Merge pull request #1782 from HKUDS/rerank
Refactor the token control system
This commit is contained in:
Daniel.y
GitHub
commit
b44c8d46a5
71 changed files with 1235 additions and 756 deletions
28
README-zh.md
28
README-zh.md
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@ -293,26 +293,19 @@ class QueryParam:
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top_k: int = int(os.getenv("TOP_K", "60"))
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"""Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode."""
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chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", "5"))
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"""Number of text chunks to retrieve initially from vector search.
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chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", "10"))
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"""Number of text chunks to retrieve initially from vector search and keep after reranking.
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If None, defaults to top_k value.
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"""
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chunk_rerank_top_k: int = int(os.getenv("CHUNK_RERANK_TOP_K", "5"))
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"""Number of text chunks to keep after reranking.
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If None, keeps all chunks returned from initial retrieval.
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"""
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max_entity_tokens: int = int(os.getenv("MAX_ENTITY_TOKENS", "10000"))
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"""Maximum number of tokens allocated for entity context in unified token control system."""
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max_token_for_text_unit: int = int(os.getenv("MAX_TOKEN_TEXT_CHUNK", "4000"))
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"""Maximum number of tokens allowed for each retrieved text chunk."""
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max_relation_tokens: int = int(os.getenv("MAX_RELATION_TOKENS", "10000"))
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"""Maximum number of tokens allocated for relationship context in unified token control system."""
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max_token_for_global_context: int = int(
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os.getenv("MAX_TOKEN_RELATION_DESC", "4000")
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)
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"""Maximum number of tokens allocated for relationship descriptions in global retrieval."""
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max_token_for_local_context: int = int(os.getenv("MAX_TOKEN_ENTITY_DESC", "4000"))
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"""Maximum number of tokens allocated for entity descriptions in local retrieval."""
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max_total_tokens: int = int(os.getenv("MAX_TOTAL_TOKENS", "32000"))
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"""Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt)."""
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hl_keywords: list[str] = field(default_factory=list)
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"""List of high-level keywords to prioritize in retrieval."""
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@ -341,6 +334,11 @@ class QueryParam:
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"""User-provided prompt for the query.
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If proivded, this will be use instead of the default vaulue from prompt template.
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"""
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enable_rerank: bool = True
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"""Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued.
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Default is True to enable reranking when rerank model is available.
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"""
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```
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> top_k的默认值可以通过环境变量TOP_K更改。
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28
README.md
28
README.md
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@ -300,26 +300,19 @@ class QueryParam:
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top_k: int = int(os.getenv("TOP_K", "60"))
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"""Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode."""
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chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", "5"))
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"""Number of text chunks to retrieve initially from vector search.
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chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", "10"))
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"""Number of text chunks to retrieve initially from vector search and keep after reranking.
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If None, defaults to top_k value.
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"""
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chunk_rerank_top_k: int = int(os.getenv("CHUNK_RERANK_TOP_K", "5"))
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"""Number of text chunks to keep after reranking.
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If None, keeps all chunks returned from initial retrieval.
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"""
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max_entity_tokens: int = int(os.getenv("MAX_ENTITY_TOKENS", "10000"))
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"""Maximum number of tokens allocated for entity context in unified token control system."""
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max_token_for_text_unit: int = int(os.getenv("MAX_TOKEN_TEXT_CHUNK", "4000"))
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"""Maximum number of tokens allowed for each retrieved text chunk."""
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max_relation_tokens: int = int(os.getenv("MAX_RELATION_TOKENS", "10000"))
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"""Maximum number of tokens allocated for relationship context in unified token control system."""
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max_token_for_global_context: int = int(
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os.getenv("MAX_TOKEN_RELATION_DESC", "4000")
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)
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"""Maximum number of tokens allocated for relationship descriptions in global retrieval."""
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max_token_for_local_context: int = int(os.getenv("MAX_TOKEN_ENTITY_DESC", "4000"))
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"""Maximum number of tokens allocated for entity descriptions in local retrieval."""
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max_total_tokens: int = int(os.getenv("MAX_TOTAL_TOKENS", "32000"))
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"""Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt)."""
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conversation_history: list[dict[str, str]] = field(default_factory=list)
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"""Stores past conversation history to maintain context.
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@ -342,6 +335,11 @@ class QueryParam:
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"""User-provided prompt for the query.
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If proivded, this will be use instead of the default vaulue from prompt template.
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"""
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enable_rerank: bool = True
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"""Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued.
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Default is True to enable reranking when rerank model is available.
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"""
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```
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> default value of Top_k can be change by environment variables TOP_K.
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@ -1,36 +1,24 @@
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# Rerank Integration in LightRAG
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# Rerank Integration Guide
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This document explains how to configure and use the rerank functionality in LightRAG to improve retrieval quality.
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LightRAG supports reranking functionality to improve retrieval quality by re-ordering documents based on their relevance to the query. Reranking is now controlled per query via the `enable_rerank` parameter (default: True).
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## Overview
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Reranking is an optional feature that improves the quality of retrieved documents by re-ordering them based on their relevance to the query. This is particularly useful when you want higher precision in document retrieval across all query modes (naive, local, global, hybrid, mix).
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## Architecture
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The rerank integration follows a simplified design pattern:
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- **Single Function Configuration**: All rerank settings (model, API keys, top_k, etc.) are contained within the rerank function
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- **Async Processing**: Non-blocking rerank operations
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- **Error Handling**: Graceful fallback to original results
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- **Optional Feature**: Can be enabled/disabled via configuration
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- **Code Reuse**: Single generic implementation for Jina/Cohere compatible APIs
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## Configuration
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## Quick Start
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### Environment Variables
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Set this variable in your `.env` file or environment:
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Set these variables in your `.env` file or environment for rerank model configuration:
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```bash
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# Enable/disable reranking
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ENABLE_RERANK=True
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# Rerank model configuration (required when enable_rerank=True in queries)
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RERANK_MODEL=BAAI/bge-reranker-v2-m3
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RERANK_BINDING_HOST=https://api.your-provider.com/v1/rerank
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RERANK_BINDING_API_KEY=your_api_key_here
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```
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### Programmatic Configuration
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```python
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from lightrag import LightRAG
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from lightrag import LightRAG, QueryParam
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from lightrag.rerank import custom_rerank, RerankModel
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# Method 1: Using a custom rerank function with all settings included
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@ -49,8 +37,19 @@ rag = LightRAG(
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working_dir="./rag_storage",
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llm_model_func=your_llm_func,
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embedding_func=your_embedding_func,
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enable_rerank=True,
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rerank_model_func=my_rerank_func,
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rerank_model_func=my_rerank_func, # Configure rerank function
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)
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# Query with rerank enabled (default)
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result = await rag.aquery(
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"your query",
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param=QueryParam(enable_rerank=True) # Control rerank per query
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)
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# Query with rerank disabled
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result = await rag.aquery(
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"your query",
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param=QueryParam(enable_rerank=False)
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)
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# Method 2: Using RerankModel wrapper
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@ -67,9 +66,17 @@ rag = LightRAG(
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working_dir="./rag_storage",
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llm_model_func=your_llm_func,
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embedding_func=your_embedding_func,
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enable_rerank=True,
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rerank_model_func=rerank_model.rerank,
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)
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# Control rerank per query
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result = await rag.aquery(
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"your query",
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param=QueryParam(
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enable_rerank=True, # Enable rerank for this query
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chunk_top_k=5 # Number of chunks to keep after reranking
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)
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)
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```
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## Supported Providers
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@ -164,7 +171,6 @@ async def main():
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working_dir="./rag_storage",
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llm_model_func=gpt_4o_mini_complete,
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embedding_func=openai_embedding,
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enable_rerank=True,
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rerank_model_func=my_rerank_func,
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)
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@ -180,7 +186,7 @@ async def main():
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# Query with rerank (automatically applied)
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result = await rag.aquery(
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"Your question here",
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param=QueryParam(mode="hybrid", top_k=5) # This top_k is passed to rerank function
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param=QueryParam(enable_rerank=True) # This top_k is passed to rerank function
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)
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print(result)
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148
env.example
148
env.example
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@ -1,6 +1,8 @@
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### This is sample file of .env
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###########################
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### Server Configuration
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###########################
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HOST=0.0.0.0
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PORT=9621
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WEBUI_TITLE='My Graph KB'
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@ -9,29 +11,17 @@ OLLAMA_EMULATING_MODEL_TAG=latest
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# WORKERS=2
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# CORS_ORIGINS=http://localhost:3000,http://localhost:8080
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### Login Configuration
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# AUTH_ACCOUNTS='admin:admin123,user1:pass456'
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# TOKEN_SECRET=Your-Key-For-LightRAG-API-Server
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# TOKEN_EXPIRE_HOURS=48
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# GUEST_TOKEN_EXPIRE_HOURS=24
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# JWT_ALGORITHM=HS256
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### API-Key to access LightRAG Server API
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# LIGHTRAG_API_KEY=your-secure-api-key-here
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# WHITELIST_PATHS=/health,/api/*
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### Optional SSL Configuration
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# SSL=true
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# SSL_CERTFILE=/path/to/cert.pem
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# SSL_KEYFILE=/path/to/key.pem
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### Directory Configuration (defaults to current working directory)
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### Should not be set if deploy by docker (Set by Dockerfile instead of .env)
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### Default value is ./inputs and ./rag_storage
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# INPUT_DIR=<absolute_path_for_doc_input_dir>
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# WORKING_DIR=<absolute_path_for_working_dir>
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### Max nodes return from grap retrieval
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### Max nodes return from grap retrieval in webui
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# MAX_GRAPH_NODES=1000
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### Logging level
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@ -42,65 +32,97 @@ OLLAMA_EMULATING_MODEL_TAG=latest
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### Logfile location (defaults to current working directory)
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# LOG_DIR=/path/to/log/directory
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### RAG Configuration
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### Chunk size for document splitting, 500~1500 is recommended
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# CHUNK_SIZE=1200
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# CHUNK_OVERLAP_SIZE=100
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#####################################
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### Login and API-Key Configuration
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#####################################
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# AUTH_ACCOUNTS='admin:admin123,user1:pass456'
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# TOKEN_SECRET=Your-Key-For-LightRAG-API-Server
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# TOKEN_EXPIRE_HOURS=48
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# GUEST_TOKEN_EXPIRE_HOURS=24
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# JWT_ALGORITHM=HS256
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### RAG Query Configuration
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### API-Key to access LightRAG Server API
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# LIGHTRAG_API_KEY=your-secure-api-key-here
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# WHITELIST_PATHS=/health,/api/*
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########################
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### Query Configuration
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########################
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# LLM responde cache for query (Not valid for streaming response
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ENABLE_LLM_CACHE=true
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# HISTORY_TURNS=3
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# MAX_TOKEN_TEXT_CHUNK=6000
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# MAX_TOKEN_RELATION_DESC=4000
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# MAX_TOKEN_ENTITY_DESC=4000
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# COSINE_THRESHOLD=0.2
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### Number of entities or relations to retrieve from KG
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# TOP_K=60
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### Number of text chunks to retrieve initially from vector search
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# CHUNK_TOP_K=5
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### Number of entities or relations retrieved from KG
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# TOP_K=40
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### Maxmium number or chunks plan to send to LLM
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# CHUNK_TOP_K=10
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### control the actual enties send to LLM
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# MAX_ENTITY_TOKENS=10000
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### control the actual relations send to LLM
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# MAX_RELATION_TOKENS=10000
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### control the maximum tokens send to LLM (include entities, raltions and chunks)
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# MAX_TOTAL_TOKENS=32000
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### maxumium related chunks grab from single entity or relations
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# RELATED_CHUNK_NUMBER=10
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### Rerank Configuration
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# ENABLE_RERANK=False
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### Number of text chunks to keep after reranking (should be <= CHUNK_TOP_K)
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# CHUNK_RERANK_TOP_K=5
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### Rerank model configuration (required when ENABLE_RERANK=True)
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### Reranker configuration (Set ENABLE_RERANK to true in reranking model is configed)
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ENABLE_RERANK=False
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# RERANK_MODEL=BAAI/bge-reranker-v2-m3
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# RERANK_BINDING_HOST=https://api.your-rerank-provider.com/v1/rerank
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# RERANK_BINDING_API_KEY=your_rerank_api_key_here
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### Entity and relation summarization configuration
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########################################
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### Document processing configuration
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########################################
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### Language: English, Chinese, French, German ...
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SUMMARY_LANGUAGE=English
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ENABLE_LLM_CACHE_FOR_EXTRACT=true
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### MAX_TOKENS: max tokens send to LLM for entity relation summaries (less than context size of the model)
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MAX_TOKENS=32000
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### Chunk size for document splitting, 500~1500 is recommended
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# CHUNK_SIZE=1200
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# CHUNK_OVERLAP_SIZE=100
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### Entity and relation summarization configuration
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### Number of duplicated entities/edges to trigger LLM re-summary on merge ( at least 3 is recommented)
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# FORCE_LLM_SUMMARY_ON_MERGE=6
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# FORCE_LLM_SUMMARY_ON_MERGE=4
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### Maximum number of entity extraction attempts for ambiguous content
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# MAX_GLEANING=1
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### Number of parallel processing documents(Less than MAX_ASYNC/2 is recommended)
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# MAX_PARALLEL_INSERT=2
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###############################
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### Concurrency Configuration
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###############################
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### Max concurrency requests of LLM (for both query and document processing)
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MAX_ASYNC=4
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### Number of parallel processing documents(between 2~10, MAX_ASYNC/4 is recommended)
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MAX_PARALLEL_INSERT=2
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### Max concurrency requests for Embedding
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# EMBEDDING_FUNC_MAX_ASYNC=8
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### Num of chunks send to Embedding in single request
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# EMBEDDING_BATCH_NUM=10
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#######################
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### LLM Configuration
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ENABLE_LLM_CACHE=true
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ENABLE_LLM_CACHE_FOR_EXTRACT=true
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#######################
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### Time out in seconds for LLM, None for infinite timeout
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TIMEOUT=240
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### Some models like o1-mini require temperature to be set to 1
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TEMPERATURE=0
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### Max concurrency requests of LLM
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MAX_ASYNC=4
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### MAX_TOKENS: max tokens send to LLM for entity relation summaries (less than context size of the model)
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MAX_TOKENS=32000
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### LLM Binding type: openai, ollama, lollms, azure_openai
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LLM_BINDING=openai
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LLM_MODEL=gpt-4o
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LLM_BINDING_HOST=https://api.openai.com/v1
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LLM_BINDING_API_KEY=your_api_key
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### Set as num_ctx option for Ollama LLM
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# OLLAMA_NUM_CTX=32768
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### Optional for Azure
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# AZURE_OPENAI_API_VERSION=2024-08-01-preview
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# AZURE_OPENAI_DEPLOYMENT=gpt-4o
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### set as num_ctx option for Ollama LLM
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# OLLAMA_NUM_CTX=32768
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### Embedding Configuration
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####################################################################################
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### Embedding Configuration (Should not be changed after the first file processed)
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####################################################################################
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### Embedding Binding type: openai, ollama, lollms, azure_openai
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EMBEDDING_BINDING=ollama
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EMBEDDING_MODEL=bge-m3:latest
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@ -108,51 +130,53 @@ EMBEDDING_DIM=1024
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EMBEDDING_BINDING_API_KEY=your_api_key
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# If the embedding service is deployed within the same Docker stack, use host.docker.internal instead of localhost
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EMBEDDING_BINDING_HOST=http://localhost:11434
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### Num of chunks send to Embedding in single request
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# EMBEDDING_BATCH_NUM=10
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### Max concurrency requests for Embedding
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# EMBEDDING_FUNC_MAX_ASYNC=8
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### Maximum tokens sent to Embedding for each chunk (no longer in use?)
