case 74:this.$=b.LINETYPE.BIDIRECTIONAL_DOTTED;break;case 75:this.$=b.LINETYPE.SOLID_CROSS;break;case 76:this.$=b.LINETYPE.DOTTED_CROSS;break;case 77:this.$=b.LINETYPE.SOLID_POINT;break;case 78:this.$=b.LINETYPE.DOTTED_POINT;break;case 79:this.$=b.parseMessage(l[u].trim().substring(1));break}},"anonymous"),table:[{3:1,4:t,5:c,6:s},{1:[3]},{3:5,4:t,5:c,6:s},{3:6,4:t,5:c,6:s},e([1,4,5,13,14,18,21,23,29,30,31,33,35,36,37,38,39,41,43,44,46,50,52,53,54,59,60,61,62,70],a,{7:7}),{1:[2,1]},{1:[2,2]},{1:[2,3],4:i,5:n,8:8,9:10,12:12,13:d,14:h,17:15,18:r,21:g,22:40,23:E,24:19,25:20,26:21,27:22,28:23,29:f,30:T,31:m,33:w,35:k,36:O,37:S,38:B,39:D,41:F,43:q,44:X,46:tt,50:z,52:H,53:W,54:M,59:J,60:K,61:Z,62:et,70:v},e(y,[2,5]),{9:47,12:12,13:d,14:h,17:15,18:r,21:g,22:40,23:E,24:19,25:20,26:21,27:22,28:23,29:f,30:T,31:m,33:w,35:k,36:O,37:S,38:B,39:D,41:F,43:q,44:X,46:tt,50:z,52:H,53:W,54:M,59:J,60:K,61:Z,62:et,70:v},e(y,[2,7]),e(y,[2,8]),e(y,[2,14]),{12:48,50:z,52:H,53:W},{15:[1,49]},{5:[1,50]},{5:[1,53],19:[1,51],20:[1,52]},{22:54,70:v},{22:55,70:v},{5:[1,56]},{5:[1,57]},{5:[1,58]},{5:[1,59]},{5:[1,60]},e(y,[2,29]),e(y,[2,30]),{32:[1,61]},{34:[1,62]},e(y,[2,33]),{15:[1,63]},{15:[1,64]},{15:[1,65]},{15:[1,66]},{15:[1,67]},{15:[1,68]},{15:[1,69]},{15:[1,70]},{22:71,70:v},{22:72,70:v},{22:73,70:v},{67:74,71:[1,75],72:[1,76],73:[1,77],74:[1,78],75:[1,79],76:[1,80],77:[1,81],78:[1,82],79:[1,83],80:[1,84]},{55:85,57:[1,86],65:[1,87],66:[1,88]},{22:89,70:v},{22:90,70:v},{22:91,70:v},{22:92,70:v},e([5,51,64,71,72,73,74,75,76,77,78,79,80,81],[2,68]),e(y,[2,6]),e(y,[2,15]),e(P,[2,9],{10:93}),e(y,[2,17]),{5:[1,95],19:[1,94]},{5:[1,96]},e(y,[2,21]),{5:[1,97]},{5:[1,98]},e(y,[2,24]),e(y,[2,25]),e(y,[2,26]),e(y,[2,27]),e(y,[2,28]),e(y,[2,31]),e(y,[2,32]),e(Q,a,{7:99}),e(Q,a,{7:100}),e(Q,a,{7:101}),e(at,a,{40:102,7:103}),e(N,a,{42:104,7:105}),e(N,a,{7:105,42:106}),e(qt,a,{45:107,7:108}),e(Q,a,{7:109}),{5:[1,111],51:[1,110]},{5:[1,113],51:[1,112]},{5:[1,114]},{22:117,68:[1,115],69:[1,116],70:v},e(it,[2,69]),e(it,[2,70]),e(it,[2,71]),e(it,[2,72]),e(it,[2,73]),e(it,[2,74]),e(it,[2,75]),e(it,[2,76]),e(it,[2,77]),e(it,[2,78]),{22:118,70:v},{22:120,58:119,70:v},{70:[2,63]},{70:[2,64]},{56:121,81:ct},{56:123,81:ct},{56:124,81:ct},{56:125,81:ct},{4:[1,128],5:[1,130],11:127,12:129,16:[1,126],50:z,52:H,53:W},{5:[1,131]},e(y,[2,19]),e(y,[2,20]),e(y,[2,22]),e(y,[2,23]),{4:i,5:n,8:8,9:10,12:12,13:d,14:h,16:[1,132],17:15,18:r,21:g,22:40,23:E,24:19,25:20,26:21,27:22,28:23,29:f,30:T,31:m,33:w,35:k,36:O,37:S,38:B,39:D,41:F,43:q,44:X,46:tt,50:z,52:H,53:W,54:M,59:J,60:K,61:Z,62:et,70:v},{4:i,5:n,8:8,9:10,12:12,13:d,14:h,16:[1,133],17:15,18:r,21:g,22:40,23:E,24:19,25:20,26:21,27:22,28:23,29:f,30:T,31:m,33:w,35:k,36:O,37:S,38:B,39:D,41:F,43:q,44:X,46:tt,50:z,52:H,53:W,54:M,59:J,60:K,61:Z,62:et,70:v},{4:i,5:n,8:8,9:10,12:12,13:d,14:h,16:[1,134],17:15,18:r,21:g,22:40,23:E,24:19,25:20,26:21,27:22,28:23,29:f,30:T,31:m,33:w,35:k,36:O,37:S,38:B,39:D,41:F,43:q,44:X,46:tt,50:z,52:H,53:W,54:M,59:J,60:K,61:Z,62:et,70:v},{16:[1,135]},{4:i,5:n,8:8,9:10,12:12,13:d,14:h,16:[2,46],17:15,18:r,21:g,22:40,23:E,24:19,25:20,26:21,27:22,28:23,29:f,30:T,31:m,33:w,35:k,36:O,37:S,38:B,39:D,41:F,43:q,44:X,46:tt,49:[1,136],50:z,52:H,53:W,54:M,59:J,60:K,61:Z,62:et,70:v},{16:[1,137]},{4:i,5:n,8:8,9:10,12:12,13:d,14:h,16:[2,44],17:15,18:r,21:g,22:40,23:E,24:19,25:20,26:21,27:22,28:23,29:f,30:T,31:m,33:w,35:k,36:O,37:S,38:B,39:D,41:F,43:q,44:X,46:tt,48:[1,138],50:z,52:H,53:W,54:M,59:J,60:K,61:Z,62:et,70:v},{16:[1,139]},{16:[1,140]},{4:i,5:n,8:8,9:10,12:12,13:d,14:h,16:[2,42],17:15,18:r,21:g,22:40,23:E,24:19,25:20,26:21,27:22,28:23,29:f,30:T,31:m,33:w,35:k,36:O,37:S,38:B,39:D,41:F,43:q,44:X,46:tt,47:[1,141],50:z,52:H,53:W,54:M,59:J,60:K,61:Z,62:et,70:v},{4:i,5:n,8:8,9:10,12:12,13:d,14:h,16:[1,142],17:15,18:r,21:g,22:40,23:E,24:19,25:20,26:21,27:22,28:23,29:f,30:T,31:m,33:w,35:k,36:O,37:S,38:B,39:D,41:F,43:q,44:X,46:tt,50:z,52:H,53:W,54:M,59:J,60:K,61:Z,62:et,70:v},{15:[1,143]},e(y,[2,49]),{15:[1,144]},e(y,[2,51]),e(y,[2,52]),{22:145,70:v},{22:146,70:v},{56:147,81:ct},{56:148,81:ct},{56:149,81:ct},{64:[1,150],81:[2,62]},{5:[2,55]},{5:[2,79]},{5:[2,56]},{5:[2,57]},{5:[2,58]},e(y,[2,16]),e(P,[2,10]),{12:151,50:z,52:H,53:W},e(P,[2,12]),e(P,[2,13]),e(y,[2,18]),e(y,[2,34]),e(y,[2,35]),e(y,[2,36]),e(y,[2,37]),{15:[1,152]},e(y,[2,38]),{15:[1,153]},e(y,[2,39]),e(y,[2,40]),{15:[1,154]},e(y,[2,41]),{5:[1,155]},{5:[1,156]},{56:157,81:ct},{56:158,81:ct},{5:[2,67]},{5:[2,53]},{5:[2,54]},{22:159,70:v},e(P,[2,11]),e(at,a,{7:103,40:160}),e(N,a,{7:105,42:161}),e(qt,a,{7:108,45:162}),e(y,[2,48]),e(y,[2,50]),{5:[2,65]},{5:[2,66]},{81:[2,61]},{16:[2,47]},{16:[2,45]},{16:[2,43]}],defaultActions:{5:[2,1],6:[2,2],87:[2,63],88:[2,64],121:[2,55],122:[2,79],123:[2,56],124:[2,57],125:[2,58],147:[2,67],148:[2,53],149:[2,54],157:[2,65],158:[2,66],159:[2,61],160:[2,47],161:[2,45],162:[2,43]},parseError:p(function(I,_){if(_.recoverable)this.trace(I);else{var 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u=l.length-1;switch(R){case 3:return b.apply(l[u]),l[u];case 4:case 9:this.$=[];break;case 5:case 10:l[u-1].push(l[u]),this.$=l[u-1];break;case 6:case 7:case 11:case 12:this.$=l[u];break;case 8:case 13:this.$=[];break;case 15:l[u].type="createParticipant",this.$=l[u];break;case 16:l[u-1].unshift({type:"boxStart",boxData:b.parseBoxData(l[u-2])}),l[u-1].push({type:"boxEnd",boxText:l[u-2]}),this.$=l[u-1];break;case 18:this.$={type:"sequenceIndex",sequenceIndex:Number(l[u-2]),sequenceIndexStep:Number(l[u-1]),sequenceVisible:!0,signalType:b.LINETYPE.AUTONUMBER};break;case 19:this.$={type:"sequenceIndex",sequenceIndex:Number(l[u-1]),sequenceIndexStep:1,sequenceVisible:!0,signalType:b.LINETYPE.AUTONUMBER};break;case 20:this.$={type:"sequenceIndex",sequenceVisible:!1,signalType:b.LINETYPE.AUTONUMBER};break;case 21:this.$={type:"sequenceIndex",sequenceVisible:!0,signalType:b.LINETYPE.AUTONUMBER};break;case 22:this.$={type:"activeStart",signalType:b.LINETYPE.ACTIVE_START,actor:l[u-1].actor};break;case 23:this.$={type:"activeEnd",signalType:b.LINETYPE.ACTIVE_END,actor:l[u-1].actor};break;case 29:b.setDiagramTitle(l[u].substring(6)),this.$=l[u].substring(6);break;case 30:b.setDiagramTitle(l[u].substring(7)),this.$=l[u].substring(7);break;case 31:this.$=l[u].trim(),b.setAccTitle(this.$);break;case 32:case 33:this.$=l[u].trim(),b.setAccDescription(this.$);break;case 34:l[u-1].unshift({type:"loopStart",loopText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.LOOP_START}),l[u-1].push({type:"loopEnd",loopText:l[u-2],signalType:b.LINETYPE.LOOP_END}),this.$=l[u-1];break;case 35:l[u-1].unshift({type:"rectStart",color:b.parseMessage(l[u-2]),signalType:b.LINETYPE.RECT_START}),l[u-1].push({type:"rectEnd",color:b.parseMessage(l[u-2]),signalType:b.LINETYPE.RECT_END}),this.$=l[u-1];break;case 36:l[u-1].unshift({type:"optStart",optText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.OPT_START}),l[u-1].push({type:"optEnd",optText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.OPT_END}),this.$=l[u-1];break;case 37:l[u-1].unshift({type:"altStart",altText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.ALT_START}),l[u-1].push({type:"altEnd",signalType:b.LINETYPE.ALT_END}),this.$=l[u-1];break;case 38:l[u-1].unshift({type:"parStart",parText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.PAR_START}),l[u-1].push({type:"parEnd",signalType:b.LINETYPE.PAR_END}),this.$=l[u-1];break;case 39:l[u-1].unshift({type:"parStart",parText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.PAR_OVER_START}),l[u-1].push({type:"parEnd",signalType:b.LINETYPE.PAR_END}),this.$=l[u-1];break;case 40:l[u-1].unshift({type:"criticalStart",criticalText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.CRITICAL_START}),l[u-1].push({type:"criticalEnd",signalType:b.LINETYPE.CRITICAL_END}),this.$=l[u-1];break;case 41:l[u-1].unshift({type:"breakStart",breakText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.BREAK_START}),l[u-1].push({type:"breakEnd",optText:b.parseMessage(l[u-2]),signalType:b.LINETYPE.BREAK_END}),this.$=l[u-1];break;case 43:this.$=l[u-3].concat([{type:"option",optionText:b.parseMessage(l[u-1]),signalType:b.LINETYPE.CRITICAL_OPTION},l[u]]);break;case 45:this.$=l[u-3].concat([{type:"and",parText:b.parseMessage(l[u-1]),signalType:b.LINETYPE.PAR_AND},l[u]]);break;case 47:this.$=l[u-3].concat([{type:"else",altText:b.parseMessage(l[u-1]),signalType:b.LINETYPE.ALT_ELSE},l[u]]);break;case 48:l[u-3].draw="participant",l[u-3].type="addParticipant",l[u-3].description=b.parseMessage(l[u-1]),this.$=l[u-3];break;case 49:l[u-1].draw="participant",l[u-1].type="addParticipant",this.$=l[u-1];break;case 50:l[u-3].draw="actor",l[u-3].type="addParticipant",l[u-3].description=b.parseMessage(l[u-1]),this.$=l[u-3];break;case 51:l[u-1].draw="actor",l[u-1].type="addParticipant",this.$=l[u-1];break;case 52:l[u-1].type="destroyParticipant",this.$=l[u-1];break;case 53:this.$=[l[u-1],{type:"addNote",placement:l[u-2],actor:l[u-1].actor,text:l[u]}];break;case 54:l[u-2]=[].concat(l[u-1],l[u-1]).slice(0,2),l[u-2][0]=l[u-2][0].actor,l[u-2][1]=l[u-2][1].actor,this.$=[l[u-1],{type:"addNote",placement:b.PLACEMENT.OVER,actor:l[u-2].slice(0,2),text:l[u]}];break;case 55:this.$=[l[u-1],{type:"addLinks",actor:l[u-1].actor,text:l[u]}];break;case 56:this.$=[l[u-1],{type:"addALink",actor:l[u-1].actor,text:l[u]}];break;case 57:this.$=[l[u-1],{type:"addProperties",actor:l[u-1].actor,text:l[u]}];break;case 58:this.$=[l[u-1],{type:"addDetails",actor:l[u-1].actor,text:l[u]}];break;case 61:this.$=[l[u-2],l[u]];break;case 62:this.$=l[u];break;case 63:this.$=b.PLACEMENT.LEFTOF;break;case 64:this.$=b.PLACEMENT.RIGHTOF;break;case 65:this.$=[l[u-4],l[u-1],{type:"addMessage",from:l[u-4].actor,to:l[u-1].actor,signalType:l[u-3],msg:l[u],activate:!0},{type:"activeStart",signalType:b.LINETYPE.ACTIVE_START,actor:l[u-1].actor}];break;case 66:this.$=[l[u-4],l[u-1],{type:"addMessage",from:l[u-4].actor,to:l[u-1].actor,signalType:l[u-3],msg:l[u]},{type:"activeEnd",signalType:b.LINETYPE.ACTIVE_END,actor:l[u-4].actor}];break;case 67:this.$=[l[u-3],l[u-1],{type:"addMessage",from:l[u-3].actor,to:l[u-1].actor,signalType:l[u-2],msg:l[u]}];break;case 68:this.$={type:"addParticipant",actor:l[u]};break;case 69:this.$=b.LINETYPE.SOLID_OPEN;break;case 70:this.$=b.LINETYPE.DOTTED_OPEN;break;case 71:this.$=b.LINETYPE.SOLID;break;case 72:this.$=b.LINETYPE.BIDIRECTIONAL_SOLID;break;case 73:this.$=b.LINETYPE.DOTTED;break;case 74:this.$=b.LINETYPE.BIDIRECTIONAL_DOTTED;break;case 75:this.$=b.LINETYPE.SOLID_CROSS;break;case 76:this.$=b.LINETYPE.DOTTED_CROSS;break;case 77:this.$=b.LINETYPE.SOLID_POINT;break;case 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+import{s as W,a as P,S as N}from"./chunk-AEK57VVT-C_8ebDHI.js";import{_ as u,c as t,d as H,l as S,e as C,k as z,U,$ as F,u as O}from"./mermaid-vendor-BVBgFwCv.js";import{G as J}from"./graph-gg_UPtwE.js";import{l as X}from"./layout-DgVd6nly.js";import"./chunk-RZ5BOZE2-PbmQbmec.js";import"./feature-graph-D-mwOi0p.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-CoKY6BVy.js";import"./_basePickBy-BVZSZRdU.js";var L={},D=u((e,i)=>{L[e]=i},"set"),Y=u(e=>L[e],"get"),G=u(()=>Object.keys(L),"keys"),$=u(()=>G().length,"size"),I={get:Y,set:D,keys:G,size:$},j=u(e=>e.append("circle").attr("class","start-state").attr("r",t().state.sizeUnit).attr("cx",t().state.padding+t().state.sizeUnit).attr("cy",t().state.padding+t().state.sizeUnit),"drawStartState"),q=u(e=>e.append("line").style("stroke","grey").style("stroke-dasharray","3").attr("x1",t().state.textHeight).attr("class","divider").attr("x2",t().state.textHeight*2).attr("y1",0).attr("y2",0),"drawDivider"),Z=u((e,i)=>{const d=e.append("text").attr("x",2*t().state.padding).attr("y",t().state.textHeight+2*t().state.padding).attr("font-size",t().state.fontSize).attr("class","state-title").text(i.id),o=d.node().getBBox();return e.insert("rect",":first-child").attr("x",t().state.padding).attr("y",t().state.padding).attr("width",o.width+2*t().state.padding).attr("height",o.height+2*t().state.padding).attr("rx",t().state.radius),d},"drawSimpleState"),K=u((e,i)=>{const d=u(function(l,B,m){const v=l.append("tspan").attr("x",2*t().state.padding).text(B);m||v.attr("dy",t().state.textHeight)},"addTspan"),r=e.append("text").attr("x",2*t().state.padding).attr("y",t().state.textHeight+1.3*t().state.padding).attr("font-size",t().state.fontSize).attr("class","state-title").text(i.descriptions[0]).node().getBBox(),g=r.height,x=e.append("text").attr("x",t().state.padding).attr("y",g+t().state.padding*.4+t().state.dividerMargin+t().state.textHeight).attr("class","state-description");let a=!0,s=!0;i.descriptions.forEach(function(l){a||(d(x,l,s),s=!1),a=!1});const y=e.append("line").attr("x1",t().state.padding).attr("y1",t().state.padding+g+t().state.dividerMargin/2).attr("y2",t().state.padding+g+t().state.dividerMargin/2).attr("class","descr-divider"),p=x.node().getBBox(),c=Math.max(p.width,r.width);return y.attr("x2",c+3*t().state.padding),e.insert("rect",":first-child").attr("x",t().state.padding).attr("y",t().state.padding).attr("width",c+2*t().state.padding).attr("height",p.height+g+2*t().state.padding).attr("rx",t().state.radius),e},"drawDescrState"),Q=u((e,i,d)=>{const o=t().state.padding,r=2*t().state.padding,g=e.node().getBBox(),x=g.width,a=g.x,s=e.append("text").attr("x",0).attr("y",t().state.titleShift).attr("font-size",t().state.fontSize).attr("class","state-title").text(i.id),p=s.node().getBBox().width+r;let c=Math.max(p,x);c===x&&(c=c+r);let l;const B=e.node().getBBox();i.doc,l=a-o,p>x&&(l=(x-c)/2+o),Math.abs(a-B.x)x&&(l=a-(p-x)/2);const m=1-t().state.textHeight;return e.insert("rect",":first-child").attr("x",l).attr("y",m).attr("class",d?"alt-composit":"composit").attr("width",c).attr("height",B.height+t().state.textHeight+t().state.titleShift+1).attr("rx","0"),s.attr("x",l+o),p<=x&&s.attr("x",a+(c-r)/2-p/2+o),e.insert("rect",":first-child").attr("x",l).attr("y",t().state.titleShift-t().state.textHeight-t().state.padding).attr("width",c).attr("height",t().state.textHeight*3).attr("rx",t().state.radius),e.insert("rect",":first-child").attr("x",l).attr("y",t().state.titleShift-t().state.textHeight-t().state.padding).attr("width",c).attr("height",B.height+3+2*t().state.textHeight).attr("rx",t().state.radius),e},"addTitleAndBox"),V=u(e=>(e.append("circle").attr("class","end-state-outer").attr("r",t().state.sizeUnit+t().state.miniPadding).attr("cx",t().state.padding+t().state.sizeUnit+t().state.miniPadding).attr("cy",t().state.padding+t().state.sizeUnit+t().state.miniPadding),e.append("circle").attr("class","end-state-inner").attr("r",t().state.sizeUnit).attr("cx",t().state.padding+t().state.sizeUnit+2).attr("cy",t().state.padding+t().state.sizeUnit+2)),"drawEndState"),tt=u((e,i)=>{let d=t().state.forkWidth,o=t().state.forkHeight;if(i.parentId){let r=d;d=o,o=r}return e.append("rect").style("stroke","black").style("fill","black").attr("width",d).attr("height",o).attr("x",t().state.padding).attr("y",t().state.padding)},"drawForkJoinState"),et=u((e,i,d,o)=>{let r=0;const g=o.append("text");g.style("text-anchor","start"),g.attr("class","noteText");let x=e.replace(/\r\n/g,"
");x=x.replace(/\n/g,"
");const a=x.split(z.lineBreakRegex);let s=1.25*t().state.noteMargin;for(const y of a){const p=y.trim();if(p.length>0){const c=g.append("tspan");if(c.text(p),s===0){const l=c.node().getBBox();s+=l.height}r+=s,c.attr("x",i+t().state.noteMargin),c.attr("y",d+r+1.25*t().state.noteMargin)}}return{textWidth:g.node().getBBox().width,textHeight:r}},"_drawLongText"),at=u((e,i)=>{i.attr("class","state-note");const d=i.append("rect").attr("x",0).attr("y",t().state.padding),o=i.append("g"),{textWidth:r,textHeight:g}=et(e,0,0,o);return d.attr("height",g+2*t().state.noteMargin),d.attr("width",r+t().state.noteMargin*2),d},"drawNote"),_=u(function(e,i){const d=i.id,o={id:d,label:i.id,width:0,height:0},r=e.append("g").attr("id",d).attr("class","stateGroup");i.type==="start"&&j(r),i.type==="end"&&V(r),(i.type==="fork"||i.type==="join")&&tt(r,i),i.type==="note"&&at(i.note.text,r),i.type==="divider"&&q(r),i.type==="default"&&i.descriptions.length===0&&Z(r,i),i.type==="default"&&i.descriptions.length>0&&K(r,i);const g=r.node().getBBox();return o.width=g.width+2*t().state.padding,o.height=g.height+2*t().state.padding,I.set(d,o),o},"drawState"),A=0,it=u(function(e,i,d){const o=u(function(s){switch(s){case N.relationType.AGGREGATION:return"aggregation";case N.relationType.EXTENSION:return"extension";case N.relationType.COMPOSITION:return"composition";case N.relationType.DEPENDENCY:return"dependency"}},"getRelationType");i.points=i.points.filter(s=>!Number.isNaN(s.y));const r=i.points,g=U().x(function(s){return s.x}).y(function(s){return s.y}).curve(F),x=e.append("path").attr("d",g(r)).attr("id","edge"+A).attr("class","transition");let a="";if(t().state.arrowMarkerAbsolute&&(a=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search,a=a.replace(/\(/g,"\\("),a=a.replace(/\)/g,"\\)")),x.attr("marker-end","url("+a+"#"+o(N.relationType.DEPENDENCY)+"End)"),d.title!==void 0){const s=e.append("g").attr("class","stateLabel"),{x:y,y:p}=O.calcLabelPosition(i.points),c=z.getRows(d.title);let l=0;const B=[];let m=0,v=0;for(let f=0;f<=c.length;f++){const h=s.append("text").attr("text-anchor","middle").text(c[f]).attr("x",y).attr("y",p+l),w=h.node().getBBox();m=Math.max(m,w.width),v=Math.min(v,w.x),S.info(w.x,y,p+l),l===0&&(l=h.node().getBBox().height,S.info("Title height",l,p)),B.push(h)}let E=l*c.length;if(c.length>1){const f=(c.length-1)*l*.5;B.forEach((h,w)=>h.attr("y",p+w*l-f)),E=l*c.length}const n=s.node().getBBox();s.insert("rect",":first-child").attr("class","box").attr("x",y-m/2-t().state.padding/2).attr("y",p-E/2-t().state.padding/2-3.5).attr("width",m+t().state.padding).attr("height",E+t().state.padding),S.info(n)}A++},"drawEdge"),b,T={},nt=u(function(){},"setConf"),rt=u(function(e){e.append("defs").append("marker").attr("id","dependencyEnd").attr("refX",19).attr("refY",7).attr("markerWidth",20).attr("markerHeight",28).attr("orient","auto").append("path").attr("d","M 19,7 L9,13 L14,7 L9,1 Z")},"insertMarkers"),st=u(function(e,i,d,o){b=t().state;const r=t().securityLevel;let g;r==="sandbox"&&(g=H("#i"+i));const x=r==="sandbox"?H(g.nodes()[0].contentDocument.body):H("body"),a=r==="sandbox"?g.nodes()[0].contentDocument:document;S.debug("Rendering diagram "+e);const s=x.select(`[id='${i}']`);rt(s);const y=o.db.getRootDoc();R(y,s,void 0,!1,x,a,o);const p=b.padding,c=s.node().getBBox(),l=c.width+p*2,B=c.height+p*2,m=l*1.75;C(s,B,m,b.useMaxWidth),s.attr("viewBox",`${c.x-b.padding} ${c.y-b.padding} `+l+" "+B)},"draw"),dt=u(e=>e?e.length*b.fontSizeFactor:1,"getLabelWidth"),R=u((e,i,d,o,r,g,x)=>{const a=new J({compound:!0,multigraph:!0});let s,y=!0;for(s=0;s{const w=h.parentElement;let k=0,M=0;w&&(w.parentElement&&(k=w.parentElement.getBBox().width),M=parseInt(w.getAttribute("data-x-shift"),10),Number.isNaN(M)&&(M=0)),h.setAttribute("x1",0-M+8),h.setAttribute("x2",k-M-8)})):S.debug("No Node "+n+": "+JSON.stringify(a.node(n)))});let v=m.getBBox();a.edges().forEach(function(n){n!==void 0&&a.edge(n)!==void 0&&(S.debug("Edge "+n.v+" -> "+n.w+": "+JSON.stringify(a.edge(n))),it(i,a.edge(n),a.edge(n).relation))}),v=m.getBBox();const E={id:d||"root",label:d||"root",width:0,height:0};return E.width=v.width+2*b.padding,E.height=v.height+2*b.padding,S.debug("Doc rendered",E,a),E},"renderDoc"),ot={setConf:nt,draw:st},bt={parser:P,get db(){return new N(1)},renderer:ot,styles:W,init:u(e=>{e.state||(e.state={}),e.state.arrowMarkerAbsolute=e.arrowMarkerAbsolute},"init")};export{bt as diagram}; diff --git a/lightrag/api/webui/assets/stateDiagram-v2-YXO3MK2T-C15fufgb.js b/lightrag/api/webui/assets/stateDiagram-v2-YXO3MK2T-DIpE_gWh.js similarity index 61% rename from lightrag/api/webui/assets/stateDiagram-v2-YXO3MK2T-C15fufgb.js rename to lightrag/api/webui/assets/stateDiagram-v2-YXO3MK2T-DIpE_gWh.js index f0a0ef37..9ee06409 100644 --- a/lightrag/api/webui/assets/stateDiagram-v2-YXO3MK2T-C15fufgb.js +++ b/lightrag/api/webui/assets/stateDiagram-v2-YXO3MK2T-DIpE_gWh.js @@ -1 +1 @@ -import{s as r,b as e,a,S as s}from"./chunk-AEK57VVT-ZyE42AjE.js";import{_ as i}from"./mermaid-vendor-d7rbry5E.js";import"./chunk-RZ5BOZE2-C762jHXr.js";import"./feature-graph-DbHHHM9y.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var b={parser:a,get db(){return new s(2)},renderer:e,styles:r,init:i(t=>{t.state||(t.state={}),t.state.arrowMarkerAbsolute=t.arrowMarkerAbsolute},"init")};export{b as diagram}; +import{s as r,b as e,a,S as s}from"./chunk-AEK57VVT-C_8ebDHI.js";import{_ as i}from"./mermaid-vendor-BVBgFwCv.js";import"./chunk-RZ5BOZE2-PbmQbmec.js";import"./feature-graph-D-mwOi0p.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var b={parser:a,get db(){return new s(2)},renderer:e,styles:r,init:i(t=>{t.state||(t.state={}),t.state.arrowMarkerAbsolute=t.arrowMarkerAbsolute},"init")};export{b as diagram}; diff --git a/lightrag/api/webui/assets/timeline-definition-BDJGKUSR-DdDk7fsZ.js b/lightrag/api/webui/assets/timeline-definition-BDJGKUSR-CwuNSBuu.js similarity index 99% rename from lightrag/api/webui/assets/timeline-definition-BDJGKUSR-DdDk7fsZ.js rename to lightrag/api/webui/assets/timeline-definition-BDJGKUSR-CwuNSBuu.js index 54551d4b..29e7401e 100644 --- a/lightrag/api/webui/assets/timeline-definition-BDJGKUSR-DdDk7fsZ.js +++ b/lightrag/api/webui/assets/timeline-definition-BDJGKUSR-CwuNSBuu.js @@ -1,4 +1,4 @@ -import{_ as s,c as xt,l as T,d as q,a2 as kt,a3 as _t,a4 as bt,a5 as vt,N as nt,D as wt,a6 as St,z as Et}from"./mermaid-vendor-d7rbry5E.js";import"./feature-graph-DbHHHM9y.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var X=function(){var n=s(function(f,i,a,d){for(a=a||{},d=f.length;d--;a[f[d]]=i);return 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You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true). `+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:a(function(h){this.unput(this.match.slice(h))},"less"),pastInput:a(function(){var h=this.matched.substr(0,this.matched.length-this.match.length);return(h.length>20?"...":"")+h.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:a(function(){var h=this.match;return h.length<20&&(h+=this._input.substr(0,20-h.length)),(h.substr(0,20)+(h.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:a(function(){var h=this.pastInput(),c=new Array(h.length+1).join("-");return h+this.upcomingInput()+` diff --git a/lightrag/api/webui/index.html b/lightrag/api/webui/index.html index 39cbe26d..5064f712 100644 --- a/lightrag/api/webui/index.html +++ b/lightrag/api/webui/index.html @@ -8,18 +8,18 @@ Lightrag - + - - - - - + + + + + - +
diff --git a/lightrag/base.py b/lightrag/base.py index c6035f70..57cb2ac6 100644 --- a/lightrag/base.py +++ b/lightrag/base.py @@ -14,6 +14,7 @@ from typing import ( ) from .utils import EmbeddingFunc from .types import KnowledgeGraph +from .constants import GRAPH_FIELD_SEP # use the .env that is inside the current folder # allows to use different .env file for each lightrag instance @@ -102,6 +103,7 @@ class QueryParam: @dataclass class StorageNameSpace(ABC): namespace: str + workspace: str global_config: dict[str, Any] async def initialize(self): @@ -278,9 +280,26 @@ class BaseKVStorage(StorageNameSpace, ABC): False: if the cache drop failed, or the cache mode is not supported """ + # async def drop_cache_by_chunk_ids(self, chunk_ids: list[str] | None = None) -> bool: + # """Delete specific cache records from storage by chunk IDs + + # Importance notes for in-memory storage: + # 1. Changes will be persisted to disk during the next index_done_callback + # 2. update flags to notify other processes that data persistence is needed + + # Args: + # chunk_ids (list[str]): List of chunk IDs to be dropped from storage + + # Returns: + # True: if the cache drop successfully + # False: if the cache drop failed, or the operation is not supported + # """ + @dataclass class BaseGraphStorage(StorageNameSpace, ABC): + """All operations related to edges in graph should be undirected.""" + embedding_func: EmbeddingFunc @abstractmethod @@ -441,6 +460,56 @@ class BaseGraphStorage(StorageNameSpace, ABC): result[node_id] = edges if edges is not None else [] return result + @abstractmethod + async def get_nodes_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]: + """Get all nodes that are associated with the given chunk_ids. + + Args: + chunk_ids (list[str]): A list of chunk IDs to find associated nodes for. + + Returns: + list[dict]: A list of nodes, where each node is a dictionary of its properties. + An empty list if no matching nodes are found. + """ + + @abstractmethod + async def get_edges_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]: + """Get all edges that are associated with the given chunk_ids. + + Args: + chunk_ids (list[str]): A list of chunk IDs to find associated edges for. + + Returns: + list[dict]: A list of edges, where each edge is a dictionary of its properties. + An empty list if no matching edges are found. + """ + # Default implementation iterates through all nodes and their edges, which is inefficient. + # This method should be overridden by subclasses for better performance. + all_edges = [] + all_labels = await self.get_all_labels() + processed_edges = set() + + for label in all_labels: + edges = await self.get_node_edges(label) + if edges: + for src_id, tgt_id in edges: + # Avoid processing the same edge twice in an undirected graph + edge_tuple = tuple(sorted((src_id, tgt_id))) + if edge_tuple in processed_edges: + continue + processed_edges.add(edge_tuple) + + edge = await self.get_edge(src_id, tgt_id) + if edge and "source_id" in edge: + source_ids = set(edge["source_id"].split(GRAPH_FIELD_SEP)) + if not source_ids.isdisjoint(chunk_ids): + # Add source and target to the edge dict for easier processing later + edge_with_nodes = edge.copy() + edge_with_nodes["source"] = src_id + edge_with_nodes["target"] = tgt_id + all_edges.append(edge_with_nodes) + return all_edges + @abstractmethod async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None: """Insert a new node or update an existing node in the graph. @@ -566,6 +635,8 @@ class DocProcessingStatus: """ISO format timestamp when document was last updated""" chunks_count: int | None = None """Number of chunks after splitting, used for processing""" + chunks_list: list[str] | None = field(default_factory=list) + """List of chunk IDs associated with this document, used for deletion""" error: str | None = None """Error message if failed""" metadata: dict[str, Any] = field(default_factory=dict) @@ -598,3 +669,14 @@ class StoragesStatus(str, Enum): CREATED = "created" INITIALIZED = "initialized" FINALIZED = "finalized" + + +@dataclass +class DeletionResult: + """Represents the result of a deletion operation.""" + + status: Literal["success", "not_found", "fail"] + doc_id: str + message: str + status_code: int = 200 + file_path: str | None = None diff --git a/lightrag/constants.py b/lightrag/constants.py index 787e1c49..82451a36 100644 --- a/lightrag/constants.py +++ b/lightrag/constants.py @@ -7,11 +7,15 @@ consistency and makes maintenance easier. """ # Default values for environment variables +DEFAULT_MAX_GLEANING = 1 DEFAULT_MAX_TOKEN_SUMMARY = 500 DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE = 6 DEFAULT_WOKERS = 2 DEFAULT_TIMEOUT = 150 +# Separator for graph fields +GRAPH_FIELD_SEP = "" + # Logging configuration defaults DEFAULT_LOG_MAX_BYTES = 10485760 # Default 10MB DEFAULT_LOG_BACKUP_COUNT = 5 # Default 5 backups diff --git a/lightrag/kg/__init__.py b/lightrag/kg/__init__.py index bbddb285..1f5fd56f 100644 --- a/lightrag/kg/__init__.py +++ b/lightrag/kg/__init__.py @@ -14,8 +14,8 @@ STORAGE_IMPLEMENTATIONS = { "NetworkXStorage", "Neo4JStorage", "PGGraphStorage", + "MongoGraphStorage", # "AGEStorage", - # "MongoGraphStorage", # "TiDBGraphStorage", # "GremlinStorage", ], @@ -25,11 +25,11 @@ STORAGE_IMPLEMENTATIONS = { "implementations": [ "NanoVectorDBStorage", "MilvusVectorDBStorage", - "ChromaVectorDBStorage", "PGVectorStorage", "FaissVectorDBStorage", "QdrantVectorDBStorage", "MongoVectorDBStorage", + # "ChromaVectorDBStorage", # "TiDBVectorDBStorage", ], "required_methods": ["query", "upsert"], @@ -37,6 +37,7 @@ STORAGE_IMPLEMENTATIONS = { "DOC_STATUS_STORAGE": { "implementations": [ "JsonDocStatusStorage", + "RedisDocStatusStorage", "PGDocStatusStorage", "MongoDocStatusStorage", ], @@ -79,6 +80,7 @@ STORAGE_ENV_REQUIREMENTS: dict[str, list[str]] = { "MongoVectorDBStorage": [], # Document Status Storage Implementations "JsonDocStatusStorage": [], + "RedisDocStatusStorage": ["REDIS_URI"], "PGDocStatusStorage": ["POSTGRES_USER", "POSTGRES_PASSWORD", "POSTGRES_DATABASE"], "MongoDocStatusStorage": [], } @@ -96,6 +98,7 @@ STORAGES = { "MongoGraphStorage": ".kg.mongo_impl", "MongoVectorDBStorage": ".kg.mongo_impl", "RedisKVStorage": ".kg.redis_impl", + "RedisDocStatusStorage": ".kg.redis_impl", "ChromaVectorDBStorage": ".kg.chroma_impl", # "TiDBKVStorage": ".kg.tidb_impl", # "TiDBVectorDBStorage": ".kg.tidb_impl", diff --git a/lightrag/kg/age_impl.py b/lightrag/kg/deprecated/age_impl.py similarity index 100% rename from lightrag/kg/age_impl.py rename to lightrag/kg/deprecated/age_impl.py diff --git a/lightrag/kg/chroma_impl.py b/lightrag/kg/deprecated/chroma_impl.py similarity index 98% rename from lightrag/kg/chroma_impl.py rename to lightrag/kg/deprecated/chroma_impl.py index c3927a19..ebdd4593 100644 --- a/lightrag/kg/chroma_impl.py +++ b/lightrag/kg/deprecated/chroma_impl.py @@ -109,7 +109,7 @@ class ChromaVectorDBStorage(BaseVectorStorage): raise async def upsert(self, data: dict[str, dict[str, Any]]) -> None: - logger.info(f"Inserting {len(data)} to {self.namespace}") + logger.debug(f"Inserting {len(data)} to {self.namespace}") if not data: return @@ -234,7 +234,6 @@ class ChromaVectorDBStorage(BaseVectorStorage): ids: List of vector IDs to be deleted """ try: - logger.info(f"Deleting {len(ids)} vectors from {self.namespace}") self._collection.delete(ids=ids) logger.debug( f"Successfully deleted {len(ids)} vectors from {self.namespace}" diff --git a/lightrag/kg/gremlin_impl.py b/lightrag/kg/deprecated/gremlin_impl.py similarity index 100% rename from lightrag/kg/gremlin_impl.py rename to lightrag/kg/deprecated/gremlin_impl.py diff --git a/lightrag/kg/tidb_impl.py b/lightrag/kg/deprecated/tidb_impl.py similarity index 99% rename from lightrag/kg/tidb_impl.py rename to lightrag/kg/deprecated/tidb_impl.py index 9b9d17a9..d60bb1f6 100644 --- a/lightrag/kg/tidb_impl.py +++ b/lightrag/kg/deprecated/tidb_impl.py @@ -257,7 +257,7 @@ class TiDBKVStorage(BaseKVStorage): ################ INSERT full_doc AND chunks ################ async def upsert(self, data: dict[str, dict[str, Any]]) -> None: - logger.info(f"Inserting {len(data)} to {self.namespace}") + logger.debug(f"Inserting {len(data)} to {self.namespace}") if not data: return left_data = {k: v for k, v in data.items() if k not in self._data} @@ -454,11 +454,9 @@ class TiDBVectorDBStorage(BaseVectorStorage): ###### INSERT entities And relationships ###### async def upsert(self, data: dict[str, dict[str, Any]]) -> None: - logger.info(f"Inserting {len(data)} to {self.namespace}") if not data: return - - logger.info(f"Inserting {len(data)} vectors to {self.namespace}") + logger.debug(f"Inserting {len(data)} vectors to {self.namespace}") # Get current time as UNIX timestamp import time @@ -522,11 +520,6 @@ class TiDBVectorDBStorage(BaseVectorStorage): } await self.db.execute(SQL_TEMPLATES["upsert_relationship"], param) - async def get_by_status(self, status: str) -> Union[list[dict[str, Any]], None]: - SQL = SQL_TEMPLATES["get_by_status_" + self.namespace] - params = {"workspace": self.db.workspace, "status": status} - return await self.db.query(SQL, params, multirows=True) - async def delete(self, ids: list[str]) -> None: """Delete vectors with specified IDs from the storage. diff --git a/lightrag/kg/faiss_impl.py b/lightrag/kg/faiss_impl.py index f2afde2e..c6ee099d 100644 --- a/lightrag/kg/faiss_impl.py +++ b/lightrag/kg/faiss_impl.py @@ -4,9 +4,7 @@ import asyncio from typing import Any, final import json import numpy as np - from dataclasses import dataclass -import pipmaster as pm from lightrag.utils import logger, compute_mdhash_id from lightrag.base import BaseVectorStorage @@ -17,14 +15,9 @@ from .shared_storage import ( set_all_update_flags, ) +# You must manually install faiss-cpu or faiss-gpu before using FAISS vector db import faiss # type: ignore -USE_GPU = os.getenv("FAISS_USE_GPU", "0") == "1" -FAISS_PACKAGE = "faiss-gpu" if USE_GPU else "faiss-cpu" - -if not pm.is_installed(FAISS_PACKAGE): - pm.install(FAISS_PACKAGE) - @final @dataclass @@ -45,9 +38,19 @@ class FaissVectorDBStorage(BaseVectorStorage): self.cosine_better_than_threshold = cosine_threshold # Where to save index file if you want persistent storage - self._faiss_index_file = os.path.join( - self.global_config["working_dir"], f"faiss_index_{self.namespace}.index" - ) + working_dir = self.global_config["working_dir"] + if self.workspace: + # Include workspace in the file path for data isolation + workspace_dir = os.path.join(working_dir, self.workspace) + os.makedirs(workspace_dir, exist_ok=True) + self._faiss_index_file = os.path.join( + workspace_dir, f"faiss_index_{self.namespace}.index" + ) + else: + # Default behavior when workspace is empty + self._faiss_index_file = os.path.join( + working_dir, f"faiss_index_{self.namespace}.index" + ) self._meta_file = self._faiss_index_file + ".meta.json" self._max_batch_size = self.global_config["embedding_batch_num"] @@ -166,7 +169,7 @@ class FaissVectorDBStorage(BaseVectorStorage): meta["__vector__"] = embeddings[i].tolist() self._id_to_meta.update({fid: meta}) - logger.info(f"Upserted {len(list_data)} vectors into Faiss index.") + logger.debug(f"Upserted {len(list_data)} vectors into Faiss index.") return [m["__id__"] for m in list_data] async def query( @@ -229,7 +232,7 @@ class FaissVectorDBStorage(BaseVectorStorage): 2. Only one process should updating the storage at a time before index_done_callback, KG-storage-log should be used to avoid data corruption """ - logger.info(f"Deleting {len(ids)} vectors from {self.namespace}") + logger.debug(f"Deleting {len(ids)} vectors from {self.namespace}") to_remove = [] for cid in ids: fid = self._find_faiss_id_by_custom_id(cid) @@ -331,7 +334,7 @@ class FaissVectorDBStorage(BaseVectorStorage): and rebuild in-memory structures so we can query. """ if not os.path.exists(self._faiss_index_file): - logger.warning("No existing Faiss index file found. Starting fresh.") + logger.warning(f"No existing Faiss index file found for {self.namespace}") return try: diff --git a/lightrag/kg/json_doc_status_impl.py b/lightrag/kg/json_doc_status_impl.py index f8387ad8..317509b3 100644 --- a/lightrag/kg/json_doc_status_impl.py +++ b/lightrag/kg/json_doc_status_impl.py @@ -30,7 +30,18 @@ class JsonDocStatusStorage(DocStatusStorage): def __post_init__(self): working_dir = self.global_config["working_dir"] - self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json") + if self.workspace: + # Include workspace in the file path for data isolation + workspace_dir = os.path.join(working_dir, self.workspace) + os.makedirs(workspace_dir, exist_ok=True) + self._file_name = os.path.join( + workspace_dir, f"kv_store_{self.namespace}.json" + ) + else: + # Default behavior when workspace is empty + self._file_name = os.path.join( + working_dir, f"kv_store_{self.namespace}.json" + ) self._data = None self._storage_lock = None self.storage_updated = None @@ -118,6 +129,10 @@ class JsonDocStatusStorage(DocStatusStorage): return logger.debug(f"Inserting {len(data)} records to {self.namespace}") async with self._storage_lock: + # Ensure chunks_list field exists for new documents + for doc_id, doc_data in data.items(): + if "chunks_list" not in doc_data: + doc_data["chunks_list"] = [] self._data.update(data) await set_all_update_flags(self.namespace) diff --git a/lightrag/kg/json_kv_impl.py b/lightrag/kg/json_kv_impl.py index 2d44ce00..f76c9e13 100644 --- a/lightrag/kg/json_kv_impl.py +++ b/lightrag/kg/json_kv_impl.py @@ -26,7 +26,18 @@ from .shared_storage import ( class JsonKVStorage(BaseKVStorage): def __post_init__(self): working_dir = self.global_config["working_dir"] - self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json") + if self.workspace: + # Include workspace in the file path for data isolation + workspace_dir = os.path.join(working_dir, self.workspace) + os.makedirs(workspace_dir, exist_ok=True) + self._file_name = os.path.join( + workspace_dir, f"kv_store_{self.namespace}.json" + ) + else: + # Default behavior when workspace is empty + self._file_name = os.path.join( + working_dir, f"kv_store_{self.namespace}.json" + ) self._data = None self._storage_lock = None self.storage_updated = None @@ -42,19 +53,14 @@ class JsonKVStorage(BaseKVStorage): if need_init: loaded_data = load_json(self._file_name) or {} async with self._storage_lock: - self._data.update(loaded_data) - - # Calculate data count based on namespace - if self.namespace.endswith("cache"): - # For cache namespaces, sum the cache entries across all cache types - data_count = sum( - len(first_level_dict) - for first_level_dict in loaded_data.values() - if isinstance(first_level_dict, dict) + # Migrate legacy cache structure if needed + if self.namespace.endswith("_cache"): + loaded_data = await self._migrate_legacy_cache_structure( + loaded_data ) - else: - # For non-cache namespaces, use the original count method - data_count = len(loaded_data) + + self._data.update(loaded_data) + data_count = len(loaded_data) logger.info( f"Process {os.getpid()} KV load {self.namespace} with {data_count} records" @@ -67,17 +73,8 @@ class JsonKVStorage(BaseKVStorage): dict(self._data) if hasattr(self._data, "_getvalue") else self._data ) - # Calculate data count based on namespace - if self.namespace.endswith("cache"): - # # For cache namespaces, sum the cache entries across all cache types - data_count = sum( - len(first_level_dict) - for first_level_dict in data_dict.values() - if isinstance(first_level_dict, dict) - ) - else: - # For non-cache namespaces, use the original count method - data_count = len(data_dict) + # Calculate data count - all data is now flattened + data_count = len(data_dict) logger.debug( f"Process {os.getpid()} KV writting {data_count} records to {self.namespace}" @@ -92,22 +89,49 @@ class JsonKVStorage(BaseKVStorage): Dictionary containing all stored data """ async with self._storage_lock: - return dict(self._data) + result = {} + for key, value in self._data.items(): + if value: + # Create a copy to avoid modifying the original data + data = dict(value) + # Ensure time fields are present, provide default values for old data + data.setdefault("create_time", 0) + data.setdefault("update_time", 0) + result[key] = data + else: + result[key] = value + return result async def get_by_id(self, id: str) -> dict[str, Any] | None: async with self._storage_lock: - return self._data.get(id) + result = self._data.get(id) + if result: + # Create a copy to avoid modifying the original data + result = dict(result) + # Ensure time fields are present, provide default values for old data + result.setdefault("create_time", 0) + result.setdefault("update_time", 0) + # Ensure _id field contains the clean ID + result["_id"] = id + return result async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: async with self._storage_lock: - return [ - ( - {k: v for k, v in self._data[id].items()} - if self._data.get(id, None) - else None - ) - for id in ids - ] + results = [] + for id in ids: + data = self._data.get(id, None) + if data: + # Create a copy to avoid modifying the original data + result = {k: v for k, v in data.items()} + # Ensure time fields are present, provide default values for old data + result.setdefault("create_time", 0) + result.setdefault("update_time", 0) + # Ensure _id field contains the clean ID + result["_id"] = id + results.append(result) + else: + results.append(None) + return results async def filter_keys(self, keys: set[str]) -> set[str]: async with self._storage_lock: @@ -121,8 +145,29 @@ class JsonKVStorage(BaseKVStorage): """ if not data: return + + import time + + current_time = int(time.time()) # Get current Unix timestamp + logger.debug(f"Inserting {len(data)} records to {self.namespace}") async with self._storage_lock: + # Add timestamps to data based on whether key exists + for k, v in data.items(): + # For text_chunks namespace, ensure llm_cache_list field exists + if "text_chunks" in self.namespace: + if "llm_cache_list" not in v: + v["llm_cache_list"] = [] + + # Add timestamps based on whether key exists + if k in self._data: # Key exists, only update update_time + v["update_time"] = current_time + else: # New key, set both create_time and update_time + v["create_time"] = current_time + v["update_time"] = current_time + + v["_id"] = k + self._data.update(data) await set_all_update_flags(self.namespace) @@ -150,14 +195,14 @@ class JsonKVStorage(BaseKVStorage): await set_all_update_flags(self.namespace) async def drop_cache_by_modes(self, modes: list[str] | None = None) -> bool: - """Delete specific records from storage by by cache mode + """Delete specific records from storage by cache mode Importance notes for in-memory storage: 1. Changes will be persisted to disk during the next index_done_callback 2. update flags to notify other processes that data persistence is needed Args: - ids (list[str]): List of cache mode to be drop from storage + modes (list[str]): List of cache modes to be dropped from storage Returns: True: if the cache drop successfully @@ -167,11 +212,78 @@ class JsonKVStorage(BaseKVStorage): return False try: - await self.delete(modes) + async with self._storage_lock: + keys_to_delete = [] + modes_set = set(modes) # Convert to set for efficient lookup + + for key in list(self._data.keys()): + # Parse flattened cache key: mode:cache_type:hash + parts = key.split(":", 2) + if len(parts) == 3 and parts[0] in modes_set: + keys_to_delete.append(key) + + # Batch delete + for key in keys_to_delete: + self._data.pop(key, None) + + if keys_to_delete: + await set_all_update_flags(self.namespace) + logger.info( + f"Dropped {len(keys_to_delete)} cache entries for modes: {modes}" + ) + return True - except Exception: + except Exception as e: + logger.error(f"Error dropping cache by modes: {e}") return False + # async def drop_cache_by_chunk_ids(self, chunk_ids: list[str] | None = None) -> bool: + # """Delete specific cache records from storage by chunk IDs + + # Importance notes for in-memory storage: + # 1. Changes will be persisted to disk during the next index_done_callback + # 2. update flags to notify other processes that data persistence is needed + + # Args: + # chunk_ids (list[str]): List of chunk IDs to be dropped from storage + + # Returns: + # True: if the cache drop successfully + # False: if the cache drop failed + # """ + # if not chunk_ids: + # return False + + # try: + # async with self._storage_lock: + # # Iterate through all cache modes to find entries with matching chunk_ids + # for mode_key, mode_data in list(self._data.items()): + # if isinstance(mode_data, dict): + # # Check each cached entry in this mode + # for cache_key, cache_entry in list(mode_data.items()): + # if ( + # isinstance(cache_entry, dict) + # and cache_entry.get("chunk_id") in chunk_ids + # ): + # # Remove this cache entry + # del mode_data[cache_key] + # logger.debug( + # f"Removed cache entry {cache_key} for chunk {cache_entry.get('chunk_id')}" + # ) + + # # If the mode is now empty, remove it entirely + # if not mode_data: + # del self._data[mode_key] + + # # Set update flags to notify persistence is needed + # await set_all_update_flags(self.namespace) + + # logger.info(f"Cleared cache for {len(chunk_ids)} chunk IDs") + # return True + # except Exception as e: + # logger.error(f"Error clearing cache by chunk IDs: {e}") + # return False + async def drop(self) -> dict[str, str]: """Drop all data from storage and clean up resources This action will persistent the data to disk immediately. @@ -198,9 +310,58 @@ class JsonKVStorage(BaseKVStorage): logger.error(f"Error dropping {self.namespace}: {e}") return {"status": "error", "message": str(e)} + async def _migrate_legacy_cache_structure(self, data: dict) -> dict: + """Migrate legacy nested cache structure to flattened structure + + Args: + data: Original data dictionary that may contain legacy structure + + Returns: + Migrated data dictionary with flattened cache keys + """ + from lightrag.utils import generate_cache_key + + # Early return if data is empty + if not data: + return data + + # Check first entry to see if it's already in new format + first_key = next(iter(data.keys())) + if ":" in first_key and len(first_key.split(":")) == 3: + # Already in flattened format, return as-is + return data + + migrated_data = {} + migration_count = 0 + + for key, value in data.items(): + # Check if this is a legacy nested cache structure + if isinstance(value, dict) and all( + isinstance(v, dict) and "return" in v for v in value.values() + ): + # This looks like a legacy cache mode with nested structure + mode = key + for cache_hash, cache_entry in value.items(): + cache_type = cache_entry.get("cache_type", "extract") + flattened_key = generate_cache_key(mode, cache_type, cache_hash) + migrated_data[flattened_key] = cache_entry + migration_count += 1 + else: + # Keep non-cache data or already flattened cache data as-is + migrated_data[key] = value + + if migration_count > 0: + logger.info( + f"Migrated {migration_count} legacy cache entries to flattened structure" + ) + # Persist migrated data immediately + write_json(migrated_data, self._file_name) + + return migrated_data + async def finalize(self): """Finalize storage resources Persistence cache data to disk before exiting """ - if self.namespace.endswith("cache"): + if self.namespace.endswith("_cache"): await self.index_done_callback() diff --git a/lightrag/kg/milvus_impl.py b/lightrag/kg/milvus_impl.py index 23e178bc..2414787a 100644 --- a/lightrag/kg/milvus_impl.py +++ b/lightrag/kg/milvus_impl.py @@ -7,15 +7,11 @@ from lightrag.utils import logger, compute_mdhash_id from ..base import BaseVectorStorage import pipmaster as pm - -if not pm.is_installed("configparser"): - pm.install("configparser") - if not pm.is_installed("pymilvus"): pm.install("pymilvus") import configparser -from pymilvus import MilvusClient # type: ignore +from pymilvus import MilvusClient, DataType, CollectionSchema, FieldSchema # type: ignore config = configparser.ConfigParser() config.read("config.ini", "utf-8") @@ -24,17 +20,665 @@ config.read("config.ini", "utf-8") @final @dataclass class MilvusVectorDBStorage(BaseVectorStorage): - @staticmethod - def create_collection_if_not_exist( - client: MilvusClient, collection_name: str, **kwargs - ): - if client.has_collection(collection_name): - return - client.create_collection( - collection_name, max_length=64, id_type="string", **kwargs + def _create_schema_for_namespace(self) -> CollectionSchema: + """Create schema based on the current instance's namespace""" + + # Get vector dimension from embedding_func + dimension = self.embedding_func.embedding_dim + + # Base fields (common to all collections) + base_fields = [ + FieldSchema( + name="id", dtype=DataType.VARCHAR, max_length=64, is_primary=True + ), + FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=dimension), + FieldSchema(name="created_at", dtype=DataType.INT64), + ] + + # Determine specific fields based on namespace + if "entities" in self.namespace.lower(): + specific_fields = [ + FieldSchema( + name="entity_name", + dtype=DataType.VARCHAR, + max_length=256, + nullable=True, + ), + FieldSchema( + name="entity_type", + dtype=DataType.VARCHAR, + max_length=64, + nullable=True, + ), + FieldSchema( + name="file_path", + dtype=DataType.VARCHAR, + max_length=512, + nullable=True, + ), + ] + description = "LightRAG entities vector storage" + + elif "relationships" in self.namespace.lower(): + specific_fields = [ + FieldSchema( + name="src_id", dtype=DataType.VARCHAR, max_length=256, nullable=True + ), + FieldSchema( + name="tgt_id", dtype=DataType.VARCHAR, max_length=256, nullable=True + ), + FieldSchema(name="weight", dtype=DataType.DOUBLE, nullable=True), + FieldSchema( + name="file_path", + dtype=DataType.VARCHAR, + max_length=512, + nullable=True, + ), + ] + description = "LightRAG relationships vector storage" + + elif "chunks" in self.namespace.lower(): + specific_fields = [ + FieldSchema( + name="full_doc_id", + dtype=DataType.VARCHAR, + max_length=64, + nullable=True, + ), + FieldSchema( + name="file_path", + dtype=DataType.VARCHAR, + max_length=512, + nullable=True, + ), + ] + description = "LightRAG chunks vector storage" + + else: + # Default generic schema (backward compatibility) + specific_fields = [ + FieldSchema( + name="file_path", + dtype=DataType.VARCHAR, + max_length=512, + nullable=True, + ), + ] + description = "LightRAG generic vector storage" + + # Merge all fields + all_fields = base_fields + specific_fields + + return CollectionSchema( + fields=all_fields, + description=description, + enable_dynamic_field=True, # Support dynamic fields ) + def _get_index_params(self): + """Get IndexParams in a version-compatible way""" + try: + # Try to use client's prepare_index_params method (most common) + if hasattr(self._client, "prepare_index_params"): + return self._client.prepare_index_params() + except Exception: + pass + + try: + # Try to import IndexParams from different possible locations + from pymilvus.client.prepare import IndexParams + + return IndexParams() + except ImportError: + pass + + try: + from pymilvus.client.types import IndexParams + + return IndexParams() + except ImportError: + pass + + try: + from pymilvus import IndexParams + + return IndexParams() + except ImportError: + pass + + # If all else fails, return None to use fallback method + return None + + def _create_vector_index_fallback(self): + """Fallback method to create vector index using direct API""" + try: + self._client.create_index( + collection_name=self.namespace, + field_name="vector", + index_params={ + "index_type": "HNSW", + "metric_type": "COSINE", + "params": {"M": 16, "efConstruction": 256}, + }, + ) + logger.debug("Created vector index using fallback method") + except Exception as e: + logger.warning(f"Failed to create vector index using fallback method: {e}") + + def _create_scalar_index_fallback(self, field_name: str, index_type: str): + """Fallback method to create scalar index using direct API""" + # Skip unsupported index types + if index_type == "SORTED": + logger.info( + f"Skipping SORTED index for {field_name} (not supported in this Milvus version)" + ) + return + + try: + self._client.create_index( + collection_name=self.namespace, + field_name=field_name, + index_params={"index_type": index_type}, + ) + logger.debug(f"Created {field_name} index using fallback method") + except Exception as e: + logger.info( + f"Could not create {field_name} index using fallback method: {e}" + ) + + def _create_indexes_after_collection(self): + """Create indexes after collection is created""" + try: + # Try to get IndexParams in a version-compatible way + IndexParamsClass = self._get_index_params() + + if IndexParamsClass is not None: + # Use IndexParams approach if available + try: + # Create vector index first (required for most operations) + vector_index = IndexParamsClass + vector_index.add_index( + field_name="vector", + index_type="HNSW", + metric_type="COSINE", + params={"M": 16, "efConstruction": 256}, + ) + self._client.create_index( + collection_name=self.namespace, index_params=vector_index + ) + logger.debug("Created vector index using IndexParams") + except Exception as e: + logger.debug(f"IndexParams method failed for vector index: {e}") + self._create_vector_index_fallback() + + # Create scalar indexes based on namespace + if "entities" in self.namespace.lower(): + # Create indexes for entity fields + try: + entity_name_index = self._get_index_params() + entity_name_index.add_index( + field_name="entity_name", index_type="INVERTED" + ) + self._client.create_index( + collection_name=self.namespace, + index_params=entity_name_index, + ) + except Exception as e: + logger.debug(f"IndexParams method failed for entity_name: {e}") + self._create_scalar_index_fallback("entity_name", "INVERTED") + + try: + entity_type_index = self._get_index_params() + entity_type_index.add_index( + field_name="entity_type", index_type="INVERTED" + ) + self._client.create_index( + collection_name=self.namespace, + index_params=entity_type_index, + ) + except Exception as e: + logger.debug(f"IndexParams method failed for entity_type: {e}") + self._create_scalar_index_fallback("entity_type", "INVERTED") + + elif "relationships" in self.namespace.lower(): + # Create indexes for relationship fields + try: + src_id_index = self._get_index_params() + src_id_index.add_index( + field_name="src_id", index_type="INVERTED" + ) + self._client.create_index( + collection_name=self.namespace, index_params=src_id_index + ) + except Exception as e: + logger.debug(f"IndexParams method failed for src_id: {e}") + self._create_scalar_index_fallback("src_id", "INVERTED") + + try: + tgt_id_index = self._get_index_params() + tgt_id_index.add_index( + field_name="tgt_id", index_type="INVERTED" + ) + self._client.create_index( + collection_name=self.namespace, index_params=tgt_id_index + ) + except Exception as e: + logger.debug(f"IndexParams method failed for tgt_id: {e}") + self._create_scalar_index_fallback("tgt_id", "INVERTED") + + elif "chunks" in self.namespace.lower(): + # Create indexes for chunk fields + try: + doc_id_index = self._get_index_params() + doc_id_index.add_index( + field_name="full_doc_id", index_type="INVERTED" + ) + self._client.create_index( + collection_name=self.namespace, index_params=doc_id_index + ) + except Exception as e: + logger.debug(f"IndexParams method failed for full_doc_id: {e}") + self._create_scalar_index_fallback("full_doc_id", "INVERTED") + + # No common indexes needed + + else: + # Fallback to direct API calls if IndexParams is not available + logger.info( + f"IndexParams not available, using fallback methods for {self.namespace}" + ) + + # Create vector index using fallback + self._create_vector_index_fallback() + + # Create scalar indexes using fallback + if "entities" in self.namespace.lower(): + self._create_scalar_index_fallback("entity_name", "INVERTED") + self._create_scalar_index_fallback("entity_type", "INVERTED") + elif "relationships" in self.namespace.lower(): + self._create_scalar_index_fallback("src_id", "INVERTED") + self._create_scalar_index_fallback("tgt_id", "INVERTED") + elif "chunks" in self.namespace.lower(): + self._create_scalar_index_fallback("full_doc_id", "INVERTED") + + logger.info(f"Created indexes for collection: {self.namespace}") + + except Exception as e: + logger.warning(f"Failed to create some indexes for {self.namespace}: {e}") + + def _get_required_fields_for_namespace(self) -> dict: + """Get required core field definitions for current namespace""" + + # Base fields (common to all types) + base_fields = { + "id": {"type": "VarChar", "is_primary": True}, + "vector": {"type": "FloatVector"}, + "created_at": {"type": "Int64"}, + } + + # Add specific fields based on namespace + if "entities" in self.namespace.lower(): + specific_fields = { + "entity_name": {"type": "VarChar"}, + "entity_type": {"type": "VarChar"}, + "file_path": {"type": "VarChar"}, + } + elif "relationships" in self.namespace.lower(): + specific_fields = { + "src_id": {"type": "VarChar"}, + "tgt_id": {"type": "VarChar"}, + "weight": {"type": "Double"}, + "file_path": {"type": "VarChar"}, + } + elif "chunks" in self.namespace.lower(): + specific_fields = { + "full_doc_id": {"type": "VarChar"}, + "file_path": {"type": "VarChar"}, + } + else: + specific_fields = { + "file_path": {"type": "VarChar"}, + } + + return {**base_fields, **specific_fields} + + def _is_field_compatible(self, existing_field: dict, expected_config: dict) -> bool: + """Check compatibility of a single field""" + field_name = existing_field.get("name", "unknown") + existing_type = existing_field.get("type") + expected_type = expected_config.get("type") + + logger.debug( + f"Checking field '{field_name}': existing_type={existing_type} (type={type(existing_type)}), expected_type={expected_type}" + ) + + # Convert DataType enum values to string names if needed + original_existing_type = existing_type + if hasattr(existing_type, "name"): + existing_type = existing_type.name + logger.debug( + f"Converted enum to name: {original_existing_type} -> {existing_type}" + ) + elif isinstance(existing_type, int): + # Map common Milvus internal type codes to type names for backward compatibility + type_mapping = { + 21: "VarChar", + 101: "FloatVector", + 5: "Int64", + 9: "Double", + } + mapped_type = type_mapping.get(existing_type, str(existing_type)) + logger.debug(f"Mapped numeric type: {existing_type} -> {mapped_type}") + existing_type = mapped_type + + # Normalize type names for comparison + type_aliases = { + "VARCHAR": "VarChar", + "String": "VarChar", + "FLOAT_VECTOR": "FloatVector", + "INT64": "Int64", + "BigInt": "Int64", + "DOUBLE": "Double", + "Float": "Double", + } + + original_existing = existing_type + original_expected = expected_type + existing_type = type_aliases.get(existing_type, existing_type) + expected_type = type_aliases.get(expected_type, expected_type) + + if original_existing != existing_type or original_expected != expected_type: + logger.debug( + f"Applied aliases: {original_existing} -> {existing_type}, {original_expected} -> {expected_type}" + ) + + # Basic type compatibility check + type_compatible = existing_type == expected_type + logger.debug( + f"Type compatibility for '{field_name}': {existing_type} == {expected_type} -> {type_compatible}" + ) + + if not type_compatible: + logger.warning( + f"Type mismatch for field '{field_name}': expected {expected_type}, got {existing_type}" + ) + return False + + # Primary key check - be more flexible about primary key detection + if expected_config.get("is_primary"): + # Check multiple possible field names for primary key status + is_primary = ( + existing_field.get("is_primary_key", False) + or existing_field.get("is_primary", False) + or existing_field.get("primary_key", False) + ) + logger.debug( + f"Primary key check for '{field_name}': expected=True, actual={is_primary}" + ) + logger.debug(f"Raw field data for '{field_name}': {existing_field}") + + # For ID field, be more lenient - if it's the ID field, assume it should be primary + if field_name == "id" and not is_primary: + logger.info( + f"ID field '{field_name}' not marked as primary in existing collection, but treating as compatible" + ) + # Don't fail for ID field primary key mismatch + elif not is_primary: + logger.warning( + f"Primary key mismatch for field '{field_name}': expected primary key, but field is not primary" + ) + return False + + logger.debug(f"Field '{field_name}' is compatible") + return True + + def _check_vector_dimension(self, collection_info: dict): + """Check vector dimension compatibility""" + current_dimension = self.embedding_func.embedding_dim + + # Find vector field dimension + for field in collection_info.get("fields", []): + if field.get("name") == "vector": + field_type = field.get("type") + + # Extract type name from DataType enum or string + type_name = None + if hasattr(field_type, "name"): + type_name = field_type.name + elif isinstance(field_type, str): + type_name = field_type + else: + type_name = str(field_type) + + # Check if it's a vector type (supports multiple formats) + if type_name in ["FloatVector", "FLOAT_VECTOR"]: + existing_dimension = field.get("params", {}).get("dim") + + if existing_dimension != current_dimension: + raise ValueError( + f"Vector dimension mismatch for collection '{self.namespace}': " + f"existing={existing_dimension}, current={current_dimension}" + ) + + logger.debug(f"Vector dimension check passed: {current_dimension}") + return + + # If no vector field found, this might be an old collection created with simple schema + logger.warning( + f"Vector field not found in collection '{self.namespace}'. This might be an old collection created with simple schema." + ) + logger.warning("Consider recreating the collection for optimal performance.") + return + + def _check_schema_compatibility(self, collection_info: dict): + """Check schema field compatibility""" + existing_fields = { + field["name"]: field for field in collection_info.get("fields", []) + } + + # Check if this is an old collection created with simple schema + has_vector_field = any( + field.get("name") == "vector" for field in collection_info.get("fields", []) + ) + + if not has_vector_field: + logger.warning( + f"Collection {self.namespace} appears to be created with old simple schema (no vector field)" + ) + logger.warning( + "This collection will work but may have suboptimal performance" + ) + logger.warning("Consider recreating the collection for optimal performance") + return + + # For collections with vector field, check basic compatibility + # Only check for critical incompatibilities, not missing optional fields + critical_fields = {"id": {"type": "VarChar", "is_primary": True}} + + incompatible_fields = [] + + for field_name, expected_config in critical_fields.items(): + if field_name in existing_fields: + existing_field = existing_fields[field_name] + if not self._is_field_compatible(existing_field, expected_config): + incompatible_fields.append( + f"{field_name}: expected {expected_config['type']}, " + f"got {existing_field.get('type')}" + ) + + if incompatible_fields: + raise ValueError( + f"Critical schema incompatibility in collection '{self.namespace}': {incompatible_fields}" + ) + + # Get all expected fields for informational purposes + expected_fields = self._get_required_fields_for_namespace() + missing_fields = [ + field for field in expected_fields if field not in existing_fields + ] + + if missing_fields: + logger.info( + f"Collection {self.namespace} missing optional fields: {missing_fields}" + ) + logger.info( + "These fields would be available in a newly created collection for better performance" + ) + + logger.debug(f"Schema compatibility check passed for {self.namespace}") + + def _validate_collection_compatibility(self): + """Validate existing collection's dimension and schema compatibility""" + try: + collection_info = self._client.describe_collection(self.namespace) + + # 1. Check vector dimension + self._check_vector_dimension(collection_info) + + # 2. Check schema compatibility + self._check_schema_compatibility(collection_info) + + logger.info( + f"VectorDB Collection '{self.namespace}' compatibility validation passed" + ) + + except Exception as e: + logger.error( + f"Collection compatibility validation failed for {self.namespace}: {e}" + ) + raise + + def _ensure_collection_loaded(self): + """Ensure the collection is loaded into memory for search operations""" + try: + # Check if collection exists first + if not self._client.has_collection(self.namespace): + logger.error(f"Collection {self.namespace} does not exist") + raise ValueError(f"Collection {self.namespace} does not exist") + + # Load the collection if it's not already loaded + # In Milvus, collections need to be loaded before they can be searched + self._client.load_collection(self.namespace) + logger.debug(f"Collection {self.namespace} loaded successfully") + + except Exception as e: + logger.error(f"Failed to load collection {self.namespace}: {e}") + raise + + def _create_collection_if_not_exist(self): + """Create collection if not exists and check existing collection compatibility""" + + try: + # First, list all collections to see what actually exists + try: + all_collections = self._client.list_collections() + logger.debug(f"All collections in database: {all_collections}") + except Exception as list_error: + logger.warning(f"Could not list collections: {list_error}") + all_collections = [] + + # Check if our specific collection exists + collection_exists = self._client.has_collection(self.namespace) + logger.info( + f"VectorDB collection '{self.namespace}' exists check: {collection_exists}" + ) + + if collection_exists: + # Double-check by trying to describe the collection + try: + self._client.describe_collection(self.namespace) + self._validate_collection_compatibility() + # Ensure the collection is loaded after validation + self._ensure_collection_loaded() + return + except Exception as describe_error: + logger.warning( + f"Collection '{self.namespace}' exists but cannot be described: {describe_error}" + ) + logger.info( + "Treating as if collection doesn't exist and creating new one..." + ) + # Fall through to creation logic + + # Collection doesn't exist, create new collection + logger.info(f"Creating new collection: {self.namespace}") + schema = self._create_schema_for_namespace() + + # Create collection with schema only first + self._client.create_collection( + collection_name=self.namespace, schema=schema + ) + + # Then create indexes + self._create_indexes_after_collection() + + # Load the newly created collection + self._ensure_collection_loaded() + + logger.info(f"Successfully created Milvus collection: {self.namespace}") + + except Exception as e: + logger.error( + f"Error in _create_collection_if_not_exist for {self.namespace}: {e}" + ) + + # If there's any error, try to force create the collection + logger.info(f"Attempting to force create collection {self.namespace}...") + try: + # Try to drop the collection first if it exists in a bad state + try: + if self._client.has_collection(self.namespace): + logger.info( + f"Dropping potentially corrupted collection {self.namespace}" + ) + self._client.drop_collection(self.namespace) + except Exception as drop_error: + logger.warning( + f"Could not drop collection {self.namespace}: {drop_error}" + ) + + # Create fresh collection + schema = self._create_schema_for_namespace() + self._client.create_collection( + collection_name=self.namespace, schema=schema + ) + self._create_indexes_after_collection() + + # Load the newly created collection + self._ensure_collection_loaded() + + logger.info(f"Successfully force-created collection {self.namespace}") + + except Exception as create_error: + logger.error( + f"Failed to force-create collection {self.namespace}: {create_error}" + ) + raise + def __post_init__(self): + # Check for MILVUS_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all Milvus storage instances + milvus_workspace = os.environ.get("MILVUS_WORKSPACE") + if milvus_workspace and milvus_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = milvus_workspace.strip() + logger.info( + f"Using MILVUS_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build namespace with workspace prefix for data isolation + if effective_workspace: + self.namespace = f"{effective_workspace}_{self.namespace}" + logger.debug(f"Final namespace with workspace prefix: '{self.namespace}'") + # When workspace is empty, keep the original namespace unchanged + kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {}) cosine_threshold = kwargs.get("cosine_better_than_threshold") if cosine_threshold is None: @@ -43,6 +687,10 @@ class MilvusVectorDBStorage(BaseVectorStorage): ) self.cosine_better_than_threshold = cosine_threshold + # Ensure created_at is in meta_fields + if "created_at" not in self.meta_fields: + self.meta_fields.add("created_at") + self._client = MilvusClient( uri=os.environ.get( "MILVUS_URI", @@ -68,17 +716,18 @@ class MilvusVectorDBStorage(BaseVectorStorage): ), ) self._max_batch_size = self.global_config["embedding_batch_num"] - MilvusVectorDBStorage.create_collection_if_not_exist( - self._client, - self.namespace, - dimension=self.embedding_func.embedding_dim, - ) + + # Create collection and check compatibility + self._create_collection_if_not_exist() async def upsert(self, data: dict[str, dict[str, Any]]) -> None: - logger.info(f"Inserting {len(data)} to {self.namespace}") + logger.debug(f"Inserting {len(data)} to {self.namespace}") if not data: return + # Ensure collection is loaded before upserting + self._ensure_collection_loaded() + import time current_time = int(time.time()) @@ -109,26 +758,31 @@ class MilvusVectorDBStorage(BaseVectorStorage): async def query( self, query: str, top_k: int, ids: list[str] | None = None ) -> list[dict[str, Any]]: + # Ensure collection is loaded before querying + self._ensure_collection_loaded() + embedding = await self.embedding_func( [query], _priority=5 ) # higher priority for query + + # Include all meta_fields (created_at is now always included) + output_fields = list(self.meta_fields) + results = self._client.search( collection_name=self.namespace, data=embedding, limit=top_k, - output_fields=list(self.meta_fields) + ["created_at"], + output_fields=output_fields, search_params={ "metric_type": "COSINE", "params": {"radius": self.cosine_better_than_threshold}, }, ) - print(results) return [ { **dp["entity"], "id": dp["id"], "distance": dp["distance"], - # created_at is requested in output_fields, so it should be a top-level key in the result dict (dp) "created_at": dp.get("created_at"), } for dp in results[0] @@ -171,6 +825,9 @@ class MilvusVectorDBStorage(BaseVectorStorage): entity_name: The name of the entity whose relations should be deleted """ try: + # Ensure collection is loaded before querying + self._ensure_collection_loaded() + # Search for relations where entity is either source or target expr = f'src_id == "{entity_name}" or tgt_id == "{entity_name}"' @@ -209,6 +866,9 @@ class MilvusVectorDBStorage(BaseVectorStorage): ids: List of vector IDs to be deleted """ try: + # Ensure collection is loaded before deleting + self._ensure_collection_loaded() + # Delete vectors by IDs result = self._client.delete(collection_name=self.namespace, pks=ids) @@ -232,20 +892,22 @@ class MilvusVectorDBStorage(BaseVectorStorage): The vector data if found, or None if not found """ try: + # Ensure collection is loaded before querying + self._ensure_collection_loaded() + + # Include all meta_fields (created_at is now always included) plus id + output_fields = list(self.meta_fields) + ["id"] + # Query Milvus for a specific ID result = self._client.query( collection_name=self.namespace, filter=f'id == "{id}"', - output_fields=list(self.meta_fields) + ["id", "created_at"], + output_fields=output_fields, ) if not result or len(result) == 0: return None - # Ensure the result contains created_at field - if "created_at" not in result[0]: - result[0]["created_at"] = None - return result[0] except Exception as e: logger.error(f"Error retrieving vector data for ID {id}: {e}") @@ -264,6 +926,12 @@ class MilvusVectorDBStorage(BaseVectorStorage): return [] try: + # Ensure collection is loaded before querying + self._ensure_collection_loaded() + + # Include all meta_fields (created_at is now always included) plus id + output_fields = list(self.meta_fields) + ["id"] + # Prepare the ID filter expression id_list = '", "'.join(ids) filter_expr = f'id in ["{id_list}"]' @@ -272,14 +940,9 @@ class MilvusVectorDBStorage(BaseVectorStorage): result = self._client.query( collection_name=self.namespace, filter=filter_expr, - output_fields=list(self.meta_fields) + ["id", "created_at"], + output_fields=output_fields, ) - # Ensure each result contains created_at field - for item in result: - if "created_at" not in item: - item["created_at"] = None - return result or [] except Exception as e: logger.error(f"Error retrieving vector data for IDs {ids}: {e}") @@ -301,11 +964,7 @@ class MilvusVectorDBStorage(BaseVectorStorage): self._client.drop_collection(self.namespace) # Recreate the collection - MilvusVectorDBStorage.create_collection_if_not_exist( - self._client, - self.namespace, - dimension=self.embedding_func.embedding_dim, - ) + self._create_collection_if_not_exist() logger.info( f"Process {os.getpid()} drop Milvus collection {self.namespace}" diff --git a/lightrag/kg/mongo_impl.py b/lightrag/kg/mongo_impl.py index d49a36b7..dcf99327 100644 --- a/lightrag/kg/mongo_impl.py +++ b/lightrag/kg/mongo_impl.py @@ -1,10 +1,11 @@ import os +import time from dataclasses import dataclass, field import numpy as np import configparser import asyncio -from typing import Any, List, Union, final +from typing import Any, Union, final from ..base import ( BaseGraphStorage, @@ -14,35 +15,34 @@ from ..base import ( DocStatus, DocStatusStorage, ) -from ..namespace import NameSpace, is_namespace from ..utils import logger, compute_mdhash_id from ..types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge +from ..constants import GRAPH_FIELD_SEP + import pipmaster as pm if not pm.is_installed("pymongo"): pm.install("pymongo") -if not pm.is_installed("motor"): - pm.install("motor") - -from motor.motor_asyncio import ( # type: ignore - AsyncIOMotorClient, - AsyncIOMotorDatabase, - AsyncIOMotorCollection, -) +from pymongo import AsyncMongoClient # type: ignore +from pymongo import UpdateOne # type: ignore +from pymongo.asynchronous.database import AsyncDatabase # type: ignore +from pymongo.asynchronous.collection import AsyncCollection # type: ignore from pymongo.operations import SearchIndexModel # type: ignore from pymongo.errors import PyMongoError # type: ignore config = configparser.ConfigParser() config.read("config.ini", "utf-8") +GRAPH_BFS_MODE = os.getenv("MONGO_GRAPH_BFS_MODE", "bidirectional") + class ClientManager: _instances = {"db": None, "ref_count": 0} _lock = asyncio.Lock() @classmethod - async def get_client(cls) -> AsyncIOMotorDatabase: + async def get_client(cls) -> AsyncMongoClient: async with cls._lock: if cls._instances["db"] is None: uri = os.environ.get( @@ -57,7 +57,7 @@ class ClientManager: "MONGO_DATABASE", config.get("mongodb", "database", fallback="LightRAG"), ) - client = AsyncIOMotorClient(uri) + client = AsyncMongoClient(uri) db = client.get_database(database_name) cls._instances["db"] = db cls._instances["ref_count"] = 0 @@ -65,7 +65,7 @@ class ClientManager: return cls._instances["db"] @classmethod - async def release_client(cls, db: AsyncIOMotorDatabase): + async def release_client(cls, db: AsyncDatabase): async with cls._lock: if db is not None: if db is cls._instances["db"]: @@ -77,10 +77,42 @@ class ClientManager: @final @dataclass class MongoKVStorage(BaseKVStorage): - db: AsyncIOMotorDatabase = field(default=None) - _data: AsyncIOMotorCollection = field(default=None) + db: AsyncDatabase = field(default=None) + _data: AsyncCollection = field(default=None) + + def __init__(self, namespace, global_config, embedding_func, workspace=None): + super().__init__( + namespace=namespace, + workspace=workspace or "", + global_config=global_config, + embedding_func=embedding_func, + ) + self.__post_init__() def __post_init__(self): + # Check for MONGODB_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all MongoDB storage instances + mongodb_workspace = os.environ.get("MONGODB_WORKSPACE") + if mongodb_workspace and mongodb_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = mongodb_workspace.strip() + logger.info( + f"Using MONGODB_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build namespace with workspace prefix for data isolation + if effective_workspace: + self.namespace = f"{effective_workspace}_{self.namespace}" + logger.debug(f"Final namespace with workspace prefix: '{self.namespace}'") + # When workspace is empty, keep the original namespace unchanged + self._collection_name = self.namespace async def initialize(self): @@ -96,55 +128,84 @@ class MongoKVStorage(BaseKVStorage): self._data = None async def get_by_id(self, id: str) -> dict[str, Any] | None: - return await self._data.find_one({"_id": id}) + # Unified handling for flattened keys + doc = await self._data.find_one({"_id": id}) + if doc: + # Ensure time fields are present, provide default values for old data + doc.setdefault("create_time", 0) + doc.setdefault("update_time", 0) + return doc async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: cursor = self._data.find({"_id": {"$in": ids}}) - return await cursor.to_list() + docs = await cursor.to_list() + # Ensure time fields are present for all documents + for doc in docs: + doc.setdefault("create_time", 0) + doc.setdefault("update_time", 0) + return docs async def filter_keys(self, keys: set[str]) -> set[str]: cursor = self._data.find({"_id": {"$in": list(keys)}}, {"_id": 1}) existing_ids = {str(x["_id"]) async for x in cursor} return keys - existing_ids + async def get_all(self) -> dict[str, Any]: + """Get all data from storage + + Returns: + Dictionary containing all stored data + """ + cursor = self._data.find({}) + result = {} + async for doc in cursor: + doc_id = doc.pop("_id") + # Ensure time fields are present for all documents + doc.setdefault("create_time", 0) + doc.setdefault("update_time", 0) + result[doc_id] = doc + return result + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: - logger.info(f"Inserting {len(data)} to {self.namespace}") + logger.debug(f"Inserting {len(data)} to {self.namespace}") if not data: return - if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE): - update_tasks: list[Any] = [] - for mode, items in data.items(): - for k, v in items.items(): - key = f"{mode}_{k}" - data[mode][k]["_id"] = f"{mode}_{k}" - update_tasks.append( - self._data.update_one( - {"_id": key}, {"$setOnInsert": v}, upsert=True - ) - ) - await asyncio.gather(*update_tasks) - else: - update_tasks = [] - for k, v in data.items(): - data[k]["_id"] = k - update_tasks.append( - self._data.update_one({"_id": k}, {"$set": v}, upsert=True) - ) - await asyncio.gather(*update_tasks) + # Unified handling for all namespaces with flattened keys + # Use bulk_write for better performance - async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]: - if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE): - res = {} - v = await self._data.find_one({"_id": mode + "_" + id}) - if v: - res[id] = v - logger.debug(f"llm_response_cache find one by:{id}") - return res - else: - return None - else: - return None + operations = [] + current_time = int(time.time()) # Get current Unix timestamp + + for k, v in data.items(): + # For text_chunks namespace, ensure llm_cache_list field exists + if self.namespace.endswith("text_chunks"): + if "llm_cache_list" not in v: + v["llm_cache_list"] = [] + + # Create a copy of v for $set operation, excluding create_time to avoid conflicts + v_for_set = v.copy() + v_for_set["_id"] = k # Use flattened key as _id + v_for_set["update_time"] = current_time # Always update update_time + + # Remove create_time from $set to avoid conflict with $setOnInsert + v_for_set.pop("create_time", None) + + operations.append( + UpdateOne( + {"_id": k}, + { + "$set": v_for_set, # Update all fields except create_time + "$setOnInsert": { + "create_time": current_time + }, # Set create_time only on insert + }, + upsert=True, + ) + ) + + if operations: + await self._data.bulk_write(operations) async def index_done_callback(self) -> None: # Mongo handles persistence automatically @@ -159,6 +220,10 @@ class MongoKVStorage(BaseKVStorage): if not ids: return + # Convert to list if it's a set (MongoDB BSON cannot encode sets) + if isinstance(ids, set): + ids = list(ids) + try: result = await self._data.delete_many({"_id": {"$in": ids}}) logger.info( @@ -180,8 +245,8 @@ class MongoKVStorage(BaseKVStorage): return False try: - # Build regex pattern to match documents with the specified modes - pattern = f"^({'|'.join(modes)})_" + # Build regex pattern to match flattened key format: mode:cache_type:hash + pattern = f"^({'|'.join(modes)}):" result = await self._data.delete_many({"_id": {"$regex": pattern}}) logger.info(f"Deleted {result.deleted_count} documents by modes: {modes}") return True @@ -214,10 +279,42 @@ class MongoKVStorage(BaseKVStorage): @final @dataclass class MongoDocStatusStorage(DocStatusStorage): - db: AsyncIOMotorDatabase = field(default=None) - _data: AsyncIOMotorCollection = field(default=None) + db: AsyncDatabase = field(default=None) + _data: AsyncCollection = field(default=None) + + def __init__(self, namespace, global_config, embedding_func, workspace=None): + super().__init__( + namespace=namespace, + workspace=workspace or "", + global_config=global_config, + embedding_func=embedding_func, + ) + self.__post_init__() def __post_init__(self): + # Check for MONGODB_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all MongoDB storage instances + mongodb_workspace = os.environ.get("MONGODB_WORKSPACE") + if mongodb_workspace and mongodb_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = mongodb_workspace.strip() + logger.info( + f"Using MONGODB_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build namespace with workspace prefix for data isolation + if effective_workspace: + self.namespace = f"{effective_workspace}_{self.namespace}" + logger.debug(f"Final namespace with workspace prefix: '{self.namespace}'") + # When workspace is empty, keep the original namespace unchanged + self._collection_name = self.namespace async def initialize(self): @@ -245,11 +342,14 @@ class MongoDocStatusStorage(DocStatusStorage): return data - existing_ids async def upsert(self, data: dict[str, dict[str, Any]]) -> None: - logger.info(f"Inserting {len(data)} to {self.namespace}") + logger.debug(f"Inserting {len(data)} to {self.namespace}") if not data: return update_tasks: list[Any] = [] for k, v in data.items(): + # Ensure chunks_list field exists and is an array + if "chunks_list" not in v: + v["chunks_list"] = [] data[k]["_id"] = k update_tasks.append( self._data.update_one({"_id": k}, {"$set": v}, upsert=True) @@ -259,7 +359,7 @@ class MongoDocStatusStorage(DocStatusStorage): async def get_status_counts(self) -> dict[str, int]: """Get counts of documents in each status""" pipeline = [{"$group": {"_id": "$status", "count": {"$sum": 1}}}] - cursor = self._data.aggregate(pipeline) + cursor = self._data.aggregate(pipeline, allowDiskUse=True) result = await cursor.to_list() counts = {} for doc in result: @@ -282,6 +382,7 @@ class MongoDocStatusStorage(DocStatusStorage): updated_at=doc.get("updated_at"), chunks_count=doc.get("chunks_count", -1), file_path=doc.get("file_path", doc["_id"]), + chunks_list=doc.get("chunks_list", []), ) for doc in result } @@ -311,6 +412,9 @@ class MongoDocStatusStorage(DocStatusStorage): logger.error(f"Error dropping doc status {self._collection_name}: {e}") return {"status": "error", "message": str(e)} + async def delete(self, ids: list[str]) -> None: + await self._data.delete_many({"_id": {"$in": ids}}) + @final @dataclass @@ -319,16 +423,44 @@ class MongoGraphStorage(BaseGraphStorage): A concrete implementation using MongoDB's $graphLookup to demonstrate multi-hop queries. """ - db: AsyncIOMotorDatabase = field(default=None) - collection: AsyncIOMotorCollection = field(default=None) + db: AsyncDatabase = field(default=None) + # node collection storing node_id, node_properties + collection: AsyncCollection = field(default=None) + # edge collection storing source_node_id, target_node_id, and edge_properties + edgeCollection: AsyncCollection = field(default=None) - def __init__(self, namespace, global_config, embedding_func): + def __init__(self, namespace, global_config, embedding_func, workspace=None): super().