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# MAX_EMBED_TOKENS=8192
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### Optional for Azure
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# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
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# AZURE_EMBEDDING_API_VERSION=2023-05-15
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# AZURE_EMBEDDING_ENDPOINT=your_endpoint
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# AZURE_EMBEDDING_API_KEY=your_api_key
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###########################
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############################
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### Data storage selection
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###########################
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### In-memory database with local file persistence(Recommended for small scale deployment)
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############################
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### Default storage (Recommended for small scale deployment)
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# LIGHTRAG_KV_STORAGE=JsonKVStorage
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# LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage
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# LIGHTRAG_GRAPH_STORAGE=NetworkXStorage
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# LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage
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### Redis Storage (Recommended for production deployment)
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# LIGHTRAG_KV_STORAGE=RedisKVStorage
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# LIGHTRAG_DOC_STATUS_STORAGE=RedisDocStatusStorage
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### Vector Storage (Recommended for production deployment)
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# LIGHTRAG_VECTOR_STORAGE=MilvusVectorDBStorage
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# LIGHTRAG_VECTOR_STORAGE=QdrantVectorDBStorage
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# LIGHTRAG_VECTOR_STORAGE=FaissVectorDBStorage
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### Graph Storage (Recommended for production deployment)
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# LIGHTRAG_GRAPH_STORAGE=Neo4JStorage
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# LIGHTRAG_GRAPH_STORAGE=MemgraphStorage
|
||||
|
||||
### PostgreSQL
|
||||
# LIGHTRAG_KV_STORAGE=PGKVStorage
|
||||
# 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
|
||||
# LIGHTRAG_GRAPH_STORAGE=MemgraphStorage
|
||||
|
||||
####################################################################
|
||||
### 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 setting workspace name for all storage types
|
||||
### in the purpose of isolating data from LightRAG instances.
|
||||
### Valid workspace name constraints: a-z, A-Z, 0-9, and _
|
||||
####################################################################
|
||||
# WORKSPACE=space1
|
||||
|
||||
|
|
|
|||
|
|
@ -9,7 +9,11 @@ Configuration Required:
|
|||
2. Set your embedding API key and base URL in embedding_func()
|
||||
3. Set your rerank API key and base URL in the rerank configuration
|
||||
4. Or use environment variables (.env file):
|
||||
- ENABLE_RERANK=True
|
||||
- RERANK_MODEL=your_rerank_model
|
||||
- RERANK_BINDING_HOST=your_rerank_endpoint
|
||||
- RERANK_BINDING_API_KEY=your_rerank_api_key
|
||||
|
||||
Note: Rerank is now controlled per query via the 'enable_rerank' parameter (default: True)
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
|
|
@ -83,8 +87,7 @@ async def create_rag_with_rerank():
|
|||
max_token_size=8192,
|
||||
func=embedding_func,
|
||||
),
|
||||
# Simplified Rerank Configuration
|
||||
enable_rerank=True,
|
||||
# Rerank Configuration - provide the rerank function
|
||||
rerank_model_func=my_rerank_func,
|
||||
)
|
||||
|
||||
|
|
@ -120,7 +123,6 @@ async def create_rag_with_rerank_model():
|
|||
max_token_size=8192,
|
||||
func=embedding_func,
|
||||
),
|
||||
enable_rerank=True,
|
||||
rerank_model_func=rerank_model.rerank,
|
||||
)
|
||||
|
||||
|
|
@ -130,9 +132,9 @@ async def create_rag_with_rerank_model():
|
|||
return rag
|
||||
|
||||
|
||||
async def test_rerank_with_different_topk():
|
||||
async def test_rerank_with_different_settings():
|
||||
"""
|
||||
Test rerank functionality with different top_k settings
|
||||
Test rerank functionality with different enable_rerank settings
|
||||
"""
|
||||
print("🚀 Setting up LightRAG with Rerank functionality...")
|
||||
|
||||
|
|
@ -154,16 +156,41 @@ async def test_rerank_with_different_topk():
|
|||
print(f"\n🔍 Testing query: '{query}'")
|
||||
print("=" * 80)
|
||||
|
||||
# Test different top_k values to show parameter priority
|
||||
top_k_values = [2, 5, 10]
|
||||
# Test with rerank enabled (default)
|
||||
print("\n📊 Testing with enable_rerank=True (default):")
|
||||
result_with_rerank = await rag.aquery(
|
||||
query,
|
||||
param=QueryParam(
|
||||
mode="naive",
|
||||
top_k=10,
|
||||
chunk_top_k=5,
|
||||
enable_rerank=True, # Explicitly enable rerank
|
||||
),
|
||||
)
|
||||
print(f" Result length: {len(result_with_rerank)} characters")
|
||||
print(f" Preview: {result_with_rerank[:100]}...")
|
||||
|
||||
for top_k in top_k_values:
|
||||
print(f"\n📊 Testing with QueryParam(top_k={top_k}):")
|
||||
# Test with rerank disabled
|
||||
print("\n📊 Testing with enable_rerank=False:")
|
||||
result_without_rerank = await rag.aquery(
|
||||
query,
|
||||
param=QueryParam(
|
||||
mode="naive",
|
||||
top_k=10,
|
||||
chunk_top_k=5,
|
||||
enable_rerank=False, # Disable rerank
|
||||
),
|
||||
)
|
||||
print(f" Result length: {len(result_without_rerank)} characters")
|
||||
print(f" Preview: {result_without_rerank[:100]}...")
|
||||
|
||||
# Test naive mode with specific top_k
|
||||
result = await rag.aquery(query, param=QueryParam(mode="naive", top_k=top_k))
|
||||
print(f" Result length: {len(result)} characters")
|
||||
print(f" Preview: {result[:100]}...")
|
||||
# Test with default settings (enable_rerank defaults to True)
|
||||
print("\n📊 Testing with default settings (enable_rerank defaults to True):")
|
||||
result_default = await rag.aquery(
|
||||
query, param=QueryParam(mode="naive", top_k=10, chunk_top_k=5)
|
||||
)
|
||||
print(f" Result length: {len(result_default)} characters")
|
||||
print(f" Preview: {result_default[:100]}...")
|
||||
|
||||
|
||||
async def test_direct_rerank():
|
||||
|
|
@ -209,17 +236,21 @@ async def main():
|
|||
print("=" * 60)
|
||||
|
||||
try:
|
||||
# Test rerank with different top_k values
|
||||
await test_rerank_with_different_topk()
|
||||
# Test rerank with different enable_rerank settings
|
||||
await test_rerank_with_different_settings()
|
||||
|
||||
# Test direct rerank
|
||||
await test_direct_rerank()
|
||||
|
||||
print("\n✅ Example completed successfully!")
|
||||
print("\n💡 Key Points:")
|
||||
print(" ✓ All rerank configurations are contained within rerank_model_func")
|
||||
print(" ✓ Rerank improves document relevance ordering")
|
||||
print(" ✓ Configure API keys within your rerank function")
|
||||
print(" ✓ Rerank is now controlled per query via 'enable_rerank' parameter")
|
||||
print(" ✓ Default value for enable_rerank is True")
|
||||
print(" ✓ Rerank function is configured at LightRAG initialization")
|
||||
print(" ✓ Per-query enable_rerank setting overrides default behavior")
|
||||
print(
|
||||
" ✓ If enable_rerank=True but no rerank model is configured, a warning is issued"
|
||||
)
|
||||
print(" ✓ Monitor API usage and costs when using rerank services")
|
||||
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -11,6 +11,14 @@ from lightrag.utils import get_env_value
|
|||
from lightrag.constants import (
|
||||
DEFAULT_WOKERS,
|
||||
DEFAULT_TIMEOUT,
|
||||
DEFAULT_TOP_K,
|
||||
DEFAULT_CHUNK_TOP_K,
|
||||
DEFAULT_HISTORY_TURNS,
|
||||
DEFAULT_MAX_ENTITY_TOKENS,
|
||||
DEFAULT_MAX_RELATION_TOKENS,
|
||||
DEFAULT_MAX_TOTAL_TOKENS,
|
||||
DEFAULT_COSINE_THRESHOLD,
|
||||
DEFAULT_RELATED_CHUNK_NUMBER,
|
||||
)
|
||||
|
||||
# use the .env that is inside the current folder
|
||||
|
|
@ -151,45 +159,6 @@ def parse_args() -> argparse.Namespace:
|
|||
help="Path to SSL private key file (required if --ssl is enabled)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--history-turns",
|
||||
type=int,
|
||||
default=get_env_value("HISTORY_TURNS", 3, int),
|
||||
help="Number of conversation history turns to include (default: from env or 3)",
|
||||
)
|
||||
|
||||
# Search parameters
|
||||
parser.add_argument(
|
||||
"--top-k",
|
||||
type=int,
|
||||
default=get_env_value("TOP_K", 60, int),
|
||||
help="Number of most similar results to return (default: from env or 60)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--chunk-top-k",
|
||||
type=int,
|
||||
default=get_env_value("CHUNK_TOP_K", 15, int),
|
||||
help="Number of text chunks to retrieve initially from vector search (default: from env or 15)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--chunk-rerank-top-k",
|
||||
type=int,
|
||||
default=get_env_value("CHUNK_RERANK_TOP_K", 5, int),
|
||||
help="Number of text chunks to keep after reranking (default: from env or 5)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--enable-rerank",
|
||||
action="store_true",
|
||||
default=get_env_value("ENABLE_RERANK", False, bool),
|
||||
help="Enable rerank functionality (default: from env or False)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cosine-threshold",
|
||||
type=float,
|
||||
default=get_env_value("COSINE_THRESHOLD", 0.2, float),
|
||||
help="Cosine similarity threshold (default: from env or 0.4)",
|
||||
)
|
||||
|
||||
# Ollama model name
|
||||
parser.add_argument(
|
||||
"--simulated-model-name",
|
||||
|
|
@ -321,6 +290,26 @@ def parse_args() -> argparse.Namespace:
|
|||
args.rerank_binding_host = get_env_value("RERANK_BINDING_HOST", None)
|
||||
args.rerank_binding_api_key = get_env_value("RERANK_BINDING_API_KEY", None)
|
||||
|
||||
# Query configuration
|
||||
args.history_turns = get_env_value("HISTORY_TURNS", DEFAULT_HISTORY_TURNS, int)
|
||||
args.top_k = get_env_value("TOP_K", DEFAULT_TOP_K, int)
|
||||
args.chunk_top_k = get_env_value("CHUNK_TOP_K", DEFAULT_CHUNK_TOP_K, int)
|
||||
args.max_entity_tokens = get_env_value(
|
||||
"MAX_ENTITY_TOKENS", DEFAULT_MAX_ENTITY_TOKENS, int
|
||||
)
|
||||
args.max_relation_tokens = get_env_value(
|
||||
"MAX_RELATION_TOKENS", DEFAULT_MAX_RELATION_TOKENS, int
|
||||
)
|
||||
args.max_total_tokens = get_env_value(
|
||||
"MAX_TOTAL_TOKENS", DEFAULT_MAX_TOTAL_TOKENS, int
|
||||
)
|
||||
args.cosine_threshold = get_env_value(
|
||||
"COSINE_THRESHOLD", DEFAULT_COSINE_THRESHOLD, float
|
||||
)
|
||||
args.related_chunk_number = get_env_value(
|
||||
"RELATED_CHUNK_NUMBER", DEFAULT_RELATED_CHUNK_NUMBER, int
|
||||
)
|
||||
|
||||
ollama_server_infos.LIGHTRAG_MODEL = args.simulated_model_name
|
||||
|
||||
return args
|
||||
|
|
|
|||
|
|
@ -292,9 +292,9 @@ def create_app(args):
|
|||
),
|
||||
)
|
||||
|
||||
# Configure rerank function if enabled
|
||||
# Configure rerank function if model and API are configured
|
||||
rerank_model_func = None
|
||||
if args.enable_rerank and args.rerank_binding_api_key and args.rerank_binding_host:
|
||||
if args.rerank_binding_api_key and args.rerank_binding_host:
|
||||
from lightrag.rerank import custom_rerank
|
||||
|
||||
async def server_rerank_func(
|
||||
|
|
@ -312,10 +312,12 @@ def create_app(args):
|
|||
)
|
||||
|
||||
rerank_model_func = server_rerank_func
|
||||
logger.info(f"Rerank enabled with model: {args.rerank_model}")
|
||||
elif args.enable_rerank:
|
||||
logger.warning(
|
||||
"Rerank enabled but RERANK_BINDING_API_KEY or RERANK_BINDING_HOST not configured. Rerank will be disabled."
|
||||
logger.info(
|
||||
f"Rerank model configured: {args.rerank_model} (can be enabled per query)"
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
"Rerank model not configured. Set RERANK_BINDING_API_KEY and RERANK_BINDING_HOST to enable reranking."
|
||||
)
|
||||
|
||||
# Initialize RAG
|
||||
|
|
@ -351,7 +353,6 @@ def create_app(args):
|
|||
},
|
||||
enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract,
|
||||
enable_llm_cache=args.enable_llm_cache,
|
||||
enable_rerank=args.enable_rerank,
|
||||
rerank_model_func=rerank_model_func,
|
||||
auto_manage_storages_states=False,
|
||||
max_parallel_insert=args.max_parallel_insert,
|
||||
|
|
@ -381,7 +382,6 @@ def create_app(args):
|
|||
},
|
||||
enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract,
|
||||
enable_llm_cache=args.enable_llm_cache,
|
||||
enable_rerank=args.enable_rerank,
|
||||
rerank_model_func=rerank_model_func,
|
||||
auto_manage_storages_states=False,
|
||||
max_parallel_insert=args.max_parallel_insert,
|
||||
|
|
@ -512,11 +512,13 @@ def create_app(args):
|
|||
"enable_llm_cache": args.enable_llm_cache,
|
||||
"workspace": args.workspace,
|
||||
"max_graph_nodes": args.max_graph_nodes,
|
||||
# Rerank configuration
|
||||
"enable_rerank": args.enable_rerank,
|
||||
"rerank_model": args.rerank_model if args.enable_rerank else None,
|
||||
# Rerank configuration (based on whether rerank model is configured)
|
||||
"enable_rerank": rerank_model_func is not None,
|
||||
"rerank_model": args.rerank_model
|
||||
if rerank_model_func is not None
|
||||
else None,
|
||||
"rerank_binding_host": args.rerank_binding_host
|
||||
if args.enable_rerank
|
||||
if rerank_model_func is not None
|
||||
else None,
|
||||
},
|
||||
"auth_mode": auth_mode,
|
||||
|
|
|
|||
|
|
@ -52,31 +52,25 @@ class QueryRequest(BaseModel):
|
|||
chunk_top_k: Optional[int] = Field(
|
||||
ge=1,
|
||||
default=None,
|
||||
description="Number of text chunks to retrieve initially from vector search.",
|
||||
description="Number of text chunks to retrieve initially from vector search and keep after reranking.",
|
||||
)
|
||||
|
||||
chunk_rerank_top_k: Optional[int] = Field(
|
||||
max_entity_tokens: Optional[int] = Field(
|
||||
default=None,
|
||||
description="Maximum number of tokens allocated for entity context in unified token control system.",
|
||||
ge=1,
|
||||
default=None,
|
||||
description="Number of text chunks to keep after reranking.",
|
||||
)
|
||||
|
||||
max_token_for_text_unit: Optional[int] = Field(
|
||||
gt=1,
|
||||
max_relation_tokens: Optional[int] = Field(
|
||||
default=None,
|
||||
description="Maximum number of tokens allowed for each retrieved text chunk.",
|
||||
description="Maximum number of tokens allocated for relationship context in unified token control system.",
|
||||
ge=1,
|
||||
)
|
||||
|
||||
max_token_for_global_context: Optional[int] = Field(
|
||||
gt=1,
|
||||
max_total_tokens: Optional[int] = Field(
|
||||
default=None,
|
||||
description="Maximum number of tokens allocated for relationship descriptions in global retrieval.",
|
||||
)
|
||||
|
||||
max_token_for_local_context: Optional[int] = Field(
|
||||
gt=1,
|
||||
default=None,
|
||||
description="Maximum number of tokens allocated for entity descriptions in local retrieval.",
|
||||
description="Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt).",
|
||||
ge=1,
|
||||
)
|
||||
|
||||
conversation_history: Optional[List[Dict[str, Any]]] = Field(
|
||||
|
|
@ -99,6 +93,11 @@ class QueryRequest(BaseModel):
|
|||
description="User-provided prompt for the query. If provided, this will be used instead of the default value from prompt template.",
|
||||
)
|
||||
|
||||
enable_rerank: Optional[bool] = Field(
|
||||
default=None,
|
||||
description="Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued. Default is True.",
|
||||
)
|
||||
|
||||
@field_validator("query", mode="after")
|
||||
@classmethod
|
||||
def query_strip_after(cls, query: str) -> str:
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
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import{e as v,c as b,g as m,k as O,h as P,j as p,l as w,m as c,n as x,t as A,o as N}from"./_baseUniq-BZ3hvks1.js";import{aU as g,aq as _,aV as $,aW as E,aX as F,aY as I,aZ as M,a_ as y,a$ as B,b0 as T}from"./mermaid-vendor-CR44n-lC.js";var S=/\s/;function q(n){for(var r=n.length;r--&&S.test(n.charAt(r)););return r}var G=/^\s+/;function H(n){return n&&n.slice(0,q(n)+1).replace(G,"")}var o=NaN,L=/^[-+]0x[0-9a-f]+$/i,R=/^0b[01]+$/i,W=/^0o[0-7]+$/i,X=parseInt;function Y(n){if(typeof n=="number")return n;if(v(n))return o;if(g(n)){var r=typeof n.valueOf=="function"?n.valueOf():n;n=g(r)?r+"":r}if(typeof n!="string")return n===0?n:+n;n=H(n);var t=R.test(n);return t||W.test(n)?X(n.slice(2),t?2:8):L.test(n)?o:+n}var z=1/0,C=17976931348623157e292;function K(n){if(!n)return n===0?n:0;if(n=Y(n),n===z||n===-1/0){var r=n<0?-1:1;return r*C}return n===n?n:0}function U(n){var r=K(n),t=r%1;return r===r?t?r-t:r:0}function fn(n){var r=n==null?0:n.length;return r?b(n):[]}var l=Object.prototype,Z=l.hasOwnProperty,dn=_(function(n,r){n=Object(n);var t=-1,e=r.length,a=e>2?r[2]:void 0;for(a&&$(r[0],r[1],a)&&(e=1);++t<e;)for(var f=r[t],i=E(f),s=-1,d=i.length;++s<d;){var u=i[s],h=n[u];(h===void 0||F(h,l[u])&&!Z.call(n,u))&&(n[u]=f[u])}return n});function un(n){var r=n==null?0:n.length;return r?n[r-1]:void 0}function D(n){return function(r,t,e){var a=Object(r);if(!I(r)){var f=m(t);r=O(r),t=function(s){return f(a[s],s,a)}}var i=n(r,t,e);return i>-1?a[f?r[i]:i]:void 0}}var J=Math.max;function Q(n,r,t){var e=n==null?0:n.length;if(!e)return-1;var a=t==null?0:U(t);return a<0&&(a=J(e+a,0)),P(n,m(r),a)}var hn=D(Q);function V(n,r){var t=-1,e=I(n)?Array(n.length):[];return p(n,function(a,f,i){e[++t]=r(a,f,i)}),e}function gn(n,r){var t=M(n)?w:V;return t(n,m(r))}var j=Object.prototype,k=j.hasOwnProperty;function nn(n,r){return n!=null&&k.call(n,r)}function mn(n,r){return n!=null&&c(n,r,nn)}function rn(n,r){return n<r}function tn(n,r,t){for(var e=-1,a=n.length;++e<a;){var f=n[e],i=r(f);if(i!=null&&(s===void 0?i===i&&!v(i):t(i,s)))var s=i,d=f}return d}function on(n){return n&&n.length?tn(n,y,rn):void 0}function an(n,r,t,e){if(!g(n))return n;r=x(r,n);for(var a=-1,f=r.length,i=f-1,s=n;s!=null&&++a<f;){var d=A(r[a]),u=t;if(d==="__proto__"||d==="constructor"||d==="prototype")return n;if(a!=i){var h=s[d];u=void 0,u===void 0&&(u=g(h)?h:B(r[a+1])?[]:{})}T(s,d,u),s=s[d]}return n}function vn(n,r,t){for(var e=-1,a=r.length,f={};++e<a;){var i=r[e],s=N(n,i);t(s,i)&&an(f,x(i,n),s)}return f}export{rn as a,tn as b,V as c,vn as d,on as e,fn as f,hn as g,mn as h,dn as i,U as j,un as l,gn as m,K as t};
|
||||
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|
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import{_ as l}from"./mermaid-vendor-D0f_SE0h.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-CR44n-lC.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};
|
||||
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|
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|||
import{_ as n,a1 as x,j as l}from"./mermaid-vendor-D0f_SE0h.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",`#${i}`)},"drawEmbeddedImage"),y=n(()=>({x:0,y:0,width:100,height:100,fill:"#EDF2AE",stroke:"#666",anchor:"start",rx:0,ry:0}),"getNoteRect"),p=n(()=>({x:0,y:0,width:100,height:100,"text-anchor":"start",style:"#666",textMargin:0,rx:0,ry:0,tspan:!0}),"getTextObj");export{d as a,p as b,m as c,c as d,h as e,g as f,y as g};
|
||||
import{_ as n,a1 as x,j as l}from"./mermaid-vendor-CR44n-lC.js";var c=n((a,t)=>{const e=a.append("rect");if(e.attr("x",t.x),e.attr("y",t.y),e.attr("fill",t.fill),e.attr("stroke",t.stroke),e.attr("width",t.width),e.attr("height",t.height),t.name&&e.attr("name",t.name),t.rx&&e.attr("rx",t.rx),t.ry&&e.attr("ry",t.ry),t.attrs!==void 0)for(const r in t.attrs)e.attr(r,t.attrs[r]);return t.class&&e.attr("class",t.class),e},"drawRect"),d=n((a,t)=>{const e={x:t.startx,y:t.starty,width:t.stopx-t.startx,height:t.stopy-t.starty,fill:t.fill,stroke:t.stroke,class:"rect"};c(a,e).lower()},"drawBackgroundRect"),g=n((a,t)=>{const e=t.text.replace(x," "),r=a.append("text");r.attr("x",t.x),r.attr("y",t.y),r.attr("class","legend"),r.style("text-anchor",t.anchor),t.class&&r.attr("class",t.class);const s=r.append("tspan");return s.attr("x",t.x+t.textMargin*2),s.text(e),r},"drawText"),h=n((a,t,e,r)=>{const s=a.append("image");s.attr("x",t),s.attr("y",e);const i=l.sanitizeUrl(r);s.attr("xlink:href",i)},"drawImage"),m=n((a,t,e,r)=>{const s=a.append("use");s.attr("x",t),s.attr("y",e);const i=l.sanitizeUrl(r);s.attr("xlink:href",`#${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};
|
||||
|
|
@ -1 +1 @@
|
|||
import{_ as n,d as r,e as d,l as g}from"./mermaid-vendor-D0f_SE0h.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} ${i}`,"createViewBox");export{u as g,b as s};
|
||||
import{_ as n,d as r,e as d,l as g}from"./mermaid-vendor-CR44n-lC.js";var u=n((e,t)=>{let o;return t==="sandbox"&&(o=r("#i"+e)),(t==="sandbox"?r(o.nodes()[0].contentDocument.body):r("body")).select(`[id="${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};
|
||||
|
|
@ -1 +1 @@
|
|||
import{_ as s}from"./mermaid-vendor-D0f_SE0h.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};
|
||||
import{_ as s}from"./mermaid-vendor-CR44n-lC.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};
|
||||
|
|
@ -1 +1 @@
|
|||
import{s as a,c as s,a as e,C as t}from"./chunk-A2AXSNBT-B91iiasA.js";import{_ as i}from"./mermaid-vendor-D0f_SE0h.js";import"./chunk-RZ5BOZE2-B615FLH4.js";import"./feature-graph-NODQb6qW.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-CRex3-yW.js";import{_ as i}from"./mermaid-vendor-CR44n-lC.js";import"./chunk-RZ5BOZE2-CdnIs5Fb.js";import"./feature-graph-wF7LCIjH.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};
|
||||
|
|
@ -1 +1 @@
|
|||
import{s as a,c as s,a as e,C as t}from"./chunk-A2AXSNBT-B91iiasA.js";import{_ as i}from"./mermaid-vendor-D0f_SE0h.js";import"./chunk-RZ5BOZE2-B615FLH4.js";import"./feature-graph-NODQb6qW.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-CRex3-yW.js";import{_ as i}from"./mermaid-vendor-CR44n-lC.js";import"./chunk-RZ5BOZE2-CdnIs5Fb.js";import"./feature-graph-wF7LCIjH.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};
|
||||
1
lightrag/api/webui/assets/clone-Dm5jEAXQ.js
generated
1
lightrag/api/webui/assets/clone-Dm5jEAXQ.js
generated
|
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|
|||
import{b as r}from"./_baseUniq-CtAZZJ8e.js";var e=4;function a(o){return r(o,e)}export{a as c};
|
||||
1
lightrag/api/webui/assets/clone-eVzB-9-f.js
generated
Normal file
1
lightrag/api/webui/assets/clone-eVzB-9-f.js
generated
Normal file
|
|
@ -0,0 +1 @@
|
|||
import{b as r}from"./_baseUniq-BZ3hvks1.js";var e=4;function a(o){return r(o,e)}export{a as c};
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|
|||
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||||
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|
||||
.radarCurve-${r} {
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||||
color: ${s};
|
||||
fill: ${s};
|
||||
|
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@ -1,4 +1,4 @@
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|||
import{p as w}from"./chunk-4BMEZGHF-CAhtCpmT.js";import{_ as n,s as B,g as S,t as F,q as z,a as P,b as W,F as x,K as T,e as D,z as _,G as A,H as E,l as v}from"./mermaid-vendor-D0f_SE0h.js";import{p as N}from"./radar-MK3ICKWK-DOAXm8cx.js";import"./feature-graph-NODQb6qW.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-CtAZZJ8e.js";import"./_basePickBy-D3PHsJjq.js";import"./clone-Dm5jEAXQ.js";var C={packet:[]},h=structuredClone(C),L=E.packet,Y=n(()=>{const t=x({...L,...A().packet});return t.showBits&&(t.paddingY+=10),t},"getConfig"),G=n(()=>h.packet,"getPacket"),H=n(t=>{t.length>0&&h.packet.push(t)},"pushWord"),I=n(()=>{_(),h=structuredClone(C)},"clear"),m={pushWord:H,getPacket:G,getConfig:Y,clear:I,setAccTitle:W,getAccTitle:P,setDiagramTitle:z,getDiagramTitle:F,getAccDescription:S,setAccDescription:B},K=1e4,M=n(t=>{w(t,m);let e=-1,o=[],s=1;const{bitsPerRow:i}=m.getConfig();for(let{start:a,end:r,label:p}of t.blocks){if(r&&r<a)throw new Error(`Packet block ${a} - ${r} is invalid. End must be greater than start.`);if(a!==e+1)throw new Error(`Packet block ${a} - ${r??a} is not contiguous. It should start from ${e+1}.`);for(e=r??a,v.debug(`Packet block ${a} - ${e} with label ${p}`);o.length<=i+1&&m.getPacket().length<K;){const[b,c]=O({start:a,end:r,label:p},s,i);if(o.push(b),b.end+1===s*i&&(m.pushWord(o),o=[],s++),!c)break;({start:a,end:r,label:p}=c)}}m.pushWord(o)},"populate"),O=n((t,e,o)=>{if(t.end===void 0&&(t.end=t.start),t.start>t.end)throw new Error(`Block start ${t.start} is greater than block end ${t.end}.`);return t.end+1<=e*o?[t,void 0]:[{start:t.start,end:e*o-1,label:t.label},{start:e*o,end:t.end,label:t.label}]},"getNextFittingBlock"),q={parse:n(async t=>{const e=await N("packet",t);v.debug(e),M(e)},"parse")},R=n((t,e,o,s)=>{const i=s.db,a=i.getConfig(),{rowHeight:r,paddingY:p,bitWidth:b,bitsPerRow:c}=a,u=i.getPacket(),l=i.getDiagramTitle(),g=r+p,d=g*(u.length+1)-(l?0:r),k=b*c+2,f=T(e);f.attr("viewbox",`0 0 ${k} ${d}`),D(f,d,k,a.useMaxWidth);for(const[$,y]of u.entries())U(f,y,$,a);f.append("text").text(l).attr("x",k/2).attr("y",d-g/2).attr("dominant-baseline","middle").attr("text-anchor","middle").attr("class","packetTitle")},"draw"),U=n((t,e,o,{rowHeight:s,paddingX:i,paddingY:a,bitWidth:r,bitsPerRow:p,showBits:b})=>{const c=t.append("g"),u=o*(s+a)+a;for(const l of e){const g=l.start%p*r+1,d=(l.end-l.start+1)*r-i;if(c.append("rect").attr("x",g).attr("y",u).attr("width",d).attr("height",s).attr("class","packetBlock"),c.append("text").attr("x",g+d/2).attr("y",u+s/2).attr("class","packetLabel").attr("dominant-baseline","middle").attr("text-anchor","middle").text(l.label),!b)continue;const k=l.end===l.start,f=u-2;c.append("text").attr("x",g+(k?d/2:0)).attr("y",f).attr("class","packetByte start").attr("dominant-baseline","auto").attr("text-anchor",k?"middle":"start").text(l.start),k||c.append("text").attr("x",g+d).attr("y",f).attr("class","packetByte end").attr("dominant-baseline","auto").attr("text-anchor","end").text(l.end)}},"drawWord"),X={draw:R},j={byteFontSize:"10px",startByteColor:"black",endByteColor:"black",labelColor:"black",labelFontSize:"12px",titleColor:"black",titleFontSize:"14px",blockStrokeColor:"black",blockStrokeWidth:"1",blockFillColor:"#efefef"},J=n(({packet:t}={})=>{const e=x(j,t);return`
|
||||
import{p as w}from"./chunk-4BMEZGHF-DM9xX3Iw.js";import{_ as n,s as B,g as S,t as F,q as z,a as P,b as W,F as x,K as T,e as D,z as _,G as A,H as E,l as v}from"./mermaid-vendor-CR44n-lC.js";import{p as N}from"./radar-MK3ICKWK-B97XRKGx.js";import"./feature-graph-wF7LCIjH.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-BZ3hvks1.js";import"./_basePickBy-DV1dBXEu.js";import"./clone-eVzB-9-f.js";var C={packet:[]},h=structuredClone(C),L=E.packet,Y=n(()=>{const t=x({...L,...A().packet});return t.showBits&&(t.paddingY+=10),t},"getConfig"),G=n(()=>h.packet,"getPacket"),H=n(t=>{t.length>0&&h.packet.push(t)},"pushWord"),I=n(()=>{_(),h=structuredClone(C)},"clear"),m={pushWord:H,getPacket:G,getConfig:Y,clear:I,setAccTitle:W,getAccTitle:P,setDiagramTitle:z,getDiagramTitle:F,getAccDescription:S,setAccDescription:B},K=1e4,M=n(t=>{w(t,m);let e=-1,o=[],s=1;const{bitsPerRow:i}=m.getConfig();for(let{start:a,end:r,label:p}of t.blocks){if(r&&r<a)throw new Error(`Packet block ${a} - ${r} is invalid. End must be greater than start.`);if(a!==e+1)throw new Error(`Packet block ${a} - ${r??a} is not contiguous. It should start from ${e+1}.`);for(e=r??a,v.debug(`Packet block ${a} - ${e} with label ${p}`);o.length<=i+1&&m.getPacket().length<K;){const[b,c]=O({start:a,end:r,label:p},s,i);if(o.push(b),b.end+1===s*i&&(m.pushWord(o),o=[],s++),!c)break;({start:a,end:r,label:p}=c)}}m.pushWord(o)},"populate"),O=n((t,e,o)=>{if(t.end===void 0&&(t.end=t.start),t.start>t.end)throw new Error(`Block start ${t.start} is greater than block end ${t.end}.`);return t.end+1<=e*o?[t,void 0]:[{start:t.start,end:e*o-1,label:t.label},{start:e*o,end:t.end,label:t.label}]},"getNextFittingBlock"),q={parse:n(async t=>{const e=await N("packet",t);v.debug(e),M(e)},"parse")},R=n((t,e,o,s)=>{const i=s.db,a=i.getConfig(),{rowHeight:r,paddingY:p,bitWidth:b,bitsPerRow:c}=a,u=i.getPacket(),l=i.getDiagramTitle(),g=r+p,d=g*(u.length+1)-(l?0:r),k=b*c+2,f=T(e);f.attr("viewbox",`0 0 ${k} ${d}`),D(f,d,k,a.useMaxWidth);for(const[$,y]of u.entries())U(f,y,$,a);f.append("text").text(l).attr("x",k/2).attr("y",d-g/2).attr("dominant-baseline","middle").attr("text-anchor","middle").attr("class","packetTitle")},"draw"),U=n((t,e,o,{rowHeight:s,paddingX:i,paddingY:a,bitWidth:r,bitsPerRow:p,showBits:b})=>{const c=t.append("g"),u=o*(s+a)+a;for(const l of e){const g=l.start%p*r+1,d=(l.end-l.start+1)*r-i;if(c.append("rect").attr("x",g).attr("y",u).attr("width",d).attr("height",s).attr("class","packetBlock"),c.append("text").attr("x",g+d/2).attr("y",u+s/2).attr("class","packetLabel").attr("dominant-baseline","middle").attr("text-anchor","middle").text(l.label),!b)continue;const k=l.end===l.start,f=u-2;c.append("text").attr("x",g+(k?d/2:0)).attr("y",f).attr("class","packetByte start").attr("dominant-baseline","auto").attr("text-anchor",k?"middle":"start").text(l.start),k||c.append("text").attr("x",g+d).attr("y",f).attr("class","packetByte end").attr("dominant-baseline","auto").attr("text-anchor","end").text(l.end)}},"drawWord"),X={draw:R},j={byteFontSize:"10px",startByteColor:"black",endByteColor:"black",labelColor:"black",labelFontSize:"12px",titleColor:"black",titleFontSize:"14px",blockStrokeColor:"black",blockStrokeWidth:"1",blockFillColor:"#efefef"},J=n(({packet:t}={})=>{const e=x(j,t);return`