__init__( namespace=namespace, + workspace=workspace or "", global_config=global_config, embedding_func=embedding_func, ) + # Check for MONGODB_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all MongoDB storage instances + mongodb_workspace = os.environ.get("MONGODB_WORKSPACE") + if mongodb_workspace and mongodb_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = mongodb_workspace.strip() + logger.info( + f"Using MONGODB_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build namespace with workspace prefix for data isolation + if effective_workspace: + self.namespace = f"{effective_workspace}_{self.namespace}" + logger.debug(f"Final namespace with workspace prefix: '{self.namespace}'") + # When workspace is empty, keep the original namespace unchanged + self._collection_name = self.namespace + self._edge_collection_name = f"{self._collection_name}_edges" async def initialize(self): if self.db is None: @@ -336,6 +468,9 @@ class MongoGraphStorage(BaseGraphStorage): self.collection = await get_or_create_collection( self.db, self._collection_name ) + self.edge_collection = await get_or_create_collection( + self.db, self._edge_collection_name + ) logger.debug(f"Use MongoDB as KG {self._collection_name}") async def finalize(self): @@ -343,58 +478,36 @@ class MongoGraphStorage(BaseGraphStorage): await ClientManager.release_client(self.db) self.db = None self.collection = None + self.edge_collection = None - # - # ------------------------------------------------------------------------- - # HELPER: $graphLookup pipeline - # ------------------------------------------------------------------------- - # + # Sample entity document + # "source_ids" is Array representation of "source_id" split by GRAPH_FIELD_SEP - async def _graph_lookup( - self, start_node_id: str, max_depth: int = None - ) -> List[dict]: - """ - Performs a $graphLookup starting from 'start_node_id' and returns - all reachable documents (including the start node itself). + # { + # "_id" : "CompanyA", + # "entity_id" : "CompanyA", + # "entity_type" : "Organization", + # "description" : "A major technology company", + # "source_id" : "chunk-eeec0036b909839e8ec4fa150c939eec", + # "source_ids": ["chunk-eeec0036b909839e8ec4fa150c939eec"], + # "file_path" : "custom_kg", + # "created_at" : 1749904575 + # } - Pipeline Explanation: - - 1) $match: We match the start node document by _id = start_node_id. - - 2) $graphLookup: - "from": same collection, - "startWith": "$edges.target" (the immediate neighbors in 'edges'), - "connectFromField": "edges.target", - "connectToField": "_id", - "as": "reachableNodes", - "maxDepth": max_depth (if provided), - "depthField": "depth" (used for debugging or filtering). - - 3) We add an $project or $unwind as needed to extract data. - """ - pipeline = [ - {"$match": {"_id": start_node_id}}, - { - "$graphLookup": { - "from": self.collection.name, - "startWith": "$edges.target", - "connectFromField": "edges.target", - "connectToField": "_id", - "as": "reachableNodes", - "depthField": "depth", - } - }, - ] - - # If you want a limited depth (e.g., only 1 or 2 hops), set maxDepth - if max_depth is not None: - pipeline[1]["$graphLookup"]["maxDepth"] = max_depth - - # Return the matching doc plus a field "reachableNodes" - cursor = self.collection.aggregate(pipeline) - results = await cursor.to_list(None) - - # If there's no matching node, results = []. - # Otherwise, results[0] is the start node doc, - # plus results[0]["reachableNodes"] is the array of connected docs. - return results + # Sample relation document + # { + # "_id" : ObjectId("6856ac6e7c6bad9b5470b678"), // MongoDB build-in ObjectId + # "description" : "CompanyA develops ProductX", + # "source_node_id" : "CompanyA", + # "target_node_id" : "ProductX", + # "relationship": "Develops", // To distinguish multiple same-target relations + # "weight" : Double("1"), + # "keywords" : "develop, produce", + # "source_id" : "chunk-eeec0036b909839e8ec4fa150c939eec", + # "source_ids": ["chunk-eeec0036b909839e8ec4fa150c939eec"], + # "file_path" : "custom_kg", + # "created_at" : 1749904575 + # } # # ------------------------------------------------------------------------- @@ -412,45 +525,24 @@ class MongoGraphStorage(BaseGraphStorage): async def has_edge(self, source_node_id: str, target_node_id: str) -> bool: """ - Check if there's a direct single-hop edge from source_node_id to target_node_id. - - We'll do a $graphLookup with maxDepth=0 from the source node—meaning - "Look up zero expansions." Actually, for a direct edge check, we can do maxDepth=1 - and then see if the target node is in the "reachableNodes" at depth=0. - - But typically for a direct edge, we might just do a find_one. - Below is a demonstration approach. + Check if there's a direct single-hop edge between source_node_id and target_node_id. """ - # We can do a single-hop graphLookup (maxDepth=0 or 1). - # Then check if the target_node appears among the edges array. - pipeline = [ - {"$match": {"_id": source_node_id}}, + doc = await self.edge_collection.find_one( { - "$graphLookup": { - "from": self.collection.name, - "startWith": "$edges.target", - "connectFromField": "edges.target", - "connectToField": "_id", - "as": "reachableNodes", - "depthField": "depth", - "maxDepth": 0, # means: do not follow beyond immediate edges - } + "$or": [ + { + "source_node_id": source_node_id, + "target_node_id": target_node_id, + }, + { + "source_node_id": target_node_id, + "target_node_id": source_node_id, + }, + ] }, - { - "$project": { - "_id": 0, - "reachableNodes._id": 1, # only keep the _id from the subdocs - } - }, - ] - cursor = self.collection.aggregate(pipeline) - results = await cursor.to_list(None) - if not results: - return False - - # results[0]["reachableNodes"] are the immediate neighbors - reachable_ids = [d["_id"] for d in results[0].get("reachableNodes", [])] - return target_node_id in reachable_ids + {"_id": 1}, + ) + return doc is not None # # ------------------------------------------------------------------------- @@ -461,85 +553,25 @@ class MongoGraphStorage(BaseGraphStorage): async def node_degree(self, node_id: str) -> int: """ Returns the total number of edges connected to node_id (both inbound and outbound). - The easiest approach is typically two queries: - - count of edges array in node_id's doc - - count of how many other docs have node_id in their edges.target. - - But we'll do a $graphLookup demonstration for inbound edges: - 1) Outbound edges: direct from node's edges array - 2) Inbound edges: we can do a special $graphLookup from all docs - or do an explicit match. - - For demonstration, let's do this in two steps (with second step $graphLookup). """ - # --- 1) Outbound edges (direct from doc) --- - doc = await self.collection.find_one({"_id": node_id}, {"edges": 1}) - if not doc: - return 0 - outbound_count = len(doc.get("edges", [])) - - # --- 2) Inbound edges: - # A simple way is: find all docs where "edges.target" == node_id. - # But let's do a $graphLookup from `node_id` in REVERSE. - # There's a trick to do "reverse" graphLookups: you'd store - # reversed edges or do a more advanced pipeline. Typically you'd do - # a direct match. We'll just do a direct match for inbound. - inbound_count_pipeline = [ - {"$match": {"edges.target": node_id}}, - { - "$project": { - "matchingEdgesCount": { - "$size": { - "$filter": { - "input": "$edges", - "as": "edge", - "cond": {"$eq": ["$$edge.target", node_id]}, - } - } - } - } - }, - {"$group": {"_id": None, "totalInbound": {"$sum": "$matchingEdgesCount"}}}, - ] - inbound_cursor = self.collection.aggregate(inbound_count_pipeline) - inbound_result = await inbound_cursor.to_list(None) - inbound_count = inbound_result[0]["totalInbound"] if inbound_result else 0 - - return outbound_count + inbound_count + return await self.edge_collection.count_documents( + {"$or": [{"source_node_id": node_id}, {"target_node_id": node_id}]} + ) async def edge_degree(self, src_id: str, tgt_id: str) -> int: - """ - If your graph can hold multiple edges from the same src to the same tgt - (e.g. different 'relation' values), you can sum them. If it's always - one edge, this is typically 1 or 0. + """Get the total degree (sum of relationships) of two nodes. - We'll do a single-hop $graphLookup from src_id, - then count how many edges reference tgt_id at depth=0. - """ - pipeline = [ - {"$match": {"_id": src_id}}, - { - "$graphLookup": { - "from": self.collection.name, - "startWith": "$edges.target", - "connectFromField": "edges.target", - "connectToField": "_id", - "as": "neighbors", - "depthField": "depth", - "maxDepth": 0, - } - }, - {"$project": {"edges": 1, "neighbors._id": 1, "neighbors.type": 1}}, - ] - cursor = self.collection.aggregate(pipeline) - results = await cursor.to_list(None) - if not results: - return 0 + Args: + src_id: Label of the source node + tgt_id: Label of the target node - # We can simply count how many edges in `results[0].edges` have target == tgt_id. - edges = results[0].get("edges", []) - count = sum(1 for e in edges if e.get("target") == tgt_id) - return count + Returns: + int: Sum of the degrees of both nodes + """ + src_degree = await self.node_degree(src_id) + trg_degree = await self.node_degree(tgt_id) + + return src_degree + trg_degree # # ------------------------------------------------------------------------- @@ -549,65 +581,171 @@ class MongoGraphStorage(BaseGraphStorage): async def get_node(self, node_id: str) -> dict[str, str] | None: """ - Return the full node document (including "edges"), or None if missing. + Return the full node document, or None if missing. """ return await self.collection.find_one({"_id": node_id}) async def get_edge( self, source_node_id: str, target_node_id: str ) -> dict[str, str] | None: - pipeline = [ - {"$match": {"_id": source_node_id}}, + return await self.edge_collection.find_one( { - "$graphLookup": { - "from": self.collection.name, - "startWith": "$edges.target", - "connectFromField": "edges.target", - "connectToField": "_id", - "as": "neighbors", - "depthField": "depth", - "maxDepth": 0, - } - }, - {"$project": {"edges": 1}}, - ] - cursor = self.collection.aggregate(pipeline) - docs = await cursor.to_list(None) - if not docs: - return None - - for e in docs[0].get("edges", []): - if e.get("target") == target_node_id: - return e - return None + "$or": [ + { + "source_node_id": source_node_id, + "target_node_id": target_node_id, + }, + { + "source_node_id": target_node_id, + "target_node_id": source_node_id, + }, + ] + } + ) async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None: """ - Return a list of (source_id, target_id) for direct edges from source_node_id. - Demonstrates $graphLookup at maxDepth=0, though direct doc retrieval is simpler. - """ - pipeline = [ - {"$match": {"_id": source_node_id}}, - { - "$graphLookup": { - "from": self.collection.name, - "startWith": "$edges.target", - "connectFromField": "edges.target", - "connectToField": "_id", - "as": "neighbors", - "depthField": "depth", - "maxDepth": 0, - } - }, - {"$project": {"_id": 0, "edges": 1}}, - ] - cursor = self.collection.aggregate(pipeline) - result = await cursor.to_list(None) - if not result: - return None + Retrieves all edges (relationships) for a particular node identified by its label. - edges = result[0].get("edges", []) - return [(source_node_id, e["target"]) for e in edges] + Args: + source_node_id: Label of the node to get edges for + + Returns: + list[tuple[str, str]]: List of (source_label, target_label) tuples representing edges + None: If no edges found + """ + cursor = self.edge_collection.find( + { + "$or": [ + {"source_node_id": source_node_id}, + {"target_node_id": source_node_id}, + ] + }, + {"source_node_id": 1, "target_node_id": 1}, + ) + + return [ + (e.get("source_node_id"), e.get("target_node_id")) async for e in cursor + ] + + async def get_nodes_batch(self, node_ids: list[str]) -> dict[str, dict]: + result = {} + + async for doc in self.collection.find({"_id": {"$in": node_ids}}): + result[doc.get("_id")] = doc + return result + + async def node_degrees_batch(self, node_ids: list[str]) -> dict[str, int]: + # merge the outbound and inbound results with the same "_id" and sum the "degree" + merged_results = {} + + # Outbound degrees + outbound_pipeline = [ + {"$match": {"source_node_id": {"$in": node_ids}}}, + {"$group": {"_id": "$source_node_id", "degree": {"$sum": 1}}}, + ] + + cursor = await self.edge_collection.aggregate( + outbound_pipeline, allowDiskUse=True + ) + async for doc in cursor: + merged_results[doc.get("_id")] = doc.get("degree") + + # Inbound degrees + inbound_pipeline = [ + {"$match": {"target_node_id": {"$in": node_ids}}}, + {"$group": {"_id": "$target_node_id", "degree": {"$sum": 1}}}, + ] + + cursor = await self.edge_collection.aggregate( + inbound_pipeline, allowDiskUse=True + ) + async for doc in cursor: + merged_results[doc.get("_id")] = merged_results.get( + doc.get("_id"), 0 + ) + doc.get("degree") + + return merged_results + + async def get_nodes_edges_batch( + self, node_ids: list[str] + ) -> dict[str, list[tuple[str, str]]]: + """ + Batch retrieve edges for multiple nodes. + For each node, returns both outgoing and incoming edges to properly represent + the undirected graph nature. + + Args: + node_ids: List of node IDs (entity_id) for which to retrieve edges. + + Returns: + A dictionary mapping each node ID to its list of edge tuples (source, target). + For each node, the list includes both: + - Outgoing edges: (queried_node, connected_node) + - Incoming edges: (connected_node, queried_node) + """ + result = {node_id: [] for node_id in node_ids} + + # Query outgoing edges (where node is the source) + outgoing_cursor = self.edge_collection.find( + {"source_node_id": {"$in": node_ids}}, + {"source_node_id": 1, "target_node_id": 1}, + ) + async for edge in outgoing_cursor: + source = edge["source_node_id"] + target = edge["target_node_id"] + result[source].append((source, target)) + + # Query incoming edges (where node is the target) + incoming_cursor = self.edge_collection.find( + {"target_node_id": {"$in": node_ids}}, + {"source_node_id": 1, "target_node_id": 1}, + ) + async for edge in incoming_cursor: + source = edge["source_node_id"] + target = edge["target_node_id"] + result[target].append((source, target)) + + return result + + async def get_nodes_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]: + """Get all nodes that are associated with the given chunk_ids. + + Args: + chunk_ids (list[str]): A list of chunk IDs to find associated nodes for. + + Returns: + list[dict]: A list of nodes, where each node is a dictionary of its properties. + An empty list if no matching nodes are found. + """ + if not chunk_ids: + return [] + + cursor = self.collection.find({"source_ids": {"$in": chunk_ids}}) + return [doc async for doc in cursor] + + async def get_edges_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]: + """Get all edges that are associated with the given chunk_ids. + + Args: + chunk_ids (list[str]): A list of chunk IDs to find associated edges for. + + Returns: + list[dict]: A list of edges, where each edge is a dictionary of its properties. + An empty list if no matching edges are found. + """ + if not chunk_ids: + return [] + + cursor = self.edge_collection.find({"source_ids": {"$in": chunk_ids}}) + + edges = [] + async for edge in cursor: + edge["source"] = edge["source_node_id"] + edge["target"] = edge["target_node_id"] + edges.append(edge) + + return edges # # ------------------------------------------------------------------------- @@ -617,33 +755,50 @@ class MongoGraphStorage(BaseGraphStorage): async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None: """ - Insert or update a node document. If new, create an empty edges array. + Insert or update a node document. """ - # By default, preserve existing 'edges'. - # We'll only set 'edges' to [] on insert (no overwrite). - update_doc = {"$set": {**node_data}, "$setOnInsert": {"edges": []}} + update_doc = {"$set": {**node_data}} + if node_data.get("source_id", ""): + update_doc["$set"]["source_ids"] = node_data["source_id"].split( + GRAPH_FIELD_SEP + ) + await self.collection.update_one({"_id": node_id}, update_doc, upsert=True) async def upsert_edge( self, source_node_id: str, target_node_id: str, edge_data: dict[str, str] ) -> None: """ - Upsert an edge from source_node_id -> target_node_id with optional 'relation'. + Upsert an edge between source_node_id and target_node_id with optional 'relation'. If an edge with the same target exists, we remove it and re-insert with updated data. """ # Ensure source node exists await self.upsert_node(source_node_id, {}) - # Remove existing edge (if any) - await self.collection.update_one( - {"_id": source_node_id}, {"$pull": {"edges": {"target": target_node_id}}} - ) + update_doc = {"$set": edge_data} + if edge_data.get("source_id", ""): + update_doc["$set"]["source_ids"] = edge_data["source_id"].split( + GRAPH_FIELD_SEP + ) - # Insert new edge - new_edge = {"target": target_node_id} - new_edge.update(edge_data) - await self.collection.update_one( - {"_id": source_node_id}, {"$push": {"edges": new_edge}} + edge_data["source_node_id"] = source_node_id + edge_data["target_node_id"] = target_node_id + + await self.edge_collection.update_one( + { + "$or": [ + { + "source_node_id": source_node_id, + "target_node_id": target_node_id, + }, + { + "source_node_id": target_node_id, + "target_node_id": source_node_id, + }, + ] + }, + update_doc, + upsert=True, ) # @@ -655,10 +810,12 @@ class MongoGraphStorage(BaseGraphStorage): async def delete_node(self, node_id: str) -> None: """ 1) Remove node's doc entirely. - 2) Remove inbound edges from any doc that references node_id. + 2) Remove inbound & outbound edges from any doc that references node_id. """ - # Remove inbound edges from all other docs - await self.collection.update_many({}, {"$pull": {"edges": {"target": node_id}}}) + # Remove all edges + await self.edge_collection.delete_many( + {"$or": [{"source_node_id": node_id}, {"target_node_id": node_id}]} + ) # Remove the node doc await self.collection.delete_one({"_id": node_id}) @@ -675,187 +832,408 @@ class MongoGraphStorage(BaseGraphStorage): Returns: [id1, id2, ...] # Alphabetically sorted id list """ - # Use MongoDB's distinct and aggregation to get all unique labels - pipeline = [ - {"$group": {"_id": "$_id"}}, # Group by _id - {"$sort": {"_id": 1}}, # Sort alphabetically - ] - cursor = self.collection.aggregate(pipeline) + # Use aggregation with allowDiskUse for large datasets + pipeline = [{"$project": {"_id": 1}}, {"$sort": {"_id": 1}}] + cursor = await self.collection.aggregate(pipeline, allowDiskUse=True) labels = [] async for doc in cursor: labels.append(doc["_id"]) return labels - async def get_knowledge_graph( - self, node_label: str, max_depth: int = 5 + def _construct_graph_node( + self, node_id, node_data: dict[str, str] + ) -> KnowledgeGraphNode: + return KnowledgeGraphNode( + id=node_id, + labels=[node_id], + properties={ + k: v + for k, v in node_data.items() + if k + not in [ + "_id", + "connected_edges", + "source_ids", + "edge_count", + ] + }, + ) + + def _construct_graph_edge(self, edge_id: str, edge: dict[str, str]): + return KnowledgeGraphEdge( + id=edge_id, + type=edge.get("relationship", ""), + source=edge["source_node_id"], + target=edge["target_node_id"], + properties={ + k: v + for k, v in edge.items() + if k + not in [ + "_id", + "source_node_id", + "target_node_id", + "relationship", + "source_ids", + ] + }, + ) + + async def get_knowledge_graph_all_by_degree( + self, max_depth: int, max_nodes: int ) -> KnowledgeGraph: """ - Get complete connected subgraph for specified node (including the starting node itself) - - Args: - node_label: Label of the nodes to start from - max_depth: Maximum depth of traversal (default: 5) - - Returns: - KnowledgeGraph object containing nodes and edges of the subgraph + It's possible that the node with one or multiple relationships is retrieved, + while its neighbor is not. Then this node might seem like disconnected in UI. """ - label = node_label + + total_node_count = await self.collection.count_documents({}) result = KnowledgeGraph() - seen_nodes = set() seen_edges = set() - try: - if label == "*": - # Get all nodes and edges - async for node_doc in self.collection.find({}): - node_id = str(node_doc["_id"]) - if node_id not in seen_nodes: - result.nodes.append( - KnowledgeGraphNode( - id=node_id, - labels=[node_doc.get("_id")], - properties={ - k: v - for k, v in node_doc.items() - if k not in ["_id", "edges"] - }, - ) - ) - seen_nodes.add(node_id) + result.is_truncated = total_node_count > max_nodes + if result.is_truncated: + # Get all node_ids ranked by degree if max_nodes exceeds total node count + pipeline = [ + {"$project": {"source_node_id": 1, "_id": 0}}, + {"$group": {"_id": "$source_node_id", "degree": {"$sum": 1}}}, + { + "$unionWith": { + "coll": self._edge_collection_name, + "pipeline": [ + {"$project": {"target_node_id": 1, "_id": 0}}, + { + "$group": { + "_id": "$target_node_id", + "degree": {"$sum": 1}, + } + }, + ], + } + }, + {"$group": {"_id": "$_id", "degree": {"$sum": "$degree"}}}, + {"$sort": {"degree": -1}}, + {"$limit": max_nodes}, + ] + cursor = await self.edge_collection.aggregate(pipeline, allowDiskUse=True) - # Process edges - for edge in node_doc.get("edges", []): - edge_id = f"{node_id}-{edge['target']}" - if edge_id not in seen_edges: - result.edges.append( - KnowledgeGraphEdge( - id=edge_id, - type=edge.get("relation", ""), - source=node_id, - target=edge["target"], - properties={ - k: v - for k, v in edge.items() - if k not in ["target", "relation"] - }, - ) - ) - seen_edges.add(edge_id) - else: - # Verify if starting node exists - start_nodes = self.collection.find({"_id": label}) - start_nodes_exist = await start_nodes.to_list(length=1) - if not start_nodes_exist: - logger.warning(f"Starting node with label {label} does not exist!") + node_ids = [] + async for doc in cursor: + node_id = str(doc["_id"]) + node_ids.append(node_id) + + cursor = self.collection.find({"_id": {"$in": node_ids}}, {"source_ids": 0}) + async for doc in cursor: + result.nodes.append(self._construct_graph_node(doc["_id"], doc)) + + # As node count reaches the limit, only need to fetch the edges that directly connect to these nodes + edge_cursor = self.edge_collection.find( + { + "$and": [ + {"source_node_id": {"$in": node_ids}}, + {"target_node_id": {"$in": node_ids}}, + ] + } + ) + else: + # All nodes and edges are needed + cursor = self.collection.find({}, {"source_ids": 0}) + + async for doc in cursor: + node_id = str(doc["_id"]) + result.nodes.append(self._construct_graph_node(doc["_id"], doc)) + + edge_cursor = self.edge_collection.find({}) + + async for edge in edge_cursor: + edge_id = f"{edge['source_node_id']}-{edge['target_node_id']}" + if edge_id not in seen_edges: + seen_edges.add(edge_id) + result.edges.append(self._construct_graph_edge(edge_id, edge)) + + return result + + async def _bidirectional_bfs_nodes( + self, + node_labels: list[str], + seen_nodes: set[str], + result: KnowledgeGraph, + depth: int, + max_depth: int, + max_nodes: int, + ) -> KnowledgeGraph: + if depth > max_depth or len(result.nodes) > max_nodes: + return result + + cursor = self.collection.find({"_id": {"$in": node_labels}}) + + async for node in cursor: + node_id = node["_id"] + if node_id not in seen_nodes: + seen_nodes.add(node_id) + result.nodes.append(self._construct_graph_node(node_id, node)) + if len(result.nodes) > max_nodes: return result - # Use $graphLookup for traversal - pipeline = [ - { - "$match": {"_id": label} - }, # Start with nodes having the specified label - { - "$graphLookup": { - "from": self._collection_name, - "startWith": "$edges.target", - "connectFromField": "edges.target", - "connectToField": "_id", - "maxDepth": max_depth, - "depthField": "depth", - "as": "connected_nodes", - } - }, + # Collect neighbors + # Get both inbound and outbound one hop nodes + cursor = self.edge_collection.find( + { + "$or": [ + {"source_node_id": {"$in": node_labels}}, + {"target_node_id": {"$in": node_labels}}, ] + } + ) - async for doc in self.collection.aggregate(pipeline): - # Add the start node - node_id = str(doc["_id"]) - if node_id not in seen_nodes: - result.nodes.append( - KnowledgeGraphNode( - id=node_id, - labels=[ - doc.get( - "_id", - ) - ], - properties={ - k: v - for k, v in doc.items() - if k - not in [ - "_id", - "edges", - "connected_nodes", - "depth", - ] - }, - ) - ) - seen_nodes.add(node_id) + neighbor_nodes = [] + async for edge in cursor: + if edge["source_node_id"] not in seen_nodes: + neighbor_nodes.append(edge["source_node_id"]) + if edge["target_node_id"] not in seen_nodes: + neighbor_nodes.append(edge["target_node_id"]) - # Add edges from start node - for edge in doc.get("edges", []): - edge_id = f"{node_id}-{edge['target']}" - if edge_id not in seen_edges: - result.edges.append( - KnowledgeGraphEdge( - id=edge_id, - type=edge.get("relation", ""), - source=node_id, - target=edge["target"], - properties={ - k: v - for k, v in edge.items() - if k not in ["target", "relation"] - }, - ) - ) - seen_edges.add(edge_id) + if neighbor_nodes: + result = await self._bidirectional_bfs_nodes( + neighbor_nodes, seen_nodes, result, depth + 1, max_depth, max_nodes + ) - # Add connected nodes and their edges - for connected in doc.get("connected_nodes", []): - node_id = str(connected["_id"]) - if node_id not in seen_nodes: - result.nodes.append( - KnowledgeGraphNode( - id=node_id, - labels=[connected.get("_id")], - properties={ - k: v - for k, v in connected.items() - if k not in ["_id", "edges", "depth"] - }, - ) - ) - seen_nodes.add(node_id) + return result - # Add edges from connected nodes - for edge in connected.get("edges", []): - edge_id = f"{node_id}-{edge['target']}" - if edge_id not in seen_edges: - result.edges.append( - KnowledgeGraphEdge( - id=edge_id, - type=edge.get("relation", ""), - source=node_id, - target=edge["target"], - properties={ - k: v - for k, v in edge.items() - if k not in ["target", "relation"] - }, - ) - ) - seen_edges.add(edge_id) + async def get_knowledge_subgraph_bidirectional_bfs( + self, + node_label: str, + depth: int, + max_depth: int, + max_nodes: int, + ) -> KnowledgeGraph: + seen_nodes = set() + seen_edges = set() + result = KnowledgeGraph() + + result = await self._bidirectional_bfs_nodes( + [node_label], seen_nodes, result, depth, max_depth, max_nodes + ) + + # Get all edges from seen_nodes + all_node_ids = list(seen_nodes) + cursor = self.edge_collection.find( + { + "$and": [ + {"source_node_id": {"$in": all_node_ids}}, + {"target_node_id": {"$in": all_node_ids}}, + ] + } + ) + + async for edge in cursor: + edge_id = f"{edge['source_node_id']}-{edge['target_node_id']}" + if edge_id not in seen_edges: + result.edges.append(self._construct_graph_edge(edge_id, edge)) + seen_edges.add(edge_id) + + return result + + async def get_knowledge_subgraph_in_out_bound_bfs( + self, node_label: str, max_depth: int, max_nodes: int + ) -> KnowledgeGraph: + seen_nodes = set() + seen_edges = set() + result = KnowledgeGraph() + project_doc = { + "source_ids": 0, + "created_at": 0, + "entity_type": 0, + "file_path": 0, + } + + # Verify if starting node exists + start_node = await self.collection.find_one({"_id": node_label}) + if not start_node: + logger.warning(f"Starting node with label {node_label} does not exist!") + return result + + seen_nodes.add(node_label) + result.nodes.append(self._construct_graph_node(node_label, start_node)) + + if max_depth == 0: + return result + + # In MongoDB, depth = 0 means one-hop + max_depth = max_depth - 1 + + pipeline = [ + {"$match": {"_id": node_label}}, + {"$project": project_doc}, + { + "$graphLookup": { + "from": self._edge_collection_name, + "startWith": "$_id", + "connectFromField": "target_node_id", + "connectToField": "source_node_id", + "maxDepth": max_depth, + "depthField": "depth", + "as": "connected_edges", + }, + }, + { + "$unionWith": { + "coll": self._collection_name, + "pipeline": [ + {"$match": {"_id": node_label}}, + {"$project": project_doc}, + { + "$graphLookup": { + "from": self._edge_collection_name, + "startWith": "$_id", + "connectFromField": "source_node_id", + "connectToField": "target_node_id", + "maxDepth": max_depth, + "depthField": "depth", + "as": "connected_edges", + } + }, + ], + } + }, + ] + + cursor = await self.collection.aggregate(pipeline, allowDiskUse=True) + node_edges = [] + + # Two records for node_label are returned capturing outbound and inbound connected_edges + async for doc in cursor: + if doc.get("connected_edges", []): + node_edges.extend(doc.get("connected_edges")) + + # Sort the connected edges by depth ascending and weight descending + # And stores the source_node_id and target_node_id in sequence to retrieve the neighbouring nodes + node_edges = sorted( + node_edges, + key=lambda x: (x["depth"], -x["weight"]), + ) + + # As order matters, we need to use another list to store the node_id + # And only take the first max_nodes ones + node_ids = [] + for edge in node_edges: + if len(node_ids) < max_nodes and edge["source_node_id"] not in seen_nodes: + node_ids.append(edge["source_node_id"]) + seen_nodes.add(edge["source_node_id"]) + + if len(node_ids) < max_nodes and edge["target_node_id"] not in seen_nodes: + node_ids.append(edge["target_node_id"]) + seen_nodes.add(edge["target_node_id"]) + + # Filter out all the node whose id is same as node_label so that we do not check existence next step + cursor = self.collection.find({"_id": {"$in": node_ids}}) + + async for doc in cursor: + result.nodes.append(self._construct_graph_node(str(doc["_id"]), doc)) + + for edge in node_edges: + if ( + edge["source_node_id"] not in seen_nodes + or edge["target_node_id"] not in seen_nodes + ): + continue + + edge_id = f"{edge['source_node_id']}-{edge['target_node_id']}" + if edge_id not in seen_edges: + result.edges.append(self._construct_graph_edge(edge_id, edge)) + seen_edges.add(edge_id) + + return result + + async def get_knowledge_graph( + self, + node_label: str, + max_depth: int = 3, + max_nodes: int = None, + ) -> KnowledgeGraph: + """ + Retrieve a connected subgraph of nodes where the label includes the specified `node_label`. + + Args: + node_label: Label of the starting node, * means all nodes + max_depth: Maximum depth of the subgraph, Defaults to 3 + max_nodes: Maximum nodes to return, Defaults to global_config max_graph_nodes + + Returns: + KnowledgeGraph object containing nodes and edges, with an is_truncated flag + indicating whether the graph was truncated due to max_nodes limit + + If a graph is like this and starting from B: + A → B ← C ← F, B -> E, C → D + + Outbound BFS: + B → E + + Inbound BFS: + A → B + C → B + F → C + + Bidirectional BFS: + A → B + B → E + F → C + C → B + C → D + """ + # Use global_config max_graph_nodes as default if max_nodes is None + if max_nodes is None: + max_nodes = self.global_config.get("max_graph_nodes", 1000) + else: + # Limit max_nodes to not exceed global_config max_graph_nodes + max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000)) + + result = KnowledgeGraph() + start = time.perf_counter() + + try: + # Optimize pipeline to avoid memory issues with large datasets + if node_label == "*": + result = await self.get_knowledge_graph_all_by_degree( + max_depth, max_nodes + ) + elif GRAPH_BFS_MODE == "in_out_bound": + result = await self.get_knowledge_subgraph_in_out_bound_bfs( + node_label, max_depth, max_nodes + ) + else: + result = await self.get_knowledge_subgraph_bidirectional_bfs( + node_label, 0, max_depth, max_nodes + ) + + duration = time.perf_counter() - start logger.info( - f"Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}" + f"Subgraph query successful in {duration:.4f} seconds | Node count: {len(result.nodes)} | Edge count: {len(result.edges)} | Truncated: {result.is_truncated}" ) except PyMongoError as e: - logger.error(f"MongoDB query failed: {str(e)}") + # Handle memory limit errors specifically + if "memory limit" in str(e).lower() or "sort exceeded" in str(e).lower(): + logger.warning( + f"MongoDB memory limit exceeded, falling back to simple query: {str(e)}" + ) + # Fallback to a simple query without complex aggregation + try: + simple_cursor = self.collection.find({}).limit(max_nodes) + async for doc in simple_cursor: + result.nodes.append( + self._construct_graph_node(str(doc["_id"]), doc) + ) + result.is_truncated = True + logger.info( + f"Fallback query completed | Node count: {len(result.nodes)}" + ) + except PyMongoError as fallback_error: + logger.error(f"Fallback query also failed: {str(fallback_error)}") + else: + logger.error(f"MongoDB query failed: {str(e)}") return result @@ -873,9 +1251,14 @@ class MongoGraphStorage(BaseGraphStorage): if not nodes: return - # 1. Remove all edges referencing these nodes (remove from edges array of other nodes) - await self.collection.update_many( - {}, {"$pull": {"edges": {"target": {"$in": nodes}}}} + # 1. Remove all edges referencing these nodes + await self.edge_collection.delete_many( + { + "$or": [ + {"source_node_id": {"$in": nodes}}, + {"target_node_id": {"$in": nodes}}, + ] + } ) # 2. Delete the node documents @@ -893,17 +1276,16 @@ class MongoGraphStorage(BaseGraphStorage): if not edges: return - update_tasks = [] - for source, target in edges: - # Remove edge pointing to target from source node's edges array - update_tasks.append( - self.collection.update_one( - {"_id": source}, {"$pull": {"edges": {"target": target}}} - ) + all_edge_pairs = [] + for source_id, target_id in edges: + all_edge_pairs.append( + {"source_node_id": source_id, "target_node_id": target_id} + ) + all_edge_pairs.append( + {"source_node_id": target_id, "target_node_id": source_id} ) - if update_tasks: - await asyncio.gather(*update_tasks) + await self.edge_collection.delete_many({"$or": all_edge_pairs}) logger.debug(f"Successfully deleted edges: {edges}") @@ -920,9 +1302,16 @@ class MongoGraphStorage(BaseGraphStorage): logger.info( f"Dropped {deleted_count} documents from graph {self._collection_name}" ) + + result = await self.edge_collection.delete_many({}) + edge_count = result.deleted_count + logger.info( + f"Dropped {edge_count} edges from graph {self._edge_collection_name}" + ) + return { "status": "success", - "message": f"{deleted_count} documents dropped", + "message": f"{deleted_count} documents and {edge_count} edges dropped", } except PyMongoError as e: logger.error(f"Error dropping graph {self._collection_name}: {e}") @@ -932,10 +1321,54 @@ class MongoGraphStorage(BaseGraphStorage): @final @dataclass class MongoVectorDBStorage(BaseVectorStorage): - db: AsyncIOMotorDatabase | None = field(default=None) - _data: AsyncIOMotorCollection | None = field(default=None) + db: AsyncDatabase | None = field(default=None) + _data: AsyncCollection | None = field(default=None) + _index_name: str = field(default="", init=False) + + def __init__( + self, namespace, global_config, embedding_func, workspace=None, meta_fields=None + ): + super().__init__( + namespace=namespace, + workspace=workspace or "", + global_config=global_config, + embedding_func=embedding_func, + meta_fields=meta_fields or set(), + ) + self.__post_init__() def __post_init__(self): + # Check for MONGODB_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all MongoDB storage instances + mongodb_workspace = os.environ.get("MONGODB_WORKSPACE") + if mongodb_workspace and mongodb_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = mongodb_workspace.strip() + logger.info( + f"Using MONGODB_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build namespace with workspace prefix for data isolation + if effective_workspace: + self.namespace = f"{effective_workspace}_{self.namespace}" + logger.debug(f"Final namespace with workspace prefix: '{self.namespace}'") + # When workspace is empty, keep the original namespace unchanged + + # Set index name based on workspace for backward compatibility + if effective_workspace: + # Use collection-specific index name for workspaced collections to avoid conflicts + self._index_name = f"vector_knn_index_{self.namespace}" + else: + # Keep original index name for backward compatibility with existing deployments + self._index_name = "vector_knn_index" + kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {}) cosine_threshold = kwargs.get("cosine_better_than_threshold") if cosine_threshold is None: @@ -965,12 +1398,11 @@ class MongoVectorDBStorage(BaseVectorStorage): async def create_vector_index_if_not_exists(self): """Creates an Atlas Vector Search index.""" try: - index_name = "vector_knn_index" - - indexes = await self._data.list_search_indexes().to_list(length=None) + indexes_cursor = await self._data.list_search_indexes() + indexes = await indexes_cursor.to_list(length=None) for index in indexes: - if index["name"] == index_name: - logger.debug("vector index already exist") + if index["name"] == self._index_name: + logger.info(f"vector index {self._index_name} already exist") return search_index_model = SearchIndexModel( @@ -984,24 +1416,26 @@ class MongoVectorDBStorage(BaseVectorStorage): } ] }, - name=index_name, + name=self._index_name, type="vectorSearch", ) await self._data.create_search_index(search_index_model) - logger.info("Vector index created successfully.") + logger.info(f"Vector index {self._index_name} created successfully.") - except PyMongoError as _: - logger.debug("vector index already exist") + except PyMongoError as e: + error_msg = f"Error creating vector index {self._index_name}: {e}" + logger.error(error_msg) + raise SystemExit( + f"Failed to create MongoDB vector index. Program cannot continue. {error_msg}" + ) async def upsert(self, data: dict[str, dict[str, Any]]) -> None: - logger.info(f"Inserting {len(data)} to {self.namespace}") + logger.debug(f"Inserting {len(data)} to {self.namespace}") if not data: return # Add current time as Unix timestamp - import time - current_time = int(time.time()) list_data = [ @@ -1049,7 +1483,7 @@ class MongoVectorDBStorage(BaseVectorStorage): pipeline = [ { "$vectorSearch": { - "index": "vector_knn_index", # Ensure this matches the created index name + "index": self._index_name, # Use stored index name for consistency "path": "vector", "queryVector": query_vector, "numCandidates": 100, # Adjust for performance @@ -1062,8 +1496,8 @@ class MongoVectorDBStorage(BaseVectorStorage): ] # Execute the aggregation pipeline - cursor = self._data.aggregate(pipeline) - results = await cursor.to_list() + cursor = await self._data.aggregate(pipeline, allowDiskUse=True) + results = await cursor.to_list(length=None) # Format and return the results with created_at field return [ @@ -1086,10 +1520,14 @@ class MongoVectorDBStorage(BaseVectorStorage): Args: ids: List of vector IDs to be deleted """ - logger.info(f"Deleting {len(ids)} vectors from {self.namespace}") + logger.debug(f"Deleting {len(ids)} vectors from {self.namespace}") if not ids: return + # Convert to list if it's a set (MongoDB BSON cannot encode sets) + if isinstance(ids, set): + ids = list(ids) + try: result = await self._data.delete_many({"_id": {"$in": ids}}) logger.debug( @@ -1232,7 +1670,7 @@ class MongoVectorDBStorage(BaseVectorStorage): return {"status": "error", "message": str(e)} -async def get_or_create_collection(db: AsyncIOMotorDatabase, collection_name: str): +async def get_or_create_collection(db: AsyncDatabase, collection_name: str): collection_names = await db.list_collection_names() if collection_name not in collection_names: diff --git a/lightrag/kg/nano_vector_db_impl.py b/lightrag/kg/nano_vector_db_impl.py index fa56a214..4eae2db5 100644 --- a/lightrag/kg/nano_vector_db_impl.py +++ b/lightrag/kg/nano_vector_db_impl.py @@ -41,9 +41,19 @@ class NanoVectorDBStorage(BaseVectorStorage): ) self.cosine_better_than_threshold = cosine_threshold - self._client_file_name = os.path.join( - self.global_config["working_dir"], f"vdb_{self.namespace}.json" - ) + working_dir = self.global_config["working_dir"] + if self.workspace: + # Include workspace in the file path for data isolation + workspace_dir = os.path.join(working_dir, self.workspace) + os.makedirs(workspace_dir, exist_ok=True) + self._client_file_name = os.path.join( + workspace_dir, f"vdb_{self.namespace}.json" + ) + else: + # Default behavior when workspace is empty + self._client_file_name = os.path.join( + working_dir, f"vdb_{self.namespace}.json" + ) self._max_batch_size = self.global_config["embedding_batch_num"] self._client = NanoVectorDB( diff --git a/lightrag/kg/neo4j_impl.py b/lightrag/kg/neo4j_impl.py index 3c57ae34..b0efc4b5 100644 --- a/lightrag/kg/neo4j_impl.py +++ b/lightrag/kg/neo4j_impl.py @@ -1,4 +1,3 @@ -import inspect import os import re from dataclasses import dataclass @@ -17,6 +16,7 @@ import logging from ..utils import logger from ..base import BaseGraphStorage from ..types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge +from ..constants import GRAPH_FIELD_SEP import pipmaster as pm if not pm.is_installed("neo4j"): @@ -36,9 +36,6 @@ from dotenv import load_dotenv # the OS environment variables take precedence over the .env file load_dotenv(dotenv_path=".env", override=False) -# Get maximum number of graph nodes from environment variable, default is 1000 -MAX_GRAPH_NODES = int(os.getenv("MAX_GRAPH_NODES", 1000)) - config = configparser.ConfigParser() config.read("config.ini", "utf-8") @@ -50,14 +47,25 @@ logging.getLogger("neo4j").setLevel(logging.ERROR) @final @dataclass class Neo4JStorage(BaseGraphStorage): - def __init__(self, namespace, global_config, embedding_func): + def __init__(self, namespace, global_config, embedding_func, workspace=None): + # Check NEO4J_WORKSPACE environment variable and override workspace if set + neo4j_workspace = os.environ.get("NEO4J_WORKSPACE") + if neo4j_workspace and neo4j_workspace.strip(): + workspace = neo4j_workspace + super().__init__( namespace=namespace, + workspace=workspace or "", global_config=global_config, embedding_func=embedding_func, ) self._driver = None + def _get_workspace_label(self) -> str: + """Get workspace label, return 'base' for compatibility when workspace is empty""" + workspace = getattr(self, "workspace", None) + return workspace if workspace else "base" + async def initialize(self): URI = os.environ.get("NEO4J_URI", config.get("neo4j", "uri", fallback=None)) USERNAME = os.environ.get( @@ -153,13 +161,14 @@ class Neo4JStorage(BaseGraphStorage): raise e if connected: - # Create index for base nodes on entity_id if it doesn't exist + # Create index for workspace nodes on entity_id if it doesn't exist + workspace_label = self._get_workspace_label() try: async with self._driver.session(database=database) as session: # Check if index exists first - check_query = """ + check_query = f""" CALL db.indexes() YIELD name, labelsOrTypes, properties - WHERE labelsOrTypes = ['base'] AND properties = ['entity_id'] + WHERE labelsOrTypes = ['{workspace_label}'] AND properties = ['entity_id'] RETURN count(*) > 0 AS exists """ try: @@ -172,16 +181,16 @@ class Neo4JStorage(BaseGraphStorage): if not index_exists: # Create index only if it doesn't exist result = await session.run( - "CREATE INDEX FOR (n:base) ON (n.entity_id)" + f"CREATE INDEX FOR (n:`{workspace_label}`) ON (n.entity_id)" ) await result.consume() logger.info( - f"Created index for base nodes on entity_id in {database}" + f"Created index for {workspace_label} nodes on entity_id in {database}" ) except Exception: # Fallback if db.indexes() is not supported in this Neo4j version result = await session.run( - "CREATE INDEX IF NOT EXISTS FOR (n:base) ON (n.entity_id)" + f"CREATE INDEX IF NOT EXISTS FOR (n:`{workspace_label}`) ON (n.entity_id)" ) await result.consume() except Exception as e: @@ -216,11 +225,12 @@ class Neo4JStorage(BaseGraphStorage): ValueError: If node_id is invalid Exception: If there is an error executing the query """ + workspace_label = self._get_workspace_label() async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: try: - query = "MATCH (n:base {entity_id: $entity_id}) RETURN count(n) > 0 AS node_exists" + query = f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN count(n) > 0 AS node_exists" result = await session.run(query, entity_id=node_id) single_result = await result.single() await result.consume() # Ensure result is fully consumed @@ -245,12 +255,13 @@ class Neo4JStorage(BaseGraphStorage): ValueError: If either node_id is invalid Exception: If there is an error executing the query """ + workspace_label = self._get_workspace_label() async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: try: query = ( - "MATCH (a:base {entity_id: $source_entity_id})-[r]-(b:base {entity_id: $target_entity_id}) " + f"MATCH (a:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(b:`{workspace_label}` {{entity_id: $target_entity_id}}) " "RETURN COUNT(r) > 0 AS edgeExists" ) result = await session.run( @@ -282,11 +293,14 @@ class Neo4JStorage(BaseGraphStorage): ValueError: If node_id is invalid Exception: If there is an error executing the query """ + workspace_label = self._get_workspace_label() async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: try: - query = "MATCH (n:base {entity_id: $entity_id}) RETURN n" + query = ( + f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN n" + ) result = await session.run(query, entity_id=node_id) try: records = await result.fetch( @@ -300,14 +314,14 @@ class Neo4JStorage(BaseGraphStorage): if records: node = records[0]["n"] node_dict = dict(node) - # Remove base label from labels list if it exists + # Remove workspace label from labels list if it exists if "labels" in node_dict: node_dict["labels"] = [ label for label in node_dict["labels"] - if label != "base" + if label != workspace_label ] - logger.debug(f"Neo4j query node {query} return: {node_dict}") + # logger.debug(f"Neo4j query node {query} return: {node_dict}") return node_dict return None finally: @@ -326,12 +340,13 @@ class Neo4JStorage(BaseGraphStorage): Returns: A dictionary mapping each node_id to its node data (or None if not found). """ + workspace_label = self._get_workspace_label() async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: - query = """ + query = f""" UNWIND $node_ids AS id - MATCH (n:base {entity_id: id}) + MATCH (n:`{workspace_label}` {{entity_id: id}}) RETURN n.entity_id AS entity_id, n """ result = await session.run(query, node_ids=node_ids) @@ -340,10 +355,12 @@ class Neo4JStorage(BaseGraphStorage): entity_id = record["entity_id"] node = record["n"] node_dict = dict(node) - # Remove the 'base' label if present in a 'labels' property + # Remove the workspace label if present in a 'labels' property if "labels" in node_dict: node_dict["labels"] = [ - label for label in node_dict["labels"] if label != "base" + label + for label in node_dict["labels"] + if label != workspace_label ] nodes[entity_id] = node_dict await result.consume() # Make sure to consume the result fully @@ -364,12 +381,13 @@ class Neo4JStorage(BaseGraphStorage): ValueError: If node_id is invalid Exception: If there is an error executing the query """ + workspace_label = self._get_workspace_label() async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: try: - query = """ - MATCH (n:base {entity_id: $entity_id}) + query = f""" + MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) OPTIONAL MATCH (n)-[r]-() RETURN COUNT(r) AS degree """ @@ -382,9 +400,9 @@ class Neo4JStorage(BaseGraphStorage): return 0 degree = record["degree"] - logger.debug( - f"Neo4j query node degree for {node_id} return: {degree}" - ) + # logger.debug( + # f"Neo4j query node degree for {node_id} return: {degree}" + # ) return degree finally: await result.consume() # Ensure result is fully consumed @@ -403,13 +421,14 @@ class Neo4JStorage(BaseGraphStorage): A dictionary mapping each node_id to its degree (number of relationships). If a node is not found, its degree will be set to 0. """ + workspace_label = self._get_workspace_label() async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: - query = """ + query = f""" UNWIND $node_ids AS id - MATCH (n:base {entity_id: id}) - RETURN n.entity_id AS entity_id, count { (n)--() } AS degree; + MATCH (n:`{workspace_label}` {{entity_id: id}}) + RETURN n.entity_id AS entity_id, count {{ (n)--() }} AS degree; """ result = await session.run(query, node_ids=node_ids) degrees = {} @@ -424,7 +443,7 @@ class Neo4JStorage(BaseGraphStorage): logger.warning(f"No node found with label '{nid}'") degrees[nid] = 0 - logger.debug(f"Neo4j batch node degree query returned: {degrees}") + # logger.debug(f"Neo4j batch node degree query returned: {degrees}") return degrees async def edge_degree(self, src_id: str, tgt_id: str) -> int: @@ -489,12 +508,13 @@ class Neo4JStorage(BaseGraphStorage): ValueError: If either node_id is invalid Exception: If there is an error executing the query """ + workspace_label = self._get_workspace_label() try: async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: - query = """ - MATCH (start:base {entity_id: $source_entity_id})-[r]-(end:base {entity_id: $target_entity_id}) + query = f""" + MATCH (start:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(end:`{workspace_label}` {{entity_id: $target_entity_id}}) RETURN properties(r) as edge_properties """ result = await session.run( @@ -512,7 +532,7 @@ class Neo4JStorage(BaseGraphStorage): if records: try: edge_result = dict(records[0]["edge_properties"]) - logger.debug(f"Result: {edge_result}") + # logger.debug(f"Result: {edge_result}") # Ensure required keys exist with defaults required_keys = { "weight": 0.0, @@ -528,9 +548,9 @@ class Neo4JStorage(BaseGraphStorage): f"missing {key}, using default: {default_value}" ) - logger.debug( - f"{inspect.currentframe().f_code.co_name}:query:{query}:result:{edge_result}" - ) + # logger.debug( + # f"{inspect.currentframe().f_code.co_name}:query:{query}:result:{edge_result}" + # ) return edge_result except (KeyError, TypeError, ValueError) as e: logger.error( @@ -545,9 +565,9 @@ class Neo4JStorage(BaseGraphStorage): "keywords": None, } - logger.debug( - f"{inspect.currentframe().f_code.co_name}: No edge found between {source_node_id} and {target_node_id}" - ) + # logger.debug( + # f"{inspect.currentframe().f_code.co_name}: No edge found between {source_node_id} and {target_node_id}" + # ) # Return None when no edge found return None finally: @@ -571,12 +591,13 @@ class Neo4JStorage(BaseGraphStorage): Returns: A dictionary mapping (src, tgt) tuples to their edge properties. """ + workspace_label = self._get_workspace_label() async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: - query = """ + query = f""" UNWIND $pairs AS pair - MATCH (start:base {entity_id: pair.src})-[r:DIRECTED]-(end:base {entity_id: pair.tgt}) + MATCH (start:`{workspace_label}` {{entity_id: pair.src}})-[r:DIRECTED]-(end:`{workspace_label}` {{entity_id: pair.tgt}}) RETURN pair.src AS src_id, pair.tgt AS tgt_id, collect(properties(r)) AS edges """ result = await session.run(query, pairs=pairs) @@ -627,8 +648,9 @@ class Neo4JStorage(BaseGraphStorage): database=self._DATABASE, default_access_mode="READ" ) as session: try: - query = """MATCH (n:base {entity_id: $entity_id}) - OPTIONAL MATCH (n)-[r]-(connected:base) + workspace_label = self._get_workspace_label() + query = f"""MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) + OPTIONAL MATCH (n)-[r]-(connected:`{workspace_label}`) WHERE connected.entity_id IS NOT NULL RETURN n, r, connected""" results = await session.run(query, entity_id=source_node_id) @@ -689,10 +711,11 @@ class Neo4JStorage(BaseGraphStorage): database=self._DATABASE, default_access_mode="READ" ) as session: # Query to get both outgoing and incoming edges - query = """ + workspace_label = self._get_workspace_label() + query = f""" UNWIND $node_ids AS id - MATCH (n:base {entity_id: id}) - OPTIONAL MATCH (n)-[r]-(connected:base) + MATCH (n:`{workspace_label}` {{entity_id: id}}) + OPTIONAL MATCH (n)-[r]-(connected:`{workspace_label}`) RETURN id AS queried_id, n.entity_id AS node_entity_id, connected.entity_id AS connected_entity_id, startNode(r).entity_id AS start_entity_id @@ -726,6 +749,49 @@ class Neo4JStorage(BaseGraphStorage): await result.consume() # Ensure results are fully consumed return edges_dict + async def get_nodes_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]: + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + UNWIND $chunk_ids AS chunk_id + MATCH (n:`{workspace_label}`) + WHERE n.source_id IS NOT NULL AND chunk_id IN split(n.source_id, $sep) + RETURN DISTINCT n + """ + result = await session.run(query, chunk_ids=chunk_ids, sep=GRAPH_FIELD_SEP) + nodes = [] + async for record in result: + node = record["n"] + node_dict = dict(node) + # Add node id (entity_id) to the dictionary for easier access + node_dict["id"] = node_dict.get("entity_id") + nodes.append(node_dict) + await result.consume() + return nodes + + async def get_edges_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]: + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + UNWIND $chunk_ids AS chunk_id + MATCH (a:`{workspace_label}`)-[r]-(b:`{workspace_label}`) + WHERE r.source_id IS NOT NULL AND chunk_id IN split(r.source_id, $sep) + RETURN DISTINCT a.entity_id AS source, b.entity_id AS target, properties(r) AS properties + """ + result = await session.run(query, chunk_ids=chunk_ids, sep=GRAPH_FIELD_SEP) + edges = [] + async for record in result: + edge_properties = record["properties"] + edge_properties["source"] = record["source"] + edge_properties["target"] = record["target"] + edges.append(edge_properties) + await result.consume() + return edges + @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10), @@ -746,6 +812,7 @@ class Neo4JStorage(BaseGraphStorage): node_id: The unique identifier for the node (used as label) node_data: Dictionary of node properties """ + workspace_label = self._get_workspace_label() properties = node_data entity_type = properties["entity_type"] if "entity_id" not in properties: @@ -755,20 +822,14 @@ class Neo4JStorage(BaseGraphStorage): async with self._driver.session(database=self._DATABASE) as session: async def execute_upsert(tx: AsyncManagedTransaction): - query = ( - """ - MERGE (n:base {entity_id: $entity_id}) + query = f""" + MERGE (n:`{workspace_label}` {{entity_id: $entity_id}}) SET n += $properties - SET n:`%s` + SET n:`{entity_type}` """ - % entity_type - ) result = await tx.run( query, entity_id=node_id, properties=properties ) - logger.debug( - f"Upserted node with entity_id '{node_id}' and properties: {properties}" - ) await result.consume() # Ensure result is fully consumed await session.execute_write(execute_upsert) @@ -809,10 +870,11 @@ class Neo4JStorage(BaseGraphStorage): async with self._driver.session(database=self._DATABASE) as session: async def execute_upsert(tx: AsyncManagedTransaction): - query = """ - MATCH (source:base {entity_id: $source_entity_id}) + workspace_label = self._get_workspace_label() + query = f""" + MATCH (source:`{workspace_label}` {{entity_id: $source_entity_id}}) WITH source - MATCH (target:base {entity_id: $target_entity_id}) + MATCH (target:`{workspace_label}` {{entity_id: $target_entity_id}}) MERGE (source)-[r:DIRECTED]-(target) SET r += $properties RETURN r, source, target @@ -824,12 +886,7 @@ class Neo4JStorage(BaseGraphStorage): properties=edge_properties, ) try: - records = await result.fetch(2) - if records: - logger.debug( - f"Upserted edge from '{source_node_id}' to '{target_node_id}'" - f"with properties: {edge_properties}" - ) + await result.fetch(2) finally: await result.consume() # Ensure result is consumed @@ -842,7 +899,7 @@ class Neo4JStorage(BaseGraphStorage): self, node_label: str, max_depth: int = 3, - max_nodes: int = MAX_GRAPH_NODES, + max_nodes: int = None, ) -> KnowledgeGraph: """ Retrieve a connected subgraph of nodes where the label includes the specified `node_label`. @@ -856,6 +913,14 @@ class Neo4JStorage(BaseGraphStorage): KnowledgeGraph object containing nodes and edges, with an is_truncated flag indicating whether the graph was truncated due to max_nodes limit """ + # Get max_nodes from global_config if not provided + if max_nodes is None: + max_nodes = self.global_config.get("max_graph_nodes", 1000) + else: + # Limit max_nodes to not exceed global_config max_graph_nodes + max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000)) + + workspace_label = self._get_workspace_label() result = KnowledgeGraph() seen_nodes = set() seen_edges = set() @@ -866,7 +931,9 @@ class Neo4JStorage(BaseGraphStorage): try: if node_label == "*": # First check total node count to determine if graph is truncated - count_query = "MATCH (n) RETURN count(n) as total" + count_query = ( + f"MATCH (n:`{workspace_label}`) RETURN count(n) as total" + ) count_result = None try: count_result = await session.run(count_query) @@ -882,13 +949,13 @@ class Neo4JStorage(BaseGraphStorage): await count_result.consume() # Run main query to get nodes with highest degree - main_query = """ - MATCH (n) + main_query = f""" + MATCH (n:`{workspace_label}`) OPTIONAL MATCH (n)-[r]-() WITH n, COALESCE(count(r), 0) AS degree ORDER BY degree DESC LIMIT $max_nodes - WITH collect({node: n}) AS filtered_nodes + WITH collect({{node: n}}) AS filtered_nodes UNWIND filtered_nodes AS node_info WITH collect(node_info.node) AS kept_nodes, filtered_nodes OPTIONAL MATCH (a)-[r]-(b) @@ -910,20 +977,21 @@ class Neo4JStorage(BaseGraphStorage): else: # return await self._robust_fallback(node_label, max_depth, max_nodes) # First try without limit to check if we need to truncate - full_query = """ - MATCH (start) + full_query = f""" + MATCH (start:`{workspace_label}`) WHERE start.entity_id = $entity_id WITH start - CALL apoc.path.subgraphAll(start, { + CALL apoc.path.subgraphAll(start, {{ relationshipFilter: '', + labelFilter: '{workspace_label}', minLevel: 0, maxLevel: $max_depth, bfs: true - }) + }}) YIELD nodes, relationships WITH nodes, relationships, size(nodes) AS total_nodes UNWIND nodes AS node - WITH collect({node: node}) AS node_info, relationships, total_nodes + WITH collect({{node: node}}) AS node_info, relationships, total_nodes RETURN node_info, relationships, total_nodes """ @@ -961,20 +1029,21 @@ class Neo4JStorage(BaseGraphStorage): ) # Run limited query - limited_query = """ - MATCH (start) + limited_query = f""" + MATCH (start:`{workspace_label}`) WHERE start.entity_id = $entity_id WITH start - CALL apoc.path.subgraphAll(start, { + CALL apoc.path.subgraphAll(start, {{ relationshipFilter: '', + labelFilter: '{workspace_label}', minLevel: 0, maxLevel: $max_depth, limit: $max_nodes, bfs: true - }) + }}) YIELD nodes, relationships UNWIND nodes AS node - WITH collect({node: node}) AS node_info, relationships + WITH collect({{node: node}}) AS node_info, relationships RETURN node_info, relationships """ result_set = None @@ -1061,11 +1130,12 @@ class Neo4JStorage(BaseGraphStorage): visited_edge_pairs = set() # Get the starting node's data + workspace_label = self._get_workspace_label() async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: - query = """ - MATCH (n:base {entity_id: $entity_id}) + query = f""" + MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN id(n) as node_id, n """ node_result = await session.run(query, entity_id=node_label) @@ -1123,8 +1193,9 @@ class Neo4JStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: - query = """ - MATCH (a:base {entity_id: $entity_id})-[r]-(b) + workspace_label = self._get_workspace_label() + query = f""" + MATCH (a:`{workspace_label}` {{entity_id: $entity_id}})-[r]-(b) WITH r, b, id(r) as edge_id, id(b) as target_id RETURN r, b, edge_id, target_id """ @@ -1208,6 +1279,7 @@ class Neo4JStorage(BaseGraphStorage): Returns: ["Person", "Company", ...] # Alphabetically sorted label list """ + workspace_label = self._get_workspace_label() async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: @@ -1215,8 +1287,8 @@ class Neo4JStorage(BaseGraphStorage): # query = "CALL db.labels() YIELD label RETURN label" # Method 2: Query compatible with older versions - query = """ - MATCH (n:base) + query = f""" + MATCH (n:`{workspace_label}`) WHERE n.entity_id IS NOT NULL RETURN DISTINCT n.entity_id AS label ORDER BY label @@ -1252,8 +1324,9 @@ class Neo4JStorage(BaseGraphStorage): """ async def _do_delete(tx: AsyncManagedTransaction): - query = """ - MATCH (n:base {entity_id: $entity_id}) + workspace_label = self._get_workspace_label() + query = f""" + MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) DETACH DELETE n """ result = await tx.run(query, entity_id=node_id) @@ -1309,8 +1382,9 @@ class Neo4JStorage(BaseGraphStorage): for source, target in edges: async def _do_delete_edge(tx: AsyncManagedTransaction): - query = """ - MATCH (source:base {entity_id: $source_entity_id})-[r]-(target:base {entity_id: $target_entity_id}) + workspace_label = self._get_workspace_label() + query = f""" + MATCH (source:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(target:`{workspace_label}` {{entity_id: $target_entity_id}}) DELETE r """ result = await tx.run( @@ -1327,26 +1401,32 @@ class Neo4JStorage(BaseGraphStorage): raise async def drop(self) -> dict[str, str]: - """Drop all data from storage and clean up resources + """Drop all data from current workspace storage and clean up resources - This method will delete all nodes and relationships in the Neo4j database. + This method will delete all nodes and relationships in the current workspace only. Returns: dict[str, str]: Operation status and message - - On success: {"status": "success", "message": "data dropped"} + - On success: {"status": "success", "message": "workspace data dropped"} - On failure: {"status": "error", "message": ""} """ + workspace_label = self._get_workspace_label() try: async with self._driver.session(database=self._DATABASE) as session: - # Delete all nodes and relationships - query = "MATCH (n) DETACH DELETE n" + # Delete all nodes and relationships in current workspace only + query = f"MATCH (n:`{workspace_label}`) DETACH DELETE n" result = await session.run(query) await result.consume() # Ensure result is fully consumed logger.info( - f"Process {os.getpid()} drop Neo4j database {self._DATABASE}" + f"Process {os.getpid()} drop Neo4j workspace '{workspace_label}' in database {self._DATABASE}" ) - return {"status": "success", "message": "data dropped"} + return { + "status": "success", + "message": f"workspace '{workspace_label}' data dropped", + } except Exception as e: - logger.error(f"Error dropping Neo4j database {self._DATABASE}: {e}") + logger.error( + f"Error dropping Neo4j workspace '{workspace_label}' in database {self._DATABASE}: {e}" + ) return {"status": "error", "message": str(e)} diff --git a/lightrag/kg/networkx_impl.py b/lightrag/kg/networkx_impl.py index c92bbd30..01b34566 100644 --- a/lightrag/kg/networkx_impl.py +++ b/lightrag/kg/networkx_impl.py @@ -5,15 +5,13 @@ from typing import final from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge from lightrag.utils import logger from lightrag.base import BaseGraphStorage +from lightrag.constants import GRAPH_FIELD_SEP import pipmaster as pm if not pm.is_installed("networkx"): pm.install("networkx") -if not pm.is_installed("graspologic"): - pm.install("graspologic") - import networkx as nx from .shared_storage import ( get_storage_lock, @@ -28,8 +26,6 @@ from dotenv import load_dotenv # the OS environment variables take precedence over the .env file load_dotenv(dotenv_path=".env", override=False) -MAX_GRAPH_NODES = int(os.getenv("MAX_GRAPH_NODES", 1000)) - @final @dataclass @@ -48,9 +44,19 @@ class NetworkXStorage(BaseGraphStorage): nx.write_graphml(graph, file_name) def __post_init__(self): - self._graphml_xml_file = os.path.join( - self.global_config["working_dir"], f"graph_{self.namespace}.graphml" - ) + working_dir = self.global_config["working_dir"] + if self.workspace: + # Include workspace in the file path for data isolation + workspace_dir = os.path.join(working_dir, self.workspace) + os.makedirs(workspace_dir, exist_ok=True) + self._graphml_xml_file = os.path.join( + workspace_dir, f"graph_{self.namespace}.graphml" + ) + else: + # Default behavior when workspace is empty + self._graphml_xml_file = os.path.join( + working_dir, f"graph_{self.namespace}.graphml" + ) self._storage_lock = None self.storage_updated = None self._graph = None @@ -108,7 +114,9 @@ class NetworkXStorage(BaseGraphStorage): async def edge_degree(self, src_id: str, tgt_id: str) -> int: graph = await self._get_graph() - return graph.degree(src_id) + graph.degree(tgt_id) + src_degree = graph.degree(src_id) if graph.has_node(src_id) else 0 + tgt_degree = graph.degree(tgt_id) if graph.has_node(tgt_id) else 0 + return src_degree + tgt_degree async def get_edge( self, source_node_id: str, target_node_id: str @@ -208,7 +216,7 @@ class NetworkXStorage(BaseGraphStorage): self, node_label: str, max_depth: int = 3, - max_nodes: int = MAX_GRAPH_NODES, + max_nodes: int = None, ) -> KnowledgeGraph: """ Retrieve a connected subgraph of nodes where the label includes the specified `node_label`. @@ -222,6 +230,13 @@ class NetworkXStorage(BaseGraphStorage): KnowledgeGraph object containing nodes and edges, with an is_truncated flag indicating whether the graph was truncated due to max_nodes limit """ + # Get max_nodes from global_config if not provided + if max_nodes is None: + max_nodes = self.global_config.get("max_graph_nodes", 1000) + else: + # Limit max_nodes to not exceed global_config max_graph_nodes + max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000)) + graph = await self._get_graph() result = KnowledgeGraph() @@ -357,6 +372,33 @@ class NetworkXStorage(BaseGraphStorage): ) return result + async def get_nodes_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]: + chunk_ids_set = set(chunk_ids) + graph = await self._get_graph() + matching_nodes = [] + for node_id, node_data in graph.nodes(data=True): + if "source_id" in node_data: + node_source_ids = set(node_data["source_id"].split(GRAPH_FIELD_SEP)) + if not node_source_ids.isdisjoint(chunk_ids_set): + node_data_with_id = node_data.copy() + node_data_with_id["id"] = node_id + matching_nodes.append(node_data_with_id) + return matching_nodes + + async def get_edges_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]: + chunk_ids_set = set(chunk_ids) + graph = await self._get_graph() + matching_edges = [] + for u, v, edge_data in graph.edges(data=True): + if "source_id" in edge_data: + edge_source_ids = set(edge_data["source_id"].split(GRAPH_FIELD_SEP)) + if not edge_source_ids.isdisjoint(chunk_ids_set): + edge_data_with_nodes = edge_data.copy() + edge_data_with_nodes["source"] = u + edge_data_with_nodes["target"] = v + matching_edges.append(edge_data_with_nodes) + return matching_edges + async def index_done_callback(self) -> bool: """Save data to disk""" async with self._storage_lock: diff --git a/lightrag/kg/postgres_impl.py b/lightrag/kg/postgres_impl.py index 02bcdeb2..7e696c77 100644 --- a/lightrag/kg/postgres_impl.py +++ b/lightrag/kg/postgres_impl.py @@ -1,6 +1,7 @@ import asyncio import json import os +import re import datetime from datetime import timezone from dataclasses import dataclass, field @@ -27,6 +28,7 @@ from ..base import ( ) from ..namespace import NameSpace, is_namespace from ..utils import logger +from ..constants import GRAPH_FIELD_SEP import pipmaster as pm @@ -43,19 +45,16 @@ from dotenv import load_dotenv # the OS environment variables take precedence over the .env file load_dotenv(dotenv_path=".env", override=False) -# Get maximum number of graph nodes from environment variable, default is 1000 -MAX_GRAPH_NODES = int(os.getenv("MAX_GRAPH_NODES", 1000)) - class PostgreSQLDB: def __init__(self, config: dict[str, Any], **kwargs: Any): - self.host = config.get("host", "localhost") - self.port = config.get("port", 5432) - self.user = config.get("user", "postgres") - self.password = config.get("password", None) - self.database = config.get("database", "postgres") - self.workspace = config.get("workspace", "default") - self.max = int(config.get("max_connections", 12)) + self.host = config["host"] + self.port = config["port"] + self.user = config["user"] + self.password = config["password"] + self.database = config["database"] + self.workspace = config["workspace"] + self.max = int(config["max_connections"]) self.increment = 1 self.pool: Pool | None = None @@ -106,6 +105,81 @@ class PostgreSQLDB: ): pass + async def _migrate_llm_cache_add_chunk_id(self): + """Add chunk_id column to LIGHTRAG_LLM_CACHE table if it doesn't exist""" + try: + # Check if chunk_id column exists + check_column_sql = """ + SELECT column_name + FROM information_schema.columns + WHERE table_name = 'lightrag_llm_cache' + AND column_name = 'chunk_id' + """ + + column_info = await self.query(check_column_sql) + if not column_info: + logger.info("Adding chunk_id column to LIGHTRAG_LLM_CACHE table") + add_column_sql = """ + ALTER TABLE LIGHTRAG_LLM_CACHE + ADD COLUMN chunk_id VARCHAR(255) NULL + """ + await self.execute(add_column_sql) + logger.info( + "Successfully added chunk_id column to LIGHTRAG_LLM_CACHE table" + ) + else: + logger.info( + "chunk_id column already exists in LIGHTRAG_LLM_CACHE table" + ) + except Exception as e: + logger.warning(f"Failed to add chunk_id column to LIGHTRAG_LLM_CACHE: {e}") + + async def _migrate_llm_cache_add_cache_type(self): + """Add cache_type column to LIGHTRAG_LLM_CACHE table if it doesn't exist""" + try: + # Check if cache_type column exists + check_column_sql = """ + SELECT column_name + FROM information_schema.columns + WHERE table_name = 'lightrag_llm_cache' + AND column_name = 'cache_type' + """ + + column_info = await self.query(check_column_sql) + if not column_info: + logger.info("Adding cache_type column to LIGHTRAG_LLM_CACHE table") + add_column_sql = """ + ALTER TABLE LIGHTRAG_LLM_CACHE + ADD COLUMN cache_type VARCHAR(32) NULL + """ + await self.execute(add_column_sql) + logger.info( + "Successfully added cache_type column to LIGHTRAG_LLM_CACHE table" + ) + + # Migrate existing data: extract cache_type from flattened keys + logger.info( + "Migrating existing LLM cache data to populate cache_type field" + ) + update_sql = """ + UPDATE LIGHTRAG_LLM_CACHE + SET cache_type = CASE + WHEN id LIKE '%:%:%' THEN split_part(id, ':', 2) + ELSE 'extract' + END + WHERE cache_type IS NULL + """ + await self.execute(update_sql) + logger.info("Successfully migrated existing LLM cache data") + else: + logger.info( + "cache_type column already exists in LIGHTRAG_LLM_CACHE table" + ) + except Exception as e: + logger.warning( + f"Failed to add cache_type column to LIGHTRAG_LLM_CACHE: {e}" + ) + async def _migrate_timestamp_columns(self): """Migrate timestamp columns in tables to timezone-aware types, assuming original data is in UTC time""" # Tables and columns that need migration @@ -159,6 +233,243 @@ class PostgreSQLDB: # Log error but don't interrupt the process logger.warning(f"Failed to migrate {table_name}.{column_name}: {e}") + async def _migrate_doc_chunks_to_vdb_chunks(self): + """ + Migrate data from LIGHTRAG_DOC_CHUNKS to LIGHTRAG_VDB_CHUNKS if specific conditions are met. + This migration is intended for users who are upgrading and have an older table structure + where LIGHTRAG_DOC_CHUNKS contained a `content_vector` column. + + """ + try: + # 1. Check if the new table LIGHTRAG_VDB_CHUNKS is empty + vdb_chunks_count_sql = "SELECT COUNT(1) as count FROM LIGHTRAG_VDB_CHUNKS" + vdb_chunks_count_result = await self.query(vdb_chunks_count_sql) + if vdb_chunks_count_result and vdb_chunks_count_result["count"] > 0: + logger.info( + "Skipping migration: LIGHTRAG_VDB_CHUNKS already contains data." + ) + return + + # 2. Check if `content_vector` column exists in the old table + check_column_sql = """ + SELECT 1 FROM information_schema.columns + WHERE table_name = 'lightrag_doc_chunks' AND column_name = 'content_vector' + """ + column_exists = await self.query(check_column_sql) + if not column_exists: + logger.info( + "Skipping migration: `content_vector` not found in LIGHTRAG_DOC_CHUNKS" + ) + return + + # 3. Check if the old table LIGHTRAG_DOC_CHUNKS has data + doc_chunks_count_sql = "SELECT COUNT(1) as count FROM LIGHTRAG_DOC_CHUNKS" + doc_chunks_count_result = await self.query(doc_chunks_count_sql) + if not doc_chunks_count_result or doc_chunks_count_result["count"] == 0: + logger.info("Skipping migration: LIGHTRAG_DOC_CHUNKS is empty.") + return + + # 4. Perform the migration + logger.info( + "Starting data migration from LIGHTRAG_DOC_CHUNKS to LIGHTRAG_VDB_CHUNKS..." + ) + migration_sql = """ + INSERT INTO LIGHTRAG_VDB_CHUNKS ( + id, workspace, full_doc_id, chunk_order_index, tokens, content, + content_vector, file_path, create_time, update_time + ) + SELECT + id, workspace, full_doc_id, chunk_order_index, tokens, content, + content_vector, file_path, create_time, update_time + FROM LIGHTRAG_DOC_CHUNKS + ON CONFLICT (workspace, id) DO NOTHING; + """ + await self.execute(migration_sql) + logger.info("Data migration to LIGHTRAG_VDB_CHUNKS completed successfully.") + + except Exception as e: + logger.error(f"Failed during data migration to LIGHTRAG_VDB_CHUNKS: {e}") + # Do not re-raise, to allow the application to start + + async def _check_llm_cache_needs_migration(self): + """Check if LLM cache data needs migration by examining the first record""" + try: + # Only query the first record to determine format + check_sql = """ + SELECT id FROM LIGHTRAG_LLM_CACHE + ORDER BY create_time ASC + LIMIT 1 + """ + result = await self.query(check_sql) + + if result and result.get("id"): + # If id doesn't contain colon, it's old format + return ":" not in result["id"] + + return False # No data or already new format + except Exception as e: + logger.warning(f"Failed to check LLM cache migration status: {e}") + return False + + async def _migrate_llm_cache_to_flattened_keys(self): + """Migrate LLM cache to flattened key format, recalculating hash values""" + try: + # Get all old format data + old_data_sql = """ + SELECT id, mode, original_prompt, return_value, chunk_id, + workspace, create_time, update_time + FROM LIGHTRAG_LLM_CACHE + WHERE id NOT LIKE '%:%' + """ + + old_records = await self.query(old_data_sql, multirows=True) + + if not old_records: + logger.info("No old format LLM cache data found, skipping migration") + return + + logger.info( + f"Found {len(old_records)} old format cache records, starting migration..." + ) + + # Import hash calculation function + from ..utils import compute_args_hash + + migrated_count = 0 + + # Migrate data in batches + for record in old_records: + try: + # Recalculate hash using correct method + new_hash = compute_args_hash( + record["mode"], record["original_prompt"] + ) + + # Determine cache_type based on mode + cache_type = "extract" if record["mode"] == "default" else "unknown" + + # Generate new flattened key + new_key = f"{record['mode']}:{cache_type}:{new_hash}" + + # Insert new format data with cache_type field + insert_sql = """ + INSERT INTO LIGHTRAG_LLM_CACHE + (workspace, id, mode, original_prompt, return_value, chunk_id, cache_type, create_time, update_time) + VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9) + ON CONFLICT (workspace, mode, id) DO NOTHING + """ + + await self.execute( + insert_sql, + { + "workspace": record[ + "workspace" + ], # Use original record's workspace + "id": new_key, + "mode": record["mode"], + "original_prompt": record["original_prompt"], + "return_value": record["return_value"], + "chunk_id": record["chunk_id"], + "cache_type": cache_type, # Add cache_type field + "create_time": record["create_time"], + "update_time": record["update_time"], + }, + ) + + # Delete old data + delete_sql = """ + DELETE FROM LIGHTRAG_LLM_CACHE + WHERE workspace=$1 AND mode=$2 AND id=$3 + """ + await self.execute( + delete_sql, + { + "workspace": record[ + "workspace" + ], # Use original record's workspace + "mode": record["mode"], + "id": record["id"], # Old id + }, + ) + + migrated_count += 1 + + except Exception as e: + logger.warning( + f"Failed to migrate cache record {record['id']}: {e}" + ) + continue + + logger.info( + f"Successfully migrated {migrated_count} cache records to flattened format" + ) + + except Exception as e: + logger.error(f"LLM cache migration failed: {e}") + # Don't raise exception, allow system to continue startup + + async def _migrate_doc_status_add_chunks_list(self): + """Add chunks_list column to LIGHTRAG_DOC_STATUS table if it doesn't exist""" + try: + # Check if chunks_list column exists + check_column_sql = """ + SELECT column_name + FROM information_schema.columns + WHERE table_name = 'lightrag_doc_status' + AND column_name = 'chunks_list' + """ + + column_info = await self.query(check_column_sql) + if not column_info: + logger.info("Adding chunks_list column to LIGHTRAG_DOC_STATUS table") + add_column_sql = """ + ALTER TABLE LIGHTRAG_DOC_STATUS + ADD COLUMN chunks_list JSONB NULL DEFAULT '[]'::jsonb + """ + await self.execute(add_column_sql) + logger.info( + "Successfully added chunks_list column to LIGHTRAG_DOC_STATUS table" + ) + else: + logger.info( + "chunks_list column already exists in LIGHTRAG_DOC_STATUS table" + ) + except Exception as e: + logger.warning( + f"Failed to add chunks_list column to LIGHTRAG_DOC_STATUS: {e}" + ) + + async def _migrate_text_chunks_add_llm_cache_list(self): + """Add llm_cache_list column to LIGHTRAG_DOC_CHUNKS table if it doesn't exist""" + try: + # Check if llm_cache_list column exists + check_column_sql = """ + SELECT column_name + FROM information_schema.columns + WHERE table_name = 'lightrag_doc_chunks' + AND column_name = 'llm_cache_list' + """ + + column_info = await self.query(check_column_sql) + if not column_info: + logger.info("Adding llm_cache_list column to LIGHTRAG_DOC_CHUNKS table") + add_column_sql = """ + ALTER TABLE LIGHTRAG_DOC_CHUNKS + ADD COLUMN llm_cache_list JSONB NULL DEFAULT '[]'::jsonb + """ + await self.execute(add_column_sql) + logger.info( + "Successfully added llm_cache_list column to LIGHTRAG_DOC_CHUNKS table" + ) + else: + logger.info( + "llm_cache_list column already exists in LIGHTRAG_DOC_CHUNKS table" + ) + except Exception as e: + logger.warning( + f"Failed to add llm_cache_list column to LIGHTRAG_DOC_CHUNKS: {e}" + ) + async def check_tables(self): # First create all tables for k, v in TABLES.items(): @@ -196,6 +507,29 @@ class PostgreSQLDB: f"PostgreSQL, Failed to create index on table {k}, Got: {e}" ) + # Create composite index for (workspace, id) columns in each table + try: + composite_index_name = f"idx_{k.lower()}_workspace_id" + check_composite_index_sql = f""" + SELECT 1 FROM pg_indexes + WHERE indexname = '{composite_index_name}' + AND tablename = '{k.lower()}' + """ + composite_index_exists = await self.query(check_composite_index_sql) + + if not composite_index_exists: + create_composite_index_sql = ( + f"CREATE INDEX {composite_index_name} ON {k}(workspace, id)" + ) + logger.info( + f"PostgreSQL, Creating composite index {composite_index_name} on table {k}" + ) + await self.execute(create_composite_index_sql) + except Exception as e: + logger.error( + f"PostgreSQL, Failed to create composite index on table {k}, Got: {e}" + ) + # After all tables are created, attempt to migrate timestamp fields try: await self._migrate_timestamp_columns() @@ -203,6 +537,51 @@ class PostgreSQLDB: logger.error(f"PostgreSQL, Failed to migrate timestamp columns: {e}") # Don't throw an exception, allow the initialization process to continue + # Migrate LLM cache table to add chunk_id field if needed + try: + await self._migrate_llm_cache_add_chunk_id() + except Exception as e: + logger.error(f"PostgreSQL, Failed to migrate LLM cache chunk_id field: {e}") + # Don't throw an exception, allow the initialization process to continue + + # Migrate LLM cache table to add cache_type field if needed + try: + await self._migrate_llm_cache_add_cache_type() + except Exception as e: + logger.error( + f"PostgreSQL, Failed to migrate LLM cache cache_type field: {e}" + ) + # Don't throw an exception, allow the initialization process to continue + + # Finally, attempt to migrate old doc chunks data if needed + try: + await self._migrate_doc_chunks_to_vdb_chunks() + except Exception as e: + logger.error(f"PostgreSQL, Failed to migrate doc_chunks to vdb_chunks: {e}") + + # Check and migrate LLM cache to flattened keys if needed + try: + if await self._check_llm_cache_needs_migration(): + await self._migrate_llm_cache_to_flattened_keys() + except Exception as e: + logger.error(f"PostgreSQL, LLM cache migration failed: {e}") + + # Migrate doc status to add chunks_list field if needed + try: + await self._migrate_doc_status_add_chunks_list() + except Exception as e: + logger.error( + f"PostgreSQL, Failed to migrate doc status chunks_list field: {e}" + ) + + # Migrate text chunks to add llm_cache_list field if needed + try: + await self._migrate_text_chunks_add_llm_cache_list() + except Exception as e: + logger.error( + f"PostgreSQL, Failed to migrate text chunks llm_cache_list field: {e}" + ) + async def query( self, sql: str, @@ -253,25 +632,31 @@ class PostgreSQLDB: sql: str, data: dict[str, Any] | None = None, upsert: bool = False, + ignore_if_exists: bool = False, with_age: bool = False, graph_name: str | None = None, ): try: async with self.pool.acquire() as connection: # type: ignore if with_age and graph_name: - await self.configure_age(connection, graph_name) # type: ignore + await self.configure_age(connection, graph_name) elif with_age and not graph_name: raise ValueError("Graph name is required when with_age is True") if data is None: - await connection.execute(sql) # type: ignore + await connection.execute(sql) else: - await connection.execute(sql, *data.values()) # type: ignore + await connection.execute(sql, *data.values()) except ( asyncpg.exceptions.UniqueViolationError, asyncpg.exceptions.DuplicateTableError, + asyncpg.exceptions.DuplicateObjectError, # Catch "already exists" error + asyncpg.exceptions.InvalidSchemaNameError, # Also catch for AGE extension "already exists" ) as e: - if upsert: + if ignore_if_exists: + # If the flag is set, just ignore these specific errors + pass + elif upsert: print("Key value duplicate, but upsert succeeded.") else: logger.error(f"Upsert error: {e}") @@ -298,7 +683,7 @@ class ClientManager: "POSTGRES_PORT", config.get("postgres", "port", fallback=5432) ), "user": os.environ.get( - "POSTGRES_USER", config.get("postgres", "user", fallback=None) + "POSTGRES_USER", config.get("postgres", "user", fallback="postgres") ), "password": os.environ.get( "POSTGRES_PASSWORD", @@ -306,15 +691,15 @@ class ClientManager: ), "database": os.environ.get( "POSTGRES_DATABASE", - config.get("postgres", "database", fallback=None), + config.get("postgres", "database", fallback="postgres"), ), "workspace": os.environ.get( "POSTGRES_WORKSPACE", - config.get("postgres", "workspace", fallback="default"), + config.get("postgres", "workspace", fallback=None), ), "max_connections": os.environ.get( "POSTGRES_MAX_CONNECTIONS", - config.get("postgres", "max_connections", fallback=12), + config.get("postgres", "max_connections", fallback=20), ), } @@ -356,6 +741,18 @@ class PGKVStorage(BaseKVStorage): async def initialize(self): if self.db is None: self.db = await ClientManager.get_client() + # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default" + if self.db.workspace: + # Use PostgreSQLDB's workspace (highest priority) + final_workspace = self.db.workspace + elif hasattr(self, "workspace") and self.workspace: + # Use storage class's workspace (medium priority) + final_workspace = self.workspace + self.db.workspace = final_workspace + else: + # Use "default" for compatibility (lowest priority) + final_workspace = "default" + self.db.workspace = final_workspace async def finalize(self): if self.db is not None: @@ -380,74 +777,139 @@ class PGKVStorage(BaseKVStorage): try: results = await self.db.query(sql, params, multirows=True) + # Special handling for LLM cache to ensure compatibility with _get_cached_extraction_results if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE): - result_dict = {} + processed_results = {} for row in results: - mode = row["mode"] - if mode not in result_dict: - result_dict[mode] = {} - result_dict[mode][row["id"]] = row - return result_dict - else: - return {row["id"]: row for row in results} + create_time = row.get("create_time", 0) + update_time = row.get("update_time", 0) + # Map field names and add cache_type for compatibility + processed_row = { + **row, + "return": row.get("return_value", ""), + "cache_type": row.get("original_prompt", "unknow"), + "original_prompt": row.get("original_prompt", ""), + "chunk_id": row.get("chunk_id"), + "mode": row.get("mode", "default"), + "create_time": create_time, + "update_time": create_time if update_time == 0 else update_time, + } + processed_results[row["id"]] = processed_row + return processed_results + + # For text_chunks namespace, parse llm_cache_list JSON string back to list + if is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS): + processed_results = {} + for row in results: + llm_cache_list = row.get("llm_cache_list", []) + if isinstance(llm_cache_list, str): + try: + llm_cache_list = json.loads(llm_cache_list) + except json.JSONDecodeError: + llm_cache_list = [] + row["llm_cache_list"] = llm_cache_list + create_time = row.get("create_time", 0) + update_time = row.get("update_time", 0) + row["create_time"] = create_time + row["update_time"] = ( + create_time if update_time == 0 else update_time + ) + processed_results[row["id"]] = row + return processed_results + + # For other namespaces, return as-is + return {row["id"]: row for row in results} except Exception as e: logger.error(f"Error retrieving all data from {self.namespace}: {e}") return {} async def get_by_id(self, id: str) -> dict[str, Any] | None: - """Get doc_full data by id.""" + """Get data by id.""" sql = SQL_TEMPLATES["get_by_id_" + self.namespace] params = {"workspace": self.db.workspace, "id": id} - if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE): - array_res = await self.db.query(sql, params, multirows=True) - res = {} - for row in array_res: - res[row["id"]] = row - return res if res else None - else: - response = await self.db.query(sql, params) - return response if response else None + response = await self.db.query(sql, params) - async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]: - """Specifically for llm_response_cache.""" - sql = SQL_TEMPLATES["get_by_mode_id_" + self.namespace] - params = {"workspace": self.db.workspace, "mode": mode, "id": id} - if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE): - array_res = await self.db.query(sql, params, multirows=True) - res = {} - for row in array_res: - res[row["id"]] = row - return res - else: - return None + if response and is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS): + # Parse llm_cache_list JSON string back to list + llm_cache_list = response.get("llm_cache_list", []) + if isinstance(llm_cache_list, str): + try: + llm_cache_list = json.loads(llm_cache_list) + except json.JSONDecodeError: + llm_cache_list = [] + response["llm_cache_list"] = llm_cache_list + create_time = response.get("create_time", 0) + update_time = response.get("update_time", 0) + response["create_time"] = create_time + response["update_time"] = create_time if update_time == 0 else update_time + + # Special handling for LLM cache to ensure compatibility with _get_cached_extraction_results + if response and is_namespace( + self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE + ): + create_time = response.get("create_time", 0) + update_time = response.get("update_time", 0) + # Map field names and add cache_type for compatibility + response = { + **response, + "return": response.get("return_value", ""), + "cache_type": response.get("cache_type"), + "original_prompt": response.get("original_prompt", ""), + "chunk_id": response.get("chunk_id"), + "mode": response.get("mode", "default"), + "create_time": create_time, + "update_time": create_time if update_time == 0 else update_time, + } + + return response if response else None # Query by id async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: - """Get doc_chunks data by id""" + """Get data by ids""" sql = SQL_TEMPLATES["get_by_ids_" + self.namespace].format( ids=",".join([f"'{id}'" for id in ids]) ) params = {"workspace": self.db.workspace} - if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE): - array_res = await self.db.query(sql, params, multirows=True) - modes = set() - dict_res: dict[str, dict] = {} - for row in array_res: - modes.add(row["mode"]) - for mode in modes: - if mode not in dict_res: - dict_res[mode] = {} - for row in array_res: - dict_res[row["mode"]][row["id"]] = row - return [{k: v} for k, v in dict_res.items()] - else: - return await self.db.query(sql, params, multirows=True) + results = await self.db.query(sql, params, multirows=True) - async def get_by_status(self, status: str) -> Union[list[dict[str, Any]], None]: - """Specifically for llm_response_cache.""" - SQL = SQL_TEMPLATES["get_by_status_" + self.namespace] - params = {"workspace": self.db.workspace, "status": status} - return await self.db.query(SQL, params, multirows=True) + if results and is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS): + # Parse llm_cache_list JSON string back to list for each result + for result in results: + llm_cache_list = result.get("llm_cache_list", []) + if isinstance(llm_cache_list, str): + try: + llm_cache_list = json.loads(llm_cache_list) + except json.JSONDecodeError: + llm_cache_list = [] + result["llm_cache_list"] = llm_cache_list + create_time = result.get("create_time", 0) + update_time = result.get("update_time", 0) + result["create_time"] = create_time + result["update_time"] = create_time if update_time == 0 else update_time + + # Special handling for LLM cache to ensure compatibility with _get_cached_extraction_results + if results and is_namespace( + self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE + ): + processed_results = [] + for row in results: + create_time = row.get("create_time", 0) + update_time = row.get("update_time", 0) + # Map field names and add cache_type for compatibility + processed_row = { + **row, + "return": row.get("return_value", ""), + "cache_type": row.get("cache_type"), + "original_prompt": row.get("original_prompt", ""), + "chunk_id": row.get("chunk_id"), + "mode": row.get("mode", "default"), + "create_time": create_time, + "update_time": create_time if update_time == 0 else update_time, + } + processed_results.append(processed_row) + return processed_results + + return results if results else [] async def filter_keys(self, keys: set[str]) -> set[str]: """Filter out duplicated content""" @@ -477,7 +939,22 @@ class PGKVStorage(BaseKVStorage): return if is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS): - pass + current_time = datetime.datetime.now(timezone.utc) + for k, v in data.items(): + upsert_sql = SQL_TEMPLATES["upsert_text_chunk"] + _data = { + "workspace": self.db.workspace, + "id": k, + "tokens": v["tokens"], + "chunk_order_index": v["chunk_order_index"], + "full_doc_id": v["full_doc_id"], + "content": v["content"], + "file_path": v["file_path"], + "llm_cache_list": json.dumps(v.get("llm_cache_list", [])), + "create_time": current_time, + "update_time": current_time, + } + await self.db.execute(upsert_sql, _data) elif is_namespace(self.namespace, NameSpace.KV_STORE_FULL_DOCS): for k, v in data.items(): upsert_sql = SQL_TEMPLATES["upsert_doc_full"] @@ -488,18 +965,21 @@ class PGKVStorage(BaseKVStorage): } await self.db.execute(upsert_sql, _data) elif is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE): - for mode, items in data.items(): - for k, v in items.items(): - upsert_sql = SQL_TEMPLATES["upsert_llm_response_cache"] - _data = { - "workspace": self.db.workspace, - "id": k, - "original_prompt": v["original_prompt"], - "return_value": v["return"], - "mode": mode, - } + for k, v in data.items(): + upsert_sql = SQL_TEMPLATES["upsert_llm_response_cache"] + _data = { + "workspace": self.db.workspace, + "id": k, # Use flattened key as id + "original_prompt": v["original_prompt"], + "return_value": v["return"], + "mode": v.get("mode", "default"), # Get mode from data + "chunk_id": v.get("chunk_id"), + "cache_type": v.get( + "cache_type", "extract" + ), # Get cache_type from data + } - await self.db.execute(upsert_sql, _data) + await self.db.execute(upsert_sql, _data) async def index_done_callback(self) -> None: # PG handles persistence automatically @@ -604,6 +1084,18 @@ class PGVectorStorage(BaseVectorStorage): async def initialize(self): if self.db is None: self.db = await ClientManager.get_client() + # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default" + if self.db.workspace: + # Use PostgreSQLDB's workspace (highest priority) + final_workspace = self.db.workspace + elif hasattr(self, "workspace") and self.workspace: + # Use storage class's workspace (medium priority) + final_workspace = self.workspace + self.db.workspace = final_workspace + else: + # Use "default" for compatibility (lowest priority) + final_workspace = "default" + self.db.workspace = final_workspace async def finalize(self): if self.db is not None: @@ -885,6 +1377,18 @@ class PGDocStatusStorage(DocStatusStorage): async def initialize(self): if self.db is None: self.db = await ClientManager.get_client() + # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default" + if self.db.workspace: + # Use PostgreSQLDB's workspace (highest priority) + final_workspace = self.db.workspace + elif hasattr(self, "workspace") and self.workspace: + # Use storage class's workspace (medium priority) + final_workspace = self.workspace + self.db.workspace = final_workspace + else: + # Use "default" for compatibility (lowest priority) + final_workspace = "default" + self.db.workspace = final_workspace async def finalize(self): if self.db is not None: @@ -905,8 +1409,8 @@ class PGDocStatusStorage(DocStatusStorage): else: exist_keys = [] new_keys = set([s for s in keys if s not in exist_keys]) - print(f"keys: {keys}") - print(f"new_keys: {new_keys}") + # print(f"keys: {keys}") + # print(f"new_keys: {new_keys}") return new_keys except Exception as e: logger.error( @@ -921,6 +1425,14 @@ class PGDocStatusStorage(DocStatusStorage): if result is None or result == []: return None else: + # Parse chunks_list JSON string back to list + chunks_list = result[0].get("chunks_list", []) + if isinstance(chunks_list, str): + try: + chunks_list = json.loads(chunks_list) + except json.JSONDecodeError: + chunks_list = [] + return dict( content=result[0]["content"], content_length=result[0]["content_length"], @@ -930,6 +1442,7 @@ class PGDocStatusStorage(DocStatusStorage): created_at=result[0]["created_at"], updated_at=result[0]["updated_at"], file_path=result[0]["file_path"], + chunks_list=chunks_list, ) async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: @@ -944,19 +1457,32 @@ class PGDocStatusStorage(DocStatusStorage): if not results: return [] - return [ - { - "content": row["content"], - "content_length": row["content_length"], - "content_summary": row["content_summary"], - "status": row["status"], - "chunks_count": row["chunks_count"], - "created_at": row["created_at"], - "updated_at": row["updated_at"], - "file_path": row["file_path"], - } - for row in results - ] + + processed_results = [] + for row in results: + # Parse chunks_list JSON string back to list + chunks_list = row.get("chunks_list", []) + if isinstance(chunks_list, str): + try: + chunks_list = json.loads(chunks_list) + except json.JSONDecodeError: + chunks_list = [] + + processed_results.append( + { + "content": row["content"], + "content_length": row["content_length"], + "content_summary": row["content_summary"], + "status": row["status"], + "chunks_count": row["chunks_count"], + "created_at": row["created_at"], + "updated_at": row["updated_at"], + "file_path": row["file_path"], + "chunks_list": chunks_list, + } + ) + + return processed_results async def get_status_counts(self) -> dict[str, int]: """Get counts of documents in each status""" @@ -977,8 +1503,18 @@ class PGDocStatusStorage(DocStatusStorage): sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and status=$2" params = {"workspace": self.db.workspace, "status": status.value} result = await self.db.query(sql, params, True) - docs_by_status = { - element["id"]: DocProcessingStatus( + + docs_by_status = {} + for element in result: + # Parse chunks_list JSON string back to list + chunks_list = element.get("chunks_list", []) + if isinstance(chunks_list, str): + try: + chunks_list = json.loads(chunks_list) + except json.JSONDecodeError: + chunks_list = [] + + docs_by_status[element["id"]] = DocProcessingStatus( content=element["content"], content_summary=element["content_summary"], content_length=element["content_length"], @@ -987,9 +1523,9 @@ class PGDocStatusStorage(DocStatusStorage): updated_at=element["updated_at"], chunks_count=element["chunks_count"], file_path=element["file_path"], + chunks_list=chunks_list, ) - for element in result - } + return docs_by_status async def index_done_callback(self) -> None: @@ -1053,10 +1589,10 @@ class PGDocStatusStorage(DocStatusStorage): logger.warning(f"Unable to parse datetime string: {dt_str}") return None - # Modified SQL to include created_at and updated_at in both INSERT and UPDATE operations - # Both fields are updated from the input data in both INSERT and UPDATE cases - sql = """insert into LIGHTRAG_DOC_STATUS(workspace,id,content,content_summary,content_length,chunks_count,status,file_path,created_at,updated_at) - values($1,$2,$3,$4,$5,$6,$7,$8,$9,$10) + # Modified SQL to include created_at, updated_at, and chunks_list in both INSERT and UPDATE operations + # All fields are updated from the input data in both INSERT and UPDATE cases + sql = """insert into LIGHTRAG_DOC_STATUS(workspace,id,content,content_summary,content_length,chunks_count,status,file_path,chunks_list,created_at,updated_at) + values($1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11) on conflict(id,workspace) do update set content = EXCLUDED.content, content_summary = EXCLUDED.content_summary, @@ -1064,6 +1600,7 @@ class PGDocStatusStorage(DocStatusStorage): chunks_count = EXCLUDED.chunks_count, status = EXCLUDED.status, file_path = EXCLUDED.file_path, + chunks_list = EXCLUDED.chunks_list, created_at = EXCLUDED.created_at, updated_at = EXCLUDED.updated_at""" for k, v in data.items(): @@ -1071,7 +1608,7 @@ class PGDocStatusStorage(DocStatusStorage): created_at = parse_datetime(v.get("created_at")) updated_at = parse_datetime(v.get("updated_at")) - # chunks_count is optional + # chunks_count and chunks_list are optional await self.db.execute( sql, { @@ -1083,6 +1620,7 @@ class PGDocStatusStorage(DocStatusStorage): "chunks_count": v["chunks_count"] if "chunks_count" in v else -1, "status": v["status"], "file_path": v["file_path"], + "chunks_list": json.dumps(v.get("chunks_list", [])), "created_at": created_at, # Use the converted datetime object "updated_at": updated_at, # Use the converted datetime object }, @@ -1129,9 +1667,34 @@ class PGGraphQueryException(Exception): @dataclass class PGGraphStorage(BaseGraphStorage): def __post_init__(self): - self.graph_name = self.namespace or os.environ.get("AGE_GRAPH_NAME", "lightrag") + # Graph name will be dynamically generated in initialize() based on workspace self.db: PostgreSQLDB | None = None + def _get_workspace_graph_name(self) -> str: + """ + Generate graph name based on workspace and namespace for data isolation. + Rules: + - If workspace is empty: graph_name = namespace + - If workspace has value: graph_name = workspace_namespace + + Args: + None + + Returns: + str: The graph name for the current workspace + """ + workspace = getattr(self, "workspace", None) + namespace = self.namespace or os.environ.get("AGE_GRAPH_NAME", "lightrag") + + if workspace and workspace.strip(): + # Ensure names comply with PostgreSQL identifier specifications + safe_workspace = re.sub(r"[^a-zA-Z0-9_]", "_", workspace.strip()) + safe_namespace = re.sub(r"[^a-zA-Z0-9_]", "_", namespace) + return f"{safe_workspace}_{safe_namespace}" + else: + # When workspace is empty, use namespace directly + return re.sub(r"[^a-zA-Z0-9_]", "_", namespace) + @staticmethod def _normalize_node_id(node_id: str) -> str: """ @@ -1152,6 +1715,27 @@ class PGGraphStorage(BaseGraphStorage): async def initialize(self): if self.db is None: self.db = await ClientManager.get_client() + # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > None + if self.db.workspace: + # Use PostgreSQLDB's workspace (highest priority) + final_workspace = self.db.workspace + elif hasattr(self, "workspace") and self.workspace: + # Use storage class's workspace (medium priority) + final_workspace = self.workspace + self.db.workspace = final_workspace + else: + # Use None for compatibility (lowest priority) + final_workspace = None + self.db.workspace = final_workspace + + # Dynamically generate graph name based on workspace + self.workspace = self.db.workspace + self.graph_name = self._get_workspace_graph_name() + + # Log the graph initialization for debugging + logger.info( + f"PostgreSQL Graph initialized: workspace='{self.workspace}', graph_name='{self.graph_name}'" + ) # Execute each statement separately and ignore errors queries = [ @@ -1175,16 +1759,15 @@ class PGGraphStorage(BaseGraphStorage): ] for query in queries: - try: - await self.db.execute( - query, - upsert=True, - with_age=True, - graph_name=self.graph_name, - ) - # logger.info(f"Successfully executed: {query}") - except Exception: - continue + # Use the new flag to silently ignore "already exists" errors + # at the source, preventing log spam. + await self.db.execute( + query, + upsert=True, + ignore_if_exists=True, # Pass the new flag + with_age=True, + graph_name=self.graph_name, + ) async def finalize(self): if self.db is not None: @@ -1380,8 +1963,6 @@ class PGGraphStorage(BaseGraphStorage): # Process string result, parse it to JSON dictionary if isinstance(node_dict, str): try: - import json - node_dict = json.loads(node_dict) except json.JSONDecodeError: logger.warning(f"Failed to parse node string: {node_dict}") @@ -1437,8 +2018,6 @@ class PGGraphStorage(BaseGraphStorage): # Process string result, parse it to JSON dictionary if isinstance(result, str): try: - import json - result = json.loads(result) except json.JSONDecodeError: logger.warning(f"Failed to parse edge string: {result}") @@ -1655,8 +2234,6 @@ class PGGraphStorage(BaseGraphStorage): # Process string result, parse it to JSON dictionary if isinstance(node_dict, str): try: - import json - node_dict = json.loads(node_dict) except json.JSONDecodeError: logger.warning( @@ -1664,10 +2241,11 @@ class PGGraphStorage(BaseGraphStorage): ) # Remove the 'base' label if present in a 'labels' property - if "labels" in node_dict: - node_dict["labels"] = [ - label for label in node_dict["labels"] if label != "base" - ] + # if "labels" in node_dict: + # node_dict["labels"] = [ + # label for label in node_dict["labels"] if label != "base" + # ] + nodes_dict[result["node_id"]] = node_dict return nodes_dict @@ -1796,14 +2374,14 @@ class PGGraphStorage(BaseGraphStorage): forward_query = f"""SELECT * FROM cypher('{self.graph_name}', $$ WITH [{src_array}] AS sources, [{tgt_array}] AS targets UNWIND range(0, size(sources)-1) AS i - MATCH (a:base {{entity_id: sources[i]}})-[r:DIRECTED]->(b:base {{entity_id: targets[i]}}) + MATCH (a:base {{entity_id: sources[i]}})-[r]->(b:base {{entity_id: targets[i]}}) RETURN sources[i] AS source, targets[i] AS target, properties(r) AS edge_properties $$) AS (source text, target text, edge_properties agtype)""" backward_query = f"""SELECT * FROM cypher('{self.graph_name}', $$ WITH [{src_array}] AS sources, [{tgt_array}] AS targets UNWIND range(0, size(sources)-1) AS i - MATCH (a:base {{entity_id: sources[i]}})<-[r:DIRECTED]-(b:base {{entity_id: targets[i]}}) + MATCH (a:base {{entity_id: sources[i]}})<-[r]-(b:base {{entity_id: targets[i]}}) RETURN sources[i] AS source, targets[i] AS target, properties(r) AS edge_properties $$) AS (source text, target text, edge_properties agtype)""" @@ -1819,8 +2397,6 @@ class PGGraphStorage(BaseGraphStorage): # Process string result, parse it to JSON dictionary if isinstance(edge_props, str): try: - import json - edge_props = json.loads(edge_props) except json.JSONDecodeError: logger.warning( @@ -1837,8 +2413,6 @@ class PGGraphStorage(BaseGraphStorage): # Process string result, parse it to JSON dictionary if isinstance(edge_props, str): try: - import json - edge_props = json.loads(edge_props) except json.JSONDecodeError: logger.warning( @@ -1933,6 +2507,102 @@ class PGGraphStorage(BaseGraphStorage): labels.append(result["label"]) return labels + async def get_nodes_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]: + """ + Retrieves nodes from the graph that are associated with a given list of chunk IDs. + This method uses a Cypher query with UNWIND to efficiently find all nodes + where the `source_id` property contains any of the specified chunk IDs. + """ + # The string representation of the list for the cypher query + chunk_ids_str = json.dumps(chunk_ids) + + query = f""" + SELECT * FROM cypher('{self.graph_name}', $$ + UNWIND {chunk_ids_str} AS chunk_id + MATCH (n:base) + WHERE n.source_id IS NOT NULL AND chunk_id IN split(n.source_id, '{GRAPH_FIELD_SEP}') + RETURN n + $$) AS (n agtype); + """ + results = await self._query(query) + + # Build result list + nodes = [] + for result in results: + if result["n"]: + node_dict = result["n"]["properties"] + + # Process string result, parse it to JSON dictionary + if isinstance(node_dict, str): + try: + node_dict = json.loads(node_dict) + except json.JSONDecodeError: + logger.warning( + f"Failed to parse node string in batch: {node_dict}" + ) + + node_dict["id"] = node_dict["entity_id"] + nodes.append(node_dict) + + return nodes + + async def get_edges_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]: + """ + Retrieves edges from the graph that are associated with a given list of chunk IDs. + This method uses a Cypher query with UNWIND to efficiently find all edges + where the `source_id` property contains any of the specified chunk IDs. + """ + chunk_ids_str = json.dumps(chunk_ids) + + query = f""" + SELECT * FROM cypher('{self.graph_name}', $$ + UNWIND {chunk_ids_str} AS chunk_id + MATCH (a:base)-[r]-(b:base) + WHERE r.source_id IS NOT NULL AND chunk_id IN split(r.source_id, '{GRAPH_FIELD_SEP}') + RETURN DISTINCT r, startNode(r) AS source, endNode(r) AS target + $$) AS (edge agtype, source agtype, target agtype); + """ + results = await self._query(query) + edges = [] + if results: + for item in results: + edge_agtype = item["edge"]["properties"] + # Process string result, parse it to JSON dictionary + if isinstance(edge_agtype, str): + try: + edge_agtype = json.loads(edge_agtype) + except json.JSONDecodeError: + logger.warning( + f"Failed to parse edge string in batch: {edge_agtype}" + ) + + source_agtype = item["source"]["properties"] + # Process string result, parse it to JSON dictionary + if isinstance(source_agtype, str): + try: + source_agtype = json.loads(source_agtype) + except json.JSONDecodeError: + logger.warning( + f"Failed to parse node string in batch: {source_agtype}" + ) + + target_agtype = item["target"]["properties"] + # Process string result, parse it to JSON dictionary + if isinstance(target_agtype, str): + try: + target_agtype = json.loads(target_agtype) + except json.JSONDecodeError: + logger.warning( + f"Failed to parse node string in batch: {target_agtype}" + ) + + if edge_agtype and source_agtype and target_agtype: + edge_properties = edge_agtype + edge_properties["source"] = source_agtype["entity_id"] + edge_properties["target"] = target_agtype["entity_id"] + edges.append(edge_properties) + return edges + async def _bfs_subgraph( self, node_label: str, max_depth: int, max_nodes: int ) -> KnowledgeGraph: @@ -2146,7 +2816,7 @@ class PGGraphStorage(BaseGraphStorage): self, node_label: str, max_depth: int = 3, - max_nodes: int = MAX_GRAPH_NODES, + max_nodes: int = None, ) -> KnowledgeGraph: """ Retrieve a connected subgraph of nodes where the label includes the specified `node_label`. @@ -2154,12 +2824,18 @@ class PGGraphStorage(BaseGraphStorage): Args: node_label: Label of the starting node, * means all nodes max_depth: Maximum depth of the subgraph, Defaults to 3 - max_nodes: Maxiumu nodes to return, Defaults to 1000 + max_nodes: Maximum nodes to return, Defaults to global_config max_graph_nodes Returns: KnowledgeGraph object containing nodes and edges, with an is_truncated flag indicating whether the graph was truncated due to max_nodes limit """ + # Use global_config max_graph_nodes as default if max_nodes is None + if max_nodes is None: + max_nodes = self.global_config.get("max_graph_nodes", 1000) + else: + # Limit max_nodes to not exceed global_config max_graph_nodes + max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000)) kg = KnowledgeGraph() # Handle wildcard query - get all nodes @@ -2270,7 +2946,10 @@ class PGGraphStorage(BaseGraphStorage): $$) AS (result agtype)""" await self._query(drop_query, readonly=False) - return {"status": "success", "message": "graph data dropped"} + return { + "status": "success", + "message": f"workspace '{self.workspace}' graph data dropped", + } except Exception as e: logger.error(f"Error dropping graph: {e}") return {"status": "error", "message": str(e)} @@ -2279,7 +2958,7 @@ class PGGraphStorage(BaseGraphStorage): NAMESPACE_TABLE_MAP = { NameSpace.KV_STORE_FULL_DOCS: "LIGHTRAG_DOC_FULL", NameSpace.KV_STORE_TEXT_CHUNKS: "LIGHTRAG_DOC_CHUNKS", - NameSpace.VECTOR_STORE_CHUNKS: "LIGHTRAG_DOC_CHUNKS", + NameSpace.VECTOR_STORE_CHUNKS: "LIGHTRAG_VDB_CHUNKS", NameSpace.VECTOR_STORE_ENTITIES: "LIGHTRAG_VDB_ENTITY", NameSpace.VECTOR_STORE_RELATIONSHIPS: "LIGHTRAG_VDB_RELATION", NameSpace.DOC_STATUS: "LIGHTRAG_DOC_STATUS", @@ -2308,6 +2987,21 @@ TABLES = { }, "LIGHTRAG_DOC_CHUNKS": { "ddl": """CREATE TABLE LIGHTRAG_DOC_CHUNKS ( + id VARCHAR(255), + workspace VARCHAR(255), + full_doc_id VARCHAR(256), + chunk_order_index INTEGER, + tokens INTEGER, + content TEXT, + file_path VARCHAR(256), + llm_cache_list JSONB NULL DEFAULT '[]'::jsonb, + create_time TIMESTAMP(0) WITH TIME ZONE, + update_time TIMESTAMP(0) WITH TIME ZONE, + CONSTRAINT LIGHTRAG_DOC_CHUNKS_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_VDB_CHUNKS": { + "ddl": """CREATE TABLE LIGHTRAG_VDB_CHUNKS ( id VARCHAR(255), workspace VARCHAR(255), full_doc_id VARCHAR(256), @@ -2318,7 +3012,7 @@ TABLES = { file_path VARCHAR(256), create_time TIMESTAMP(0) WITH TIME ZONE, update_time TIMESTAMP(0) WITH TIME ZONE, - CONSTRAINT LIGHTRAG_DOC_CHUNKS_PK PRIMARY KEY (workspace, id) + CONSTRAINT LIGHTRAG_VDB_CHUNKS_PK PRIMARY KEY (workspace, id) )""" }, "LIGHTRAG_VDB_ENTITY": { @@ -2357,6 +3051,7 @@ TABLES = { mode varchar(32) NOT NULL, original_prompt TEXT, return_value TEXT, + chunk_id VARCHAR(255) NULL, create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP, update_time TIMESTAMP, CONSTRAINT LIGHTRAG_LLM_CACHE_PK PRIMARY KEY (workspace, mode, id) @@ -2372,6 +3067,7 @@ TABLES = { chunks_count int4 NULL, status varchar(64) NULL, file_path TEXT NULL, + chunks_list JSONB NULL DEFAULT '[]'::jsonb, created_at timestamp with time zone DEFAULT CURRENT_TIMESTAMP NULL, updated_at timestamp with time zone DEFAULT CURRENT_TIMESTAMP NULL, CONSTRAINT LIGHTRAG_DOC_STATUS_PK PRIMARY KEY (workspace, id) @@ -2386,24 +3082,30 @@ SQL_TEMPLATES = { FROM LIGHTRAG_DOC_FULL WHERE workspace=$1 AND id=$2 """, "get_by_id_text_chunks": """SELECT id, tokens, COALESCE(content, '') as content, - chunk_order_index, full_doc_id, file_path + chunk_order_index, full_doc_id, file_path, + COALESCE(llm_cache_list, '[]'::jsonb) as llm_cache_list, + create_time, update_time FROM LIGHTRAG_DOC_CHUNKS WHERE workspace=$1 AND id=$2 """, - "get_by_id_llm_response_cache": """SELECT id, original_prompt, COALESCE(return_value, '') as "return", mode - FROM LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND mode=$2 + "get_by_id_llm_response_cache": """SELECT id, original_prompt, return_value, mode, chunk_id, cache_type, + create_time, update_time + FROM LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND id=$2 """, - "get_by_mode_id_llm_response_cache": """SELECT id, original_prompt, COALESCE(return_value, '') as "return", mode + "get_by_mode_id_llm_response_cache": """SELECT id, original_prompt, return_value, mode, chunk_id FROM LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND mode=$2 AND id=$3 """, "get_by_ids_full_docs": """SELECT id, COALESCE(content, '') as content FROM LIGHTRAG_DOC_FULL WHERE workspace=$1 AND id IN ({ids}) """, "get_by_ids_text_chunks": """SELECT id, tokens, COALESCE(content, '') as content, - chunk_order_index, full_doc_id, file_path + chunk_order_index, full_doc_id, file_path, + COALESCE(llm_cache_list, '[]'::jsonb) as llm_cache_list, + create_time, update_time FROM LIGHTRAG_DOC_CHUNKS WHERE workspace=$1 AND id IN ({ids}) """, - "get_by_ids_llm_response_cache": """SELECT id, original_prompt, COALESCE(return_value, '') as "return", mode - FROM LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND mode= IN ({ids}) + "get_by_ids_llm_response_cache": """SELECT id, original_prompt, return_value, mode, chunk_id, cache_type, + create_time, update_time + FROM LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND id IN ({ids}) """, "filter_keys": "SELECT id FROM {table_name} WHERE workspace=$1 AND id IN ({ids})", "upsert_doc_full": """INSERT INTO LIGHTRAG_DOC_FULL (id, content, workspace) @@ -2411,15 +3113,31 @@ SQL_TEMPLATES = { ON CONFLICT (workspace,id) DO UPDATE SET content = $2, update_time = CURRENT_TIMESTAMP """, - "upsert_llm_response_cache": """INSERT INTO LIGHTRAG_LLM_CACHE(workspace,id,original_prompt,return_value,mode) - VALUES ($1, $2, $3, $4, $5) + "upsert_llm_response_cache": """INSERT INTO LIGHTRAG_LLM_CACHE(workspace,id,original_prompt,return_value,mode,chunk_id,cache_type) + VALUES ($1, $2, $3, $4, $5, $6, $7) ON CONFLICT (workspace,mode,id) DO UPDATE SET original_prompt = EXCLUDED.original_prompt, return_value=EXCLUDED.return_value, mode=EXCLUDED.mode, + chunk_id=EXCLUDED.chunk_id, + cache_type=EXCLUDED.cache_type, update_time = CURRENT_TIMESTAMP """, - "upsert_chunk": """INSERT INTO LIGHTRAG_DOC_CHUNKS (workspace, id, tokens, + "upsert_text_chunk": """INSERT INTO LIGHTRAG_DOC_CHUNKS (workspace, id, tokens, + chunk_order_index, full_doc_id, content, file_path, llm_cache_list, + create_time, update_time) + VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) + ON CONFLICT (workspace,id) DO UPDATE + SET tokens=EXCLUDED.tokens, + chunk_order_index=EXCLUDED.chunk_order_index, + full_doc_id=EXCLUDED.full_doc_id, + content = EXCLUDED.content, + file_path=EXCLUDED.file_path, + llm_cache_list=EXCLUDED.llm_cache_list, + update_time = EXCLUDED.update_time + """, + # SQL for VectorStorage + "upsert_chunk": """INSERT INTO LIGHTRAG_VDB_CHUNKS (workspace, id, tokens, chunk_order_index, full_doc_id, content, content_vector, file_path, create_time, update_time) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) @@ -2432,7 +3150,6 @@ SQL_TEMPLATES = { file_path=EXCLUDED.file_path, update_time = EXCLUDED.update_time """, - # SQL for VectorStorage "upsert_entity": """INSERT INTO LIGHTRAG_VDB_ENTITY (workspace, id, entity_name, content, content_vector, chunk_ids, file_path, create_time, update_time) VALUES ($1, $2, $3, $4, $5, $6::varchar[], $7, $8, $9) @@ -2459,7 +3176,7 @@ SQL_TEMPLATES = { "relationships": """ WITH relevant_chunks AS ( SELECT id as chunk_id - FROM LIGHTRAG_DOC_CHUNKS + FROM LIGHTRAG_VDB_CHUNKS WHERE $2::varchar[] IS NULL OR full_doc_id = ANY($2::varchar[]) ) SELECT source_id as src_id, target_id as tgt_id, EXTRACT(EPOCH FROM create_time)::BIGINT as created_at @@ -2476,7 +3193,7 @@ SQL_TEMPLATES = { "entities": """ WITH relevant_chunks AS ( SELECT id as chunk_id - FROM LIGHTRAG_DOC_CHUNKS + FROM LIGHTRAG_VDB_CHUNKS WHERE $2::varchar[] IS NULL OR full_doc_id = ANY($2::varchar[]) ) SELECT entity_name, EXTRACT(EPOCH FROM create_time)::BIGINT as created_at FROM @@ -2493,13 +3210,13 @@ SQL_TEMPLATES = { "chunks": """ WITH relevant_chunks AS ( SELECT id as chunk_id - FROM LIGHTRAG_DOC_CHUNKS + FROM LIGHTRAG_VDB_CHUNKS WHERE $2::varchar[] IS NULL OR full_doc_id = ANY($2::varchar[]) ) SELECT id, content, file_path, EXTRACT(EPOCH FROM create_time)::BIGINT as created_at FROM ( SELECT id, content, file_path, create_time, 1 - (content_vector <=> '[{embedding_string}]'::vector) as distance - FROM LIGHTRAG_DOC_CHUNKS + FROM LIGHTRAG_VDB_CHUNKS WHERE workspace=$1 AND id IN (SELECT chunk_id FROM relevant_chunks) ) as chunk_distances diff --git a/lightrag/kg/qdrant_impl.py b/lightrag/kg/qdrant_impl.py index 885a23ca..9cdeff7a 100644 --- a/lightrag/kg/qdrant_impl.py +++ b/lightrag/kg/qdrant_impl.py @@ -50,6 +50,18 @@ def compute_mdhash_id_for_qdrant( @final @dataclass class QdrantVectorDBStorage(BaseVectorStorage): + def __init__( + self, namespace, global_config, embedding_func, workspace=None, meta_fields=None + ): + super().__init__( + namespace=namespace, + workspace=workspace or "", + global_config=global_config, + embedding_func=embedding_func, + meta_fields=meta_fields or set(), + ) + self.__post_init__() + @staticmethod def create_collection_if_not_exist( client: QdrantClient, collection_name: str, **kwargs @@ -59,6 +71,29 @@ class QdrantVectorDBStorage(BaseVectorStorage): client.create_collection(collection_name, **kwargs) def __post_init__(self): + # Check for QDRANT_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all Qdrant storage instances + qdrant_workspace = os.environ.get("QDRANT_WORKSPACE") + if qdrant_workspace and qdrant_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = qdrant_workspace.strip() + logger.info( + f"Using QDRANT_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build namespace with workspace prefix for data isolation + if effective_workspace: + self.namespace = f"{effective_workspace}_{self.namespace}" + logger.debug(f"Final namespace with workspace prefix: '{self.namespace}'") + # When workspace is empty, keep the original namespace unchanged + kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {}) cosine_threshold = kwargs.get("cosine_better_than_threshold") if cosine_threshold is None: @@ -85,7 +120,7 @@ class QdrantVectorDBStorage(BaseVectorStorage): ) async def upsert(self, data: dict[str, dict[str, Any]]) -> None: - logger.info(f"Inserting {len(data)} to {self.namespace}") + logger.debug(f"Inserting {len(data)} to {self.namespace}") if not data: return diff --git a/lightrag/kg/redis_impl.py b/lightrag/kg/redis_impl.py index 65c25bfc..06e51384 100644 --- a/lightrag/kg/redis_impl.py +++ b/lightrag/kg/redis_impl.py @@ -1,9 +1,10 @@ import os -from typing import Any, final +from typing import Any, final, Union from dataclasses import dataclass import pipmaster as pm import configparser from contextlib import asynccontextmanager +import threading if not pm.is_installed("redis"): pm.install("redis") @@ -13,7 +14,12 @@ from redis.asyncio import Redis, ConnectionPool # type: ignore from redis.exceptions import RedisError, ConnectionError # type: ignore from lightrag.utils import logger -from lightrag.base import BaseKVStorage +from lightrag.base import ( + BaseKVStorage, + DocStatusStorage, + DocStatus, + DocProcessingStatus, +) import json @@ -26,26 +32,93 @@ SOCKET_TIMEOUT = 5.0 SOCKET_CONNECT_TIMEOUT = 3.0 +class RedisConnectionManager: + """Shared Redis connection pool manager to avoid creating multiple pools for the same Redis URI""" + + _pools = {} + _lock = threading.Lock() + + @classmethod + def get_pool(cls, redis_url: str) -> ConnectionPool: + """Get or create a connection pool for the given Redis URL""" + if redis_url not in cls._pools: + with cls._lock: + if redis_url not in cls._pools: + cls._pools[redis_url] = ConnectionPool.from_url( + redis_url, + max_connections=MAX_CONNECTIONS, + decode_responses=True, + socket_timeout=SOCKET_TIMEOUT, + socket_connect_timeout=SOCKET_CONNECT_TIMEOUT, + ) + logger.info(f"Created shared Redis connection pool for {redis_url}") + return cls._pools[redis_url] + + @classmethod + def close_all_pools(cls): + """Close all connection pools (for cleanup)""" + with cls._lock: + for url, pool in cls._pools.items(): + try: + pool.disconnect() + logger.info(f"Closed Redis connection pool for {url}") + except Exception as e: + logger.error(f"Error closing Redis pool for {url}: {e}") + cls._pools.clear() + + @final @dataclass class RedisKVStorage(BaseKVStorage): def __post_init__(self): + # Check for REDIS_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all Redis storage instances + redis_workspace = os.environ.get("REDIS_WORKSPACE") + if redis_workspace and redis_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = redis_workspace.strip() + logger.info( + f"Using REDIS_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build namespace with workspace prefix for data isolation + if effective_workspace: + self.namespace = f"{effective_workspace}_{self.namespace}" + logger.debug(f"Final namespace with workspace prefix: '{self.namespace}'") + # When workspace is empty, keep the original namespace unchanged + redis_url = os.environ.get( "REDIS_URI", config.get("redis", "uri", fallback="redis://localhost:6379") ) - # Create a connection pool with limits - self._pool = ConnectionPool.from_url( - redis_url, - max_connections=MAX_CONNECTIONS, - decode_responses=True, - socket_timeout=SOCKET_TIMEOUT, - socket_connect_timeout=SOCKET_CONNECT_TIMEOUT, - ) + # Use shared connection pool + self._pool = RedisConnectionManager.get_pool(redis_url) self._redis = Redis(connection_pool=self._pool) logger.info( - f"Initialized Redis connection pool for {self.namespace} with max {MAX_CONNECTIONS} connections" + f"Initialized Redis KV storage for {self.namespace} using shared connection pool" ) + async def initialize(self): + """Initialize Redis connection and migrate legacy cache structure if needed""" + # Test connection + try: + async with self._get_redis_connection() as redis: + await redis.ping() + logger.info(f"Connected to Redis for namespace {self.namespace}") + except Exception as e: + logger.error(f"Failed to connect to Redis: {e}") + raise + + # Migrate legacy cache structure if this is a cache namespace + if self.namespace.endswith("_cache"): + await self._migrate_legacy_cache_structure() + @asynccontextmanager async def _get_redis_connection(self): """Safe context manager for Redis operations.""" @@ -82,7 +155,13 @@ class RedisKVStorage(BaseKVStorage): async with self._get_redis_connection() as redis: try: data = await redis.get(f"{self.namespace}:{id}") - return json.loads(data) if data else None + if data: + result = json.loads(data) + # Ensure time fields are present, provide default values for old data + result.setdefault("create_time", 0) + result.setdefault("update_time", 0) + return result + return None except json.JSONDecodeError as e: logger.error(f"JSON decode error for id {id}: {e}") return None @@ -94,35 +173,113 @@ class RedisKVStorage(BaseKVStorage): for id in ids: pipe.get(f"{self.namespace}:{id}") results = await pipe.execute() - return [json.loads(result) if result else None for result in results] + + processed_results = [] + for result in results: + if result: + data = json.loads(result) + # Ensure time fields are present for all documents + data.setdefault("create_time", 0) + data.setdefault("update_time", 0) + processed_results.append(data) + else: + processed_results.append(None) + + return processed_results except json.JSONDecodeError as e: logger.error(f"JSON decode error in batch get: {e}") return [None] * len(ids) + async def get_all(self) -> dict[str, Any]: + """Get all data from storage + + Returns: + Dictionary containing all stored data + """ + async with self._get_redis_connection() as redis: + try: + # Get all keys for this namespace + keys = await redis.keys(f"{self.namespace}:*") + + if not keys: + return {} + + # Get all values in batch + pipe = redis.pipeline() + for key in keys: + pipe.get(key) + values = await pipe.execute() + + # Build result dictionary + result = {} + for key, value in zip(keys, values): + if value: + # Extract the ID part (after namespace:) + key_id = key.split(":", 1)[1] + try: + data = json.loads(value) + # Ensure time fields are present for all documents + data.setdefault("create_time", 0) + data.setdefault("update_time", 0) + result[key_id] = data + except json.JSONDecodeError as e: + logger.error(f"JSON decode error for key {key}: {e}") + continue + + return result + except Exception as e: + logger.error(f"Error getting all data from Redis: {e}") + return {} + async def filter_keys(self, keys: set[str]) -> set[str]: async with self._get_redis_connection() as redis: pipe = redis.pipeline() - for key in keys: + keys_list = list(keys) # Convert set to list for indexing + for key in keys_list: pipe.exists(f"{self.namespace}:{key}") results = await pipe.execute() - existing_ids = {keys[i] for i, exists in enumerate(results) if exists} + existing_ids = {keys_list[i] for i, exists in enumerate(results) if exists} return set(keys) - existing_ids async def upsert(self, data: dict[str, dict[str, Any]]) -> None: if not data: return - logger.info(f"Inserting {len(data)} items to {self.namespace}") + import time + + current_time = int(time.time()) # Get current Unix timestamp + async with self._get_redis_connection() as redis: try: + # Check which keys already exist to determine create vs update + pipe = redis.pipeline() + for k in data.keys(): + pipe.exists(f"{self.namespace}:{k}") + exists_results = await pipe.execute() + + # Add timestamps to data + for i, (k, v) in enumerate(data.items()): + # For text_chunks namespace, ensure llm_cache_list field exists + if "text_chunks" in self.namespace: + if "llm_cache_list" not in v: + v["llm_cache_list"] = [] + + # Add timestamps based on whether key exists + if exists_results[i]: # Key exists, only update update_time + v["update_time"] = current_time + else: # New key, set both create_time and update_time + v["create_time"] = current_time + v["update_time"] = current_time + + v["_id"] = k + + # Store the data pipe = redis.pipeline() for k, v in data.items(): pipe.set(f"{self.namespace}:{k}", json.dumps(v)) await pipe.execute() - for k in data: - data[k]["_id"] = k except json.JSONEncodeError as e: logger.error(f"JSON encode error during upsert: {e}") raise @@ -148,13 +305,13 @@ class RedisKVStorage(BaseKVStorage): ) async def drop_cache_by_modes(self, modes: list[str] | None = None) -> bool: - """Delete specific records from storage by by cache mode + """Delete specific records from storage by cache mode Importance notes for Redis storage: 1. This will immediately delete the specified cache modes from Redis Args: - modes (list[str]): List of cache mode to be drop from storage + modes (list[str]): List of cache modes to be dropped from storage Returns: True: if the cache drop successfully @@ -164,9 +321,47 @@ class RedisKVStorage(BaseKVStorage): return False try: - await self.delete(modes) + async with self._get_redis_connection() as redis: + keys_to_delete = [] + + # Find matching keys for each mode using SCAN + for mode in modes: + # Use correct pattern to match flattened cache key format {namespace}:{mode}:{cache_type}:{hash} + pattern = f"{self.namespace}:{mode}:*" + cursor = 0 + mode_keys = [] + + while True: + cursor, keys = await redis.scan( + cursor, match=pattern, count=1000 + ) + if keys: + mode_keys.extend(keys) + + if cursor == 0: + break + + keys_to_delete.extend(mode_keys) + logger.info( + f"Found {len(mode_keys)} keys for mode '{mode}' with pattern '{pattern}'" + ) + + if keys_to_delete: + # Batch delete + pipe = redis.pipeline() + for key in keys_to_delete: + pipe.delete(key) + results = await pipe.execute() + deleted_count = sum(results) + logger.info( + f"Dropped {deleted_count} cache entries for modes: {modes}" + ) + else: + logger.warning(f"No cache entries found for modes: {modes}") + return True - except Exception: + except Exception as e: + logger.error(f"Error dropping cache by modes in Redis: {e}") return False async def drop(self) -> dict[str, str]: @@ -177,24 +372,393 @@ class RedisKVStorage(BaseKVStorage): """ async with self._get_redis_connection() as redis: try: - keys = await redis.keys(f"{self.namespace}:*") + # Use SCAN to find all keys with the namespace prefix + pattern = f"{self.namespace}:*" + cursor = 0 + deleted_count = 0 - if keys: - pipe = redis.pipeline() - for key in keys: - pipe.delete(key) - results = await pipe.execute() - deleted_count = sum(results) + while True: + cursor, keys = await redis.scan(cursor, match=pattern, count=1000) + if keys: + # Delete keys in batches + pipe = redis.pipeline() + for key in keys: + pipe.delete(key) + results = await pipe.execute() + deleted_count += sum(results) - logger.info(f"Dropped {deleted_count} keys from {self.namespace}") - return { - "status": "success", - "message": f"{deleted_count} keys dropped", - } - else: - logger.info(f"No keys found to drop in {self.namespace}") - return {"status": "success", "message": "no keys to drop"} + if cursor == 0: + break + + logger.info(f"Dropped {deleted_count} keys from {self.namespace}") + return { + "status": "success", + "message": f"{deleted_count} keys dropped", + } except Exception as e: logger.error(f"Error dropping keys from {self.namespace}: {e}") return {"status": "error", "message": str(e)} + + async def _migrate_legacy_cache_structure(self): + """Migrate legacy nested cache structure to flattened structure for Redis + + Redis already stores data in a flattened way, but we need to check for + legacy keys that might contain nested JSON structures and migrate them. + + Early exit if any flattened key is found (indicating migration already done). + """ + from lightrag.utils import generate_cache_key + + async with self._get_redis_connection() as redis: + # Get all keys for this namespace + keys = await redis.keys(f"{self.namespace}:*") + + if not keys: + return + + # Check if we have any flattened keys already - if so, skip migration + has_flattened_keys = False + keys_to_migrate = [] + + for key in keys: + # Extract the ID part (after namespace:) + key_id = key.split(":", 1)[1] + + # Check if already in flattened format (contains exactly 2 colons for mode:cache_type:hash) + if ":" in key_id and len(key_id.split(":")) == 3: + has_flattened_keys = True + break # Early exit - migration already done + + # Get the data to check if it's a legacy nested structure + data = await redis.get(key) + if data: + try: + parsed_data = json.loads(data) + # Check if this looks like a legacy cache mode with nested structure + if isinstance(parsed_data, dict) and all( + isinstance(v, dict) and "return" in v + for v in parsed_data.values() + ): + keys_to_migrate.append((key, key_id, parsed_data)) + except json.JSONDecodeError: + continue + + # If we found any flattened keys, assume migration is already done + if has_flattened_keys: + logger.debug( + f"Found flattened cache keys in {self.namespace}, skipping migration" + ) + return + + if not keys_to_migrate: + return + + # Perform migration + pipe = redis.pipeline() + migration_count = 0 + + for old_key, mode, nested_data in keys_to_migrate: + # Delete the old key + pipe.delete(old_key) + + # Create new flattened keys + for cache_hash, cache_entry in nested_data.items(): + cache_type = cache_entry.get("cache_type", "extract") + flattened_key = generate_cache_key(mode, cache_type, cache_hash) + full_key = f"{self.namespace}:{flattened_key}" + pipe.set(full_key, json.dumps(cache_entry)) + migration_count += 1 + + await pipe.execute() + + if migration_count > 0: + logger.info( + f"Migrated {migration_count} legacy cache entries to flattened structure in Redis" + ) + + +@final +@dataclass +class RedisDocStatusStorage(DocStatusStorage): + """Redis implementation of document status storage""" + + def __post_init__(self): + # Check for REDIS_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all Redis storage instances + redis_workspace = os.environ.get("REDIS_WORKSPACE") + if redis_workspace and redis_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = redis_workspace.strip() + logger.info( + f"Using REDIS_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build namespace with workspace prefix for data isolation + if effective_workspace: + self.namespace = f"{effective_workspace}_{self.namespace}" + logger.debug(f"Final namespace with workspace prefix: '{self.namespace}'") + # When workspace is empty, keep the original namespace unchanged + + redis_url = os.environ.get( + "REDIS_URI", config.get("redis", "uri", fallback="redis://localhost:6379") + ) + # Use shared connection pool + self._pool = RedisConnectionManager.get_pool(redis_url) + self._redis = Redis(connection_pool=self._pool) + logger.info( + f"Initialized Redis doc status storage for {self.namespace} using shared connection pool" + ) + + async def initialize(self): + """Initialize Redis connection""" + try: + async with self._get_redis_connection() as redis: + await redis.ping() + logger.info( + f"Connected to Redis for doc status namespace {self.namespace}" + ) + except Exception as e: + logger.error(f"Failed to connect to Redis for doc status: {e}") + raise + + @asynccontextmanager + async def _get_redis_connection(self): + """Safe context manager for Redis operations.""" + try: + yield self._redis + except ConnectionError as e: + logger.error(f"Redis connection error in doc status {self.namespace}: {e}") + raise + except RedisError as e: + logger.error(f"Redis operation error in doc status {self.namespace}: {e}") + raise + except Exception as e: + logger.error( + f"Unexpected error in Redis doc status operation for {self.namespace}: {e}" + ) + raise + + async def close(self): + """Close the Redis connection.""" + if hasattr(self, "_redis") and self._redis: + await self._redis.close() + logger.debug(f"Closed Redis connection for doc status {self.namespace}") + + async def __aenter__(self): + """Support for async context manager.""" + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + """Ensure Redis resources are cleaned up when exiting context.""" + await self.close() + + async def filter_keys(self, keys: set[str]) -> set[str]: + """Return keys that should be processed (not in storage or not successfully processed)""" + async with self._get_redis_connection() as redis: + pipe = redis.pipeline() + keys_list = list(keys) + for key in keys_list: + pipe.exists(f"{self.namespace}:{key}") + results = await pipe.execute() + + existing_ids = {keys_list[i] for i, exists in enumerate(results) if exists} + return set(keys) - existing_ids + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + result: list[dict[str, Any]] = [] + async with self._get_redis_connection() as redis: + try: + pipe = redis.pipeline() + for id in ids: + pipe.get(f"{self.namespace}:{id}") + results = await pipe.execute() + + for result_data in results: + if result_data: + try: + result.append(json.loads(result_data)) + except json.JSONDecodeError as e: + logger.error(f"JSON decode error in get_by_ids: {e}") + continue + except Exception as e: + logger.error(f"Error in get_by_ids: {e}") + return result + + async def get_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status""" + counts = {status.value: 0 for status in DocStatus} + async with self._get_redis_connection() as redis: + try: + # Use SCAN to iterate through all keys in the namespace + cursor = 0 + while True: + cursor, keys = await redis.scan( + cursor, match=f"{self.namespace}:*", count=1000 + ) + if keys: + # Get all values in batch + pipe = redis.pipeline() + for key in keys: + pipe.get(key) + values = await pipe.execute() + + # Count statuses + for value in values: + if value: + try: + doc_data = json.loads(value) + status = doc_data.get("status") + if status in counts: + counts[status] += 1 + except json.JSONDecodeError: + continue + + if cursor == 0: + break + except Exception as e: + logger.error(f"Error getting status counts: {e}") + + return counts + + async def get_docs_by_status( + self, status: DocStatus + ) -> dict[str, DocProcessingStatus]: + """Get all documents with a specific status""" + result = {} + async with self._get_redis_connection() as redis: + try: + # Use SCAN to iterate through all keys in the namespace + cursor = 0 + while True: + cursor, keys = await redis.scan( + cursor, match=f"{self.namespace}:*", count=1000 + ) + if keys: + # Get all values in batch + pipe = redis.pipeline() + for key in keys: + pipe.get(key) + values = await pipe.execute() + + # Filter by status and create DocProcessingStatus objects + for key, value in zip(keys, values): + if value: + try: + doc_data = json.loads(value) + if doc_data.get("status") == status.value: + # Extract document ID from key + doc_id = key.split(":", 1)[1] + + # Make a copy of the data to avoid modifying the original + data = doc_data.copy() + # If content is missing, use content_summary as content + if ( + "content" not in data + and "content_summary" in data + ): + data["content"] = data["content_summary"] + # If file_path is not in data, use document id as file path + if "file_path" not in data: + data["file_path"] = "no-file-path" + + result[doc_id] = DocProcessingStatus(**data) + except (json.JSONDecodeError, KeyError) as e: + logger.error( + f"Error processing document {key}: {e}" + ) + continue + + if cursor == 0: + break + except Exception as e: + logger.error(f"Error getting docs by status: {e}") + + return result + + async def index_done_callback(self) -> None: + """Redis handles persistence automatically""" + pass + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + """Insert or update document status data""" + if not data: + return + + logger.debug(f"Inserting {len(data)} records to {self.namespace}") + async with self._get_redis_connection() as redis: + try: + # Ensure chunks_list field exists for new documents + for doc_id, doc_data in data.items(): + if "chunks_list" not in doc_data: + doc_data["chunks_list"] = [] + + pipe = redis.pipeline() + for k, v in data.items(): + pipe.set(f"{self.namespace}:{k}", json.dumps(v)) + await pipe.execute() + except json.JSONEncodeError as e: + logger.error(f"JSON encode error during upsert: {e}") + raise + + async