|
||||
.packetByte {
|
||||
font-size: ${e.byteFontSize};
|
||||
}
|
||||
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10
lightrag/api/webui/assets/feature-retrieval-DdCvVec9.js
generated
Normal file
10
lightrag/api/webui/assets/feature-retrieval-DdCvVec9.js
generated
Normal file
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|
|
@ -1,4 +1,4 @@
|
|||
import{_ as m,o as O1,l as Z,c as Ge,d as Ce,p as H1,r as q1,u as i1,b as X1,s as Q1,q as J1,a as Z1,g as $1,t as et,k as tt,v as st,J as it,x as rt,y as s1,z as nt,A as at,B as ut,C as lt}from"./mermaid-vendor-D0f_SE0h.js";import{g as ot,s as ct}from"./chunk-RZ5BOZE2-B615FLH4.js";import"./feature-graph-NODQb6qW.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var ht="flowchart-",Pe,dt=(Pe=class{constructor(){this.vertexCounter=0,this.config=Ge(),this.vertices=new Map,this.edges=[],this.classes=new Map,this.subGraphs=[],this.subGraphLookup=new Map,this.tooltips=new Map,this.subCount=0,this.firstGraphFlag=!0,this.secCount=-1,this.posCrossRef=[],this.funs=[],this.setAccTitle=X1,this.setAccDescription=Q1,this.setDiagramTitle=J1,this.getAccTitle=Z1,this.getAccDescription=$1,this.getDiagramTitle=et,this.funs.push(this.setupToolTips.bind(this)),this.addVertex=this.addVertex.bind(this),this.firstGraph=this.firstGraph.bind(this),this.setDirection=this.setDirection.bind(this),this.addSubGraph=this.addSubGraph.bind(this),this.addLink=this.addLink.bind(this),this.setLink=this.setLink.bind(this),this.updateLink=this.updateLink.bind(this),this.addClass=this.addClass.bind(this),this.setClass=this.setClass.bind(this),this.destructLink=this.destructLink.bind(this),this.setClickEvent=this.setClickEvent.bind(this),this.setTooltip=this.setTooltip.bind(this),this.updateLinkInterpolate=this.updateLinkInterpolate.bind(this),this.setClickFun=this.setClickFun.bind(this),this.bindFunctions=this.bindFunctions.bind(this),this.lex={firstGraph:this.firstGraph.bind(this)},this.clear(),this.setGen("gen-2")}sanitizeText(i){return tt.sanitizeText(i,this.config)}lookUpDomId(i){for(const n of this.vertices.values())if(n.id===i)return n.domId;return i}addVertex(i,n,a,u,l,f,c={},A){var U,T;if(!i||i.trim().length===0)return;let r;if(A!==void 0){let d;A.includes(`
|
||||
import{_ as m,o as O1,l as Z,c as Ge,d as Ce,p as H1,r as q1,u as i1,b as X1,s as Q1,q as J1,a as Z1,g as $1,t as et,k as tt,v as st,J as it,x as rt,y as s1,z as nt,A as at,B as ut,C as lt}from"./mermaid-vendor-CR44n-lC.js";import{g as ot,s as ct}from"./chunk-RZ5BOZE2-CdnIs5Fb.js";import"./feature-graph-wF7LCIjH.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var ht="flowchart-",Pe,dt=(Pe=class{constructor(){this.vertexCounter=0,this.config=Ge(),this.vertices=new Map,this.edges=[],this.classes=new Map,this.subGraphs=[],this.subGraphLookup=new Map,this.tooltips=new Map,this.subCount=0,this.firstGraphFlag=!0,this.secCount=-1,this.posCrossRef=[],this.funs=[],this.setAccTitle=X1,this.setAccDescription=Q1,this.setDiagramTitle=J1,this.getAccTitle=Z1,this.getAccDescription=$1,this.getDiagramTitle=et,this.funs.push(this.setupToolTips.bind(this)),this.addVertex=this.addVertex.bind(this),this.firstGraph=this.firstGraph.bind(this),this.setDirection=this.setDirection.bind(this),this.addSubGraph=this.addSubGraph.bind(this),this.addLink=this.addLink.bind(this),this.setLink=this.setLink.bind(this),this.updateLink=this.updateLink.bind(this),this.addClass=this.addClass.bind(this),this.setClass=this.setClass.bind(this),this.destructLink=this.destructLink.bind(this),this.setClickEvent=this.setClickEvent.bind(this),this.setTooltip=this.setTooltip.bind(this),this.updateLinkInterpolate=this.updateLinkInterpolate.bind(this),this.setClickFun=this.setClickFun.bind(this),this.bindFunctions=this.bindFunctions.bind(this),this.lex={firstGraph:this.firstGraph.bind(this)},this.clear(),this.setGen("gen-2")}sanitizeText(i){return tt.sanitizeText(i,this.config)}lookUpDomId(i){for(const n of this.vertices.values())if(n.id===i)return n.domId;return i}addVertex(i,n,a,u,l,f,c={},A){var U,T;if(!i||i.trim().length===0)return;let r;if(A!==void 0){let d;A.includes(`
|
||||
`)?d=A+`
|
||||
`:d=`{
|
||||
`+A+`
|
||||
File diff suppressed because one or more lines are too long
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@ -1,2 +1,2 @@
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import{_ as e,l as o,K as i,e as n,L as p}from"./mermaid-vendor-D0f_SE0h.js";import{p as m}from"./radar-MK3ICKWK-DOAXm8cx.js";import"./feature-graph-NODQb6qW.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-CtAZZJ8e.js";import"./_basePickBy-D3PHsJjq.js";import"./clone-Dm5jEAXQ.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-CR44n-lC.js";import{p as m}from"./radar-MK3ICKWK-B97XRKGx.js";import"./feature-graph-wF7LCIjH.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-BZ3hvks1.js";import"./_basePickBy-DV1dBXEu.js";import"./clone-eVzB-9-f.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};
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
import{a as pt,g as at,f as gt,d as mt}from"./chunk-D6G4REZN-CGaqGId9.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-D0f_SE0h.js";import"./feature-graph-NODQb6qW.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 a},"o"),e=[6,8,10,11,12,14,16,17,18],i=[1,9],c=[1,10],n=[1,11],u=[1,12],h=[1,13],f=[1,14],d={trace:s(function(){},"trace"),yy:{},symbols_:{error:2,start:3,journey:4,document:5,EOF:6,line:7,SPACE:8,statement:9,NEWLINE:10,title:11,acc_title:12,acc_title_value:13,acc_descr:14,acc_descr_value:15,acc_descr_multiline_value:16,section:17,taskName:18,taskData:19,$accept:0,$end:1},terminals_:{2:"error",4:"journey",6:"EOF",8:"SPACE",10:"NEWLINE",11:"title",12:"acc_title",13:"acc_title_value",14:"acc_descr",15:"acc_descr_value",16:"acc_descr_multiline_value",17:"section",18:"taskName",19:"taskData"},productions_:[0,[3,3],[5,0],[5,2],[7,2],[7,1],[7,1],[7,1],[9,1],[9,2],[9,2],[9,1],[9,1],[9,2]],performAction:s(function(r,a,l,y,p,o,S){var _=o.length-1;switch(p){case 1:return o[_-1];case 2:this.$=[];break;case 3:o[_-1].push(o[_]),this.$=o[_-1];break;case 4:case 5:this.$=o[_];break;case 6:case 7:this.$=[];break;case 8:y.setDiagramTitle(o[_].substr(6)),this.$=o[_].substr(6);break;case 9:this.$=o[_].trim(),y.setAccTitle(this.$);break;case 10:case 11:this.$=o[_].trim(),y.setAccDescription(this.$);break;case 12:y.addSection(o[_].substr(8)),this.$=o[_].substr(8);break;case 13:y.addTask(o[_-1],o[_]),this.$="task";break}},"anonymous"),table:[{3:1,4:[1,2]},{1:[3]},t(e,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:i,12:c,14:n,16:u,17:h,18:f},t(e,[2,7],{1:[2,1]}),t(e,[2,3]),{9:15,11:i,12:c,14:n,16:u,17:h,18:f},t(e,[2,5]),t(e,[2,6]),t(e,[2,8]),{13:[1,16]},{15:[1,17]},t(e,[2,11]),t(e,[2,12]),{19:[1,18]},t(e,[2,4]),t(e,[2,9]),t(e,[2,10]),t(e,[2,13])],defaultActions:{},parseError:s(function(r,a){if(a.recoverable)this.trace(r);else{var l=new Error(r);throw l.hash=a,l}},"parseError"),parse:s(function(r){var a=this,l=[0],y=[],p=[null],o=[],S=this.table,_="",B=0,J=0,ut=2,K=1,yt=o.slice.call(arguments,1),k=Object.create(this.lexer),E={yy:{}};for(var O in this.yy)Object.prototype.hasOwnProperty.call(this.yy,O)&&(E.yy[O]=this.yy[O]);k.setInput(r,E.yy),E.yy.lexer=k,E.yy.parser=this,typeof k.yylloc>"u"&&(k.yylloc={});var Y=k.yylloc;o.push(Y);var dt=k.options&&k.options.ranges;typeof E.yy.parseError=="function"?this.parseError=E.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function ft(v){l.length=l.length-2*v,p.length=p.length-v,o.length=o.length-v}s(ft,"popStack");function Q(){var v;return v=y.pop()||k.lex()||K,typeof v!="number"&&(v instanceof Array&&(y=v,v=y.pop()),v=a.symbols_[v]||v),v}s(Q,"lex");for(var b,P,w,q,C={},N,$,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)+`:
|
||||
import{a as pt,g as at,f as gt,d as mt}from"./chunk-D6G4REZN-5j_Vlndu.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-CR44n-lC.js";import"./feature-graph-wF7LCIjH.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 a},"o"),e=[6,8,10,11,12,14,16,17,18],i=[1,9],c=[1,10],n=[1,11],u=[1,12],h=[1,13],f=[1,14],d={trace:s(function(){},"trace"),yy:{},symbols_:{error:2,start:3,journey:4,document:5,EOF:6,line:7,SPACE:8,statement:9,NEWLINE:10,title:11,acc_title:12,acc_title_value:13,acc_descr:14,acc_descr_value:15,acc_descr_multiline_value:16,section:17,taskName:18,taskData:19,$accept:0,$end:1},terminals_:{2:"error",4:"journey",6:"EOF",8:"SPACE",10:"NEWLINE",11:"title",12:"acc_title",13:"acc_title_value",14:"acc_descr",15:"acc_descr_value",16:"acc_descr_multiline_value",17:"section",18:"taskName",19:"taskData"},productions_:[0,[3,3],[5,0],[5,2],[7,2],[7,1],[7,1],[7,1],[9,1],[9,2],[9,2],[9,1],[9,1],[9,2]],performAction:s(function(r,a,l,y,p,o,S){var _=o.length-1;switch(p){case 1:return o[_-1];case 2:this.$=[];break;case 3:o[_-1].push(o[_]),this.$=o[_-1];break;case 4:case 5:this.$=o[_];break;case 6:case 7:this.$=[];break;case 8:y.setDiagramTitle(o[_].substr(6)),this.$=o[_].substr(6);break;case 9:this.$=o[_].trim(),y.setAccTitle(this.$);break;case 10:case 11:this.$=o[_].trim(),y.setAccDescription(this.$);break;case 12:y.addSection(o[_].substr(8)),this.$=o[_].substr(8);break;case 13:y.addTask(o[_-1],o[_]),this.$="task";break}},"anonymous"),table:[{3:1,4:[1,2]},{1:[3]},t(e,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:i,12:c,14:n,16:u,17:h,18:f},t(e,[2,7],{1:[2,1]}),t(e,[2,3]),{9:15,11:i,12:c,14:n,16:u,17:h,18:f},t(e,[2,5]),t(e,[2,6]),t(e,[2,8]),{13:[1,16]},{15:[1,17]},t(e,[2,11]),t(e,[2,12]),{19:[1,18]},t(e,[2,4]),t(e,[2,9]),t(e,[2,10]),t(e,[2,13])],defaultActions:{},parseError:s(function(r,a){if(a.recoverable)this.trace(r);else{var l=new Error(r);throw l.hash=a,l}},"parseError"),parse:s(function(r){var a=this,l=[0],y=[],p=[null],o=[],S=this.table,_="",B=0,J=0,ut=2,K=1,yt=o.slice.call(arguments,1),k=Object.create(this.lexer),E={yy:{}};for(var O in this.yy)Object.prototype.hasOwnProperty.call(this.yy,O)&&(E.yy[O]=this.yy[O]);k.setInput(r,E.yy),E.yy.lexer=k,E.yy.parser=this,typeof k.yylloc>"u"&&(k.yylloc={});var Y=k.yylloc;o.push(Y);var dt=k.options&&k.options.ranges;typeof E.yy.parseError=="function"?this.parseError=E.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function ft(v){l.length=l.length-2*v,p.length=p.length-v,o.length=o.length-v}s(ft,"popStack");function Q(){var v;return v=y.pop()||k.lex()||K,typeof v!="number"&&(v instanceof Array&&(y=v,v=y.pop()),v=a.symbols_[v]||v),v}s(Q,"lex");for(var b,P,w,q,C={},N,$,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 line "+(B+1)+": Unexpected "+(b==K?"end of input":"'"+(this.terminals_[b]||b)+"'"),this.parseError(G,{text:k.match,token:this.terminals_[b]||b,line:k.yylineno,loc:Y,expected:j})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+P+", token: "+b);switch(w[0]){case 1:l.push(b),p.push(k.yytext),o.push(k.yylloc),l.push(w[1]),b=null,J=k.yyleng,_=k.yytext,B=k.yylineno,Y=k.yylloc;break;case 2:if($=this.productions_[w[1]][1],C.$=p[p.length-$],C._$={first_line:o[o.length-($||1)].first_line,last_line:o[o.length-1].last_line,first_column:o[o.length-($||1)].first_column,last_column:o[o.length-1].last_column},dt&&(C._$.range=[o[o.length-($||1)].range[0],o[o.length-1].range[1]]),q=this.performAction.apply(C,[_,J,B,E.yy,w[1],p,o].concat(yt)),typeof q<"u")return q;$&&(l=l.slice(0,-1*$*2),p=p.slice(0,-1*$),o=o.slice(0,-1*$)),l.push(this.productions_[w[1]][0]),p.push(C.$),o.push(C._$),D=S[l[l.length-2]][l[l.length-1]],l.push(D);break;case 3:return!0}}return!0},"parse")},x=function(){var g={EOF:1,parseError:s(function(a,l){if(this.yy.parser)this.yy.parser.parseError(a,l);else throw new Error(a)},"parseError"),setInput:s(function(r,a){return this.yy=a||this.yy||{},this._input=r,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 r=this._input[0];this.yytext+=r,this.yyleng++,this.offset++,this.match+=r,this.matched+=r;var a=r.match(/(?:\r\n?|\n).*/g);return a?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),r},"input"),unput:s(function(r){var 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)+`. 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:s(function(r){this.unput(this.match.slice(r))},"less"),pastInput:s(function(){var r=this.matched.substr(0,this.matched.length-this.match.length);return(r.length>20?"...":"")+r.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:s(function(){var r=this.match;return r.length<20&&(r+=this._input.substr(0,20-r.length)),(r.substr(0,20)+(r.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:s(function(){var r=this.pastInput(),a=new Array(r.length+1).join("-");return r+this.upcomingInput()+`
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