def get_by_id(self, id: str) -> Union[dict[str, Any], None]: + async with self._get_redis_connection() as redis: + try: + data = await redis.get(f"{self.namespace}:{id}") + return json.loads(data) if data else None + except json.JSONDecodeError as e: + logger.error(f"JSON decode error for id {id}: {e}") + return None + + async def delete(self, doc_ids: list[str]) -> None: + """Delete specific records from storage by their IDs""" + if not doc_ids: + return + + async with self._get_redis_connection() as redis: + pipe = redis.pipeline() + for doc_id in doc_ids: + pipe.delete(f"{self.namespace}:{doc_id}") + + results = await pipe.execute() + deleted_count = sum(results) + logger.info( + f"Deleted {deleted_count} of {len(doc_ids)} doc status entries from {self.namespace}" + ) + + async def drop(self) -> dict[str, str]: + """Drop all document status data from storage and clean up resources""" + try: + async with self._get_redis_connection() as redis: + # Use SCAN to find all keys with the namespace prefix + pattern = f"{self.namespace}:*" + cursor = 0 + deleted_count = 0 + + while True: + cursor, keys = await redis.scan(cursor, match=pattern, count=1000) + if keys: + # Delete keys in batches + pipe = redis.pipeline() + for key in keys: + pipe.delete(key) + results = await pipe.execute() + deleted_count += sum(results) + + if cursor == 0: + break + + logger.info( + f"Dropped {deleted_count} doc status keys from {self.namespace}" + ) + return {"status": "success", "message": "data dropped"} + except Exception as e: + logger.error(f"Error dropping doc status {self.namespace}: {e}") + return {"status": "error", "message": str(e)} diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index e5b447c0..7fde953b 100644 --- a/lightrag/lightrag.py +++ b/lightrag/lightrag.py @@ -4,6 +4,7 @@ import traceback import asyncio import configparser import os +import time import warnings from dataclasses import asdict, dataclass, field from datetime import datetime, timezone @@ -21,6 +22,7 @@ from typing import ( Dict, ) from lightrag.constants import ( + DEFAULT_MAX_GLEANING, DEFAULT_MAX_TOKEN_SUMMARY, DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE, ) @@ -34,6 +36,7 @@ from lightrag.kg import ( from lightrag.kg.shared_storage import ( get_namespace_data, get_pipeline_status_lock, + get_graph_db_lock, ) from .base import ( @@ -46,8 +49,9 @@ from .base import ( QueryParam, StorageNameSpace, StoragesStatus, + DeletionResult, ) -from .namespace import NameSpace, make_namespace +from .namespace import NameSpace from .operate import ( chunking_by_token_size, extract_entities, @@ -55,8 +59,9 @@ from .operate import ( kg_query, naive_query, query_with_keywords, + _rebuild_knowledge_from_chunks, ) -from .prompt import GRAPH_FIELD_SEP +from .constants import GRAPH_FIELD_SEP from .utils import ( Tokenizer, TiktokenTokenizer, @@ -92,9 +97,7 @@ class LightRAG: # Directory # --- - working_dir: str = field( - default=f"./lightrag_cache_{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}" - ) + working_dir: str = field(default="./rag_storage") """Directory where cache and temporary files are stored.""" # Storage @@ -112,6 +115,12 @@ class LightRAG: doc_status_storage: str = field(default="JsonDocStatusStorage") """Storage type for tracking document processing statuses.""" + # Workspace + # --- + + workspace: str = field(default_factory=lambda: os.getenv("WORKSPACE", "")) + """Workspace for data isolation. Defaults to empty string if WORKSPACE environment variable is not set.""" + # Logging (Deprecated, use setup_logger in utils.py instead) # --- log_level: int | None = field(default=None) @@ -120,7 +129,9 @@ class LightRAG: # Entity extraction # --- - entity_extract_max_gleaning: int = field(default=1) + entity_extract_max_gleaning: int = field( + default=get_env_value("MAX_GLEANING", DEFAULT_MAX_GLEANING, int) + ) """Maximum number of entity extraction attempts for ambiguous content.""" summary_to_max_tokens: int = field( @@ -190,7 +201,7 @@ class LightRAG: embedding_func: EmbeddingFunc | None = field(default=None) """Function for computing text embeddings. Must be set before use.""" - embedding_batch_num: int = field(default=int(os.getenv("EMBEDDING_BATCH_NUM", 32))) + embedding_batch_num: int = field(default=int(os.getenv("EMBEDDING_BATCH_NUM", 10))) """Batch size for embedding computations.""" embedding_func_max_async: int = field( @@ -235,10 +246,6 @@ class LightRAG: vector_db_storage_cls_kwargs: dict[str, Any] = field(default_factory=dict) """Additional parameters for vector database storage.""" - # TODO：deprecated, remove in the future, use WORKSPACE instead - namespace_prefix: str = field(default="") - """Prefix for namespacing stored data across different environments.""" - enable_llm_cache: bool = field(default=True) """Enables caching for LLM responses to avoid redundant computations.""" @@ -251,6 +258,9 @@ class LightRAG: max_parallel_insert: int = field(default=int(os.getenv("MAX_PARALLEL_INSERT", 2))) """Maximum number of parallel insert operations.""" + max_graph_nodes: int = field(default=get_env_value("MAX_GRAPH_NODES", 1000, int)) + """Maximum number of graph nodes to return in knowledge graph queries.""" + addon_params: dict[str, Any] = field( default_factory=lambda: { "language": get_env_value("SUMMARY_LANGUAGE", "English", str) @@ -342,6 +352,7 @@ class LightRAG: # Fix global_config now global_config = asdict(self) + _print_config = ",\n ".join([f"{k} = {v}" for k, v in global_config.items()]) logger.debug(f"LightRAG init with param:\n {_print_config}\n") @@ -374,61 +385,53 @@ class LightRAG: self.doc_status_storage_cls = self._get_storage_class(self.doc_status_storage) self.llm_response_cache: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore - namespace=make_namespace( - self.namespace_prefix, NameSpace.KV_STORE_LLM_RESPONSE_CACHE - ), - global_config=asdict( - self - ), # Add global_config to ensure cache works properly + namespace=NameSpace.KV_STORE_LLM_RESPONSE_CACHE, + workspace=self.workspace, + global_config=global_config, embedding_func=self.embedding_func, ) self.full_docs: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore - namespace=make_namespace( - self.namespace_prefix, NameSpace.KV_STORE_FULL_DOCS - ), + namespace=NameSpace.KV_STORE_FULL_DOCS, + workspace=self.workspace, embedding_func=self.embedding_func, ) - # TODO: deprecating, text_chunks is redundant with chunks_vdb self.text_chunks: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore - namespace=make_namespace( - self.namespace_prefix, NameSpace.KV_STORE_TEXT_CHUNKS - ), + namespace=NameSpace.KV_STORE_TEXT_CHUNKS, + workspace=self.workspace, embedding_func=self.embedding_func, ) + self.chunk_entity_relation_graph: BaseGraphStorage = self.graph_storage_cls( # type: ignore - namespace=make_namespace( - self.namespace_prefix, NameSpace.GRAPH_STORE_CHUNK_ENTITY_RELATION - ), + namespace=NameSpace.GRAPH_STORE_CHUNK_ENTITY_RELATION, + workspace=self.workspace, embedding_func=self.embedding_func, ) self.entities_vdb: BaseVectorStorage = self.vector_db_storage_cls( # type: ignore - namespace=make_namespace( - self.namespace_prefix, NameSpace.VECTOR_STORE_ENTITIES - ), + namespace=NameSpace.VECTOR_STORE_ENTITIES, + workspace=self.workspace, embedding_func=self.embedding_func, meta_fields={"entity_name", "source_id", "content", "file_path"}, ) self.relationships_vdb: BaseVectorStorage = self.vector_db_storage_cls( # type: ignore - namespace=make_namespace( - self.namespace_prefix, NameSpace.VECTOR_STORE_RELATIONSHIPS - ), + namespace=NameSpace.VECTOR_STORE_RELATIONSHIPS, + workspace=self.workspace, embedding_func=self.embedding_func, meta_fields={"src_id", "tgt_id", "source_id", "content", "file_path"}, ) self.chunks_vdb: BaseVectorStorage = self.vector_db_storage_cls( # type: ignore - namespace=make_namespace( - self.namespace_prefix, NameSpace.VECTOR_STORE_CHUNKS - ), + namespace=NameSpace.VECTOR_STORE_CHUNKS, + workspace=self.workspace, embedding_func=self.embedding_func, meta_fields={"full_doc_id", "content", "file_path"}, ) # Initialize document status storage self.doc_status: DocStatusStorage = self.doc_status_storage_cls( - namespace=make_namespace(self.namespace_prefix, NameSpace.DOC_STATUS), + namespace=NameSpace.DOC_STATUS, + workspace=self.workspace, global_config=global_config, embedding_func=None, ) @@ -526,18 +529,24 @@ class LightRAG: self, node_label: str, max_depth: int = 3, - max_nodes: int = 1000, + max_nodes: int = None, ) -> KnowledgeGraph: """Get knowledge graph for a given label Args: node_label (str): Label to get knowledge graph for max_depth (int): Maximum depth of graph - max_nodes (int, optional): Maximum number of nodes to return. Defaults to 1000. + max_nodes (int, optional): Maximum number of nodes to return. Defaults to self.max_graph_nodes. Returns: KnowledgeGraph: Knowledge graph containing nodes and edges """ + # Use self.max_graph_nodes as default if max_nodes is None + if max_nodes is None: + max_nodes = self.max_graph_nodes + else: + # Limit max_nodes to not exceed self.max_graph_nodes + max_nodes = min(max_nodes, self.max_graph_nodes) return await self.chunk_entity_relation_graph.get_knowledge_graph( node_label, max_depth, max_nodes @@ -945,6 +954,7 @@ class LightRAG: **dp, "full_doc_id": doc_id, "file_path": file_path, # Add file path to each chunk + "llm_cache_list": [], # Initialize empty LLM cache list for each chunk } for dp in self.chunking_func( self.tokenizer, @@ -956,14 +966,20 @@ class LightRAG: ) } - # Process document (text chunks and full docs) in parallel - # Create tasks with references for potential cancellation + if not chunks: + logger.warning("No document chunks to process") + + # Process document in two stages + # Stage 1: Process text chunks and docs (parallel execution) doc_status_task = asyncio.create_task( self.doc_status.upsert( { doc_id: { "status": DocStatus.PROCESSING, "chunks_count": len(chunks), + "chunks_list": list( + chunks.keys() + ), # Save chunks list "content": status_doc.content, "content_summary": status_doc.content_summary, "content_length": status_doc.content_length, @@ -979,11 +995,6 @@ class LightRAG: chunks_vdb_task = asyncio.create_task( self.chunks_vdb.upsert(chunks) ) - entity_relation_task = asyncio.create_task( - self._process_entity_relation_graph( - chunks, pipeline_status, pipeline_status_lock - ) - ) full_docs_task = asyncio.create_task( self.full_docs.upsert( {doc_id: {"content": status_doc.content}} @@ -992,20 +1003,32 @@ class LightRAG: text_chunks_task = asyncio.create_task( self.text_chunks.upsert(chunks) ) - tasks = [ + + # First stage tasks (parallel execution) + first_stage_tasks = [ doc_status_task, chunks_vdb_task, - entity_relation_task, full_docs_task, text_chunks_task, ] - await asyncio.gather(*tasks) + entity_relation_task = None + + # Execute first stage tasks + await asyncio.gather(*first_stage_tasks) + + # Stage 2: Process entity relation graph (after text_chunks are saved) + entity_relation_task = asyncio.create_task( + self._process_entity_relation_graph( + chunks, pipeline_status, pipeline_status_lock + ) + ) + await entity_relation_task file_extraction_stage_ok = True except Exception as e: # Log error and update pipeline status logger.error(traceback.format_exc()) - error_msg = f"Failed to extrat document {current_file_number}/{total_files}: {file_path}" + error_msg = f"Failed to extract document {current_file_number}/{total_files}: {file_path}" logger.error(error_msg) async with pipeline_status_lock: pipeline_status["latest_message"] = error_msg @@ -1014,14 +1037,14 @@ class LightRAG: ) pipeline_status["history_messages"].append(error_msg) - # Cancel other tasks as they are no longer meaningful - for task in [ - chunks_vdb_task, - entity_relation_task, - full_docs_task, - text_chunks_task, - ]: - if not task.done(): + # Cancel tasks that are not yet completed + all_tasks = first_stage_tasks + ( + [entity_relation_task] + if entity_relation_task + else [] + ) + for task in all_tasks: + if task and not task.done(): task.cancel() # Persistent llm cache @@ -1068,22 +1091,25 @@ class LightRAG: file_path=file_path, ) - await self.doc_status.upsert( - { - doc_id: { - "status": DocStatus.PROCESSED, - "chunks_count": len(chunks), - "content": status_doc.content, - "content_summary": status_doc.content_summary, - "content_length": status_doc.content_length, - "created_at": status_doc.created_at, - "updated_at": datetime.now( - timezone.utc - ).isoformat(), - "file_path": file_path, - } + await self.doc_status.upsert( + { + doc_id: { + "status": DocStatus.PROCESSED, + "chunks_count": len(chunks), + "chunks_list": list( + chunks.keys() + ), # 保留 chunks_list + "content": status_doc.content, + "content_summary": status_doc.content_summary, + "content_length": status_doc.content_length, + "created_at": status_doc.created_at, + "updated_at": datetime.now( + timezone.utc + ).isoformat(), + "file_path": file_path, } - ) + } + ) # Call _insert_done after processing each file await self._insert_done() @@ -1191,6 +1217,7 @@ class LightRAG: pipeline_status=pipeline_status, pipeline_status_lock=pipeline_status_lock, llm_response_cache=self.llm_response_cache, + text_chunks_storage=self.text_chunks, ) return chunk_results except Exception as e: @@ -1208,6 +1235,7 @@ class LightRAG: cast(StorageNameSpace, storage_inst).index_done_callback() for storage_inst in [ # type: ignore self.full_docs, + self.doc_status, self.text_chunks, self.llm_response_cache, self.entities_vdb, @@ -1237,7 +1265,6 @@ class LightRAG: self, custom_kg: dict[str, Any], full_doc_id: str = None, - file_path: str = "custom_kg", ) -> None: update_storage = False try: @@ -1247,6 +1274,7 @@ class LightRAG: for chunk_data in custom_kg.get("chunks", []): chunk_content = clean_text(chunk_data["content"]) source_id = chunk_data["source_id"] + file_path = chunk_data.get("file_path", "custom_kg") tokens = len(self.tokenizer.encode(chunk_content)) chunk_order_index = ( 0 @@ -1263,7 +1291,7 @@ class LightRAG: "full_doc_id": full_doc_id if full_doc_id is not None else source_id, - "file_path": file_path, # Add file path + "file_path": file_path, "status": DocStatus.PROCESSED, } all_chunks_data[chunk_id] = chunk_entry @@ -1284,6 +1312,7 @@ class LightRAG: description = entity_data.get("description", "No description provided") source_chunk_id = entity_data.get("source_id", "UNKNOWN") source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN") + file_path = entity_data.get("file_path", "custom_kg") # Log if source_id is UNKNOWN if source_id == "UNKNOWN": @@ -1297,6 +1326,8 @@ class LightRAG: "entity_type": entity_type, "description": description, "source_id": source_id, + "file_path": file_path, + "created_at": int(time.time()), } # Insert node data into the knowledge graph await self.chunk_entity_relation_graph.upsert_node( @@ -1316,6 +1347,7 @@ class LightRAG: weight = relationship_data.get("weight", 1.0) source_chunk_id = relationship_data.get("source_id", "UNKNOWN") source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN") + file_path = relationship_data.get("file_path", "custom_kg") # Log if source_id is UNKNOWN if source_id == "UNKNOWN": @@ -1335,6 +1367,8 @@ class LightRAG: "source_id": source_id, "description": "UNKNOWN", "entity_type": "UNKNOWN", + "file_path": file_path, + "created_at": int(time.time()), }, ) @@ -1347,8 +1381,11 @@ class LightRAG: "description": description, "keywords": keywords, "source_id": source_id, + "file_path": file_path, + "created_at": int(time.time()), }, ) + edge_data: dict[str, str] = { "src_id": src_id, "tgt_id": tgt_id, @@ -1356,6 +1393,8 @@ class LightRAG: "keywords": keywords, "source_id": source_id, "weight": weight, + "file_path": file_path, + "created_at": int(time.time()), } all_relationships_data.append(edge_data) update_storage = True @@ -1368,7 +1407,7 @@ class LightRAG: "source_id": dp["source_id"], "description": dp["description"], "entity_type": dp["entity_type"], - "file_path": file_path, # Add file path + "file_path": dp.get("file_path", "custom_kg"), } for dp in all_entities_data } @@ -1384,7 +1423,7 @@ class LightRAG: "keywords": dp["keywords"], "description": dp["description"], "weight": dp["weight"], - "file_path": file_path, # Add file path + "file_path": dp.get("file_path", "custom_kg"), } for dp in all_relationships_data } @@ -1664,269 +1703,327 @@ class LightRAG: # Return the dictionary containing statuses only for the found document IDs return found_statuses - # TODO: Deprecated (Deleting documents can cause hallucinations in RAG.) - # Document delete is not working properly for most of the storage implementations. - async def adelete_by_doc_id(self, doc_id: str) -> None: - """Delete a document and all its related data + async def adelete_by_doc_id(self, doc_id: str) -> DeletionResult: + """Delete a document and all its related data, including chunks, graph elements, and cached entries. + + This method orchestrates a comprehensive deletion process for a given document ID. + It ensures that not only the document itself but also all its derived and associated + data across different storage layers are removed. If entities or relationships are partially affected, it triggers. Args: - doc_id: Document ID to delete + doc_id (str): The unique identifier of the document to be deleted. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + - `status` (str): "success", "not_found", or "failure". + - `doc_id` (str): The ID of the document attempted to be deleted. + - `message` (str): A summary of the operation's result. + - `status_code` (int): HTTP status code (e.g., 200, 404, 500). + - `file_path` (str | None): The file path of the deleted document, if available. """ + deletion_operations_started = False + original_exception = None + + # Get pipeline status shared data and lock for status updates + pipeline_status = await get_namespace_data("pipeline_status") + pipeline_status_lock = get_pipeline_status_lock() + + async with pipeline_status_lock: + log_message = f"Starting deletion process for document {doc_id}" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + try: # 1. Get the document status and related data - if not await self.doc_status.get_by_id(doc_id): + doc_status_data = await self.doc_status.get_by_id(doc_id) + file_path = doc_status_data.get("file_path") if doc_status_data else None + if not doc_status_data: logger.warning(f"Document {doc_id} not found") - return + return DeletionResult( + status="not_found", + doc_id=doc_id, + message=f"Document {doc_id} not found.", + status_code=404, + file_path="", + ) - logger.debug(f"Starting deletion for document {doc_id}") + # 2. Get chunk IDs from document status + chunk_ids = set(doc_status_data.get("chunks_list", [])) - # 2. Get all chunks related to this document - # Find all chunks where full_doc_id equals the current doc_id - all_chunks = await self.text_chunks.get_all() - related_chunks = { - chunk_id: chunk_data - for chunk_id, chunk_data in all_chunks.items() - if isinstance(chunk_data, dict) - and chunk_data.get("full_doc_id") == doc_id - } - - if not related_chunks: + if not chunk_ids: logger.warning(f"No chunks found for document {doc_id}") - return + # Mark that deletion operations have started + deletion_operations_started = True + try: + # Still need to delete the doc status and full doc + await self.full_docs.delete([doc_id]) + await self.doc_status.delete([doc_id]) + logger.info(f"Deleted document {doc_id} with no associated chunks") + except Exception as e: + logger.error( + f"Failed to delete document {doc_id} with no chunks: {e}" + ) + raise Exception(f"Failed to delete document entry: {e}") from e - # Get all related chunk IDs - chunk_ids = set(related_chunks.keys()) - logger.debug(f"Found {len(chunk_ids)} chunks to delete") + async with pipeline_status_lock: + log_message = ( + f"Document {doc_id} is deleted without associated chunks." + ) + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) - # TODO: self.entities_vdb.client_storage only works for local storage, need to fix this + return DeletionResult( + status="success", + doc_id=doc_id, + message=log_message, + status_code=200, + file_path=file_path, + ) - # 3. Before deleting, check the related entities and relationships for these chunks - for chunk_id in chunk_ids: - # Check entities - entities_storage = await self.entities_vdb.client_storage - entities = [ - dp - for dp in entities_storage["data"] - if chunk_id in dp.get("source_id") - ] - logger.debug(f"Chunk {chunk_id} has {len(entities)} related entities") + # Mark that deletion operations have started + deletion_operations_started = True - # Check relationships - relationships_storage = await self.relationships_vdb.client_storage - relations = [ - dp - for dp in relationships_storage["data"] - if chunk_id in dp.get("source_id") - ] - logger.debug(f"Chunk {chunk_id} has {len(relations)} related relations") - - # Continue with the original deletion process... - - # 4. Delete chunks from vector database - if chunk_ids: - await self.chunks_vdb.delete(chunk_ids) - await self.text_chunks.delete(chunk_ids) - - # 5. Find and process entities and relationships that have these chunks as source - # Get all nodes and edges from the graph storage using storage-agnostic methods + # 4. Analyze entities and relationships that will be affected entities_to_delete = set() - entities_to_update = {} # entity_name -> new_source_id + entities_to_rebuild = {} # entity_name -> remaining_chunk_ids relationships_to_delete = set() - relationships_to_update = {} # (src, tgt) -> new_source_id + relationships_to_rebuild = {} # (src, tgt) -> remaining_chunk_ids - # Process entities - use storage-agnostic methods - all_labels = await self.chunk_entity_relation_graph.get_all_labels() - for node_label in all_labels: - node_data = await self.chunk_entity_relation_graph.get_node(node_label) - if node_data and "source_id" in node_data: - # Split source_id using GRAPH_FIELD_SEP - sources = set(node_data["source_id"].split(GRAPH_FIELD_SEP)) - sources.difference_update(chunk_ids) - if not sources: - entities_to_delete.add(node_label) - logger.debug( - f"Entity {node_label} marked for deletion - no remaining sources" - ) - else: - new_source_id = GRAPH_FIELD_SEP.join(sources) - entities_to_update[node_label] = new_source_id - logger.debug( - f"Entity {node_label} will be updated with new source_id: {new_source_id}" + # Use graph database lock to ensure atomic merges and updates + graph_db_lock = get_graph_db_lock(enable_logging=False) + async with graph_db_lock: + try: + # Get all affected nodes and edges in batch + # logger.info( + # f"Analyzing affected entities and relationships for {len(chunk_ids)} chunks" + # ) + affected_nodes = ( + await self.chunk_entity_relation_graph.get_nodes_by_chunk_ids( + list(chunk_ids) ) + ) - # Process relationships - for node_label in all_labels: - node_edges = await self.chunk_entity_relation_graph.get_node_edges( - node_label - ) - if node_edges: - for src, tgt in node_edges: - edge_data = await self.chunk_entity_relation_graph.get_edge( - src, tgt + affected_edges = ( + await self.chunk_entity_relation_graph.get_edges_by_chunk_ids( + list(chunk_ids) ) - if edge_data and "source_id" in edge_data: - # Split source_id using GRAPH_FIELD_SEP + ) + + except Exception as e: + logger.error(f"Failed to analyze affected graph elements: {e}") + raise Exception(f"Failed to analyze graph dependencies: {e}") from e + + try: + # Process entities + for node_data in affected_nodes: + node_label = node_data.get("entity_id") + if node_label and "source_id" in node_data: + sources = set(node_data["source_id"].split(GRAPH_FIELD_SEP)) + remaining_sources = sources - chunk_ids + + if not remaining_sources: + entities_to_delete.add(node_label) + elif remaining_sources != sources: + entities_to_rebuild[node_label] = remaining_sources + + async with pipeline_status_lock: + log_message = ( + f"Found {len(entities_to_rebuild)} affected entities" + ) + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + # Process relationships + for edge_data in affected_edges: + src = edge_data.get("source") + tgt = edge_data.get("target") + + if src and tgt and "source_id" in edge_data: + edge_tuple = tuple(sorted((src, tgt))) + if ( + edge_tuple in relationships_to_delete + or edge_tuple in relationships_to_rebuild + ): + continue + sources = set(edge_data["source_id"].split(GRAPH_FIELD_SEP)) - sources.difference_update(chunk_ids) - if not sources: - relationships_to_delete.add((src, tgt)) - logger.debug( - f"Relationship {src}-{tgt} marked for deletion - no remaining sources" - ) - else: - new_source_id = GRAPH_FIELD_SEP.join(sources) - relationships_to_update[(src, tgt)] = new_source_id - logger.debug( - f"Relationship {src}-{tgt} will be updated with new source_id: {new_source_id}" - ) + remaining_sources = sources - chunk_ids - # Delete entities - if entities_to_delete: - for entity in entities_to_delete: - await self.entities_vdb.delete_entity(entity) - logger.debug(f"Deleted entity {entity} from vector DB") - await self.chunk_entity_relation_graph.remove_nodes( - list(entities_to_delete) - ) - logger.debug(f"Deleted {len(entities_to_delete)} entities from graph") + if not remaining_sources: + relationships_to_delete.add(edge_tuple) + elif remaining_sources != sources: + relationships_to_rebuild[edge_tuple] = remaining_sources - # Update entities - for entity, new_source_id in entities_to_update.items(): - node_data = await self.chunk_entity_relation_graph.get_node(entity) - if node_data: - node_data["source_id"] = new_source_id - await self.chunk_entity_relation_graph.upsert_node( - entity, node_data - ) - logger.debug( - f"Updated entity {entity} with new source_id: {new_source_id}" - ) + async with pipeline_status_lock: + log_message = ( + f"Found {len(relationships_to_rebuild)} affected relations" + ) + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) - # Delete relationships - if relationships_to_delete: - for src, tgt in relationships_to_delete: - rel_id_0 = compute_mdhash_id(src + tgt, prefix="rel-") - rel_id_1 = compute_mdhash_id(tgt + src, prefix="rel-") - await self.relationships_vdb.delete([rel_id_0, rel_id_1]) - logger.debug(f"Deleted relationship {src}-{tgt} from vector DB") - await self.chunk_entity_relation_graph.remove_edges( - list(relationships_to_delete) - ) - logger.debug( - f"Deleted {len(relationships_to_delete)} relationships from graph" - ) + except Exception as e: + logger.error(f"Failed to process graph analysis results: {e}") + raise Exception(f"Failed to process graph dependencies: {e}") from e - # Update relationships - for (src, tgt), new_source_id in relationships_to_update.items(): - edge_data = await self.chunk_entity_relation_graph.get_edge(src, tgt) - if edge_data: - edge_data["source_id"] = new_source_id - await self.chunk_entity_relation_graph.upsert_edge( - src, tgt, edge_data - ) - logger.debug( - f"Updated relationship {src}-{tgt} with new source_id: {new_source_id}" - ) + # 5. Delete chunks from storage + if chunk_ids: + try: + await self.chunks_vdb.delete(chunk_ids) + await self.text_chunks.delete(chunk_ids) - # 6. Delete original document and status - await self.full_docs.delete([doc_id]) - await self.doc_status.delete([doc_id]) + async with pipeline_status_lock: + log_message = f"Successfully deleted {len(chunk_ids)} chunks from storage" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) - # 7. Ensure all indexes are updated - await self._insert_done() + except Exception as e: + logger.error(f"Failed to delete chunks: {e}") + raise Exception(f"Failed to delete document chunks: {e}") from e - logger.info( - f"Successfully deleted document {doc_id} and related data. " - f"Deleted {len(entities_to_delete)} entities and {len(relationships_to_delete)} relationships. " - f"Updated {len(entities_to_update)} entities and {len(relationships_to_update)} relationships." + # 6. Delete entities that have no remaining sources + if entities_to_delete: + try: + # Delete from vector database + entity_vdb_ids = [ + compute_mdhash_id(entity, prefix="ent-") + for entity in entities_to_delete + ] + await self.entities_vdb.delete(entity_vdb_ids) + + # Delete from graph + await self.chunk_entity_relation_graph.remove_nodes( + list(entities_to_delete) + ) + + async with pipeline_status_lock: + log_message = f"Successfully deleted {len(entities_to_delete)} entities" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + except Exception as e: + logger.error(f"Failed to delete entities: {e}") + raise Exception(f"Failed to delete entities: {e}") from e + + # 7. Delete relationships that have no remaining sources + if relationships_to_delete: + try: + # Delete from vector database + rel_ids_to_delete = [] + for src, tgt in relationships_to_delete: + rel_ids_to_delete.extend( + [ + compute_mdhash_id(src + tgt, prefix="rel-"), + compute_mdhash_id(tgt + src, prefix="rel-"), + ] + ) + await self.relationships_vdb.delete(rel_ids_to_delete) + + # Delete from graph + await self.chunk_entity_relation_graph.remove_edges( + list(relationships_to_delete) + ) + + async with pipeline_status_lock: + log_message = f"Successfully deleted {len(relationships_to_delete)} relations" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + except Exception as e: + logger.error(f"Failed to delete relationships: {e}") + raise Exception(f"Failed to delete relationships: {e}") from e + + # 8. Rebuild entities and relationships from remaining chunks + if entities_to_rebuild or relationships_to_rebuild: + try: + await _rebuild_knowledge_from_chunks( + entities_to_rebuild=entities_to_rebuild, + relationships_to_rebuild=relationships_to_rebuild, + knowledge_graph_inst=self.chunk_entity_relation_graph, + entities_vdb=self.entities_vdb, + relationships_vdb=self.relationships_vdb, + text_chunks_storage=self.text_chunks, + llm_response_cache=self.llm_response_cache, + global_config=asdict(self), + pipeline_status=pipeline_status, + pipeline_status_lock=pipeline_status_lock, + ) + + except Exception as e: + logger.error(f"Failed to rebuild knowledge from chunks: {e}") + raise Exception( + f"Failed to rebuild knowledge graph: {e}" + ) from e + + # 9. Delete original document and status + try: + await self.full_docs.delete([doc_id]) + await self.doc_status.delete([doc_id]) + except Exception as e: + logger.error(f"Failed to delete document and status: {e}") + raise Exception(f"Failed to delete document and status: {e}") from e + + return DeletionResult( + status="success", + doc_id=doc_id, + message=log_message, + status_code=200, + file_path=file_path, ) - async def process_data(data_type, vdb, chunk_id): - # Check data (entities or relationships) - storage = await vdb.client_storage - data_with_chunk = [ - dp - for dp in storage["data"] - if chunk_id in (dp.get("source_id") or "").split(GRAPH_FIELD_SEP) - ] - - data_for_vdb = {} - if data_with_chunk: - logger.warning( - f"found {len(data_with_chunk)} {data_type} still referencing chunk {chunk_id}" - ) - - for item in data_with_chunk: - old_sources = item["source_id"].split(GRAPH_FIELD_SEP) - new_sources = [src for src in old_sources if src != chunk_id] - - if not new_sources: - logger.info( - f"{data_type} {item.get('entity_name', 'N/A')} is deleted because source_id is not exists" - ) - await vdb.delete_entity(item) - else: - item["source_id"] = GRAPH_FIELD_SEP.join(new_sources) - item_id = item["__id__"] - data_for_vdb[item_id] = item.copy() - if data_type == "entities": - data_for_vdb[item_id]["content"] = data_for_vdb[ - item_id - ].get("content") or ( - item.get("entity_name", "") - + (item.get("description") or "") - ) - else: # relationships - data_for_vdb[item_id]["content"] = data_for_vdb[ - item_id - ].get("content") or ( - (item.get("keywords") or "") - + (item.get("src_id") or "") - + (item.get("tgt_id") or "") - + (item.get("description") or "") - ) - - if data_for_vdb: - await vdb.upsert(data_for_vdb) - logger.info(f"Successfully updated {data_type} in vector DB") - - # Add verification step - async def verify_deletion(): - # Verify if the document has been deleted - if await self.full_docs.get_by_id(doc_id): - logger.warning(f"Document {doc_id} still exists in full_docs") - - # Verify if chunks have been deleted - all_remaining_chunks = await self.text_chunks.get_all() - remaining_related_chunks = { - chunk_id: chunk_data - for chunk_id, chunk_data in all_remaining_chunks.items() - if isinstance(chunk_data, dict) - and chunk_data.get("full_doc_id") == doc_id - } - - if remaining_related_chunks: - logger.warning( - f"Found {len(remaining_related_chunks)} remaining chunks" - ) - - # Verify entities and relationships - for chunk_id in chunk_ids: - await process_data("entities", self.entities_vdb, chunk_id) - await process_data( - "relationships", self.relationships_vdb, chunk_id - ) - - await verify_deletion() - except Exception as e: - logger.error(f"Error while deleting document {doc_id}: {e}") + original_exception = e + error_message = f"Error while deleting document {doc_id}: {e}" + logger.error(error_message) + logger.error(traceback.format_exc()) + return DeletionResult( + status="fail", + doc_id=doc_id, + message=error_message, + status_code=500, + file_path=file_path, + ) - async def adelete_by_entity(self, entity_name: str) -> None: + finally: + # ALWAYS ensure persistence if any deletion operations were started + if deletion_operations_started: + try: + await self._insert_done() + except Exception as persistence_error: + persistence_error_msg = f"Failed to persist data after deletion attempt for {doc_id}: {persistence_error}" + logger.error(persistence_error_msg) + logger.error(traceback.format_exc()) + + # If there was no original exception, this persistence error becomes the main error + if original_exception is None: + return DeletionResult( + status="fail", + doc_id=doc_id, + message=f"Deletion completed but failed to persist changes: {persistence_error}", + status_code=500, + file_path=file_path, + ) + # If there was an original exception, log the persistence error but don't override the original error + # The original error result was already returned in the except block + else: + logger.debug( + f"No deletion operations were started for document {doc_id}, skipping persistence" + ) + + async def adelete_by_entity(self, entity_name: str) -> DeletionResult: """Asynchronously delete an entity and all its relationships. Args: - entity_name: Name of the entity to delete + entity_name: Name of the entity to delete. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. """ from .utils_graph import adelete_by_entity @@ -1937,16 +2034,29 @@ class LightRAG: entity_name, ) - def delete_by_entity(self, entity_name: str) -> None: + def delete_by_entity(self, entity_name: str) -> DeletionResult: + """Synchronously delete an entity and all its relationships. + + Args: + entity_name: Name of the entity to delete. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + """ loop = always_get_an_event_loop() return loop.run_until_complete(self.adelete_by_entity(entity_name)) - async def adelete_by_relation(self, source_entity: str, target_entity: str) -> None: + async def adelete_by_relation( + self, source_entity: str, target_entity: str + ) -> DeletionResult: """Asynchronously delete a relation between two entities. Args: - source_entity: Name of the source entity - target_entity: Name of the target entity + source_entity: Name of the source entity. + target_entity: Name of the target entity. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. """ from .utils_graph import adelete_by_relation @@ -1957,7 +2067,18 @@ class LightRAG: target_entity, ) - def delete_by_relation(self, source_entity: str, target_entity: str) -> None: + def delete_by_relation( + self, source_entity: str, target_entity: str + ) -> DeletionResult: + """Synchronously delete a relation between two entities. + + Args: + source_entity: Name of the source entity. + target_entity: Name of the target entity. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + """ loop = always_get_an_event_loop() return loop.run_until_complete( self.adelete_by_relation(source_entity, target_entity) diff --git a/lightrag/llm/azure_openai.py b/lightrag/llm/azure_openai.py index 1963b4fe..6354ef18 100644 --- a/lightrag/llm/azure_openai.py +++ b/lightrag/llm/azure_openai.py @@ -1,3 +1,4 @@ +from collections.abc import Iterable import os import pipmaster as pm # Pipmaster for dynamic library install @@ -11,6 +12,8 @@ from openai import ( RateLimitError, APITimeoutError, ) +from openai.types.chat import ChatCompletionMessageParam + from tenacity import ( retry, stop_after_attempt, @@ -37,31 +40,38 @@ import numpy as np async def azure_openai_complete_if_cache( model, prompt, - system_prompt=None, - history_messages=[], - base_url=None, - api_key=None, - api_version=None, + system_prompt: str | None = None, + history_messages: Iterable[ChatCompletionMessageParam] | None = None, + base_url: str | None = None, + api_key: str | None = None, + api_version: str | None = None, **kwargs, ): - if api_key: - os.environ["AZURE_OPENAI_API_KEY"] = api_key - if base_url: - os.environ["AZURE_OPENAI_ENDPOINT"] = base_url - if api_version: - os.environ["AZURE_OPENAI_API_VERSION"] = api_version + model = model or os.getenv("AZURE_OPENAI_DEPLOYMENT") or os.getenv("LLM_MODEL") + base_url = ( + base_url or os.getenv("AZURE_OPENAI_ENDPOINT") or os.getenv("LLM_BINDING_HOST") + ) + api_key = ( + api_key or os.getenv("AZURE_OPENAI_API_KEY") or os.getenv("LLM_BINDING_API_KEY") + ) + api_version = ( + api_version + or os.getenv("AZURE_OPENAI_API_VERSION") + or os.getenv("OPENAI_API_VERSION") + ) openai_async_client = AsyncAzureOpenAI( - azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), + azure_endpoint=base_url, azure_deployment=model, - api_key=os.getenv("AZURE_OPENAI_API_KEY"), - api_version=os.getenv("AZURE_OPENAI_API_VERSION"), + api_key=api_key, + api_version=api_version, ) kwargs.pop("hashing_kv", None) messages = [] if system_prompt: messages.append({"role": "system", "content": system_prompt}) - messages.extend(history_messages) + if history_messages: + messages.extend(history_messages) if prompt is not None: messages.append({"role": "user", "content": prompt}) @@ -121,23 +131,37 @@ async def azure_openai_complete( ) async def azure_openai_embed( texts: list[str], - model: str = os.getenv("EMBEDDING_MODEL", "text-embedding-3-small"), - base_url: str = None, - api_key: str = None, - api_version: str = None, + model: str | None = None, + base_url: str | None = None, + api_key: str | None = None, + api_version: str | None = None, ) -> np.ndarray: - if api_key: - os.environ["AZURE_OPENAI_API_KEY"] = api_key - if base_url: - os.environ["AZURE_OPENAI_ENDPOINT"] = base_url - if api_version: - os.environ["AZURE_OPENAI_API_VERSION"] = api_version + model = ( + model + or os.getenv("AZURE_EMBEDDING_DEPLOYMENT") + or os.getenv("EMBEDDING_MODEL", "text-embedding-3-small") + ) + base_url = ( + base_url + or os.getenv("AZURE_EMBEDDING_ENDPOINT") + or os.getenv("EMBEDDING_BINDING_HOST") + ) + api_key = ( + api_key + or os.getenv("AZURE_EMBEDDING_API_KEY") + or os.getenv("EMBEDDING_BINDING_API_KEY") + ) + api_version = ( + api_version + or os.getenv("AZURE_EMBEDDING_API_VERSION") + or os.getenv("OPENAI_API_VERSION") + ) openai_async_client = AsyncAzureOpenAI( - azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), + azure_endpoint=base_url, azure_deployment=model, - api_key=os.getenv("AZURE_OPENAI_API_KEY"), - api_version=os.getenv("AZURE_OPENAI_API_VERSION"), + api_key=api_key, + api_version=api_version, ) response = await openai_async_client.embeddings.create( diff --git a/lightrag/llm/llama_index_impl.py b/lightrag/llm/llama_index_impl.py index 4e1618b1..b8dc39f3 100644 --- a/lightrag/llm/llama_index_impl.py +++ b/lightrag/llm/llama_index_impl.py @@ -95,7 +95,7 @@ async def llama_index_complete_if_cache( prompt: str, system_prompt: Optional[str] = None, history_messages: List[dict] = [], - **kwargs, + chat_kwargs={}, ) -> str: """Complete the prompt using LlamaIndex.""" try: @@ -122,13 +122,9 @@ async def llama_index_complete_if_cache( # Add current prompt formatted_messages.append(ChatMessage(role=MessageRole.USER, content=prompt)) - # Get LLM instance from kwargs - if "llm_instance" not in kwargs: - raise ValueError("llm_instance must be provided in kwargs") - llm = kwargs["llm_instance"] - - # Get response - response: ChatResponse = await llm.achat(messages=formatted_messages) + response: ChatResponse = await model.achat( + messages=formatted_messages, **chat_kwargs + ) # In newer versions, the response is in message.content content = response.message.content diff --git a/lightrag/llm/ollama.py b/lightrag/llm/ollama.py index c86aea1c..5cd8a721 100644 --- a/lightrag/llm/ollama.py +++ b/lightrag/llm/ollama.py @@ -50,7 +50,7 @@ async def _ollama_model_if_cache( kwargs.pop("max_tokens", None) # kwargs.pop("response_format", None) # allow json host = kwargs.pop("host", None) - timeout = kwargs.pop("timeout", None) or 300 # Default timeout 300s + timeout = kwargs.pop("timeout", None) or 600 # Default timeout 600s kwargs.pop("hashing_kv", None) api_key = kwargs.pop("api_key", None) headers = { @@ -146,7 +146,7 @@ async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray: headers["Authorization"] = f"Bearer {api_key}" host = kwargs.pop("host", None) - timeout = kwargs.pop("timeout", None) or 90 # Default time out 90s + timeout = kwargs.pop("timeout", None) or 300 # Default time out 300s ollama_client = ollama.AsyncClient(host=host, timeout=timeout, headers=headers) diff --git a/lightrag/namespace.py b/lightrag/namespace.py index 77e04c9e..657d65ac 100644 --- a/lightrag/namespace.py +++ b/lightrag/namespace.py @@ -17,10 +17,6 @@ class NameSpace: DOC_STATUS = "doc_status" -def make_namespace(prefix: str, base_namespace: str): - return prefix + base_namespace - - def is_namespace(namespace: str, base_namespace: str | Iterable[str]): if isinstance(base_namespace, str): return namespace.endswith(base_namespace) diff --git a/lightrag/operate.py b/lightrag/operate.py index a062f493..cff40b0a 100644 --- a/lightrag/operate.py +++ b/lightrag/operate.py @@ -25,6 +25,7 @@ from .utils import ( CacheData, get_conversation_turns, use_llm_func_with_cache, + update_chunk_cache_list, ) from .base import ( BaseGraphStorage, @@ -33,7 +34,8 @@ from .base import ( TextChunkSchema, QueryParam, ) -from .prompt import GRAPH_FIELD_SEP, PROMPTS +from .prompt import PROMPTS +from .constants import GRAPH_FIELD_SEP import time from dotenv import load_dotenv @@ -102,8 +104,6 @@ async def _handle_entity_relation_summary( entity_or_relation_name: str, description: str, global_config: dict, - pipeline_status: dict = None, - pipeline_status_lock=None, llm_response_cache: BaseKVStorage | None = None, ) -> str: """Handle entity relation summary @@ -168,6 +168,13 @@ async def _handle_single_entity_extraction( # Normalize entity name entity_name = normalize_extracted_info(entity_name, is_entity=True) + # Check if entity name became empty after normalization + if not entity_name or not entity_name.strip(): + logger.warning( + f"Entity extraction error: entity name became empty after normalization. Original: '{record_attributes[1]}'" + ) + return None + # Clean and validate entity type entity_type = clean_str(record_attributes[2]).strip('"') if not entity_type.strip() or entity_type.startswith('("'): @@ -209,6 +216,20 @@ async def _handle_single_relationship_extraction( # Normalize source and target entity names source = normalize_extracted_info(source, is_entity=True) target = normalize_extracted_info(target, is_entity=True) + + # Check if source or target became empty after normalization + if not source or not source.strip(): + logger.warning( + f"Relationship extraction error: source entity became empty after normalization. Original: '{record_attributes[1]}'" + ) + return None + + if not target or not target.strip(): + logger.warning( + f"Relationship extraction error: target entity became empty after normalization. Original: '{record_attributes[2]}'" + ) + return None + if source == target: logger.debug( f"Relationship source and target are the same in: {record_attributes}" @@ -240,6 +261,579 @@ async def _handle_single_relationship_extraction( ) +async def _rebuild_knowledge_from_chunks( + entities_to_rebuild: dict[str, set[str]], + relationships_to_rebuild: dict[tuple[str, str], set[str]], + knowledge_graph_inst: BaseGraphStorage, + entities_vdb: BaseVectorStorage, + relationships_vdb: BaseVectorStorage, + text_chunks_storage: BaseKVStorage, + llm_response_cache: BaseKVStorage, + global_config: dict[str, str], + pipeline_status: dict | None = None, + pipeline_status_lock=None, +) -> None: + """Rebuild entity and relationship descriptions from cached extraction results + + This method uses cached LLM extraction results instead of calling LLM again, + following the same approach as the insert process. + + Args: + entities_to_rebuild: Dict mapping entity_name -> set of remaining chunk_ids + relationships_to_rebuild: Dict mapping (src, tgt) -> set of remaining chunk_ids + text_chunks_data: Pre-loaded chunk data dict {chunk_id: chunk_data} + """ + if not entities_to_rebuild and not relationships_to_rebuild: + return + rebuilt_entities_count = 0 + rebuilt_relationships_count = 0 + + # Get all referenced chunk IDs + all_referenced_chunk_ids = set() + for chunk_ids in entities_to_rebuild.values(): + all_referenced_chunk_ids.update(chunk_ids) + for chunk_ids in relationships_to_rebuild.values(): + all_referenced_chunk_ids.update(chunk_ids) + + status_message = f"Rebuilding knowledge from {len(all_referenced_chunk_ids)} cached chunk extractions" + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + # Get cached extraction results for these chunks using storage + # cached_results： chunk_id -> [list of extraction result from LLM cache sorted by created_at] + cached_results = await _get_cached_extraction_results( + llm_response_cache, + all_referenced_chunk_ids, + text_chunks_storage=text_chunks_storage, + ) + + if not cached_results: + status_message = "No cached extraction results found, cannot rebuild" + logger.warning(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + return + + # Process cached results to get entities and relationships for each chunk + chunk_entities = {} # chunk_id -> {entity_name: [entity_data]} + chunk_relationships = {} # chunk_id -> {(src, tgt): [relationship_data]} + + for chunk_id, extraction_results in cached_results.items(): + try: + # Handle multiple extraction results per chunk + chunk_entities[chunk_id] = defaultdict(list) + chunk_relationships[chunk_id] = defaultdict(list) + + # process multiple LLM extraction results for a single chunk_id + for extraction_result in extraction_results: + entities, relationships = await _parse_extraction_result( + text_chunks_storage=text_chunks_storage, + extraction_result=extraction_result, + chunk_id=chunk_id, + ) + + # Merge entities and relationships from this extraction result + # Only keep the first occurrence of each entity_name in the same chunk_id + for entity_name, entity_list in entities.items(): + if ( + entity_name not in chunk_entities[chunk_id] + or len(chunk_entities[chunk_id][entity_name]) == 0 + ): + chunk_entities[chunk_id][entity_name].extend(entity_list) + + # Only keep the first occurrence of each rel_key in the same chunk_id + for rel_key, rel_list in relationships.items(): + if ( + rel_key not in chunk_relationships[chunk_id] + or len(chunk_relationships[chunk_id][rel_key]) == 0 + ): + chunk_relationships[chunk_id][rel_key].extend(rel_list) + + except Exception as e: + status_message = ( + f"Failed to parse cached extraction result for chunk {chunk_id}: {e}" + ) + logger.info(status_message) # Per requirement, change to info + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + continue + + # Rebuild entities + for entity_name, chunk_ids in entities_to_rebuild.items(): + try: + await _rebuild_single_entity( + knowledge_graph_inst=knowledge_graph_inst, + entities_vdb=entities_vdb, + entity_name=entity_name, + chunk_ids=chunk_ids, + chunk_entities=chunk_entities, + llm_response_cache=llm_response_cache, + global_config=global_config, + ) + rebuilt_entities_count += 1 + status_message = ( + f"Rebuilt entity: {entity_name} from {len(chunk_ids)} chunks" + ) + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + except Exception as e: + status_message = f"Failed to rebuild entity {entity_name}: {e}" + logger.info(status_message) # Per requirement, change to info + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + # Rebuild relationships + for (src, tgt), chunk_ids in relationships_to_rebuild.items(): + try: + await _rebuild_single_relationship( + knowledge_graph_inst=knowledge_graph_inst, + relationships_vdb=relationships_vdb, + src=src, + tgt=tgt, + chunk_ids=chunk_ids, + chunk_relationships=chunk_relationships, + llm_response_cache=llm_response_cache, + global_config=global_config, + ) + rebuilt_relationships_count += 1 + status_message = ( + f"Rebuilt relationship: {src}->{tgt} from {len(chunk_ids)} chunks" + ) + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + except Exception as e: + status_message = f"Failed to rebuild relationship {src}->{tgt}: {e}" + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + status_message = f"KG rebuild completed: {rebuilt_entities_count} entities and {rebuilt_relationships_count} relationships." + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + +async def _get_cached_extraction_results( + llm_response_cache: BaseKVStorage, + chunk_ids: set[str], + text_chunks_storage: BaseKVStorage, +) -> dict[str, list[str]]: + """Get cached extraction results for specific chunk IDs + + Args: + llm_response_cache: LLM response cache storage + chunk_ids: Set of chunk IDs to get cached results for + text_chunks_data: Pre-loaded chunk data (optional, for performance) + text_chunks_storage: Text chunks storage (fallback if text_chunks_data is None) + + Returns: + Dict mapping chunk_id -> list of extraction_result_text + """ + cached_results = {} + + # Collect all LLM cache IDs from chunks + all_cache_ids = set() + + # Read from storage + chunk_data_list = await text_chunks_storage.get_by_ids(list(chunk_ids)) + for chunk_id, chunk_data in zip(chunk_ids, chunk_data_list): + if chunk_data and isinstance(chunk_data, dict): + llm_cache_list = chunk_data.get("llm_cache_list", []) + if llm_cache_list: + all_cache_ids.update(llm_cache_list) + else: + logger.warning( + f"Chunk {chunk_id} data is invalid or None: {type(chunk_data)}" + ) + + if not all_cache_ids: + logger.warning(f"No LLM cache IDs found for {len(chunk_ids)} chunk IDs") + return cached_results + + # Batch get LLM cache entries + cache_data_list = await llm_response_cache.get_by_ids(list(all_cache_ids)) + + # Process cache entries and group by chunk_id + valid_entries = 0 + for cache_id, cache_entry in zip(all_cache_ids, cache_data_list): + if ( + cache_entry is not None + and isinstance(cache_entry, dict) + and cache_entry.get("cache_type") == "extract" + and cache_entry.get("chunk_id") in chunk_ids + ): + chunk_id = cache_entry["chunk_id"] + extraction_result = cache_entry["return"] + create_time = cache_entry.get( + "create_time", 0 + ) # Get creation time, default to 0 + valid_entries += 1 + + # Support multiple LLM caches per chunk + if chunk_id not in cached_results: + cached_results[chunk_id] = [] + # Store tuple with extraction result and creation time for sorting + cached_results[chunk_id].append((extraction_result, create_time)) + + # Sort extraction results by create_time for each chunk + for chunk_id in cached_results: + # Sort by create_time (x[1]), then extract only extraction_result (x[0]) + cached_results[chunk_id].sort(key=lambda x: x[1]) + cached_results[chunk_id] = [item[0] for item in cached_results[chunk_id]] + + logger.info( + f"Found {valid_entries} valid cache entries, {len(cached_results)} chunks with results" + ) + return cached_results + + +async def _parse_extraction_result( + text_chunks_storage: BaseKVStorage, extraction_result: str, chunk_id: str +) -> tuple[dict, dict]: + """Parse cached extraction result using the same logic as extract_entities + + Args: + text_chunks_storage: Text chunks storage to get chunk data + extraction_result: The cached LLM extraction result + chunk_id: The chunk ID for source tracking + + Returns: + Tuple of (entities_dict, relationships_dict) + """ + + # Get chunk data for file_path from storage + chunk_data = await text_chunks_storage.get_by_id(chunk_id) + file_path = ( + chunk_data.get("file_path", "unknown_source") + if chunk_data + else "unknown_source" + ) + context_base = dict( + tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"], + record_delimiter=PROMPTS["DEFAULT_RECORD_DELIMITER"], + completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"], + ) + maybe_nodes = defaultdict(list) + maybe_edges = defaultdict(list) + + # Parse the extraction result using the same logic as in extract_entities + records = split_string_by_multi_markers( + extraction_result, + [context_base["record_delimiter"], context_base["completion_delimiter"]], + ) + for record in records: + record = re.search(r"\((.*)\)", record) + if record is None: + continue + record = record.group(1) + record_attributes = split_string_by_multi_markers( + record, [context_base["tuple_delimiter"]] + ) + + # Try to parse as entity + entity_data = await _handle_single_entity_extraction( + record_attributes, chunk_id, file_path + ) + if entity_data is not None: + maybe_nodes[entity_data["entity_name"]].append(entity_data) + continue + + # Try to parse as relationship + relationship_data = await _handle_single_relationship_extraction( + record_attributes, chunk_id, file_path + ) + if relationship_data is not None: + maybe_edges[ + (relationship_data["src_id"], relationship_data["tgt_id"]) + ].append(relationship_data) + + return dict(maybe_nodes), dict(maybe_edges) + + +async def _rebuild_single_entity( + knowledge_graph_inst: BaseGraphStorage, + entities_vdb: BaseVectorStorage, + entity_name: str, + chunk_ids: set[str], + chunk_entities: dict, + llm_response_cache: BaseKVStorage, + global_config: dict[str, str], +) -> None: + """Rebuild a single entity from cached extraction results""" + + # Get current entity data + current_entity = await knowledge_graph_inst.get_node(entity_name) + if not current_entity: + return + + # Helper function to update entity in both graph and vector storage + async def _update_entity_storage( + final_description: str, entity_type: str, file_paths: set[str] + ): + # Update entity in graph storage + updated_entity_data = { + **current_entity, + "description": final_description, + "entity_type": entity_type, + "source_id": GRAPH_FIELD_SEP.join(chunk_ids), + "file_path": GRAPH_FIELD_SEP.join(file_paths) + if file_paths + else current_entity.get("file_path", "unknown_source"), + } + await knowledge_graph_inst.upsert_node(entity_name, updated_entity_data) + + # Update entity in vector database + entity_vdb_id = compute_mdhash_id(entity_name, prefix="ent-") + + # Delete old vector record first + try: + await entities_vdb.delete([entity_vdb_id]) + except Exception as e: + logger.debug( + f"Could not delete old entity vector record {entity_vdb_id}: {e}" + ) + + # Insert new vector record + entity_content = f"{entity_name}\n{final_description}" + await entities_vdb.upsert( + { + entity_vdb_id: { + "content": entity_content, + "entity_name": entity_name, + "source_id": updated_entity_data["source_id"], + "description": final_description, + "entity_type": entity_type, + "file_path": updated_entity_data["file_path"], + } + } + ) + + # Helper function to generate final description with optional LLM summary + async def _generate_final_description(combined_description: str) -> str: + if len(combined_description) > global_config["summary_to_max_tokens"]: + return await _handle_entity_relation_summary( + entity_name, + combined_description, + global_config, + llm_response_cache=llm_response_cache, + ) + else: + return combined_description + + # Collect all entity data from relevant chunks + all_entity_data = [] + for chunk_id in chunk_ids: + if chunk_id in chunk_entities and entity_name in chunk_entities[chunk_id]: + all_entity_data.extend(chunk_entities[chunk_id][entity_name]) + + if not all_entity_data: + logger.warning( + f"No cached entity data found for {entity_name}, trying to rebuild from relationships" + ) + + # Get all edges connected to this entity + edges = await knowledge_graph_inst.get_node_edges(entity_name) + if not edges: + logger.warning(f"No relationships found for entity {entity_name}") + return + + # Collect relationship data to extract entity information + relationship_descriptions = [] + file_paths = set() + + # Get edge data for all connected relationships + for src_id, tgt_id in edges: + edge_data = await knowledge_graph_inst.get_edge(src_id, tgt_id) + if edge_data: + if edge_data.get("description"): + relationship_descriptions.append(edge_data["description"]) + + if edge_data.get("file_path"): + edge_file_paths = edge_data["file_path"].split(GRAPH_FIELD_SEP) + file_paths.update(edge_file_paths) + + # Generate description from relationships or fallback to current + if relationship_descriptions: + combined_description = GRAPH_FIELD_SEP.join(relationship_descriptions) + final_description = await _generate_final_description(combined_description) + else: + final_description = current_entity.get("description", "") + + entity_type = current_entity.get("entity_type", "UNKNOWN") + await _update_entity_storage(final_description, entity_type, file_paths) + return + + # Process cached entity data + descriptions = [] + entity_types = [] + file_paths = set() + + for entity_data in all_entity_data: + if entity_data.get("description"): + descriptions.append(entity_data["description"]) + if entity_data.get("entity_type"): + entity_types.append(entity_data["entity_type"]) + if entity_data.get("file_path"): + file_paths.add(entity_data["file_path"]) + + # Combine all descriptions + combined_description = ( + GRAPH_FIELD_SEP.join(descriptions) + if descriptions + else current_entity.get("description", "") + ) + + # Get most common entity type + entity_type = ( + max(set(entity_types), key=entity_types.count) + if entity_types + else current_entity.get("entity_type", "UNKNOWN") + ) + + # Generate final description and update storage + final_description = await _generate_final_description(combined_description) + await _update_entity_storage(final_description, entity_type, file_paths) + + +async def _rebuild_single_relationship( + knowledge_graph_inst: BaseGraphStorage, + relationships_vdb: BaseVectorStorage, + src: str, + tgt: str, + chunk_ids: set[str], + chunk_relationships: dict, + llm_response_cache: BaseKVStorage, + global_config: dict[str, str], +) -> None: + """Rebuild a single relationship from cached extraction results""" + + # Get current relationship data + current_relationship = await knowledge_graph_inst.get_edge(src, tgt) + if not current_relationship: + return + + # Collect all relationship data from relevant chunks + all_relationship_data = [] + for chunk_id in chunk_ids: + if chunk_id in chunk_relationships: + # Check both (src, tgt) and (tgt, src) since relationships can be bidirectional + for edge_key in [(src, tgt), (tgt, src)]: + if edge_key in chunk_relationships[chunk_id]: + all_relationship_data.extend( + chunk_relationships[chunk_id][edge_key] + ) + + if not all_relationship_data: + logger.warning(f"No cached relationship data found for {src}-{tgt}") + return + + # Merge descriptions and keywords + descriptions = [] + keywords = [] + weights = [] + file_paths = set() + + for rel_data in all_relationship_data: + if rel_data.get("description"): + descriptions.append(rel_data["description"]) + if rel_data.get("keywords"): + keywords.append(rel_data["keywords"]) + if rel_data.get("weight"): + weights.append(rel_data["weight"]) + if rel_data.get("file_path"): + file_paths.add(rel_data["file_path"]) + + # Combine descriptions and keywords + combined_description = ( + GRAPH_FIELD_SEP.join(descriptions) + if descriptions + else current_relationship.get("description", "") + ) + combined_keywords = ( + ", ".join(set(keywords)) + if keywords + else current_relationship.get("keywords", "") + ) + # weight = ( + # sum(weights) / len(weights) + # if weights + # else current_relationship.get("weight", 1.0) + # ) + weight = sum(weights) if weights else current_relationship.get("weight", 1.0) + + # Use summary if description is too long + if len(combined_description) > global_config["summary_to_max_tokens"]: + final_description = await _handle_entity_relation_summary( + f"{src}-{tgt}", + combined_description, + global_config, + llm_response_cache=llm_response_cache, + ) + else: + final_description = combined_description + + # Update relationship in graph storage + updated_relationship_data = { + **current_relationship, + "description": final_description, + "keywords": combined_keywords, + "weight": weight, + "source_id": GRAPH_FIELD_SEP.join(chunk_ids), + "file_path": GRAPH_FIELD_SEP.join(file_paths) + if file_paths + else current_relationship.get("file_path", "unknown_source"), + } + await knowledge_graph_inst.upsert_edge(src, tgt, updated_relationship_data) + + # Update relationship in vector database + rel_vdb_id = compute_mdhash_id(src + tgt, prefix="rel-") + rel_vdb_id_reverse = compute_mdhash_id(tgt + src, prefix="rel-") + + # Delete old vector records first (both directions to be safe) + try: + await relationships_vdb.delete([rel_vdb_id, rel_vdb_id_reverse]) + except Exception as e: + logger.debug( + f"Could not delete old relationship vector records {rel_vdb_id}, {rel_vdb_id_reverse}: {e}" + ) + + # Insert new vector record + rel_content = f"{combined_keywords}\t{src}\n{tgt}\n{final_description}" + await relationships_vdb.upsert( + { + rel_vdb_id: { + "src_id": src, + "tgt_id": tgt, + "source_id": updated_relationship_data["source_id"], + "content": rel_content, + "keywords": combined_keywords, + "description": final_description, + "weight": weight, + "file_path": updated_relationship_data["file_path"], + } + } + ) + + async def _merge_nodes_then_upsert( entity_name: str, nodes_data: list[dict], @@ -256,7 +850,7 @@ async def _merge_nodes_then_upsert( already_file_paths = [] already_node = await knowledge_graph_inst.get_node(entity_name) - if already_node is not None: + if already_node: already_entity_types.append(already_node["entity_type"]) already_source_ids.extend( split_string_by_multi_markers(already_node["source_id"], [GRAPH_FIELD_SEP]) @@ -300,8 +894,6 @@ async def _merge_nodes_then_upsert( entity_name, description, global_config, - pipeline_status, - pipeline_status_lock, llm_response_cache, ) else: @@ -468,8 +1060,6 @@ async def _merge_edges_then_upsert( f"({src_id}, {tgt_id})", description, global_config, - pipeline_status, - pipeline_status_lock, llm_response_cache, ) else: @@ -500,6 +1090,7 @@ async def _merge_edges_then_upsert( keywords=keywords, source_id=source_id, file_path=file_path, + created_at=int(time.time()), ) return edge_data @@ -646,6 +1237,7 @@ async def extract_entities( pipeline_status: dict = None, pipeline_status_lock=None, llm_response_cache: BaseKVStorage | None = None, + text_chunks_storage: BaseKVStorage | None = None, ) -> list: use_llm_func: callable = global_config["llm_model_func"] entity_extract_max_gleaning = global_config["entity_extract_max_gleaning"] @@ -752,6 +1344,9 @@ async def extract_entities( # Get file path from chunk data or use default file_path = chunk_dp.get("file_path", "unknown_source") + # Create cache keys collector for batch processing + cache_keys_collector = [] + # Get initial extraction hint_prompt = entity_extract_prompt.format( **{**context_base, "input_text": content} @@ -762,7 +1357,11 @@ async def extract_entities( use_llm_func, llm_response_cache=llm_response_cache, cache_type="extract", + chunk_id=chunk_key, + cache_keys_collector=cache_keys_collector, ) + + # Store LLM cache reference in chunk (will be handled by use_llm_func_with_cache) history = pack_user_ass_to_openai_messages(hint_prompt, final_result) # Process initial extraction with file path @@ -778,6 +1377,8 @@ async def extract_entities( llm_response_cache=llm_response_cache, history_messages=history, cache_type="extract", + chunk_id=chunk_key, + cache_keys_collector=cache_keys_collector, ) history += pack_user_ass_to_openai_messages(continue_prompt, glean_result) @@ -808,11 +1409,21 @@ async def extract_entities( llm_response_cache=llm_response_cache, history_messages=history, cache_type="extract", + cache_keys_collector=cache_keys_collector, ) if_loop_result = if_loop_result.strip().strip('"').strip("'").lower() if if_loop_result != "yes": break + # Batch update chunk's llm_cache_list with all collected cache keys + if cache_keys_collector and text_chunks_storage: + await update_chunk_cache_list( + chunk_key, + text_chunks_storage, + cache_keys_collector, + "entity_extraction", + ) + processed_chunks += 1 entities_count = len(maybe_nodes) relations_count = len(maybe_edges) @@ -885,7 +1496,7 @@ async def kg_query( use_model_func = partial(use_model_func, _priority=5) # Handle cache - args_hash = compute_args_hash(query_param.mode, query, cache_type="query") + args_hash = compute_args_hash(query_param.mode, query) cached_response, quantized, min_val, max_val = await handle_cache( hashing_kv, args_hash, query, query_param.mode, cache_type="query" ) @@ -932,7 +1543,7 @@ async def kg_query( ) if query_param.only_need_context: - return context + return context if context is not None else PROMPTS["fail_response"] if context is None: return PROMPTS["fail_response"] @@ -1044,7 +1655,7 @@ async def extract_keywords_only( """ # 1. Handle cache if needed - add cache type for keywords - args_hash = compute_args_hash(param.mode, text, cache_type="keywords") + args_hash = compute_args_hash(param.mode, text) cached_response, quantized, min_val, max_val = await handle_cache( hashing_kv, args_hash, text, param.mode, cache_type="keywords" ) @@ -1189,7 +1800,7 @@ async def _get_vector_context( f"Truncate chunks from {len(valid_chunks)} to {len(maybe_trun_chunks)} (max tokens:{query_param.max_token_for_text_unit})" ) logger.info( - f"Vector query: {len(maybe_trun_chunks)} chunks, top_k: {query_param.top_k}" + f"Query chunks: {len(maybe_trun_chunks)} chunks, top_k: {query_param.top_k}" ) if not maybe_trun_chunks: @@ -1413,7 +2024,7 @@ async def _get_node_data( ) logger.info( - f"Local query uses {len(node_datas)} entites, {len(use_relations)} relations, {len(use_text_units)} chunks" + f"Local query: {len(node_datas)} entites, {len(use_relations)} relations, {len(use_text_units)} chunks" ) # build prompt @@ -1722,7 +2333,7 @@ async def _get_edge_data( ), ) logger.info( - f"Global query uses {len(use_entities)} entites, {len(edge_datas)} relations, {len(use_text_units)} chunks" + f"Global query: {len(use_entities)} entites, {len(edge_datas)} relations, {len(use_text_units)} chunks" ) relations_context = [] @@ -1911,7 +2522,7 @@ async def naive_query( use_model_func = partial(use_model_func, _priority=5) # Handle cache - args_hash = compute_args_hash(query_param.mode, query, cache_type="query") + args_hash = compute_args_hash(query_param.mode, query) cached_response, quantized, min_val, max_val = await handle_cache( hashing_kv, args_hash, query, query_param.mode, cache_type="query" ) @@ -2027,7 +2638,7 @@ async def kg_query_with_keywords( # Apply higher priority (5) to query relation LLM function use_model_func = partial(use_model_func, _priority=5) - args_hash = compute_args_hash(query_param.mode, query, cache_type="query") + args_hash = compute_args_hash(query_param.mode, query) cached_response, quantized, min_val, max_val = await handle_cache( hashing_kv, args_hash, query, query_param.mode, cache_type="query" ) diff --git a/lightrag/prompt.py b/lightrag/prompt.py index 5ed630f9..a4641480 100644 --- a/lightrag/prompt.py +++ b/lightrag/prompt.py @@ -1,7 +1,6 @@ from __future__ import annotations from typing import Any -GRAPH_FIELD_SEP = "" PROMPTS: dict[str, Any] = {} diff --git a/lightrag/utils.py b/lightrag/utils.py index 7ecb11e3..c6e2def9 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -13,9 +13,7 @@ from dataclasses import dataclass from functools import wraps from hashlib import md5 from typing import Any, Protocol, Callable, TYPE_CHECKING, List -import xml.etree.ElementTree as ET import numpy as np -from lightrag.prompt import PROMPTS from dotenv import load_dotenv from lightrag.constants import ( DEFAULT_LOG_MAX_BYTES, @@ -279,11 +277,10 @@ def convert_response_to_json(response: str) -> dict[str, Any]: raise e from None -def compute_args_hash(*args: Any, cache_type: str | None = None) -> str: +def compute_args_hash(*args: Any) -> str: """Compute a hash for the given arguments. Args: *args: Arguments to hash - cache_type: Type of cache (e.g., 'keywords', 'query', 'extract') Returns: str: Hash string """ @@ -291,13 +288,40 @@ def compute_args_hash(*args: Any, cache_type: str | None = None) -> str: # Convert all arguments to strings and join them args_str = "".join([str(arg) for arg in args]) - if cache_type: - args_str = f"{cache_type}:{args_str}" # Compute MD5 hash return hashlib.md5(args_str.encode()).hexdigest() +def generate_cache_key(mode: str, cache_type: str, hash_value: str) -> str: + """Generate a flattened cache key in the format {mode}:{cache_type}:{hash} + + Args: + mode: Cache mode (e.g., 'default', 'local', 'global') + cache_type: Type of cache (e.g., 'extract', 'query', 'keywords') + hash_value: Hash value from compute_args_hash + + Returns: + str: Flattened cache key + """ + return f"{mode}:{cache_type}:{hash_value}" + + +def parse_cache_key(cache_key: str) -> tuple[str, str, str] | None: + """Parse a flattened cache key back into its components + + Args: + cache_key: Flattened cache key in format {mode}:{cache_type}:{hash} + + Returns: + tuple[str, str, str] | None: (mode, cache_type, hash) or None if invalid format + """ + parts = cache_key.split(":", 2) + if len(parts) == 3: + return parts[0], parts[1], parts[2] + return None + + def compute_mdhash_id(content: str, prefix: str = "") -> str: """ Compute a unique ID for a given content string. @@ -753,71 +777,6 @@ def truncate_list_by_token_size( return list_data -def save_data_to_file(data, file_name): - with open(file_name, "w", encoding="utf-8") as f: - json.dump(data, f, ensure_ascii=False, indent=4) - - -def xml_to_json(xml_file): - try: - tree = ET.parse(xml_file) - root = tree.getroot() - - # Print the root element's tag and attributes to confirm the file has been correctly loaded - print(f"Root element: {root.tag}") - print(f"Root attributes: {root.attrib}") - - data = {"nodes": [], "edges": []} - - # Use namespace - namespace = {"": "http://graphml.graphdrawing.org/xmlns"} - - for node in root.findall(".//node", namespace): - node_data = { - "id": node.get("id").strip('"'), - "entity_type": node.find("./data[@key='d0']", namespace).text.strip('"') - if node.find("./data[@key='d0']", namespace) is not None - else "", - "description": node.find("./data[@key='d1']", namespace).text - if node.find("./data[@key='d1']", namespace) is not None - else "", - "source_id": node.find("./data[@key='d2']", namespace).text - if node.find("./data[@key='d2']", namespace) is not None - else "", - } - data["nodes"].append(node_data) - - for edge in root.findall(".//edge", namespace): - edge_data = { - "source": edge.get("source").strip('"'), - "target": edge.get("target").strip('"'), - "weight": float(edge.find("./data[@key='d3']", namespace).text) - if edge.find("./data[@key='d3']", namespace) is not None - else 0.0, - "description": edge.find("./data[@key='d4']", namespace).text - if edge.find("./data[@key='d4']", namespace) is not None - else "", - "keywords": edge.find("./data[@key='d5']", namespace).text - if edge.find("./data[@key='d5']", namespace) is not None - else "", - "source_id": edge.find("./data[@key='d6']", namespace).text - if edge.find("./data[@key='d6']", namespace) is not None - else "", - } - data["edges"].append(edge_data) - - # Print the number of nodes and edges found - print(f"Found {len(data['nodes'])} nodes and {len(data['edges'])} edges") - - return data - except ET.ParseError as e: - print(f"Error parsing XML file: {e}") - return None - except Exception as e: - print(f"An error occurred: {e}") - return None - - def process_combine_contexts(*context_lists): """ Combine multiple context lists and remove duplicate content @@ -849,131 +808,6 @@ def process_combine_contexts(*context_lists): return combined_data -async def get_best_cached_response( - hashing_kv, - current_embedding, - similarity_threshold=0.95, - mode="default", - use_llm_check=False, - llm_func=None, - original_prompt=None, - cache_type=None, -) -> str | None: - logger.debug( - f"get_best_cached_response: mode={mode} cache_type={cache_type} use_llm_check={use_llm_check}" - ) - mode_cache = await hashing_kv.get_by_id(mode) - if not mode_cache: - return None - - best_similarity = -1 - best_response = None - best_prompt = None - best_cache_id = None - - # Only iterate through cache entries for this mode - for cache_id, cache_data in mode_cache.items(): - # Skip if cache_type doesn't match - if cache_type and cache_data.get("cache_type") != cache_type: - continue - - # Check if cache data is valid - if cache_data["embedding"] is None: - continue - - try: - # Safely convert cached embedding - cached_quantized = np.frombuffer( - bytes.fromhex(cache_data["embedding"]), dtype=np.uint8 - ).reshape(cache_data["embedding_shape"]) - - # Ensure min_val and max_val are valid float values - embedding_min = cache_data.get("embedding_min") - embedding_max = cache_data.get("embedding_max") - - if ( - embedding_min is None - or embedding_max is None - or embedding_min >= embedding_max - ): - logger.warning( - f"Invalid embedding min/max values: min={embedding_min}, max={embedding_max}" - ) - continue - - cached_embedding = dequantize_embedding( - cached_quantized, - embedding_min, - embedding_max, - ) - except Exception as e: - logger.warning(f"Error processing cached embedding: {str(e)}") - continue - - similarity = cosine_similarity(current_embedding, cached_embedding) - if similarity > best_similarity: - best_similarity = similarity - best_response = cache_data["return"] - best_prompt = cache_data["original_prompt"] - best_cache_id = cache_id - - if best_similarity > similarity_threshold: - # If LLM check is enabled and all required parameters are provided - if ( - use_llm_check - and llm_func - and original_prompt - and best_prompt - and best_response is not None - ): - compare_prompt = PROMPTS["similarity_check"].format( - original_prompt=original_prompt, cached_prompt=best_prompt - ) - - try: - llm_result = await llm_func(compare_prompt) - llm_result = llm_result.strip() - llm_similarity = float(llm_result) - - # Replace vector similarity with LLM similarity score - best_similarity = llm_similarity - if best_similarity < similarity_threshold: - log_data = { - "event": "cache_rejected_by_llm", - "type": cache_type, - "mode": mode, - "original_question": original_prompt[:100] + "..." - if len(original_prompt) > 100 - else original_prompt, - "cached_question": best_prompt[:100] + "..." - if len(best_prompt) > 100 - else best_prompt, - "similarity_score": round(best_similarity, 4), - "threshold": similarity_threshold, - } - logger.debug(json.dumps(log_data, ensure_ascii=False)) - logger.info(f"Cache rejected by LLM(mode:{mode} tpye:{cache_type})") - return None - except Exception as e: # Catch all possible exceptions - logger.warning(f"LLM similarity check failed: {e}") - return None # Return None directly when LLM check fails - - prompt_display = ( - best_prompt[:50] + "..." if len(best_prompt) > 50 else best_prompt - ) - log_data = { - "event": "cache_hit", - "type": cache_type, - "mode": mode, - "similarity": round(best_similarity, 4), - "cache_id": best_cache_id, - "original_prompt": prompt_display, - } - logger.debug(json.dumps(log_data, ensure_ascii=False)) - return best_response - return None - - def cosine_similarity(v1, v2): """Calculate cosine similarity between two vectors""" dot_product = np.dot(v1, v2) @@ -1023,7 +857,7 @@ async def handle_cache( mode="default", cache_type=None, ): - """Generic cache handling function""" + """Generic cache handling function with flattened cache keys""" if hashing_kv is None: return None, None, None, None @@ -1034,15 +868,14 @@ async def handle_cache( if not hashing_kv.global_config.get("enable_llm_cache_for_entity_extract"): return None, None, None, None - if exists_func(hashing_kv, "get_by_mode_and_id"): - mode_cache = await hashing_kv.get_by_mode_and_id(mode, args_hash) or {} - else: - mode_cache = await hashing_kv.get_by_id(mode) or {} - if args_hash in mode_cache: - logger.debug(f"Non-embedding cached hit(mode:{mode} type:{cache_type})") - return mode_cache[args_hash]["return"], None, None, None + # Use flattened cache key format: {mode}:{cache_type}:{hash} + flattened_key = generate_cache_key(mode, cache_type, args_hash) + cache_entry = await hashing_kv.get_by_id(flattened_key) + if cache_entry: + logger.debug(f"Flattened cache hit(key:{flattened_key})") + return cache_entry["return"], None, None, None - logger.debug(f"Non-embedding cached missed(mode:{mode} type:{cache_type})") + logger.debug(f"Cache missed(mode:{mode} type:{cache_type})") return None, None, None, None @@ -1056,10 +889,11 @@ class CacheData: max_val: float | None = None mode: str = "default" cache_type: str = "query" + chunk_id: str | None = None async def save_to_cache(hashing_kv, cache_data: CacheData): - """Save data to cache, with improved handling for streaming responses and duplicate content. + """Save data to cache using flattened key structure. Args: hashing_kv: The key-value storage for caching @@ -1074,28 +908,24 @@ async def save_to_cache(hashing_kv, cache_data: CacheData): logger.debug("Streaming response detected, skipping cache") return - # Get existing cache data - if exists_func(hashing_kv, "get_by_mode_and_id"): - mode_cache = ( - await hashing_kv.get_by_mode_and_id(cache_data.mode, cache_data.args_hash) - or {} - ) - else: - mode_cache = await hashing_kv.get_by_id(cache_data.mode) or {} + # Use flattened cache key format: {mode}:{cache_type}:{hash} + flattened_key = generate_cache_key( + cache_data.mode, cache_data.cache_type, cache_data.args_hash + ) # Check if we already have identical content cached - if cache_data.args_hash in mode_cache: - existing_content = mode_cache[cache_data.args_hash].get("return") + existing_cache = await hashing_kv.get_by_id(flattened_key) + if existing_cache: + existing_content = existing_cache.get("return") if existing_content == cache_data.content: - logger.info( - f"Cache content unchanged for {cache_data.args_hash}, skipping update" - ) + logger.info(f"Cache content unchanged for {flattened_key}, skipping update") return - # Update cache with new content - mode_cache[cache_data.args_hash] = { + # Create cache entry with flattened structure + cache_entry = { "return": cache_data.content, "cache_type": cache_data.cache_type, + "chunk_id": cache_data.chunk_id if cache_data.chunk_id is not None else None, "embedding": cache_data.quantized.tobytes().hex() if cache_data.quantized is not None else None, @@ -1107,10 +937,10 @@ async def save_to_cache(hashing_kv, cache_data: CacheData): "original_prompt": cache_data.prompt, } - logger.info(f" == LLM cache == saving {cache_data.mode}: {cache_data.args_hash}") + logger.info(f" == LLM cache == saving: {flattened_key}") - # Only upsert if there's actual new content - await hashing_kv.upsert({cache_data.mode: mode_cache}) + # Save using flattened key + await hashing_kv.upsert({flattened_key: cache_entry}) def safe_unicode_decode(content): @@ -1593,6 +1423,48 @@ def lazy_external_import(module_name: str, class_name: str) -> Callable[..., Any return import_class +async def update_chunk_cache_list( + chunk_id: str, + text_chunks_storage: "BaseKVStorage", + cache_keys: list[str], + cache_scenario: str = "batch_update", +) -> None: + """Update chunk's llm_cache_list with the given cache keys + + Args: + chunk_id: Chunk identifier + text_chunks_storage: Text chunks storage instance + cache_keys: List of cache keys to add to the list + cache_scenario: Description of the cache scenario for logging + """ + if not cache_keys: + return + + try: + chunk_data = await text_chunks_storage.get_by_id(chunk_id) + if chunk_data: + # Ensure llm_cache_list exists + if "llm_cache_list" not in chunk_data: + chunk_data["llm_cache_list"] = [] + + # Add cache keys to the list if not already present + existing_keys = set(chunk_data["llm_cache_list"]) + new_keys = [key for key in cache_keys if key not in existing_keys] + + if new_keys: + chunk_data["llm_cache_list"].extend(new_keys) + + # Update the chunk in storage + await text_chunks_storage.upsert({chunk_id: chunk_data}) + logger.debug( + f"Updated chunk {chunk_id} with {len(new_keys)} cache keys ({cache_scenario})" + ) + except Exception as e: + logger.warning( + f"Failed to update chunk {chunk_id} with cache references on {cache_scenario}: {e}" + ) + + async def use_llm_func_with_cache( input_text: str, use_llm_func: callable, @@ -1600,6 +1472,8 @@ async def use_llm_func_with_cache( max_tokens: int = None, history_messages: list[dict[str, str]] = None, cache_type: str = "extract", + chunk_id: str | None = None, + cache_keys_collector: list = None, ) -> str: """Call LLM function with cache support @@ -1613,6 +1487,9 @@ async def use_llm_func_with_cache( max_tokens: Maximum tokens for generation history_messages: History messages list cache_type: Type of cache + chunk_id: Chunk identifier to store in cache + text_chunks_storage: Text chunks storage to update llm_cache_list + cache_keys_collector: Optional list to collect cache keys for batch processing Returns: LLM response text @@ -1625,6 +1502,9 @@ async def use_llm_func_with_cache( _prompt = input_text arg_hash = compute_args_hash(_prompt) + # Generate cache key for this LLM call + cache_key = generate_cache_key("default", cache_type, arg_hash) + cached_return, _1, _2, _3 = await handle_cache( llm_response_cache, arg_hash, @@ -1635,6 +1515,11 @@ async def use_llm_func_with_cache( if cached_return: logger.debug(f"Found cache for {arg_hash}") statistic_data["llm_cache"] += 1 + + # Add cache key to collector if provided + if cache_keys_collector is not None: + cache_keys_collector.append(cache_key) + return cached_return statistic_data["llm_call"] += 1 @@ -1655,9 +1540,14 @@ async def use_llm_func_with_cache( content=res, prompt=_prompt, cache_type=cache_type, + chunk_id=chunk_id, ), ) + # Add cache key to collector if provided + if cache_keys_collector is not None: + cache_keys_collector.append(cache_key) + return res # When cache is disabled, directly call LLM diff --git a/lightrag/utils_graph.py b/lightrag/utils_graph.py index 60369ee5..c2ccd313 100644 --- a/lightrag/utils_graph.py +++ b/lightrag/utils_graph.py @@ -1,17 +1,19 @@ from __future__ import annotations +import time import asyncio from typing import Any, cast +from .base import DeletionResult from .kg.shared_storage import get_graph_db_lock -from .prompt import GRAPH_FIELD_SEP +from .constants import GRAPH_FIELD_SEP from .utils import compute_mdhash_id, logger from .base import StorageNameSpace async def adelete_by_entity( chunk_entity_relation_graph, entities_vdb, relationships_vdb, entity_name: str -) -> None: +) -> DeletionResult: """Asynchronously delete an entity and all its relationships. Args: @@ -24,18 +26,43 @@ async def adelete_by_entity( # Use graph database lock to ensure atomic graph and vector db operations async with graph_db_lock: try: + # Check if the entity exists + if not await chunk_entity_relation_graph.has_node(entity_name): + logger.warning(f"Entity '{entity_name}' not found.") + return DeletionResult( + status="not_found", + doc_id=entity_name, + message=f"Entity '{entity_name}' not found.", + status_code=404, + ) + # Retrieve related relationships before deleting the node + edges = await chunk_entity_relation_graph.get_node_edges(entity_name) + related_relations_count = len(edges) if edges else 0 + await entities_vdb.delete_entity(entity_name) await relationships_vdb.delete_entity_relation(entity_name) await chunk_entity_relation_graph.delete_node(entity_name) - logger.info( - f"Entity '{entity_name}' and its relationships have been deleted." - ) + message = f"Entity '{entity_name}' and its {related_relations_count} relationships have been deleted." + logger.info(message) await _delete_by_entity_done( entities_vdb, relationships_vdb, chunk_entity_relation_graph ) + return DeletionResult( + status="success", + doc_id=entity_name, + message=message, + status_code=200, + ) except Exception as e: - logger.error(f"Error while deleting entity '{entity_name}': {e}") + error_message = f"Error while deleting entity '{entity_name}': {e}" + logger.error(error_message) + return DeletionResult( + status="fail", + doc_id=entity_name, + message=error_message, + status_code=500, + ) async def _delete_by_entity_done( @@ -59,7 +86,7 @@ async def adelete_by_relation( relationships_vdb, source_entity: str, target_entity: str, -) -> None: +) -> DeletionResult: """Asynchronously delete a relation between two entities. Args: @@ -68,6 +95,7 @@ async def adelete_by_relation( source_entity: Name of the source entity target_entity: Name of the target entity """ + relation_str = f"{source_entity} -> {target_entity}" graph_db_lock = get_graph_db_lock(enable_logging=False) # Use graph database lock to ensure atomic graph and vector db operations async with graph_db_lock: @@ -77,29 +105,45 @@ async def adelete_by_relation( source_entity, target_entity ) if not edge_exists: - logger.warning( - f"Relation from '{source_entity}' to '{target_entity}' does not exist" + message = f"Relation from '{source_entity}' to '{target_entity}' does not exist" + logger.warning(message) + return DeletionResult( + status="not_found", + doc_id=relation_str, + message=message, + status_code=404, ) - return # Delete relation from vector database - relation_id = compute_mdhash_id( - source_entity + target_entity, prefix="rel-" - ) - await relationships_vdb.delete([relation_id]) + rel_ids_to_delete = [ + compute_mdhash_id(source_entity + target_entity, prefix="rel-"), + compute_mdhash_id(target_entity + source_entity, prefix="rel-"), + ] + + await relationships_vdb.delete(rel_ids_to_delete) # Delete relation from knowledge graph await chunk_entity_relation_graph.remove_edges( [(source_entity, target_entity)] ) - logger.info( - f"Successfully deleted relation from '{source_entity}' to '{target_entity}'" - ) + message = f"Successfully deleted relation from '{source_entity}' to '{target_entity}'" + logger.info(message) await _delete_relation_done(relationships_vdb, chunk_entity_relation_graph) + return DeletionResult( + status="success", + doc_id=relation_str, + message=message, + status_code=200, + ) except Exception as e: - logger.error( - f"Error while deleting relation from '{source_entity}' to '{target_entity}': {e}" + error_message = f"Error while deleting relation from '{source_entity}' to '{target_entity}': {e}" + logger.error(error_message) + return DeletionResult( + status="fail", + doc_id=relation_str, + message=error_message, + status_code=500, ) @@ -479,7 +523,9 @@ async def acreate_entity( "entity_id": entity_name, "entity_type": entity_data.get("entity_type", "UNKNOWN"), "description": entity_data.get("description", ""), - "source_id": entity_data.get("source_id", "manual"), + "source_id": entity_data.get("source_id", "manual_creation"), + "file_path": entity_data.get("file_path", "manual_creation"), + "created_at": int(time.time()), } # Add entity to knowledge graph @@ -575,8 +621,10 @@ async def acreate_relation( edge_data = { "description": relation_data.get("description", ""), "keywords": relation_data.get("keywords", ""), - "source_id": relation_data.get("source_id", "manual"), + "source_id": relation_data.get("source_id", "manual_creation"), "weight": float(relation_data.get("weight", 1.0)), + "file_path": relation_data.get("file_path", "manual_creation"), + "created_at": int(time.time()), } # Add relation to knowledge graph diff --git 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+ "hastscript/@types/hast": ["@types/hast@2.3.10", "", { "dependencies": { "@types/unist": "^2" } }, "sha512-McWspRw8xx8J9HurkVBfYj0xKoE25tOFlHGdx4MJ5xORQrMGZNqJhVQWaIbm6Oyla5kYOXtDiopzKRJzEOkwJw=="], "hastscript/comma-separated-tokens": ["comma-separated-tokens@1.0.8", "", {}, "sha512-GHuDRO12Sypu2cV70d1dkA2EUmXHgntrzbpvOB+Qy+49ypNfGgFQIC2fhhXbnyrJRynDCAARsT7Ou0M6hirpfw=="], @@ -1632,6 +1665,10 @@ "mdast-util-mdx-jsx/parse-entities": ["parse-entities@4.0.2", "", { "dependencies": { "@types/unist": "^2.0.0", "character-entities-legacy": "^3.0.0", "character-reference-invalid": "^2.0.0", "decode-named-character-reference": "^1.0.0", "is-alphanumerical": "^2.0.0", "is-decimal": "^2.0.0", "is-hexadecimal": "^2.0.0" } }, "sha512-GG2AQYWoLgL877gQIKeRPGO1xF9+eG1ujIb5soS5gPvLQ1y2o8FL90w2QWNdf9I361Mpp7726c+lj3U0qK1uGw=="], + "mermaid/katex": ["katex@0.16.21", "", { "dependencies": { "commander": "^8.3.0" }, "bin": { "katex": "cli.js" } }, "sha512-XvqR7FgOHtWupfMiigNzmh+MgUVmDGU2kXZm899ZkPfcuoPuFxyHmXsgATDpFZDAXCI8tvinaVcDo8PIIJSo4A=="], + + "micromark-extension-math/katex": ["katex@0.16.21", "", { "dependencies": { "commander": "^8.3.0" }, "bin": { "katex": "cli.js" } }, "sha512-XvqR7FgOHtWupfMiigNzmh+MgUVmDGU2kXZm899ZkPfcuoPuFxyHmXsgATDpFZDAXCI8tvinaVcDo8PIIJSo4A=="], + "mlly/pkg-types": ["pkg-types@1.3.1", "", { "dependencies": { "confbox": "^0.1.8", "mlly": "^1.7.4", "pathe": "^2.0.1" } }, "sha512-/Jm5M4RvtBFVkKWRu2BLUTNP8/M2a+UwuAX+ae4770q1qVGtfjG+WTCupoZixokjmHiry8uI+dlY8KXYV5HVVQ=="], "refractor/prismjs": ["prismjs@1.27.0", "", {}, "sha512-t13BGPUlFDR7wRB5kQDG4jjl7XeuH6jbJGt11JHPL96qwsEHNX2+68tFXqc1/k+/jALsbSWJKUOT/hcYAZ5LkA=="], @@ -1644,6 +1681,12 @@ "d3-sankey/d3-shape/d3-path": ["d3-path@1.0.9", "", {}, "sha512-VLaYcn81dtHVTjEHd8B+pbe9yHWpXKZUC87PzoFmsFrJqgFwDe/qxfp5MlfsfM1V5E/iVt0MmEbWQ7FVIXh/bg=="], + "hast-util-from-dom/hastscript/hast-util-parse-selector": ["hast-util-parse-selector@4.0.0", "", { "dependencies": { "@types/hast": "^3.0.0" } }, "sha512-wkQCkSYoOGCRKERFWcxMVMOcYE2K1AaNLU8DXS9arxnLOUEWbOXKXiJUNzEpqZ3JOKpnha3jkFrumEjVliDe7A=="], + + "hast-util-from-dom/hastscript/property-information": ["property-information@7.1.0", "", {}, "sha512-TwEZ+X+yCJmYfL7TPUOcvBZ4QfoT5YenQiJuX//0th53DE6w0xxLEtfK3iyryQFddXuvkIk51EEgrJQ0WJkOmQ=="], + + "hast-util-from-parse5/hastscript/hast-util-parse-selector": ["hast-util-parse-selector@4.0.0", "", { "dependencies": { "@types/hast": "^3.0.0" } }, "sha512-wkQCkSYoOGCRKERFWcxMVMOcYE2K1AaNLU8DXS9arxnLOUEWbOXKXiJUNzEpqZ3JOKpnha3jkFrumEjVliDe7A=="], + "hastscript/@types/hast/@types/unist": ["@types/unist@2.0.11", "", {}, "sha512-CmBKiL6NNo/OqgmMn95Fk9Whlp2mtvIv+KNpQKN2F4SjvrEesubTRWGYSg+BnWZOnlCaSTU1sMpsBOzgbYhnsA=="], "mdast-util-mdx-jsx/parse-entities/@types/unist": ["@types/unist@2.0.11", "", {}, "sha512-CmBKiL6NNo/OqgmMn95Fk9Whlp2mtvIv+KNpQKN2F4SjvrEesubTRWGYSg+BnWZOnlCaSTU1sMpsBOzgbYhnsA=="], diff --git a/lightrag_webui/package.json b/lightrag_webui/package.json index f043aaa7..f8e434aa 100644 --- a/lightrag_webui/package.json +++ b/lightrag_webui/package.json @@ -44,6 +44,7 @@ "graphology": "^0.26.0", "graphology-generators": "^0.11.2", "i18next": "^24.2.2", + "katex": "^0.16.22", "lucide-react": "^0.475.0", "mermaid": "^11.6.0", "minisearch": "^7.1.2", @@ -56,6 +57,7 @@ "react-number-format": "^5.4.3", "react-router-dom": "^7.3.0", "react-syntax-highlighter": "^15.6.1", + "rehype-katex": "^7.0.1", "rehype-react": "^8.0.0", "remark-gfm": "^4.0.1", "remark-math": "^6.0.0", @@ -72,6 +74,7 @@ "@stylistic/eslint-plugin-js": "^3.1.0", "@tailwindcss/vite": "^4.0.8", "@types/bun": "^1.2.3", + "@types/katex": "^0.16.7", "@types/node": "^22.13.5", "@types/react": "^19.0.10", "@types/react-dom": "^19.0.4", diff --git a/lightrag_webui/src/api/lightrag.ts b/lightrag_webui/src/api/lightrag.ts index 55046e15..24b299aa 100644 --- a/lightrag_webui/src/api/lightrag.ts +++ b/lightrag_webui/src/api/lightrag.ts @@ -40,6 +40,8 @@ export type LightragStatus = { doc_status_storage: string graph_storage: string vector_storage: string + workspace?: string + max_graph_nodes?: string } update_status?: Record core_version?: string @@ -114,6 +116,12 @@ export type DocActionResponse = { message: string } +export type DeleteDocResponse = { + status: 'deletion_started' | 'busy' | 'not_allowed' + message: string + doc_id: string +} + export type DocStatus = 'pending' | 'processing' | 'processed' | 'failed' export type DocStatusResponse = { @@ -515,6 +523,13 @@ export const clearCache = async (modes?: string[]): Promise<{ return response.data } +export const deleteDocuments = async (docIds: string[], deleteFile: boolean = false): Promise => { + const response = await axiosInstance.delete('/documents/delete_document', { + data: { doc_ids: docIds, delete_file: deleteFile } + }) + return response.data +} + export const getAuthStatus = async (): Promise => { try { // Add a timeout to the request to prevent hanging diff --git a/lightrag_webui/src/components/documents/DeleteDocumentsDialog.tsx b/lightrag_webui/src/components/documents/DeleteDocumentsDialog.tsx new file mode 100644 index 00000000..acbf011e --- /dev/null +++ b/lightrag_webui/src/components/documents/DeleteDocumentsDialog.tsx @@ -0,0 +1,182 @@ +import { useState, useCallback, useEffect } from 'react' +import Button from '@/components/ui/Button' +import { + Dialog, + DialogContent, + DialogDescription, + DialogHeader, + DialogTitle, + DialogTrigger, + DialogFooter +} from '@/components/ui/Dialog' +import Input from '@/components/ui/Input' +import { toast } from 'sonner' +import { errorMessage } from '@/lib/utils' +import { deleteDocuments } from '@/api/lightrag' + +import { TrashIcon, AlertTriangleIcon } from 'lucide-react' +import { useTranslation } from 'react-i18next' + +// Simple Label component +const Label = ({ + htmlFor, + className, + children, + ...props +}: React.LabelHTMLAttributes) => ( + +) + +interface DeleteDocumentsDialogProps { + selectedDocIds: string[] + totalCompletedCount: number + onDocumentsDeleted?: () => Promise +} + +export default function DeleteDocumentsDialog({ selectedDocIds, totalCompletedCount, onDocumentsDeleted }: DeleteDocumentsDialogProps) { + const { t } = useTranslation() + const [open, setOpen] = useState(false) + const [confirmText, setConfirmText] = useState('') + const [deleteFile, setDeleteFile] = useState(false) + const [isDeleting, setIsDeleting] = useState(false) + const isConfirmEnabled = confirmText.toLowerCase() === 'yes' && !isDeleting + + // Reset state when dialog closes + useEffect(() => { + if (!open) { + setConfirmText('') + setDeleteFile(false) + setIsDeleting(false) + } + }, [open]) + + const handleDelete = useCallback(async () => { + if (!isConfirmEnabled || selectedDocIds.length === 0) return + + // Check if user is trying to delete all completed documents + if (selectedDocIds.length === totalCompletedCount && totalCompletedCount > 0) { + toast.error(t('documentPanel.deleteDocuments.cannotDeleteAll')) + return + } + + setIsDeleting(true) + try { + const result = await deleteDocuments(selectedDocIds, deleteFile) + + if (result.status === 'deletion_started') { + toast.success(t('documentPanel.deleteDocuments.success', { count: selectedDocIds.length })) + } else if (result.status === 'busy') { + toast.error(t('documentPanel.deleteDocuments.busy')) + setConfirmText('') + setIsDeleting(false) + return + } else if (result.status === 'not_allowed') { + toast.error(t('documentPanel.deleteDocuments.notAllowed')) + setConfirmText('') + setIsDeleting(false) + return + } else { + toast.error(t('documentPanel.deleteDocuments.failed', { message: result.message })) + setConfirmText('') + setIsDeleting(false) + return + } + + // Refresh document list if provided + if (onDocumentsDeleted) { + onDocumentsDeleted().catch(console.error) + } + + // Close dialog after successful operation + setOpen(false) + } catch (err) { + toast.error(t('documentPanel.deleteDocuments.error', { error: errorMessage(err) })) + setConfirmText('') + } finally { + setIsDeleting(false) + } + }, [isConfirmEnabled, selectedDocIds, totalCompletedCount, deleteFile, setOpen, t, onDocumentsDeleted]) + + return ( + + + + + e.preventDefault()}> + + + + {t('documentPanel.deleteDocuments.title')} + + + {t('documentPanel.deleteDocuments.description', { count: selectedDocIds.length })} + + + +
+ {t('documentPanel.deleteDocuments.warning')} +
+ +
+ {t('documentPanel.deleteDocuments.confirm', { count: selectedDocIds.length })} +
+ +
+
+ + ) => setConfirmText(e.target.value)} + placeholder={t('documentPanel.deleteDocuments.confirmPlaceholder')} + className="w-full" + disabled={isDeleting} + /> +
+ +
+ setDeleteFile(e.target.checked)} + disabled={isDeleting} + className="h-4 w-4 text-red-600 focus:ring-red-500 border-gray-300 rounded" + /> + +
+
+ + + + + + + + ) +} diff --git a/lightrag_webui/src/components/documents/DeselectDocumentsDialog.tsx b/lightrag_webui/src/components/documents/DeselectDocumentsDialog.tsx new file mode 100644 index 00000000..ecb8af3e --- /dev/null +++ b/lightrag_webui/src/components/documents/DeselectDocumentsDialog.tsx @@ -0,0 +1,74 @@ +import { useState, useCallback, useEffect } from 'react' +import Button from '@/components/ui/Button' +import { + Dialog, + DialogContent, + DialogDescription, + DialogHeader, + DialogTitle, + DialogTrigger, + DialogFooter +} from '@/components/ui/Dialog' + +import { XIcon, AlertCircleIcon } from 'lucide-react' +import { useTranslation } from 'react-i18next' + +interface DeselectDocumentsDialogProps { + selectedCount: number + onDeselect: () => void +} + +export default function DeselectDocumentsDialog({ selectedCount, onDeselect }: DeselectDocumentsDialogProps) { + const { t } = useTranslation() + const [open, setOpen] = useState(false) + + // Reset state when dialog closes + useEffect(() => { + if (!open) { + // No state to reset for this simple dialog + } + }, [open]) + + const handleDeselect = useCallback(() => { + onDeselect() + setOpen(false) + }, [onDeselect, setOpen]) + + return ( + + + + + e.preventDefault()}> + + + + {t('documentPanel.deselectDocuments.title')} + + + {t('documentPanel.deselectDocuments.description', { count: selectedCount })} + + + + + + + + + + ) +} diff --git a/lightrag_webui/src/components/graph/Settings.tsx b/lightrag_webui/src/components/graph/Settings.tsx index 758f6d58..37a36305 100644 --- a/lightrag_webui/src/components/graph/Settings.tsx +++ b/lightrag_webui/src/components/graph/Settings.tsx @@ -1,4 +1,4 @@ -import { useState, useCallback} from 'react' +import { useState, useCallback, useEffect} from 'react' import { Popover, PopoverContent, PopoverTrigger } from '@/components/ui/Popover' import Checkbox from '@/components/ui/Checkbox' import Button from '@/components/ui/Button' @@ -62,6 +62,10 @@ const LabeledNumberInput = ({ // Create unique ID using the label text converted to lowercase with spaces removed const id = `input-${label.toLowerCase().replace(/\s+/g, '-')}`; + useEffect(() => { + setCurrentValue(value) + }, [value]) + const onValueChange = useCallback( (e: React.ChangeEvent) => { const text = e.target.value.trim() @@ -154,6 +158,7 @@ export default function Settings() { const maxEdgeSize = useSettingsStore.use.maxEdgeSize() const graphQueryMaxDepth = useSettingsStore.use.graphQueryMaxDepth() const graphMaxNodes = useSettingsStore.use.graphMaxNodes() + const backendMaxGraphNodes = useSettingsStore.use.backendMaxGraphNodes() const graphLayoutMaxIterations = useSettingsStore.use.graphLayoutMaxIterations() const enableHealthCheck = useSettingsStore.use.enableHealthCheck() @@ -213,14 +218,10 @@ export default function Settings() { }, []) const setGraphMaxNodes = useCallback((nodes: number) => { - if (nodes < 1 || nodes > 1000) return - useSettingsStore.setState({ graphMaxNodes: nodes }) - const currentLabel = useSettingsStore.getState().queryLabel - useSettingsStore.getState().setQueryLabel('') - setTimeout(() => { - useSettingsStore.getState().setQueryLabel(currentLabel) - }, 300) - }, []) + const maxLimit = backendMaxGraphNodes || 1000 + if (nodes < 1 || nodes > maxLimit) return + useSettingsStore.getState().setGraphMaxNodes(nodes, true) + }, [backendMaxGraphNodes]) const setGraphLayoutMaxIterations = useCallback((iterations: number) => { if (iterations < 1) return @@ -360,11 +361,11 @@ export default function Settings() { onEditFinished={setGraphQueryMaxDepth} /> { // Remove isComplete prop const { t } = useTranslation() + const { theme } = useTheme() + const [katexPlugin, setKatexPlugin] = useState(null) + + // Load KaTeX dynamically + useEffect(() => { + const loadKaTeX = async () => { + try { + const [{ default: rehypeKatex }] = await Promise.all([ + import('rehype-katex'), + import('katex/dist/katex.min.css') + ]) + setKatexPlugin(() => rehypeKatex) + } catch (error) { + console.error('Failed to load KaTeX:', error) + } + } + loadKaTeX() + }, []) const handleCopyMarkdown = useCallback(async () => { if (message.content) { try { @@ -53,9 +71,19 @@ export const ChatMessage = ({ message }: { message: MessageWithError }) => { // >
({ diff --git a/lightrag_webui/src/components/status/StatusCard.tsx b/lightrag_webui/src/components/status/StatusCard.tsx index c9e64db9..afdd554f 100644 --- a/lightrag_webui/src/components/status/StatusCard.tsx +++ b/lightrag_webui/src/components/status/StatusCard.tsx @@ -56,6 +56,10 @@ const StatusCard = ({ status }: { status: LightragStatus | null }) => { {status.configuration.graph_storage} {t('graphPanel.statusCard.vectorStorage')}: {status.configuration.vector_storage} + {t('graphPanel.statusCard.workspace')}: + {status.configuration.workspace || '-'} + {t('graphPanel.statusCard.maxGraphNodes')}: + {status.configuration.max_graph_nodes || '-'}
diff --git a/lightrag_webui/src/features/DocumentManager.tsx b/lightrag_webui/src/features/DocumentManager.tsx index f1b25472..a4149962 100644 --- a/lightrag_webui/src/features/DocumentManager.tsx +++ b/lightrag_webui/src/features/DocumentManager.tsx @@ -13,8 +13,11 @@ import { } from '@/components/ui/Table' import { Card, CardHeader, CardTitle, CardContent, CardDescription } from '@/components/ui/Card' import EmptyCard from '@/components/ui/EmptyCard' +import Checkbox from '@/components/ui/Checkbox' import UploadDocumentsDialog from '@/components/documents/UploadDocumentsDialog' import ClearDocumentsDialog from '@/components/documents/ClearDocumentsDialog' +import DeleteDocumentsDialog from '@/components/documents/DeleteDocumentsDialog' +import DeselectDocumentsDialog from '@/components/documents/DeselectDocumentsDialog' import { getDocuments, scanNewDocuments, DocsStatusesResponse, DocStatus, DocStatusResponse } from '@/api/lightrag' import { errorMessage } from '@/lib/utils' @@ -173,6 +176,25 @@ export default function DocumentManager() { // State for document status filter const [statusFilter, setStatusFilter] = useState('all'); + // State for document selection + const [selectedDocIds, setSelectedDocIds] = useState([]) + const isSelectionMode = selectedDocIds.length > 0 + + // Handle checkbox change for individual documents + const handleDocumentSelect = useCallback((docId: string, checked: boolean) => { + setSelectedDocIds(prev => { + if (checked) { + return [...prev, docId] + } else { + return prev.filter(id => id !== docId) + } + }) + }, []) + + // Handle deselect all documents + const handleDeselectAll = useCallback(() => { + setSelectedDocIds([]) + }, []) // Handle sort column click const handleSort = (field: SortField) => { @@ -463,6 +485,12 @@ export default function DocumentManager() { prevStatusCounts.current = newStatusCounts }, [docs]); + // Handle documents deleted callback + const handleDocumentsDeleted = useCallback(async () => { + setSelectedDocIds([]) + await fetchDocuments() + }, [fetchDocuments]) + // Add dependency on sort state to re-render when sort changes useEffect(() => { // This effect ensures the component re-renders when sort state changes @@ -499,7 +527,21 @@ export default function DocumentManager() {
- + {isSelectionMode && ( + + )} + {isSelectionMode ? ( + + ) : ( + + )} + + {t('documentPanel.documentManager.columns.select')} + @@ -718,6 +763,14 @@ export default function DocumentManager() { {new Date(doc.updated_at).toLocaleString()} + + handleDocumentSelect(doc.id, checked === true)} + // disabled={doc.status !== 'processed'} + className="mx-auto" + /> + ))} diff --git a/lightrag_webui/src/locales/ar.json b/lightrag_webui/src/locales/ar.json index ee274c9d..7751c05c 100644 --- a/lightrag_webui/src/locales/ar.json +++ b/lightrag_webui/src/locales/ar.json @@ -56,6 +56,32 @@ "failed": "فشل مسح المستندات:\n{{message}}", "error": "فشل مسح المستندات:\n{{error}}" }, + "deleteDocuments": { + "button": "حذف", + "tooltip": "حذف المستندات المحددة", + "title": "حذف المستندات", + "description": "سيؤدي هذا إلى حذف المستندات المحددة نهائيًا من النظام", + "warning": "تحذير: سيؤدي هذا الإجراء إلى حذف المستندات المحددة نهائيًا ولا يمكن التراجع عنه!", + "confirm": "هل تريد حقًا حذف {{count}} مستند(ات) محدد(ة)؟", + "confirmPrompt": "اكتب 'yes' لتأكيد هذا الإجراء", + "confirmPlaceholder": "اكتب yes للتأكيد", + "confirmButton": "نعم", + "deleteFileOption": "حذف الملفات المرفوعة أيضًا", + "deleteFileTooltip": "حدد هذا الخيار لحذف الملفات المرفوعة المقابلة على الخادم أيضًا", + "success": "تم بدء تشغيل خط معالجة حذف المستندات بنجاح", + "failed": "فشل حذف المستندات:\n{{message}}", + "error": "فشل حذف المستندات:\n{{error}}", + "busy": "خط المعالجة مشغول، يرجى المحاولة مرة أخرى لاحقًا", + "notAllowed": "لا توجد صلاحية لتنفيذ هذه العملية", + "cannotDeleteAll": "لا يمكن حذف جميع المستندات. إذا كنت بحاجة لحذف جميع المستندات، يرجى استخدام ميزة مسح المستندات." + }, + "deselectDocuments": { + "button": "إلغاء التحديد", + "tooltip": "إلغاء تحديد جميع المستندات المحددة", + "title": "إلغاء تحديد المستندات", + "description": "سيؤدي هذا إلى مسح جميع المستندات المحددة ({{count}} محدد)", + "confirmButton": "إلغاء تحديد الكل" + }, "uploadDocuments": { "button": "رفع", "tooltip": "رفع المستندات", @@ -105,7 +131,8 @@ "chunks": "الأجزاء", "created": "تم الإنشاء", "updated": "تم التحديث", - "metadata": "البيانات الوصفية" + "metadata": "البيانات الوصفية", + "select": "اختيار" }, "status": { "all": "الكل", @@ -236,7 +263,9 @@ "kvStorage": "تخزين المفتاح-القيمة", "docStatusStorage": "تخزين حالة المستند", "graphStorage": "تخزين الرسم البياني", - "vectorStorage": "تخزين المتجهات" + "vectorStorage": "تخزين المتجهات", + "workspace": "مساحة العمل", + "maxGraphNodes": "الحد الأقصى لعقد الرسم البياني" }, "propertiesView": { "editProperty": "تعديل {{property}}", diff --git a/lightrag_webui/src/locales/en.json b/lightrag_webui/src/locales/en.json index 643b6f2e..726ae5b9 100644 --- a/lightrag_webui/src/locales/en.json +++ b/lightrag_webui/src/locales/en.json @@ -56,6 +56,32 @@ "failed": "Clear Documents Failed:\n{{message}}", "error": "Clear Documents Failed:\n{{error}}" }, + "deleteDocuments": { + "button": "Delete", + "tooltip": "Delete selected documents", + "title": "Delete Documents", + "description": "This will permanently delete the selected documents from the system", + "warning": "WARNING: This action will permanently delete the selected documents and cannot be undone!", + "confirm": "Do you really want to delete {{count}} selected document(s)?", + "confirmPrompt": "Type 'yes' to confirm this action", + "confirmPlaceholder": "Type yes to confirm", + "confirmButton": "YES", + "deleteFileOption": "Also delete uploaded files", + "deleteFileTooltip": "Check this option to also delete the corresponding uploaded files on the server", + "success": "Document deletion pipeline started successfully", + "failed": "Delete Documents Failed:\n{{message}}", + "error": "Delete Documents Failed:\n{{error}}", + "busy": "Pipeline is busy, please try again later", + "notAllowed": "No permission to perform this operation", + "cannotDeleteAll": "Cannot delete all documents. If you need to delete all documents, please use the Clear Documents feature." + }, + "deselectDocuments": { + "button": "Deselect", + "tooltip": "Deselect all selected documents", + "title": "Deselect Documents", + "description": "This will clear all selected documents ({{count}} selected)", + "confirmButton": "Deselect All" + }, "uploadDocuments": { "button": "Upload", "tooltip": "Upload documents", @@ -105,7 +131,8 @@ "chunks": "Chunks", "created": "Created", "updated": "Updated", - "metadata": "Metadata" + "metadata": "Metadata", + "select": "Select" }, "status": { "all": "All", @@ -236,7 +263,9 @@ "kvStorage": "KV Storage", "docStatusStorage": "Doc Status Storage", "graphStorage": "Graph Storage", - "vectorStorage": "Vector Storage" + "vectorStorage": "Vector Storage", + "workspace": "Workspace", + "maxGraphNodes": "Max Graph Nodes" }, "propertiesView": { "editProperty": "Edit {{property}}", diff --git a/lightrag_webui/src/locales/fr.json b/lightrag_webui/src/locales/fr.json index 65716dde..96a85fac 100644 --- a/lightrag_webui/src/locales/fr.json +++ b/lightrag_webui/src/locales/fr.json @@ -56,6 +56,32 @@ "failed": "Échec de l'effacement des documents :\n{{message}}", "error": "Échec de l'effacement des documents :\n{{error}}" }, + "deleteDocuments": { + "button": "Supprimer", + "tooltip": "Supprimer les documents sélectionnés", + "title": "Supprimer les documents", + "description": "Cette action supprimera définitivement les documents sélectionnés du système", + "warning": "ATTENTION : Cette action supprimera définitivement les documents sélectionnés et ne peut pas être annulée !", + "confirm": "Voulez-vous vraiment supprimer {{count}} document(s) sélectionné(s) ?", + "confirmPrompt": "Tapez 'yes' pour confirmer cette action", + "confirmPlaceholder": "Tapez yes pour confirmer", + "confirmButton": "OUI", + "deleteFileOption": "Supprimer également les fichiers téléchargés", + "deleteFileTooltip": "Cochez cette option pour supprimer également les fichiers téléchargés correspondants sur le serveur", + "success": "Pipeline de suppression de documents démarré avec succès", + "failed": "Échec de la suppression des documents :\n{{message}}", + "error": "Échec de la suppression des documents :\n{{error}}", + "busy": "Le pipeline est occupé, veuillez réessayer plus tard", + "notAllowed": "Aucune autorisation pour effectuer cette opération", + "cannotDeleteAll": "Impossible de supprimer tous les documents. Si vous devez supprimer tous les documents, veuillez utiliser la fonction Effacer les documents." + }, + "deselectDocuments": { + "button": "Désélectionner", + "tooltip": "Désélectionner tous les documents sélectionnés", + "title": "Désélectionner les documents", + "description": "Cette action effacera tous les documents sélectionnés ({{count}} sélectionnés)", + "confirmButton": "Tout désélectionner" + }, "uploadDocuments": { "button": "Télécharger", "tooltip": "Télécharger des documents", @@ -105,7 +131,8 @@ "chunks": "Fragments", "created": "Créé", "updated": "Mis à jour", - "metadata": "Métadonnées" + "metadata": "Métadonnées", + "select": "Sélectionner" }, "status": { "all": "Tous", @@ -236,7 +263,9 @@ "kvStorage": "Stockage clé-valeur", "docStatusStorage": "Stockage de l'état des documents", "graphStorage": "Stockage du graphe", - "vectorStorage": "Stockage vectoriel" + "vectorStorage": "Stockage vectoriel", + "workspace": "Espace de travail", + "maxGraphNodes": "Nombre maximum de nœuds du graphe" }, "propertiesView": { "editProperty": "Modifier {{property}}", diff --git a/lightrag_webui/src/locales/zh.json b/lightrag_webui/src/locales/zh.json index 8f776de8..fa72ba1f 100644 --- a/lightrag_webui/src/locales/zh.json +++ b/lightrag_webui/src/locales/zh.json @@ -56,6 +56,32 @@ "failed": "清空文档失败：\n{{message}}", "error": "清空文档失败：\n{{error}}" }, + "deleteDocuments": { + "button": "删除", + "tooltip": "删除选中的文档", + "title": "删除文档", + "description": "此操作将永久删除选中的文档", + "warning": "警告：此操作将永久删除选中的文档，无法恢复！", + "confirm": "确定要删除 {{count}} 个选中的文档吗？", + "confirmPrompt": "请输入 yes 确认操作", + "confirmPlaceholder": "输入 yes 确认", + "confirmButton": "确定", + "deleteFileOption": "同时删除上传文件", + "deleteFileTooltip": "选中此选项将同时删除服务器上对应的上传文件", + "success": "文档删除流水线启动成功", + "failed": "删除文档失败：\n{{message}}", + "error": "删除文档失败：\n{{error}}", + "busy": "流水线被占用，请稍后再试", + "notAllowed": "没有操作权限", + "cannotDeleteAll": "无法删除所有文档。如确实需要删除所有文档请使用清空文档功能。" + }, + "deselectDocuments": { + "button": "取消选择", + "tooltip": "取消选择所有文档", + "title": "取消选择文档", + "description": "此操作将清除所有选中的文档（已选择 {{count}} 个）", + "confirmButton": "取消全部选择" + }, "uploadDocuments": { "button": "上传", "tooltip": "上传文档", @@ -105,7 +131,8 @@ "chunks": "分块", "created": "创建时间", "updated": "更新时间", - "metadata": "元数据" + "metadata": "元数据", + "select": "选择" }, "status": { "all": "全部", @@ -236,7 +263,9 @@ "kvStorage": "KV存储", "docStatusStorage": "文档状态存储", "graphStorage": "图存储", - "vectorStorage": "向量存储" + "vectorStorage": "向量存储", + "workspace": "工作空间", + "maxGraphNodes": "最大图节点数" }, "propertiesView": { "editProperty": "编辑{{property}}", diff --git a/lightrag_webui/src/locales/zh_TW.json b/lightrag_webui/src/locales/zh_TW.json index 8671f0ed..40480fd5 100644 --- a/lightrag_webui/src/locales/zh_TW.json +++ b/lightrag_webui/src/locales/zh_TW.json @@ -56,6 +56,32 @@ "failed": "清空文件失敗：\n{{message}}", "error": "清空文件失敗：\n{{error}}" }, + "deleteDocuments": { + "button": "刪除", + "tooltip": "刪除選取的文件", + "title": "刪除文件", + "description": "此操作將永久刪除選取的文件", + "warning": "警告：此操作將永久刪除選取的文件，無法復原！", + "confirm": "確定要刪除 {{count}} 個選取的文件嗎？", + "confirmPrompt": "請輸入 yes 確認操作", + "confirmPlaceholder": "輸入 yes 以確認", + "confirmButton": "確定", + "deleteFileOption": "同時刪除上傳檔案", + "deleteFileTooltip": "選取此選項將同時刪除伺服器上對應的上傳檔案", + "success": "文件刪除流水線啟動成功", + "failed": "刪除文件失敗：\n{{message}}", + "error": "刪除文件失敗：\n{{error}}", + "busy": "pipeline 被佔用，請稍後再試", + "notAllowed": "沒有操作權限", + "cannotDeleteAll": "無法刪除所有文件。如確實需要刪除所有文件請使用清空文件功能。" + }, + "deselectDocuments": { + "button": "取消選取", + "tooltip": "取消選取所有文件", + "title": "取消選取文件", + "description": "此操作將清除所有選取的文件（已選取 {{count}} 個）", + "confirmButton": "取消全部選取" + }, "uploadDocuments": { "button": "上傳", "tooltip": "上傳文件", @@ -105,7 +131,8 @@ "chunks": "分塊", "created": "建立時間", "updated": "更新時間", - "metadata": "元資料" + "metadata": "元資料", + "select": "選擇" }, "status": { "all": "全部", @@ -236,7 +263,9 @@ "kvStorage": "KV 儲存", "docStatusStorage": "文件狀態儲存", "graphStorage": "圖形儲存", - "vectorStorage": "向量儲存" + "vectorStorage": "向量儲存", + "workspace": "工作空間", + "maxGraphNodes": "最大圖形節點數" }, "propertiesView": { "editProperty": "編輯{{property}}", diff --git a/lightrag_webui/src/stores/settings.ts b/lightrag_webui/src/stores/settings.ts index fb3246ea..203502dc 100644 --- a/lightrag_webui/src/stores/settings.ts +++ b/lightrag_webui/src/stores/settings.ts @@ -36,7 +36,10 @@ interface SettingsState { setGraphQueryMaxDepth: (depth: number) => void graphMaxNodes: number - setGraphMaxNodes: (nodes: number) => void + setGraphMaxNodes: (nodes: number, triggerRefresh?: boolean) => void + + backendMaxGraphNodes: number | null + setBackendMaxGraphNodes: (maxNodes: number | null) => void graphLayoutMaxIterations: number setGraphLayoutMaxIterations: (iterations: number) => void @@ -90,6 +93,7 @@ const useSettingsStoreBase = create()( graphQueryMaxDepth: 3, graphMaxNodes: 1000, + backendMaxGraphNodes: null, graphLayoutMaxIterations: 15, queryLabel: defaultQueryLabel, @@ -143,7 +147,27 @@ const useSettingsStoreBase = create()( setGraphQueryMaxDepth: (depth: number) => set({ graphQueryMaxDepth: depth }), - setGraphMaxNodes: (nodes: number) => set({ graphMaxNodes: nodes }), + setGraphMaxNodes: (nodes: number, triggerRefresh: boolean = false) => { + const state = useSettingsStore.getState(); + if (state.graphMaxNodes === nodes) { + return; + } + + if (triggerRefresh) { + const currentLabel = state.queryLabel; + // Atomically update both the node count and the query label to trigger a refresh. + set({ graphMaxNodes: nodes, queryLabel: '' }); + + // Restore the label after a short delay. + setTimeout(() => { + set({ queryLabel: currentLabel }); + }, 300); + } else { + set({ graphMaxNodes: nodes }); + } + }, + + setBackendMaxGraphNodes: (maxNodes: number | null) => set({ backendMaxGraphNodes: maxNodes }), setMinEdgeSize: (size: number) => set({ minEdgeSize: size }), @@ -168,7 +192,7 @@ const useSettingsStoreBase = create()( { name: 'settings-storage', storage: createJSONStorage(() => localStorage), - version: 13, + version: 14, migrate: (state: any, version: number) => { if (version < 2) { state.showEdgeLabel = false @@ -232,6 +256,10 @@ const useSettingsStoreBase = create()( state.querySettings.user_prompt = '' } } + if (version < 14) { + // Add backendMaxGraphNodes field for older versions + state.backendMaxGraphNodes = null + } return state } } diff --git a/lightrag_webui/src/stores/state.ts b/lightrag_webui/src/stores/state.ts index 3bc4560e..c73b3bcd 100644 --- a/lightrag_webui/src/stores/state.ts +++ b/lightrag_webui/src/stores/state.ts @@ -1,6 +1,7 @@ import { create } from 'zustand' import { createSelectors } from '@/lib/utils' import { checkHealth, LightragStatus } from '@/api/lightrag' +import { useSettingsStore } from './settings' interface BackendState { health: boolean @@ -58,6 +59,25 @@ const useBackendStateStoreBase = create()((set) => ({ ); } + // Extract and store backend max graph nodes limit + if (health.configuration?.max_graph_nodes) { + const maxNodes = parseInt(health.configuration.max_graph_nodes, 10) + if (!isNaN(maxNodes) && maxNodes > 0) { + const currentBackendMaxNodes = useSettingsStore.getState().backendMaxGraphNodes + + // Only update if the backend limit has actually changed + if (currentBackendMaxNodes !== maxNodes) { + useSettingsStore.getState().setBackendMaxGraphNodes(maxNodes) + + // Auto-adjust current graphMaxNodes if it exceeds the new backend limit + const currentMaxNodes = useSettingsStore.getState().graphMaxNodes + if (currentMaxNodes > maxNodes) { + useSettingsStore.getState().setGraphMaxNodes(maxNodes, true) + } + } + } + } + set({ health: true, message: null, diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..2bb1f288 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,96 @@ +[build-system] +requires = ["setuptools>=64", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "lightrag-hku" +dynamic = ["version"] +authors = [ + {name = "Zirui Guo"} +] +description = "LightRAG: Simple and Fast Retrieval-Augmented Generation" +readme = "README.md" +license = {text = "MIT"} +requires-python = ">=3.10" +classifiers = [ + "Development Status :: 4 - Beta", + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + "Intended Audience :: Developers", + "Topic :: Software Development :: Libraries :: Python Modules", +] +dependencies = [ + "aiohttp", + "configparser", + "dotenv", + "future", + "numpy", + "pandas>=2.0.0", + "pipmaster", + "pydantic", + "python-dotenv", + "pyuca", + "setuptools", + "tenacity", + "tiktoken", + "xlsxwriter>=3.1.0", +] + +[project.optional-dependencies] +api = [ + # Core dependencies + "aiohttp", + "configparser", + "dotenv", + "future", + "numpy", + "openai", + "pandas>=2.0.0", + "pipmaster", + "pydantic", + "python-dotenv", + "pyuca", + "setuptools", + "tenacity", + "tiktoken", + "xlsxwriter>=3.1.0", + # API-specific dependencies + "aiofiles", + "ascii_colors", + "asyncpg", + "distro", + "fastapi", + "httpcore", + "httpx", + "jiter", + "passlib[bcrypt]", + "PyJWT", + "python-jose[cryptography]", + "python-multipart", + "pytz", + "uvicorn", +] + +[project.scripts] +lightrag-server = "lightrag.api.lightrag_server:main" +lightrag-gunicorn = "lightrag.api.run_with_gunicorn:main" + +[project.urls] +Homepage = "https://github.com/HKUDS/LightRAG" +Documentation = "https://github.com/HKUDS/LightRAG" +Repository = "https://github.com/HKUDS/LightRAG" +"Bug Tracker" = "https://github.com/HKUDS/LightRAG/issues" + +[tool.setuptools] +packages = ["lightrag"] +include-package-data = true + +[tool.setuptools.dynamic] +version = {attr = "lightrag.__version__"} + +[tool.setuptools.package-data] +lightrag = ["api/webui/**/*"] + +[tool.ruff] +target-version = "py310" diff --git a/reproduce/Step_3.py b/reproduce/Step_3.py index facb913e..f3aae83e 100644 --- a/reproduce/Step_3.py +++ b/reproduce/Step_3.py @@ -28,9 +28,10 @@ def run_queries_and_save_to_json( ): loop = always_get_an_event_loop() - with open(output_file, "a", encoding="utf-8") as result_file, open( - error_file, "a", encoding="utf-8" - ) as err_file: + with ( + open(output_file, "a", encoding="utf-8") as result_file, + open(error_file, "a", encoding="utf-8") as err_file, + ): result_file.write("[\n") first_entry = True diff --git a/reproduce/Step_3_openai_compatible.py b/reproduce/Step_3_openai_compatible.py index 885220fa..56159ee1 100644 --- a/reproduce/Step_3_openai_compatible.py +++ b/reproduce/Step_3_openai_compatible.py @@ -59,9 +59,10 @@ def run_queries_and_save_to_json( ): loop = always_get_an_event_loop() - with open(output_file, "a", encoding="utf-8") as result_file, open( - error_file, "a", encoding="utf-8" - ) as err_file: + with ( + open(output_file, "a", encoding="utf-8") as result_file, + open(error_file, "a", encoding="utf-8") as err_file, + ): result_file.write("[\n") first_entry = True diff --git a/reproduce/batch_eval.py b/reproduce/batch_eval.py new file mode 100644 index 00000000..424b4f54 --- /dev/null +++ b/reproduce/batch_eval.py @@ -0,0 +1,112 @@ +import re +import json +import jsonlines + +from openai import OpenAI + + +def batch_eval(query_file, result1_file, result2_file, output_file_path): + client = OpenAI() + + with open(query_file, "r") as f: + data = f.read() + + queries = re.findall(r"- Question \d+: (.+)", data) + + with open(result1_file, "r") as f: + answers1 = json.load(f) + answers1 = [i["result"] for i in answers1] + + with open(result2_file, "r") as f: + answers2 = json.load(f) + answers2 = [i["result"] for i in answers2] + + requests = [] + for i, (query, answer1, answer2) in enumerate(zip(queries, answers1, answers2)): + sys_prompt = """ + ---Role--- + You are an expert tasked with evaluating two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**. + """ + + prompt = f""" + You will evaluate two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**. + + - **Comprehensiveness**: How much detail does the answer provide to cover all aspects and details of the question? + - **Diversity**: How varied and rich is the answer in providing different perspectives and insights on the question? + - **Empowerment**: How well does the answer help the reader understand and make informed judgments about the topic? + + For each criterion, choose the better answer (either Answer 1 or Answer 2) and explain why. Then, select an overall winner based on these three categories. + + Here is the question: + {query} + + Here are the two answers: + + **Answer 1:** + {answer1} + + **Answer 2:** + {answer2} + + Evaluate both answers using the three criteria listed above and provide detailed explanations for each criterion. + + Output your evaluation in the following JSON format: + + {{ + "Comprehensiveness": {{ + "Winner": "[Answer 1 or Answer 2]", + "Explanation": "[Provide explanation here]" + }}, + "Diversity": {{ + "Winner": "[Answer 1 or Answer 2]", + "Explanation": "[Provide explanation here]" + }}, + "Empowerment": {{ + "Winner": "[Answer 1 or Answer 2]", + "Explanation": "[Provide explanation here]" + }}, + "Overall Winner": {{ + "Winner": "[Answer 1 or Answer 2]", + "Explanation": "[Summarize why this answer is the overall winner based on the three criteria]" + }} + }} + """ + + request_data = { + "custom_id": f"request-{i+1}", + "method": "POST", + "url": "/v1/chat/completions", + "body": { + "model": "gpt-4o-mini", + "messages": [ + {"role": "system", "content": sys_prompt}, + {"role": "user", "content": prompt}, + ], + }, + } + + requests.append(request_data) + + with jsonlines.open(output_file_path, mode="w") as writer: + for request in requests: + writer.write(request) + + print(f"Batch API requests written to {output_file_path}") + + batch_input_file = client.files.create( + file=open(output_file_path, "rb"), purpose="batch" + ) + batch_input_file_id = batch_input_file.id + + batch = client.batches.create( + input_file_id=batch_input_file_id, + endpoint="/v1/chat/completions", + completion_window="24h", + metadata={"description": "nightly eval job"}, + ) + + print(f"Batch {batch.id} has been created.") + + +if __name__ == "__main__": + batch_eval() diff --git a/setup.py b/setup.py index 14e5c56b..655e2e9e 100644 --- a/setup.py +++ b/setup.py @@ -1,107 +1,6 @@ -import setuptools -from pathlib import Path +# Minimal setup.py for backward compatibility +# Primary configuration is now in pyproject.toml +from setuptools import setup -# Reading the long description from README.md -def read_long_description(): - try: - return Path("README.md").read_text(encoding="utf-8") - except FileNotFoundError: - return "A description of LightRAG is currently unavailable." - - -# Retrieving metadata from __init__.py -def retrieve_metadata(): - vars2find = ["__author__", "__version__", "__url__"] - vars2readme = {} - try: - with open("./lightrag/__init__.py") as f: - for line in f.readlines(): - for v in vars2find: - if line.startswith(v): - line = ( - line.replace(" ", "") - .replace('"', "") - .replace("'", "") - .strip() - ) - vars2readme[v] = line.split("=")[1] - except FileNotFoundError: - raise FileNotFoundError("Metadata file './lightrag/__init__.py' not found.") - - # Checking if all required variables are found - missing_vars = [v for v in vars2find if v not in vars2readme] - if missing_vars: - raise ValueError( - f"Missing required metadata variables in __init__.py: {missing_vars}" - ) - - return vars2readme - - -# Reading dependencies from requirements.txt -def read_requirements(file_path="requirements.txt"): - deps = [] - try: - with open(file_path) as f: - deps = [ - line.strip() for line in f if line.strip() and not line.startswith("#") - ] - except FileNotFoundError: - print(f"Warning: '{file_path}' not found. No dependencies will be installed.") - return deps - - -def read_api_requirements(): - return read_requirements("lightrag/api/requirements.txt") - - -def read_extra_requirements(): - return read_requirements("lightrag/tools/lightrag_visualizer/requirements.txt") - - -metadata = retrieve_metadata() -long_description = read_long_description() -requirements = read_requirements() - -setuptools.setup( - name="lightrag-hku", - url=metadata["__url__"], - version=metadata["__version__"], - author=metadata["__author__"], - description="LightRAG: Simple and Fast Retrieval-Augmented Generation", - long_description=long_description, - long_description_content_type="text/markdown", - packages=setuptools.find_packages( - exclude=("tests*", "docs*") - ), # Automatically find packages - classifiers=[ - "Development Status :: 4 - Beta", - "Programming Language :: Python :: 3", - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", - "Intended Audience :: Developers", - "Topic :: Software Development :: Libraries :: Python Modules", - ], - python_requires=">=3.9", - install_requires=requirements, - include_package_data=True, # Includes non-code files from MANIFEST.in - project_urls={ # Additional project metadata - "Documentation": metadata.get("__url__", ""), - "Source": metadata.get("__url__", ""), - "Tracker": f"{metadata.get('__url__', '')}/issues" - if metadata.get("__url__") - else "", - }, - extras_require={ - "api": requirements + read_api_requirements(), - "tools": read_extra_requirements(), # API requirements as optional - }, - entry_points={ - "console_scripts": [ - "lightrag-server=lightrag.api.lightrag_server:main [api]", - "lightrag-gunicorn=lightrag.api.run_with_gunicorn:main [api]", - "lightrag-viewer=lightrag.tools.lightrag_visualizer.graph_visualizer:main [tools]", - ], - }, -) +setup() diff --git a/tests/test_graph_storage.py b/tests/test_graph_storage.py index fb78270d..258c8795 100644 --- a/tests/test_graph_storage.py +++ b/tests/test_graph_storage.py @@ -8,6 +8,7 @@ 支持的图存储类型包括： - NetworkXStorage - Neo4JStorage +- MongoDBStorage - PGGraphStorage """ @@ -30,6 +31,7 @@ from lightrag.kg import ( verify_storage_implementation, ) from lightrag.kg.shared_storage import initialize_share_data +from lightrag.constants import GRAPH_FIELD_SEP # 模拟的嵌入函数，返回随机向量 @@ -437,6 +439,9 @@ async def test_graph_batch_operations(storage): 5. 使用 get_nodes_edges_batch 批量获取多个节点的所有边 """ try: + chunk1_id = "1" + chunk2_id = "2" + chunk3_id = "3" # 1. 插入测试数据 # 插入节点1: 人工智能 node1_id = "人工智能" @@ -445,6 +450,7 @@ async def test_graph_batch_operations(storage): "description": "人工智能是计算机科学的一个分支，它企图了解智能的实质，并生产出一种新的能以人类智能相似的方式做出反应的智能机器。", "keywords": "AI,机器学习,深度学习", "entity_type": "技术领域", + "source_id": GRAPH_FIELD_SEP.join([chunk1_id, chunk2_id]), } print(f"插入节点1: {node1_id}") await storage.upsert_node(node1_id, node1_data) @@ -456,6 +462,7 @@ async def test_graph_batch_operations(storage): "description": "机器学习是人工智能的一个分支，它使用统计学方法让计算机系统在不被明确编程的情况下也能够学习。", "keywords": "监督学习,无监督学习,强化学习", "entity_type": "技术领域", + "source_id": GRAPH_FIELD_SEP.join([chunk2_id, chunk3_id]), } print(f"插入节点2: {node2_id}") await storage.upsert_node(node2_id, node2_data) @@ -467,6 +474,7 @@ async def test_graph_batch_operations(storage): "description": "深度学习是机器学习的一个分支，它使用多层神经网络来模拟人脑的学习过程。", "keywords": "神经网络,CNN,RNN", "entity_type": "技术领域", + "source_id": GRAPH_FIELD_SEP.join([chunk3_id]), } print(f"插入节点3: {node3_id}") await storage.upsert_node(node3_id, node3_data) @@ -498,6 +506,7 @@ async def test_graph_batch_operations(storage): "relationship": "包含", "weight": 1.0, "description": "人工智能领域包含机器学习这个子领域", + "source_id": GRAPH_FIELD_SEP.join([chunk1_id, chunk2_id]), } print(f"插入边1: {node1_id} -> {node2_id}") await storage.upsert_edge(node1_id, node2_id, edge1_data) @@ -507,6 +516,7 @@ async def test_graph_batch_operations(storage): "relationship": "包含", "weight": 1.0, "description": "机器学习领域包含深度学习这个子领域", + "source_id": GRAPH_FIELD_SEP.join([chunk2_id, chunk3_id]), } print(f"插入边2: {node2_id} -> {node3_id}") await storage.upsert_edge(node2_id, node3_id, edge2_data) @@ -516,6 +526,7 @@ async def test_graph_batch_operations(storage): "relationship": "包含", "weight": 1.0, "description": "人工智能领域包含自然语言处理这个子领域", + "source_id": GRAPH_FIELD_SEP.join([chunk3_id]), } print(f"插入边3: {node1_id} -> {node4_id}") await storage.upsert_edge(node1_id, node4_id, edge3_data) @@ -748,6 +759,76 @@ async def test_graph_batch_operations(storage): print("无向图特性验证成功：批量获取的节点边包含所有相关的边（无论方向）") + # 7. 测试 get_nodes_by_chunk_ids - 批量根据 chunk_ids 获取多个节点 + print("== 测试 get_nodes_by_chunk_ids") + + print("== 测试单个 chunk_id，匹配多个节点") + nodes = await storage.get_nodes_by_chunk_ids([chunk2_id]) + assert len(nodes) == 2, f"{chunk1_id} 应有2个节点，实际有 {len(nodes)} 个" + + has_node1 = any(node["entity_id"] == node1_id for node in nodes) + has_node2 = any(node["entity_id"] == node2_id for node in nodes) + + assert has_node1, f"节点 {node1_id} 应在返回结果中" + assert has_node2, f"节点 {node2_id} 应在返回结果中" + + print("== 测试多个 chunk_id，部分匹配多个节点") + nodes = await storage.get_nodes_by_chunk_ids([chunk2_id, chunk3_id]) + assert ( + len(nodes) == 3 + ), f"{chunk2_id}, {chunk3_id} 应有3个节点，实际有 {len(nodes)} 个" + + has_node1 = any(node["entity_id"] == node1_id for node in nodes) + has_node2 = any(node["entity_id"] == node2_id for node in nodes) + has_node3 = any(node["entity_id"] == node3_id for node in nodes) + + assert has_node1, f"节点 {node1_id} 应在返回结果中" + assert has_node2, f"节点 {node2_id} 应在返回结果中" + assert has_node3, f"节点 {node3_id} 应在返回结果中" + + # 8. 测试 get_edges_by_chunk_ids - 批量根据 chunk_ids 获取多条边 + print("== 测试 get_edges_by_chunk_ids") + + print("== 测试单个 chunk_id，匹配多条边") + edges = await storage.get_edges_by_chunk_ids([chunk2_id]) + assert len(edges) == 2, f"{chunk2_id} 应有2条边，实际有 {len(edges)} 条" + + has_edge_node1_node2 = any( + edge["source"] == node1_id and edge["target"] == node2_id for edge in edges + ) + has_edge_node2_node3 = any( + edge["source"] == node2_id and edge["target"] == node3_id for edge in edges + ) + + assert has_edge_node1_node2, f"{chunk2_id} 应包含 {node1_id} 到 {node2_id} 的边" + assert has_edge_node2_node3, f"{chunk2_id} 应包含 {node2_id} 到 {node3_id} 的边" + + print("== 测试多个 chunk_id，部分匹配多条边") + edges = await storage.get_edges_by_chunk_ids([chunk2_id, chunk3_id]) + assert ( + len(edges) == 3 + ), f"{chunk2_id}, {chunk3_id} 应有3条边，实际有 {len(edges)} 条" + + has_edge_node1_node2 = any( + edge["source"] == node1_id and edge["target"] == node2_id for edge in edges + ) + has_edge_node2_node3 = any( + edge["source"] == node2_id and edge["target"] == node3_id for edge in edges + ) + has_edge_node1_node4 = any( + edge["source"] == node1_id and edge["target"] == node4_id for edge in edges + ) + + assert ( + has_edge_node1_node2 + ), f"{chunk2_id}, {chunk3_id} 应包含 {node1_id} 到 {node2_id} 的边" + assert ( + has_edge_node2_node3 + ), f"{chunk2_id}, {chunk3_id} 应包含 {node2_id} 到 {node3_id} 的边" + assert ( + has_edge_node1_node4 + ), f"{chunk2_id}, {chunk3_id} 应包含 {node1_id} 到 {node4_id} 的边" + print("\n批量操作测试完成") return True