import{_ as c,l as te,c as W,K as fe,a7 as ye,a8 as be,a9 as me,a2 as _e,H as Y,i as G,v as Ee,J as ke,a3 as Se,a4 as le,a5 as ce}from"./mermaid-vendor-D0f_SE0h.js";import"./feature-graph-NODQb6qW.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var $=function(){var t=c(function(_,i,n,a){for(n=n||{},a=_.length;a--;n[_[a]]=i);return n},"o"),g=[1,4],d=[1,13],r=[1,12],p=[1,15],E=[1,16],f=[1,20],h=[1,19],L=[6,7,8],C=[1,26],w=[1,24],N=[1,25],s=[6,7,11],H=[1,31],x=[6,7,11,24],P=[1,6,13,16,17,20,23],M=[1,35],U=[1,36],A=[1,6,7,11,13,16,17,20,23],j=[1,38],V={trace:c(function(){},"trace"),yy:{},symbols_:{error:2,start:3,mindMap:4,spaceLines:5,SPACELINE:6,NL:7,KANBAN:8,document:9,stop:10,EOF:11,statement:12,SPACELIST:13,node:14,shapeData:15,ICON:16,CLASS:17,nodeWithId:18,nodeWithoutId:19,NODE_DSTART:20,NODE_DESCR:21,NODE_DEND:22,NODE_ID:23,SHAPE_DATA:24,$accept:0,$end:1},terminals_:{2:"error",6:"SPACELINE",7:"NL",8:"KANBAN",11:"EOF",13:"SPACELIST",16:"ICON",17:"CLASS",20:"NODE_DSTART",21:"NODE_DESCR",22:"NODE_DEND",23:"NODE_ID",24:"SHAPE_DATA"},productions_:[0,[3,1],[3,2],[5,1],[5,2],[5,2],[4,2],[4,3],[10,1],[10,1],[10,1],[10,2],[10,2],[9,3],[9,2],[12,3],[12,2],[12,2],[12,2],[12,1],[12,2],[12,1],[12,1],[12,1],[12,1],[14,1],[14,1],[19,3],[18,1],[18,4],[15,2],[15,1]],performAction:c(function(i,n,a,o,u,e,B){var l=e.length-1;switch(u){case 6:case 7:return o;case 8:o.getLogger().trace("Stop NL ");break;case 9:o.getLogger().trace("Stop EOF ");break;case 11:o.getLogger().trace("Stop NL2 ");break;case 12:o.getLogger().trace("Stop EOF2 ");break;case 15:o.getLogger().info("Node: ",e[l-1].id),o.addNode(e[l-2].length,e[l-1].id,e[l-1].descr,e[l-1].type,e[l]);break;case 16:o.getLogger().info("Node: ",e[l].id),o.addNode(e[l-1].length,e[l].id,e[l].descr,e[l].type);break;case 17:o.getLogger().trace("Icon: ",e[l]),o.decorateNode({icon:e[l]});break;case 18:case 23:o.decorateNode({class:e[l]});break;case 19:o.getLogger().trace("SPACELIST");break;case 20:o.getLogger().trace("Node: ",e[l-1].id),o.addNode(0,e[l-1].id,e[l-1].descr,e[l-1].type,e[l]);break;case 21:o.getLogger().trace("Node: ",e[l].id),o.addNode(0,e[l].id,e[l].descr,e[l].type);break;case 22:o.decorateNode({icon:e[l]});break;case 27:o.getLogger().trace("node found ..",e[l-2]),this.$={id:e[l-1],descr:e[l-1],type:o.getType(e[l-2],e[l])};break;case 28:this.$={id:e[l],descr:e[l],type:0};break;case 29:o.getLogger().trace("node found ..",e[l-3]),this.$={id:e[l-3],descr:e[l-1],type:o.getType(e[l-2],e[l])};break;case 30:this.$=e[l-1]+e[l];break;case 31:this.$=e[l];break}},"anonymous"),table:[{3:1,4:2,5:3,6:[1,5],8:g},{1:[3]},{1:[2,1]},{4:6,6:[1,7],7:[1,8],8:g},{6:d,7:[1,10],9:9,12:11,13:r,14:14,16:p,17:E,18:17,19:18,20:f,23:h},t(L,[2,3]),{1:[2,2]},t(L,[2,4]),t(L,[2,5]),{1:[2,6],6:d,12:21,13:r,14:14,16:p,17:E,18:17,19:18,20:f,23:h},{6:d,9:22,12:11,13:r,14:14,16:p,17:E,18:17,19:18,20:f,23:h},{6:C,7:w,10:23,11:N},t(s,[2,24],{18:17,19:18,14:27,16:[1,28],17:[1,29],20:f,23:h}),t(s,[2,19]),t(s,[2,21],{15:30,24:H}),t(s,[2,22]),t(s,[2,23]),t(x,[2,25]),t(x,[2,26]),t(x,[2,28],{20:[1,32]}),{21:[1,33]},{6:C,7:w,10:34,11:N},{1:[2,7],6:d,12:21,13:r,14:14,16:p,17:E,18:17,19:18,20:f,23:h},t(P,[2,14],{7:M,11:U}),t(A,[2,8]),t(A,[2,9]),t(A,[2,10]),t(s,[2,16],{15:37,24:H}),t(s,[2,17]),t(s,[2,18]),t(s,[2,20],{24:j}),t(x,[2,31]),{21:[1,39]},{22:[1,40]},t(P,[2,13],{7:M,11:U}),t(A,[2,11]),t(A,[2,12]),t(s,[2,15],{24:j}),t(x,[2,30]),{22:[1,41]},t(x,[2,27]),t(x,[2,29])],defaultActions:{2:[2,1],6:[2,2]},parseError:c(function(i,n){if(n.recoverable)this.trace(i);else{var a=new Error(i);throw a.hash=n,a}},"parseError"),parse:c(function(i){var n=this,a=[0],o=[],u=[null],e=[],B=this.table,l="",z=0,se=0,ue=2,re=1,ge=e.slice.call(arguments,1),b=Object.create(this.lexer),T={yy:{}};for(var J in this.yy)Object.prototype.hasOwnProperty.call(this.yy,J)&&(T.yy[J]=this.yy[J]);b.setInput(i,T.yy),T.yy.lexer=b,T.yy.parser=this,typeof b.yylloc>"u"&&(b.yylloc={});var q=b.yylloc;e.push(q);var de=b.options&&b.options.ranges;typeof T.yy.parseError=="function"?this.parseError=T.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function pe(S){a.length=a.length-2*S,u.length=u.length-S,e.length=e.length-S}c(pe,"popStack");function ae(){var S;return S=o.pop()||b.lex()||re,typeof S!="number"&&(S instanceof Array&&(o=S,S=o.pop()),S=n.symbols_[S]||S),S}c(ae,"lex");for(var k,R,v,Q,F={},K,I,oe,X;;){if(R=a[a.length-1],this.defaultActions[R]?v=this.defaultActions[R]:((k===null||typeof k>"u")&&(k=ae()),v=B[R]&&B[R][k]),typeof v>"u"||!v.length||!v[0]){var Z="";X=[];for(K in B[R])this.terminals_[K]&&K>ue&&X.push("'"+this.terminals_[K]+"'");b.showPosition?Z="Parse error on line "+(z+1)+`:
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import{_ as c,l as te,c as W,K as fe,a7 as ye,a8 as be,a9 as me,a2 as _e,H as Y,i as G,v as Ee,J as ke,a3 as Se,a4 as le,a5 as ce}from"./mermaid-vendor-CR44n-lC.js";import"./feature-graph-wF7LCIjH.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var $=function(){var t=c(function(_,i,n,a){for(n=n||{},a=_.length;a--;n[_[a]]=i);return n},"o"),g=[1,4],d=[1,13],r=[1,12],p=[1,15],E=[1,16],f=[1,20],h=[1,19],L=[6,7,8],C=[1,26],w=[1,24],N=[1,25],s=[6,7,11],H=[1,31],x=[6,7,11,24],P=[1,6,13,16,17,20,23],M=[1,35],U=[1,36],A=[1,6,7,11,13,16,17,20,23],j=[1,38],V={trace:c(function(){},"trace"),yy:{},symbols_:{error:2,start:3,mindMap:4,spaceLines:5,SPACELINE:6,NL:7,KANBAN:8,document:9,stop:10,EOF:11,statement:12,SPACELIST:13,node:14,shapeData:15,ICON:16,CLASS:17,nodeWithId:18,nodeWithoutId:19,NODE_DSTART:20,NODE_DESCR:21,NODE_DEND:22,NODE_ID:23,SHAPE_DATA:24,$accept:0,$end:1},terminals_:{2:"error",6:"SPACELINE",7:"NL",8:"KANBAN",11:"EOF",13:"SPACELIST",16:"ICON",17:"CLASS",20:"NODE_DSTART",21:"NODE_DESCR",22:"NODE_DEND",23:"NODE_ID",24:"SHAPE_DATA"},productions_:[0,[3,1],[3,2],[5,1],[5,2],[5,2],[4,2],[4,3],[10,1],[10,1],[10,1],[10,2],[10,2],[9,3],[9,2],[12,3],[12,2],[12,2],[12,2],[12,1],[12,2],[12,1],[12,1],[12,1],[12,1],[14,1],[14,1],[19,3],[18,1],[18,4],[15,2],[15,1]],performAction:c(function(i,n,a,o,u,e,B){var l=e.length-1;switch(u){case 6:case 7:return o;case 8:o.getLogger().trace("Stop NL ");break;case 9:o.getLogger().trace("Stop EOF ");break;case 11:o.getLogger().trace("Stop NL2 ");break;case 12:o.getLogger().trace("Stop EOF2 ");break;case 15:o.getLogger().info("Node: ",e[l-1].id),o.addNode(e[l-2].length,e[l-1].id,e[l-1].descr,e[l-1].type,e[l]);break;case 16:o.getLogger().info("Node: ",e[l].id),o.addNode(e[l-1].length,e[l].id,e[l].descr,e[l].type);break;case 17:o.getLogger().trace("Icon: ",e[l]),o.decorateNode({icon:e[l]});break;case 18:case 23:o.decorateNode({class:e[l]});break;case 19:o.getLogger().trace("SPACELIST");break;case 20:o.getLogger().trace("Node: ",e[l-1].id),o.addNode(0,e[l-1].id,e[l-1].descr,e[l-1].type,e[l]);break;case 21:o.getLogger().trace("Node: ",e[l].id),o.addNode(0,e[l].id,e[l].descr,e[l].type);break;case 22:o.decorateNode({icon:e[l]});break;case 27:o.getLogger().trace("node found ..",e[l-2]),this.$={id:e[l-1],descr:e[l-1],type:o.getType(e[l-2],e[l])};break;case 28:this.$={id:e[l],descr:e[l],type:0};break;case 29:o.getLogger().trace("node found ..",e[l-3]),this.$={id:e[l-3],descr:e[l-1],type:o.getType(e[l-2],e[l])};break;case 30:this.$=e[l-1]+e[l];break;case 31:this.$=e[l];break}},"anonymous"),table:[{3:1,4:2,5:3,6:[1,5],8:g},{1:[3]},{1:[2,1]},{4:6,6:[1,7],7:[1,8],8:g},{6:d,7:[1,10],9:9,12:11,13:r,14:14,16:p,17:E,18:17,19:18,20:f,23:h},t(L,[2,3]),{1:[2,2]},t(L,[2,4]),t(L,[2,5]),{1:[2,6],6:d,12:21,13:r,14:14,16:p,17:E,18:17,19:18,20:f,23:h},{6:d,9:22,12:11,13:r,14:14,16:p,17:E,18:17,19:18,20:f,23:h},{6:C,7:w,10:23,11:N},t(s,[2,24],{18:17,19:18,14:27,16:[1,28],17:[1,29],20:f,23:h}),t(s,[2,19]),t(s,[2,21],{15:30,24:H}),t(s,[2,22]),t(s,[2,23]),t(x,[2,25]),t(x,[2,26]),t(x,[2,28],{20:[1,32]}),{21:[1,33]},{6:C,7:w,10:34,11:N},{1:[2,7],6:d,12:21,13:r,14:14,16:p,17:E,18:17,19:18,20:f,23:h},t(P,[2,14],{7:M,11:U}),t(A,[2,8]),t(A,[2,9]),t(A,[2,10]),t(s,[2,16],{15:37,24:H}),t(s,[2,17]),t(s,[2,18]),t(s,[2,20],{24:j}),t(x,[2,31]),{21:[1,39]},{22:[1,40]},t(P,[2,13],{7:M,11:U}),t(A,[2,11]),t(A,[2,12]),t(s,[2,15],{24:j}),t(x,[2,30]),{22:[1,41]},t(x,[2,27]),t(x,[2,29])],defaultActions:{2:[2,1],6:[2,2]},parseError:c(function(i,n){if(n.recoverable)this.trace(i);else{var a=new Error(i);throw a.hash=n,a}},"parseError"),parse:c(function(i){var n=this,a=[0],o=[],u=[null],e=[],B=this.table,l="",z=0,se=0,ue=2,re=1,ge=e.slice.call(arguments,1),b=Object.create(this.lexer),T={yy:{}};for(var J in this.yy)Object.prototype.hasOwnProperty.call(this.yy,J)&&(T.yy[J]=this.yy[J]);b.setInput(i,T.yy),T.yy.lexer=b,T.yy.parser=this,typeof b.yylloc>"u"&&(b.yylloc={});var q=b.yylloc;e.push(q);var de=b.options&&b.options.ranges;typeof T.yy.parseError=="function"?this.parseError=T.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function pe(S){a.length=a.length-2*S,u.length=u.length-S,e.length=e.length-S}c(pe,"popStack");function ae(){var S;return S=o.pop()||b.lex()||re,typeof S!="number"&&(S instanceof Array&&(o=S,S=o.pop()),S=n.symbols_[S]||S),S}c(ae,"lex");for(var k,R,v,Q,F={},K,I,oe,X;;){if(R=a[a.length-1],this.defaultActions[R]?v=this.defaultActions[R]:((k===null||typeof k>"u")&&(k=ae()),v=B[R]&&B[R][k]),typeof v>"u"||!v.length||!v[0]){var Z="";X=[];for(K in B[R])this.terminals_[K]&&K>ue&&X.push("'"+this.terminals_[K]+"'");b.showPosition?Z="Parse error on line "+(z+1)+`:
|
||||
`+b.showPosition()+`
|
||||
Expecting `+X.join(", ")+", got '"+(this.terminals_[k]||k)+"'":Z="Parse error on line "+(z+1)+": Unexpected "+(k==re?"end of input":"'"+(this.terminals_[k]||k)+"'"),this.parseError(Z,{text:b.match,token:this.terminals_[k]||k,line:b.yylineno,loc:q,expected:X})}if(v[0]instanceof Array&&v.length>1)throw new Error("Parse Error: multiple actions possible at state: "+R+", token: "+k);switch(v[0]){case 1:a.push(k),u.push(b.yytext),e.push(b.yylloc),a.push(v[1]),k=null,se=b.yyleng,l=b.yytext,z=b.yylineno,q=b.yylloc;break;case 2:if(I=this.productions_[v[1]][1],F.$=u[u.length-I],F._$={first_line:e[e.length-(I||1)].first_line,last_line:e[e.length-1].last_line,first_column:e[e.length-(I||1)].first_column,last_column:e[e.length-1].last_column},de&&(F._$.range=[e[e.length-(I||1)].range[0],e[e.length-1].range[1]]),Q=this.performAction.apply(F,[l,se,z,T.yy,v[1],u,e].concat(ge)),typeof Q<"u")return Q;I&&(a=a.slice(0,-1*I*2),u=u.slice(0,-1*I),e=e.slice(0,-1*I)),a.push(this.productions_[v[1]][0]),u.push(F.$),e.push(F._$),oe=B[a[a.length-2]][a[a.length-1]],a.push(oe);break;case 3:return!0}}return!0},"parse")},m=function(){var _={EOF:1,parseError:c(function(n,a){if(this.yy.parser)this.yy.parser.parseError(n,a);else throw new Error(n)},"parseError"),setInput:c(function(i,n){return this.yy=n||this.yy||{},this._input=i,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:c(function(){var i=this._input[0];this.yytext+=i,this.yyleng++,this.offset++,this.match+=i,this.matched+=i;var n=i.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),i},"input"),unput:c(function(i){var n=i.length,a=i.split(/(?:\r\n?|\n)/g);this._input=i+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-n),this.offset-=n;var o=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),a.length-1&&(this.yylineno-=a.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:a?(a.length===o.length?this.yylloc.first_column:0)+o[o.length-a.length].length-a[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:c(function(){return this._more=!0,this},"more"),reject:c(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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:c(function(i){this.unput(this.match.slice(i))},"less"),pastInput:c(function(){var i=this.matched.substr(0,this.matched.length-this.match.length);return(i.length>20?"...":"")+i.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:c(function(){var i=this.match;return i.length<20&&(i+=this._input.substr(0,20-i.length)),(i.substr(0,20)+(i.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:c(function(){var i=this.pastInput(),n=new Array(i.length+1).join("-");return i+this.upcomingInput()+`
|
||||
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|
|
@ -1,4 +1,4 @@
|
|||
import{p as N}from"./chunk-4BMEZGHF-CAhtCpmT.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-D0f_SE0h.js";import{p as rt}from"./radar-MK3ICKWK-DOAXm8cx.js";import"./feature-graph-NODQb6qW.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-CtAZZJ8e.js";import"./_basePickBy-D3PHsJjq.js";import"./clone-Dm5jEAXQ.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 a=await rt("pie",t);C.debug(a),gt(a,F)},"parse")},mt=i(t=>`
|
||||
import{p as N}from"./chunk-4BMEZGHF-DM9xX3Iw.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-CR44n-lC.js";import{p as rt}from"./radar-MK3ICKWK-B97XRKGx.js";import"./feature-graph-wF7LCIjH.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-BZ3hvks1.js";import"./_basePickBy-DV1dBXEu.js";import"./clone-eVzB-9-f.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 a=await rt("pie",t);C.debug(a),gt(a,F)},"parse")},mt=i(t=>`
|
||||
.pieCircle{
|
||||
stroke: ${t.pieStrokeColor};
|
||||
stroke-width : ${t.pieStrokeWidth};
|
||||
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|
|
@ -1 +1 @@
|
|||
import{s as r,b as e,a,S as s}from"./chunk-AEK57VVT-gQ4j2jcG.js";import{_ as i}from"./mermaid-vendor-D0f_SE0h.js";import"./chunk-RZ5BOZE2-B615FLH4.js";import"./feature-graph-NODQb6qW.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var b={parser:a,get db(){return new s(2)},renderer:e,styles:r,init:i(t=>{t.state||(t.state={}),t.state.arrowMarkerAbsolute=t.arrowMarkerAbsolute},"init")};export{b as diagram};
|
||||
import{s as r,b as e,a,S as s}from"./chunk-AEK57VVT-DlsJi6tH.js";import{_ as i}from"./mermaid-vendor-CR44n-lC.js";import"./chunk-RZ5BOZE2-CdnIs5Fb.js";import"./feature-graph-wF7LCIjH.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var b={parser:a,get db(){return new s(2)},renderer:e,styles:r,init:i(t=>{t.state||(t.state={}),t.state.arrowMarkerAbsolute=t.arrowMarkerAbsolute},"init")};export{b as diagram};
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
import{_ as s,c as xt,l as T,d as q,a2 as kt,a3 as _t,a4 as bt,a5 as vt,N as nt,D as wt,a6 as St,z as Et}from"./mermaid-vendor-D0f_SE0h.js";import"./feature-graph-NODQb6qW.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var X=function(){var n=s(function(f,i,a,d){for(a=a||{},d=f.length;d--;a[f[d]]=i);return a},"o"),t=[6,8,10,11,12,14,16,17,20,21],e=[1,9],l=[1,10],r=[1,11],h=[1,12],c=[1,13],g=[1,16],m=[1,17],p={trace:s(function(){},"trace"),yy:{},symbols_:{error:2,start:3,timeline:4,document:5,EOF:6,line:7,SPACE:8,statement:9,NEWLINE:10,title:11,acc_title:12,acc_title_value:13,acc_descr:14,acc_descr_value:15,acc_descr_multiline_value:16,section:17,period_statement:18,event_statement:19,period:20,event:21,$accept:0,$end:1},terminals_:{2:"error",4:"timeline",6:"EOF",8:"SPACE",10:"NEWLINE",11:"title",12:"acc_title",13:"acc_title_value",14:"acc_descr",15:"acc_descr_value",16:"acc_descr_multiline_value",17:"section",20:"period",21:"event"},productions_:[0,[3,3],[5,0],[5,2],[7,2],[7,1],[7,1],[7,1],[9,1],[9,2],[9,2],[9,1],[9,1],[9,1],[9,1],[18,1],[19,1]],performAction:s(function(i,a,d,u,y,o,S){var k=o.length-1;switch(y){case 1:return o[k-1];case 2:this.$=[];break;case 3:o[k-1].push(o[k]),this.$=o[k-1];break;case 4:case 5:this.$=o[k];break;case 6:case 7:this.$=[];break;case 8:u.getCommonDb().setDiagramTitle(o[k].substr(6)),this.$=o[k].substr(6);break;case 9:this.$=o[k].trim(),u.getCommonDb().setAccTitle(this.$);break;case 10:case 11:this.$=o[k].trim(),u.getCommonDb().setAccDescription(this.$);break;case 12:u.addSection(o[k].substr(8)),this.$=o[k].substr(8);break;case 15:u.addTask(o[k],0,""),this.$=o[k];break;case 16:u.addEvent(o[k].substr(2)),this.$=o[k];break}},"anonymous"),table:[{3:1,4:[1,2]},{1:[3]},n(t,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:e,12:l,14:r,16:h,17:c,18:14,19:15,20:g,21:m},n(t,[2,7],{1:[2,1]}),n(t,[2,3]),{9:18,11:e,12:l,14:r,16:h,17:c,18:14,19:15,20:g,21:m},n(t,[2,5]),n(t,[2,6]),n(t,[2,8]),{13:[1,19]},{15:[1,20]},n(t,[2,11]),n(t,[2,12]),n(t,[2,13]),n(t,[2,14]),n(t,[2,15]),n(t,[2,16]),n(t,[2,4]),n(t,[2,9]),n(t,[2,10])],defaultActions:{},parseError:s(function(i,a){if(a.recoverable)this.trace(i);else{var d=new Error(i);throw d.hash=a,d}},"parseError"),parse:s(function(i){var a=this,d=[0],u=[],y=[null],o=[],S=this.table,k="",M=0,P=0,B=2,J=1,O=o.slice.call(arguments,1),_=Object.create(this.lexer),E={yy:{}};for(var v in this.yy)Object.prototype.hasOwnProperty.call(this.yy,v)&&(E.yy[v]=this.yy[v]);_.setInput(i,E.yy),E.yy.lexer=_,E.yy.parser=this,typeof _.yylloc>"u"&&(_.yylloc={});var L=_.yylloc;o.push(L);var A=_.options&&_.options.ranges;typeof E.yy.parseError=="function"?this.parseError=E.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function R(I){d.length=d.length-2*I,y.length=y.length-I,o.length=o.length-I}s(R,"popStack");function z(){var I;return I=u.pop()||_.lex()||J,typeof I!="number"&&(I instanceof Array&&(u=I,I=u.pop()),I=a.symbols_[I]||I),I}s(z,"lex");for(var w,C,N,K,F={},j,$,et,G;;){if(C=d[d.length-1],this.defaultActions[C]?N=this.defaultActions[C]:((w===null||typeof w>"u")&&(w=z()),N=S[C]&&S[C][w]),typeof N>"u"||!N.length||!N[0]){var Q="";G=[];for(j in S[C])this.terminals_[j]&&j>B&&G.push("'"+this.terminals_[j]+"'");_.showPosition?Q="Parse error on line "+(M+1)+`:
|
||||
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-CR44n-lC.js";import"./feature-graph-wF7LCIjH.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 a},"o"),t=[6,8,10,11,12,14,16,17,20,21],e=[1,9],l=[1,10],r=[1,11],h=[1,12],c=[1,13],g=[1,16],m=[1,17],p={trace:s(function(){},"trace"),yy:{},symbols_:{error:2,start:3,timeline:4,document:5,EOF:6,line:7,SPACE:8,statement:9,NEWLINE:10,title:11,acc_title:12,acc_title_value:13,acc_descr:14,acc_descr_value:15,acc_descr_multiline_value:16,section:17,period_statement:18,event_statement:19,period:20,event:21,$accept:0,$end:1},terminals_:{2:"error",4:"timeline",6:"EOF",8:"SPACE",10:"NEWLINE",11:"title",12:"acc_title",13:"acc_title_value",14:"acc_descr",15:"acc_descr_value",16:"acc_descr_multiline_value",17:"section",20:"period",21:"event"},productions_:[0,[3,3],[5,0],[5,2],[7,2],[7,1],[7,1],[7,1],[9,1],[9,2],[9,2],[9,1],[9,1],[9,1],[9,1],[18,1],[19,1]],performAction:s(function(i,a,d,u,y,o,S){var k=o.length-1;switch(y){case 1:return o[k-1];case 2:this.$=[];break;case 3:o[k-1].push(o[k]),this.$=o[k-1];break;case 4:case 5:this.$=o[k];break;case 6:case 7:this.$=[];break;case 8:u.getCommonDb().setDiagramTitle(o[k].substr(6)),this.$=o[k].substr(6);break;case 9:this.$=o[k].trim(),u.getCommonDb().setAccTitle(this.$);break;case 10:case 11:this.$=o[k].trim(),u.getCommonDb().setAccDescription(this.$);break;case 12:u.addSection(o[k].substr(8)),this.$=o[k].substr(8);break;case 15:u.addTask(o[k],0,""),this.$=o[k];break;case 16:u.addEvent(o[k].substr(2)),this.$=o[k];break}},"anonymous"),table:[{3:1,4:[1,2]},{1:[3]},n(t,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:e,12:l,14:r,16:h,17:c,18:14,19:15,20:g,21:m},n(t,[2,7],{1:[2,1]}),n(t,[2,3]),{9:18,11:e,12:l,14:r,16:h,17:c,18:14,19:15,20:g,21:m},n(t,[2,5]),n(t,[2,6]),n(t,[2,8]),{13:[1,19]},{15:[1,20]},n(t,[2,11]),n(t,[2,12]),n(t,[2,13]),n(t,[2,14]),n(t,[2,15]),n(t,[2,16]),n(t,[2,4]),n(t,[2,9]),n(t,[2,10])],defaultActions:{},parseError:s(function(i,a){if(a.recoverable)this.trace(i);else{var d=new Error(i);throw d.hash=a,d}},"parseError"),parse:s(function(i){var a=this,d=[0],u=[],y=[null],o=[],S=this.table,k="",M=0,P=0,B=2,J=1,O=o.slice.call(arguments,1),_=Object.create(this.lexer),E={yy:{}};for(var v in this.yy)Object.prototype.hasOwnProperty.call(this.yy,v)&&(E.yy[v]=this.yy[v]);_.setInput(i,E.yy),E.yy.lexer=_,E.yy.parser=this,typeof _.yylloc>"u"&&(_.yylloc={});var L=_.yylloc;o.push(L);var A=_.options&&_.options.ranges;typeof E.yy.parseError=="function"?this.parseError=E.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function R(I){d.length=d.length-2*I,y.length=y.length-I,o.length=o.length-I}s(R,"popStack");function z(){var I;return I=u.pop()||_.lex()||J,typeof I!="number"&&(I instanceof Array&&(u=I,I=u.pop()),I=a.symbols_[I]||I),I}s(z,"lex");for(var w,C,N,K,F={},j,$,et,G;;){if(C=d[d.length-1],this.defaultActions[C]?N=this.defaultActions[C]:((w===null||typeof w>"u")&&(w=z()),N=S[C]&&S[C][w]),typeof N>"u"||!N.length||!N[0]){var Q="";G=[];for(j in S[C])this.terminals_[j]&&j>B&&G.push("'"+this.terminals_[j]+"'");_.showPosition?Q="Parse error on line "+(M+1)+`:
|
||||
`+_.showPosition()+`
|
||||
Expecting `+G.join(", ")+", got '"+(this.terminals_[w]||w)+"'":Q="Parse error on line "+(M+1)+": Unexpected "+(w==J?"end of input":"'"+(this.terminals_[w]||w)+"'"),this.parseError(Q,{text:_.match,token:this.terminals_[w]||w,line:_.yylineno,loc:L,expected:G})}if(N[0]instanceof Array&&N.length>1)throw new Error("Parse Error: multiple actions possible at state: "+C+", token: "+w);switch(N[0]){case 1:d.push(w),y.push(_.yytext),o.push(_.yylloc),d.push(N[1]),w=null,P=_.yyleng,k=_.yytext,M=_.yylineno,L=_.yylloc;break;case 2:if($=this.productions_[N[1]][1],F.$=y[y.length-$],F._$={first_line:o[o.length-($||1)].first_line,last_line:o[o.length-1].last_line,first_column:o[o.length-($||1)].first_column,last_column:o[o.length-1].last_column},A&&(F._$.range=[o[o.length-($||1)].range[0],o[o.length-1].range[1]]),K=this.performAction.apply(F,[k,P,M,E.yy,N[1],y,o].concat(O)),typeof K<"u")return K;$&&(d=d.slice(0,-1*$*2),y=y.slice(0,-1*$),o=o.slice(0,-1*$)),d.push(this.productions_[N[1]][0]),y.push(F.$),o.push(F._$),et=S[d[d.length-2]][d[d.length-1]],d.push(et);break;case 3:return!0}}return!0},"parse")},x=function(){var f={EOF:1,parseError:s(function(a,d){if(this.yy.parser)this.yy.parser.parseError(a,d);else throw new Error(a)},"parseError"),setInput:s(function(i,a){return this.yy=a||this.yy||{},this._input=i,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 i=this._input[0];this.yytext+=i,this.yyleng++,this.offset++,this.match+=i,this.matched+=i;var a=i.match(/(?:\r\n?|\n).*/g);return a?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),i},"input"),unput:s(function(i){var a=i.length,d=i.split(/(?:\r\n?|\n)/g);this._input=i+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-a),this.offset-=a;var u=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),d.length-1&&(this.yylineno-=d.length-1);var y=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:d?(d.length===u.length?this.yylloc.first_column:0)+u[u.length-d.length].length-d[0].length:this.yylloc.first_column-a},this.options.ranges&&(this.yylloc.range=[y[0],y[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)+`. 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:s(function(i){this.unput(this.match.slice(i))},"less"),pastInput:s(function(){var i=this.matched.substr(0,this.matched.length-this.match.length);return(i.length>20?"...":"")+i.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:s(function(){var i=this.match;return i.length<20&&(i+=this._input.substr(0,20-i.length)),(i.substr(0,20)+(i.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:s(function(){var i=this.pastInput(),a=new Array(i.length+1).join("-");return i+this.upcomingInput()+`
|
||||
File diff suppressed because one or more lines are too long
12
lightrag/api/webui/index.html
generated
12
lightrag/api/webui/index.html
generated
|
|
@ -8,16 +8,16 @@
|
|||
<link rel="icon" type="image/png" href="favicon.png" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Lightrag</title>
|
||||
<script type="module" crossorigin src="/webui/assets/index-yRRg2BZk.js"></script>
|
||||
<script type="module" crossorigin src="/webui/assets/index-D3V9EKqf.js"></script>
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/react-vendor-DEwriMA6.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/ui-vendor-CeCm8EER.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/graph-vendor-B-X5JegA.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/utils-vendor-BysuhMZA.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/feature-graph-NODQb6qW.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/mermaid-vendor-D0f_SE0h.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/markdown-vendor-DmIvJdn7.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/feature-retrieval-DalFy9WB.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/feature-documents-oks3sUnM.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/feature-graph-wF7LCIjH.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/mermaid-vendor-CR44n-lC.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/markdown-vendor-ZbbHR4ge.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/feature-retrieval-DdCvVec9.js">
|
||||
<link rel="modulepreload" crossorigin href="/webui/assets/feature-documents-DZY3tMAq.js">
|
||||
<link rel="stylesheet" crossorigin href="/webui/assets/feature-graph-BipNuM18.css">
|
||||
<link rel="stylesheet" crossorigin href="/webui/assets/index-DwO2XWaU.css">
|
||||
</head>
|
||||
|
|
|
|||
|
|
@ -14,7 +14,16 @@ from typing import (
|
|||
)
|
||||
from .utils import EmbeddingFunc
|
||||
from .types import KnowledgeGraph
|
||||
from .constants import GRAPH_FIELD_SEP
|
||||
from .constants import (
|
||||
GRAPH_FIELD_SEP,
|
||||
DEFAULT_TOP_K,
|
||||
DEFAULT_CHUNK_TOP_K,
|
||||
DEFAULT_MAX_ENTITY_TOKENS,
|
||||
DEFAULT_MAX_RELATION_TOKENS,
|
||||
DEFAULT_MAX_TOTAL_TOKENS,
|
||||
DEFAULT_HISTORY_TURNS,
|
||||
DEFAULT_ENABLE_RERANK,
|
||||
)
|
||||
|
||||
# use the .env that is inside the current folder
|
||||
# allows to use different .env file for each lightrag instance
|
||||
|
|
@ -36,7 +45,7 @@ T = TypeVar("T")
|
|||
class QueryParam:
|
||||
"""Configuration parameters for query execution in LightRAG."""
|
||||
|
||||
mode: Literal["local", "global", "hybrid", "naive", "mix", "bypass"] = "global"
|
||||
mode: Literal["local", "global", "hybrid", "naive", "mix", "bypass"] = "mix"
|
||||
"""Specifies the retrieval mode:
|
||||
- "local": Focuses on context-dependent information.
|
||||
- "global": Utilizes global knowledge.
|
||||
|
|
@ -57,29 +66,28 @@ class QueryParam:
|
|||
stream: bool = False
|
||||
"""If True, enables streaming output for real-time responses."""
|
||||
|
||||
top_k: int = int(os.getenv("TOP_K", "60"))
|
||||
top_k: int = int(os.getenv("TOP_K", str(DEFAULT_TOP_K)))
|
||||
"""Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode."""
|
||||
|
||||
chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", "5"))
|
||||
"""Number of text chunks to retrieve initially from vector search.
|
||||
chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", str(DEFAULT_CHUNK_TOP_K)))
|
||||
"""Number of text chunks to retrieve initially from vector search and keep after reranking.
|
||||
If None, defaults to top_k value.
|
||||
"""
|
||||
|
||||
chunk_rerank_top_k: int = int(os.getenv("CHUNK_RERANK_TOP_K", "5"))
|
||||
"""Number of text chunks to keep after reranking.
|
||||
If None, keeps all chunks returned from initial retrieval.
|
||||
"""
|
||||
|
||||
max_token_for_text_unit: int = int(os.getenv("MAX_TOKEN_TEXT_CHUNK", "6000"))
|
||||
"""Maximum number of tokens allowed for each retrieved text chunk."""
|
||||
|
||||
max_token_for_global_context: int = int(
|
||||
os.getenv("MAX_TOKEN_RELATION_DESC", "4000")
|
||||
max_entity_tokens: int = int(
|
||||
os.getenv("MAX_ENTITY_TOKENS", str(DEFAULT_MAX_ENTITY_TOKENS))
|
||||
)
|
||||
"""Maximum number of tokens allocated for relationship descriptions in global retrieval."""
|
||||
"""Maximum number of tokens allocated for entity context in unified token control system."""
|
||||
|
||||
max_token_for_local_context: int = int(os.getenv("MAX_TOKEN_ENTITY_DESC", "4000"))
|
||||
"""Maximum number of tokens allocated for entity descriptions in local retrieval."""
|
||||
max_relation_tokens: int = int(
|
||||
os.getenv("MAX_RELATION_TOKENS", str(DEFAULT_MAX_RELATION_TOKENS))
|
||||
)
|
||||
"""Maximum number of tokens allocated for relationship context in unified token control system."""
|
||||
|
||||
max_total_tokens: int = int(
|
||||
os.getenv("MAX_TOTAL_TOKENS", str(DEFAULT_MAX_TOTAL_TOKENS))
|
||||
)
|
||||
"""Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt)."""
|
||||
|
||||
hl_keywords: list[str] = field(default_factory=list)
|
||||
"""List of high-level keywords to prioritize in retrieval."""
|
||||
|
|
@ -92,7 +100,7 @@ class QueryParam:
|
|||
Format: [{"role": "user/assistant", "content": "message"}].
|
||||
"""
|
||||
|
||||
history_turns: int = 3
|
||||
history_turns: int = int(os.getenv("HISTORY_TURNS", str(DEFAULT_HISTORY_TURNS)))
|
||||
"""Number of complete conversation turns (user-assistant pairs) to consider in the response context."""
|
||||
|
||||
ids: list[str] | None = None
|
||||
|
|
@ -109,6 +117,13 @@ class QueryParam:
|
|||
If proivded, this will be use instead of the default vaulue from prompt template.
|
||||
"""
|
||||
|
||||
enable_rerank: bool = (
|
||||
os.getenv("ENABLE_RERANK", str(DEFAULT_ENABLE_RERANK).lower()).lower() == "true"
|
||||
)
|
||||
"""Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued.
|
||||
Default is True to enable reranking when rerank model is available.
|
||||
"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class StorageNameSpace(ABC):
|
||||
|
|
|
|||
|
|
@ -12,6 +12,17 @@ DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE = 4
|
|||
DEFAULT_WOKERS = 2
|
||||
DEFAULT_TIMEOUT = 150
|
||||
|
||||
# Query and retrieval configuration defaults
|
||||
DEFAULT_TOP_K = 40
|
||||
DEFAULT_CHUNK_TOP_K = 10
|
||||
DEFAULT_MAX_ENTITY_TOKENS = 10000
|
||||
DEFAULT_MAX_RELATION_TOKENS = 10000
|
||||
DEFAULT_MAX_TOTAL_TOKENS = 32000
|
||||
DEFAULT_HISTORY_TURNS = 3
|
||||
DEFAULT_ENABLE_RERANK = True
|
||||
DEFAULT_COSINE_THRESHOLD = 0.2
|
||||
DEFAULT_RELATED_CHUNK_NUMBER = 10
|
||||
|
||||
# Separator for graph fields
|
||||
GRAPH_FIELD_SEP = "<SEP>"
|
||||
|
||||
|
|
|
|||
|
|
@ -185,10 +185,6 @@ class FaissVectorDBStorage(BaseVectorStorage):
|
|||
embedding = np.array(embedding, dtype=np.float32)
|
||||
faiss.normalize_L2(embedding) # we do in-place normalization
|
||||
|
||||
logger.info(
|
||||
f"Query: {query}, top_k: {top_k}, threshold: {self.cosine_better_than_threshold}"
|
||||
)
|
||||
|
||||
# Perform the similarity search
|
||||
index = await self._get_index()
|
||||
distances, indices = index.search(embedding, top_k)
|
||||
|
|
|
|||
|
|
@ -20,6 +20,9 @@ def direct_log(message, enable_output: bool = False, level: str = "DEBUG"):
|
|||
level: Log level (default: "DEBUG")
|
||||
enable_output: Whether to actually output the log (default: True)
|
||||
"""
|
||||
if not enable_output:
|
||||
return
|
||||
|
||||
# Get the current logger level from the lightrag logger
|
||||
try:
|
||||
from lightrag.utils import logger
|
||||
|
|
@ -40,7 +43,7 @@ def direct_log(message, enable_output: bool = False, level: str = "DEBUG"):
|
|||
message_level = level_mapping.get(level.upper(), logging.DEBUG)
|
||||
|
||||
# print(f"Diret_log: {level.upper()} {message_level} ? {current_level}", file=sys.stderr, flush=True)
|
||||
if enable_output or (message_level >= current_level):
|
||||
if message_level >= current_level:
|
||||
print(f"{level}: {message}", file=sys.stderr, flush=True)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -24,6 +24,13 @@ from typing import (
|
|||
from lightrag.constants import (
|
||||
DEFAULT_MAX_GLEANING,
|
||||
DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE,
|
||||
DEFAULT_TOP_K,
|
||||
DEFAULT_CHUNK_TOP_K,
|
||||
DEFAULT_MAX_ENTITY_TOKENS,
|
||||
DEFAULT_MAX_RELATION_TOKENS,
|
||||
DEFAULT_MAX_TOTAL_TOKENS,
|
||||
DEFAULT_COSINE_THRESHOLD,
|
||||
DEFAULT_RELATED_CHUNK_NUMBER,
|
||||
)
|
||||
from lightrag.utils import get_env_value
|
||||
|
||||
|
|
@ -125,6 +132,42 @@ class LightRAG:
|
|||
log_level: int | None = field(default=None)
|
||||
log_file_path: str | None = field(default=None)
|
||||
|
||||
# Query parameters
|
||||
# ---
|
||||
|
||||
top_k: int = field(default=get_env_value("TOP_K", DEFAULT_TOP_K, int))
|
||||
"""Number of entities/relations to retrieve for each query."""
|
||||
|
||||
chunk_top_k: int = field(
|
||||
default=get_env_value("CHUNK_TOP_K", DEFAULT_CHUNK_TOP_K, int)
|
||||
)
|
||||
"""Maximum number of chunks in context."""
|
||||
|
||||
max_entity_tokens: int = field(
|
||||
default=get_env_value("MAX_ENTITY_TOKENS", DEFAULT_MAX_ENTITY_TOKENS, int)
|
||||
)
|
||||
"""Maximum number of tokens for entity in context."""
|
||||
|
||||
max_relation_tokens: int = field(
|
||||
default=get_env_value("MAX_RELATION_TOKENS", DEFAULT_MAX_RELATION_TOKENS, int)
|
||||
)
|
||||
"""Maximum number of tokens for relation in context."""
|
||||
|
||||
max_total_tokens: int = field(
|
||||
default=get_env_value("MAX_TOTAL_TOKENS", DEFAULT_MAX_TOTAL_TOKENS, int)
|
||||
)
|
||||
"""Maximum total tokens in context (including system prompt, entities, relations and chunks)."""
|
||||
|
||||
cosine_threshold: int = field(
|
||||
default=get_env_value("COSINE_THRESHOLD", DEFAULT_COSINE_THRESHOLD, int)
|
||||
)
|
||||
"""Cosine threshold of vector DB retrieval for entities, relations and chunks."""
|
||||
|
||||
related_chunk_number: int = field(
|
||||
default=get_env_value("RELATED_CHUNK_NUMBER", DEFAULT_RELATED_CHUNK_NUMBER, int)
|
||||
)
|
||||
"""Number of related chunks to grab from single entity or relation."""
|
||||
|
||||
# Entity extraction
|
||||
# ---
|
||||
|
||||
|
|
@ -238,11 +281,6 @@ class LightRAG:
|
|||
# Rerank Configuration
|
||||
# ---
|
||||
|
||||
enable_rerank: bool = field(
|
||||
default=bool(os.getenv("ENABLE_RERANK", "False").lower() == "true")
|
||||
)
|
||||
"""Enable reranking for improved retrieval quality. Defaults to False."""
|
||||
|
||||
rerank_model_func: Callable[..., object] | None = field(default=None)
|
||||
"""Function for reranking retrieved documents. All rerank configurations (model name, API keys, top_k, etc.) should be included in this function. Optional."""
|
||||
|
||||
|
|
@ -454,9 +492,9 @@ class LightRAG:
|
|||
)
|
||||
|
||||
# Init Rerank
|
||||
if self.enable_rerank and self.rerank_model_func:
|
||||
if self.rerank_model_func:
|
||||
logger.info("Rerank model initialized for improved retrieval quality")
|
||||
elif self.enable_rerank and not self.rerank_model_func:
|
||||
else:
|
||||
logger.warning(
|
||||
"Rerank is enabled but no rerank_model_func provided. Reranking will be skipped."
|
||||
)
|
||||
|
|
|
|||
|
|
@ -36,7 +36,13 @@ from .base import (
|
|||
QueryParam,
|
||||
)
|
||||
from .prompt import PROMPTS
|
||||
from .constants import GRAPH_FIELD_SEP
|
||||
from .constants import (
|
||||
GRAPH_FIELD_SEP,
|
||||
DEFAULT_MAX_ENTITY_TOKENS,
|
||||
DEFAULT_MAX_RELATION_TOKENS,
|
||||
DEFAULT_MAX_TOTAL_TOKENS,
|
||||
DEFAULT_RELATED_CHUNK_NUMBER,
|
||||
)
|
||||
from .kg.shared_storage import get_storage_keyed_lock
|
||||
import time
|
||||
from dotenv import load_dotenv
|
||||
|
|
@ -1643,7 +1649,9 @@ async def kg_query(
|
|||
|
||||
tokenizer: Tokenizer = global_config["tokenizer"]
|
||||
len_of_prompts = len(tokenizer.encode(query + sys_prompt))
|
||||
logger.debug(f"[kg_query]Prompt Tokens: {len_of_prompts}")
|
||||
logger.debug(
|
||||
f"[kg_query] Sending to LLM: {len_of_prompts:,} tokens (Query: {len(tokenizer.encode(query))}, System: {len(tokenizer.encode(sys_prompt))})"
|
||||
)
|
||||
|
||||
response = await use_model_func(
|
||||
query,
|
||||
|
|
@ -1766,7 +1774,9 @@ async def extract_keywords_only(
|
|||
|
||||
tokenizer: Tokenizer = global_config["tokenizer"]
|
||||
len_of_prompts = len(tokenizer.encode(kw_prompt))
|
||||
logger.debug(f"[kg_query]Prompt Tokens: {len_of_prompts}")
|
||||
logger.debug(
|
||||
f"[extract_keywords] Sending to LLM: {len_of_prompts:,} tokens (Prompt: {len_of_prompts})"
|
||||
)
|
||||
|
||||
# 5. Call the LLM for keyword extraction
|
||||
if param.model_func:
|
||||
|
|
@ -1883,51 +1893,63 @@ async def _build_query_context(
|
|||
entities_context = []
|
||||
relations_context = []
|
||||
|
||||
# Store original data for later text chunk retrieval
|
||||
original_node_datas = []
|
||||
original_edge_datas = []
|
||||
|
||||
# Handle local and global modes
|
||||
if query_param.mode == "local":
|
||||
entities_context, relations_context, entity_chunks = await _get_node_data(
|
||||
(
|
||||
entities_context,
|
||||
relations_context,
|
||||
node_datas,
|
||||
use_relations,
|
||||
) = await _get_node_data(
|
||||
ll_keywords,
|
||||
knowledge_graph_inst,
|
||||
entities_vdb,
|
||||
text_chunks_db,
|
||||
query_param,
|
||||
)
|
||||
all_chunks.extend(entity_chunks)
|
||||
original_node_datas = node_datas
|
||||
original_edge_datas = use_relations
|
||||
|
||||
elif query_param.mode == "global":
|
||||
entities_context, relations_context, relationship_chunks = await _get_edge_data(
|
||||
(
|
||||
entities_context,
|
||||
relations_context,
|
||||
edge_datas,
|
||||
use_entities,
|
||||
) = await _get_edge_data(
|
||||
hl_keywords,
|
||||
knowledge_graph_inst,
|
||||
relationships_vdb,
|
||||
text_chunks_db,
|
||||
query_param,
|
||||
)
|
||||
all_chunks.extend(relationship_chunks)
|
||||
original_edge_datas = edge_datas
|
||||
original_node_datas = use_entities
|
||||
|
||||
else: # hybrid or mix mode
|
||||
ll_data = await _get_node_data(
|
||||
ll_keywords,
|
||||
knowledge_graph_inst,
|
||||
entities_vdb,
|
||||
text_chunks_db,
|
||||
query_param,
|
||||
)
|
||||
hl_data = await _get_edge_data(
|
||||
hl_keywords,
|
||||
knowledge_graph_inst,
|
||||
relationships_vdb,
|
||||
text_chunks_db,
|
||||
query_param,
|
||||
)
|
||||
|
||||
(ll_entities_context, ll_relations_context, ll_chunks) = ll_data
|
||||
(hl_entities_context, hl_relations_context, hl_chunks) = hl_data
|
||||
(ll_entities_context, ll_relations_context, ll_node_datas, ll_edge_datas) = (
|
||||
ll_data
|
||||
)
|
||||
(hl_entities_context, hl_relations_context, hl_edge_datas, hl_node_datas) = (
|
||||
hl_data
|
||||
)
|
||||
|
||||
# Collect chunks from entity and relationship sources
|
||||
all_chunks.extend(ll_chunks)
|
||||
all_chunks.extend(hl_chunks)
|
||||
|
||||
# Get vector chunks if in mix mode
|
||||
# Get vector chunks first if in mix mode
|
||||
if query_param.mode == "mix" and chunks_vdb:
|
||||
vector_chunks = await _get_vector_context(
|
||||
query,
|
||||
|
|
@ -1936,33 +1958,259 @@ async def _build_query_context(
|
|||
)
|
||||
all_chunks.extend(vector_chunks)
|
||||
|
||||
# Store original data from both sources
|
||||
original_node_datas = ll_node_datas + hl_node_datas
|
||||
original_edge_datas = ll_edge_datas + hl_edge_datas
|
||||
|
||||
# Combine entities and relations contexts
|
||||
entities_context = process_combine_contexts(
|
||||
hl_entities_context, ll_entities_context
|
||||
ll_entities_context, hl_entities_context
|
||||
)
|
||||
relations_context = process_combine_contexts(
|
||||
hl_relations_context, ll_relations_context
|
||||
)
|
||||
|
||||
# Process all chunks uniformly: deduplication, reranking, and token truncation
|
||||
processed_chunks = await process_chunks_unified(
|
||||
query=query,
|
||||
chunks=all_chunks,
|
||||
query_param=query_param,
|
||||
global_config=text_chunks_db.global_config,
|
||||
source_type="mixed",
|
||||
logger.info(
|
||||
f"Initial context: {len(entities_context)} entities, {len(relations_context)} relations, {len(all_chunks)} chunks"
|
||||
)
|
||||
|
||||
# Build final text_units_context from processed chunks
|
||||
text_units_context = []
|
||||
for i, chunk in enumerate(processed_chunks):
|
||||
text_units_context.append(
|
||||
{
|
||||
"id": i + 1,
|
||||
"content": chunk["content"],
|
||||
"file_path": chunk.get("file_path", "unknown_source"),
|
||||
}
|
||||
# Unified token control system - Apply precise token limits to entities and relations
|
||||
tokenizer = text_chunks_db.global_config.get("tokenizer")
|
||||
if tokenizer:
|
||||
# Get new token limits from query_param (with fallback to global_config)
|
||||
max_entity_tokens = getattr(
|
||||
query_param,
|
||||
"max_entity_tokens",
|
||||
text_chunks_db.global_config.get(
|
||||
"max_entity_tokens", DEFAULT_MAX_ENTITY_TOKENS
|
||||
),
|
||||
)
|
||||
max_relation_tokens = getattr(
|
||||
query_param,
|
||||
"max_relation_tokens",
|
||||
text_chunks_db.global_config.get(
|
||||
"max_relation_tokens", DEFAULT_MAX_RELATION_TOKENS
|
||||
),
|
||||
)
|
||||
max_total_tokens = getattr(
|
||||
query_param,
|
||||
"max_total_tokens",
|
||||
text_chunks_db.global_config.get(
|
||||
"max_total_tokens", DEFAULT_MAX_TOTAL_TOKENS
|
||||
),
|
||||
)
|
||||
|
||||
# Truncate entities based on complete JSON serialization
|
||||
if entities_context:
|
||||
original_entity_count = len(entities_context)
|
||||
|
||||
# Process entities context to replace GRAPH_FIELD_SEP with : in file_path fields
|
||||
for entity in entities_context:
|
||||
if "file_path" in entity and entity["file_path"]:
|
||||
entity["file_path"] = entity["file_path"].replace(
|
||||
GRAPH_FIELD_SEP, ";"
|
||||
)
|
||||
|
||||
entities_context = truncate_list_by_token_size(
|
||||
entities_context,
|
||||
key=lambda x: json.dumps(x, ensure_ascii=False),
|
||||
max_token_size=max_entity_tokens,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
if len(entities_context) < original_entity_count:
|
||||
logger.debug(
|
||||
f"Truncated entities: {original_entity_count} -> {len(entities_context)} (entity max tokens: {max_entity_tokens})"
|
||||
)
|
||||
|
||||
# Truncate relations based on complete JSON serialization
|
||||
if relations_context:
|
||||
original_relation_count = len(relations_context)
|
||||
|
||||
# Process relations context to replace GRAPH_FIELD_SEP with : in file_path fields
|
||||
for relation in relations_context:
|
||||
if "file_path" in relation and relation["file_path"]:
|
||||
relation["file_path"] = relation["file_path"].replace(
|
||||
GRAPH_FIELD_SEP, ";"
|
||||
)
|
||||
|
||||
relations_context = truncate_list_by_token_size(
|
||||
relations_context,
|
||||
key=lambda x: json.dumps(x, ensure_ascii=False),
|
||||
max_token_size=max_relation_tokens,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
if len(relations_context) < original_relation_count:
|
||||
logger.debug(
|
||||
f"Truncated relations: {original_relation_count} -> {len(relations_context)} (relation max tokens: {max_relation_tokens})"
|
||||
)
|
||||
|
||||
# After truncation, get text chunks based on final entities and relations
|
||||
logger.info("Getting text chunks based on truncated entities and relations...")
|
||||
|
||||
# Create filtered data based on truncated context
|
||||
final_node_datas = []
|
||||
if entities_context and original_node_datas:
|
||||
final_entity_names = {e["entity"] for e in entities_context}
|
||||
seen_nodes = set()
|
||||
for node in original_node_datas:
|
||||
name = node.get("entity_name")
|
||||
if name in final_entity_names and name not in seen_nodes:
|
||||
final_node_datas.append(node)
|
||||
seen_nodes.add(name)
|
||||
|
||||
final_edge_datas = []
|
||||
if relations_context and original_edge_datas:
|
||||
final_relation_pairs = {(r["entity1"], r["entity2"]) for r in relations_context}
|
||||
seen_edges = set()
|
||||
for edge in original_edge_datas:
|
||||
src, tgt = edge.get("src_id"), edge.get("tgt_id")
|
||||
if src is None or tgt is None:
|
||||
src, tgt = edge.get("src_tgt", (None, None))
|
||||
|
||||
pair = (src, tgt)
|
||||
if pair in final_relation_pairs and pair not in seen_edges:
|
||||
final_edge_datas.append(edge)
|
||||
seen_edges.add(pair)
|
||||
|
||||
# Get text chunks based on final filtered data
|
||||
text_chunk_tasks = []
|
||||
|
||||
if final_node_datas:
|
||||
text_chunk_tasks.append(
|
||||
_find_most_related_text_unit_from_entities(
|
||||
final_node_datas,
|
||||
query_param,
|
||||
text_chunks_db,
|
||||
knowledge_graph_inst,
|
||||
)
|
||||
)
|
||||
|
||||
if final_edge_datas:
|
||||
text_chunk_tasks.append(
|
||||
_find_related_text_unit_from_relationships(
|
||||
final_edge_datas,
|
||||
query_param,
|
||||
text_chunks_db,
|
||||
)
|
||||
)
|
||||
|
||||
# Execute text chunk retrieval in parallel
|
||||
if text_chunk_tasks:
|
||||
text_chunk_results = await asyncio.gather(*text_chunk_tasks)
|
||||
for chunks in text_chunk_results:
|
||||
if chunks:
|
||||
all_chunks.extend(chunks)
|
||||
|
||||
# Apply token processing to chunks if tokenizer is available
|
||||
text_units_context = []
|
||||
if tokenizer and all_chunks:
|
||||
# Calculate dynamic token limit for text chunks
|
||||
entities_str = json.dumps(entities_context, ensure_ascii=False)
|
||||
relations_str = json.dumps(relations_context, ensure_ascii=False)
|
||||
|
||||
# Calculate base context tokens (entities + relations + template)
|
||||
kg_context_template = """-----Entities(KG)-----
|
||||
|
||||
```json
|
||||
{entities_str}
|
||||
```
|
||||
|
||||
-----Relationships(KG)-----
|
||||
|
||||
```json
|
||||
{relations_str}
|
||||
```
|
||||
|
||||
-----Document Chunks(DC)-----
|
||||
|
||||
```json
|
||||
[]
|
||||
```
|
||||
|
||||
"""
|
||||
kg_context = kg_context_template.format(
|
||||
entities_str=entities_str, relations_str=relations_str
|
||||
)
|
||||
kg_context_tokens = len(tokenizer.encode(kg_context))
|
||||
|
||||
# Calculate actual system prompt overhead dynamically
|
||||
# 1. Calculate conversation history tokens
|
||||
history_context = ""
|
||||
if query_param.conversation_history:
|
||||
history_context = get_conversation_turns(
|
||||
query_param.conversation_history, query_param.history_turns
|
||||
)
|
||||
history_tokens = (
|
||||
len(tokenizer.encode(history_context)) if history_context else 0
|
||||
)
|
||||
|
||||
# 2. Calculate system prompt template tokens (excluding context_data)
|
||||
user_prompt = query_param.user_prompt if query_param.user_prompt else ""
|
||||
response_type = (
|
||||
query_param.response_type
|
||||
if query_param.response_type
|
||||
else "Multiple Paragraphs"
|
||||
)
|
||||
|
||||
# Get the system prompt template from PROMPTS
|
||||
sys_prompt_template = text_chunks_db.global_config.get(
|
||||
"system_prompt_template", PROMPTS["rag_response"]
|
||||
)
|
||||
|
||||
# Create a sample system prompt with placeholders filled (excluding context_data)
|
||||
sample_sys_prompt = sys_prompt_template.format(
|
||||
history=history_context,
|
||||
context_data="", # Empty for overhead calculation
|
||||
response_type=response_type,
|
||||
user_prompt=user_prompt,
|
||||
)
|
||||
sys_prompt_template_tokens = len(tokenizer.encode(sample_sys_prompt))
|
||||
|
||||
# Total system prompt overhead = template + query tokens
|
||||
query_tokens = len(tokenizer.encode(query))
|
||||
sys_prompt_overhead = sys_prompt_template_tokens + query_tokens
|
||||
|
||||
buffer_tokens = 100 # Safety buffer as requested
|
||||
|
||||
# Calculate available tokens for text chunks
|
||||
used_tokens = kg_context_tokens + sys_prompt_overhead + buffer_tokens
|
||||
available_chunk_tokens = max_total_tokens - used_tokens
|
||||
|
||||
logger.debug(
|
||||
f"Token allocation - Total: {max_total_tokens}, History: {history_tokens}, SysPrompt: {sys_prompt_overhead}, KG: {kg_context_tokens}, Buffer: {buffer_tokens}, Available for chunks: {available_chunk_tokens}"
|
||||
)
|
||||
|
||||
# Re-process chunks with dynamic token limit
|
||||
if all_chunks:
|
||||
# Create a temporary query_param copy with adjusted chunk token limit
|
||||
temp_chunks = [
|
||||
{"content": chunk["content"], "file_path": chunk["file_path"]}
|
||||
for chunk in all_chunks
|
||||
]
|
||||
|
||||
# Apply token truncation to chunks using the dynamic limit
|
||||
truncated_chunks = await process_chunks_unified(
|
||||
query=query,
|
||||
chunks=temp_chunks,
|
||||
query_param=query_param,
|
||||
global_config=text_chunks_db.global_config,
|
||||
source_type="mixed",
|
||||
chunk_token_limit=available_chunk_tokens, # Pass dynamic limit
|
||||
)
|
||||
|
||||
# Rebuild text_units_context with truncated chunks
|
||||
for i, chunk in enumerate(truncated_chunks):
|
||||
text_units_context.append(
|
||||
{
|
||||
"id": i + 1,
|
||||
"content": chunk["content"],
|
||||
"file_path": chunk.get("file_path", "unknown_source"),
|
||||
}
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
f"Re-truncated chunks for dynamic token limit: {len(temp_chunks)} -> {len(text_units_context)} (chunk available tokens: {available_chunk_tokens})"
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Final context: {len(entities_context)} entities, {len(relations_context)} relations, {len(text_units_context)} chunks"
|
||||
|
|
@ -2002,7 +2250,6 @@ async def _get_node_data(
|
|||
query: str,
|
||||
knowledge_graph_inst: BaseGraphStorage,
|
||||
entities_vdb: BaseVectorStorage,
|
||||
text_chunks_db: BaseKVStorage,
|
||||
query_param: QueryParam,
|
||||
):
|
||||
# get similar entities
|
||||
|
|
@ -2015,7 +2262,7 @@ async def _get_node_data(
|
|||
)
|
||||
|
||||
if not len(results):
|
||||
return "", "", ""
|
||||
return "", "", [], []
|
||||
|
||||
# Extract all entity IDs from your results list
|
||||
node_ids = [r["entity_name"] for r in results]
|
||||
|
|
@ -2042,34 +2289,16 @@ async def _get_node_data(
|
|||
}
|
||||
for k, n, d in zip(results, node_datas, node_degrees)
|
||||
if n is not None
|
||||
] # what is this text_chunks_db doing. dont remember it in airvx. check the diagram.
|
||||
# get entitytext chunk
|
||||
use_text_units = await _find_most_related_text_unit_from_entities(
|
||||
node_datas,
|
||||
query_param,
|
||||
text_chunks_db,
|
||||
knowledge_graph_inst,
|
||||
)
|
||||
]
|
||||
|
||||
use_relations = await _find_most_related_edges_from_entities(
|
||||
node_datas,
|
||||
query_param,
|
||||
knowledge_graph_inst,
|
||||
)
|
||||
|
||||
tokenizer: Tokenizer = text_chunks_db.global_config.get("tokenizer")
|
||||
len_node_datas = len(node_datas)
|
||||
node_datas = truncate_list_by_token_size(
|
||||
node_datas,
|
||||
key=lambda x: x["description"] if x["description"] is not None else "",
|
||||
max_token_size=query_param.max_token_for_local_context,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
logger.debug(
|
||||
f"Truncate entities from {len_node_datas} to {len(node_datas)} (max tokens:{query_param.max_token_for_local_context})"
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Local query: {len(node_datas)} entites, {len(use_relations)} relations, {len(use_text_units)} chunks"
|
||||
f"Local query: {len(node_datas)} entites, {len(use_relations)} relations"
|
||||
)
|
||||
|
||||
# build prompt
|
||||
|
|
@ -2088,7 +2317,6 @@ async def _get_node_data(
|
|||
"entity": n["entity_name"],
|
||||
"type": n.get("entity_type", "UNKNOWN"),
|
||||
"description": n.get("description", "UNKNOWN"),
|
||||
"rank": n["rank"],
|
||||
"created_at": created_at,
|
||||
"file_path": file_path,
|
||||
}
|
||||
|
|
@ -2110,15 +2338,12 @@ async def _get_node_data(
|
|||
"entity1": e["src_tgt"][0],
|
||||
"entity2": e["src_tgt"][1],
|
||||
"description": e["description"],
|
||||
"keywords": e["keywords"],
|
||||
"weight": e["weight"],
|
||||
"rank": e["rank"],
|
||||
"created_at": created_at,
|
||||
"file_path": file_path,
|
||||
}
|
||||
)
|
||||
|
||||
return entities_context, relations_context, use_text_units
|
||||
return entities_context, relations_context, node_datas, use_relations
|
||||
|
||||
|
||||
async def _find_most_related_text_unit_from_entities(
|
||||
|
|
@ -2127,8 +2352,14 @@ async def _find_most_related_text_unit_from_entities(
|
|||
text_chunks_db: BaseKVStorage,
|
||||
knowledge_graph_inst: BaseGraphStorage,
|
||||
):
|
||||
logger.debug(f"Searching text chunks for {len(node_datas)} entities")
|
||||
|
||||
text_units = [
|
||||
split_string_by_multi_markers(dp["source_id"], [GRAPH_FIELD_SEP])
|
||||
split_string_by_multi_markers(dp["source_id"], [GRAPH_FIELD_SEP])[
|
||||
: text_chunks_db.global_config.get(
|
||||
"related_chunk_number", DEFAULT_RELATED_CHUNK_NUMBER
|
||||
)
|
||||
]
|
||||
for dp in node_datas
|
||||
if dp["source_id"] is not None
|
||||
]
|
||||
|
|
@ -2273,20 +2504,9 @@ async def _find_most_related_edges_from_entities(
|
|||
}
|
||||
all_edges_data.append(combined)
|
||||
|
||||
tokenizer: Tokenizer = knowledge_graph_inst.global_config.get("tokenizer")
|
||||
all_edges_data = sorted(
|
||||
all_edges_data, key=lambda x: (x["rank"], x["weight"]), reverse=True
|
||||
)
|
||||
all_edges_data = truncate_list_by_token_size(
|
||||
all_edges_data,
|
||||
key=lambda x: x["description"] if x["description"] is not None else "",
|
||||
max_token_size=query_param.max_token_for_global_context,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
f"Truncate relations from {len(all_edges)} to {len(all_edges_data)} (max tokens:{query_param.max_token_for_global_context})"
|
||||
)
|
||||
|
||||
return all_edges_data
|
||||
|
||||
|
|
@ -2295,7 +2515,6 @@ async def _get_edge_data(
|
|||
keywords,
|
||||
knowledge_graph_inst: BaseGraphStorage,
|
||||
relationships_vdb: BaseVectorStorage,
|
||||
text_chunks_db: BaseKVStorage,
|
||||
query_param: QueryParam,
|
||||
):
|
||||
logger.info(
|
||||
|
|
@ -2307,7 +2526,7 @@ async def _get_edge_data(
|
|||
)
|
||||
|
||||
if not len(results):
|
||||
return "", "", ""
|
||||
return "", "", [], []
|
||||
|
||||
# Prepare edge pairs in two forms:
|
||||
# For the batch edge properties function, use dicts.
|
||||
|
|
@ -2343,31 +2562,18 @@ async def _get_edge_data(
|
|||
}
|
||||
edge_datas.append(combined)
|
||||
|
||||
tokenizer: Tokenizer = text_chunks_db.global_config.get("tokenizer")
|
||||
edge_datas = sorted(
|
||||
edge_datas, key=lambda x: (x["rank"], x["weight"]), reverse=True
|
||||
)
|
||||
edge_datas = truncate_list_by_token_size(
|
||||
|
||||
use_entities = await _find_most_related_entities_from_relationships(
|
||||
edge_datas,
|
||||
key=lambda x: x["description"] if x["description"] is not None else "",
|
||||
max_token_size=query_param.max_token_for_global_context,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
use_entities, use_text_units = await asyncio.gather(
|
||||
_find_most_related_entities_from_relationships(
|
||||
edge_datas,
|
||||
query_param,
|
||||
knowledge_graph_inst,
|
||||
),
|
||||
_find_related_text_unit_from_relationships(
|
||||
edge_datas,
|
||||
query_param,
|
||||
text_chunks_db,
|
||||
knowledge_graph_inst,
|
||||
),
|
||||
query_param,
|
||||
knowledge_graph_inst,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Global query: {len(use_entities)} entites, {len(edge_datas)} relations, {len(use_text_units)} chunks"
|
||||
f"Global query: {len(use_entities)} entites, {len(edge_datas)} relations"
|
||||
)
|
||||
|
||||
relations_context = []
|
||||
|
|
@ -2386,9 +2592,6 @@ async def _get_edge_data(
|
|||
"entity1": e["src_id"],
|
||||
"entity2": e["tgt_id"],
|
||||
"description": e["description"],
|
||||
"keywords": e["keywords"],
|
||||
"weight": e["weight"],
|
||||
"rank": e["rank"],
|
||||
"created_at": created_at,
|
||||
"file_path": file_path,
|
||||
}
|
||||
|
|
@ -2410,22 +2613,13 @@ async def _get_edge_data(
|
|||
"entity": n["entity_name"],
|
||||
"type": n.get("entity_type", "UNKNOWN"),
|
||||
"description": n.get("description", "UNKNOWN"),
|
||||
"rank": n["rank"],
|
||||
"created_at": created_at,
|
||||
"file_path": file_path,
|
||||
}
|
||||
)
|
||||
|
||||
text_units_context = []
|
||||
for i, t in enumerate(use_text_units):
|
||||
text_units_context.append(
|
||||
{
|
||||
"id": i + 1,
|
||||
"content": t["content"],
|
||||
"file_path": t.get("file_path", "unknown"),
|
||||
}
|
||||
)
|
||||
return entities_context, relations_context, text_units_context
|
||||
# Return original data for later text chunk retrieval
|
||||
return entities_context, relations_context, edge_datas, use_entities
|
||||
|
||||
|
||||
async def _find_most_related_entities_from_relationships(
|
||||
|
|
@ -2462,18 +2656,6 @@ async def _find_most_related_entities_from_relationships(
|
|||
combined = {**node, "entity_name": entity_name, "rank": degree}
|
||||
node_datas.append(combined)
|
||||
|
||||
tokenizer: Tokenizer = knowledge_graph_inst.global_config.get("tokenizer")
|
||||
len_node_datas = len(node_datas)
|
||||
node_datas = truncate_list_by_token_size(
|
||||
node_datas,
|
||||
key=lambda x: x["description"] if x["description"] is not None else "",
|
||||
max_token_size=query_param.max_token_for_local_context,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
logger.debug(
|
||||
f"Truncate entities from {len_node_datas} to {len(node_datas)} (max tokens:{query_param.max_token_for_local_context})"
|
||||
)
|
||||
|
||||
return node_datas
|
||||
|
||||
|
||||
|
|
@ -2481,10 +2663,15 @@ async def _find_related_text_unit_from_relationships(
|
|||
edge_datas: list[dict],
|
||||
query_param: QueryParam,
|
||||
text_chunks_db: BaseKVStorage,
|
||||
knowledge_graph_inst: BaseGraphStorage,
|
||||
):
|
||||
logger.debug(f"Searching text chunks for {len(edge_datas)} relationships")
|
||||
|
||||
text_units = [
|
||||
split_string_by_multi_markers(dp["source_id"], [GRAPH_FIELD_SEP])
|
||||
split_string_by_multi_markers(dp["source_id"], [GRAPH_FIELD_SEP])[
|
||||
: text_chunks_db.global_config.get(
|
||||
"related_chunk_number", DEFAULT_RELATED_CHUNK_NUMBER
|
||||
)
|
||||
]
|
||||
for dp in edge_datas
|
||||
if dp["source_id"] is not None
|
||||
]
|
||||
|
|
@ -2565,13 +2752,66 @@ async def naive_query(
|
|||
if chunks is None or len(chunks) == 0:
|
||||
return PROMPTS["fail_response"]
|
||||
|
||||
# Process chunks using unified processing
|
||||
# Calculate dynamic token limit for chunks
|
||||
# Get token limits from query_param (with fallback to global_config)
|
||||
max_total_tokens = getattr(
|
||||
query_param,
|
||||
"max_total_tokens",
|
||||
global_config.get("max_total_tokens", DEFAULT_MAX_TOTAL_TOKENS),
|
||||
)
|
||||
|
||||
# Calculate conversation history tokens
|
||||
history_context = ""
|
||||
if query_param.conversation_history:
|
||||
history_context = get_conversation_turns(
|
||||
query_param.conversation_history, query_param.history_turns
|
||||
)
|
||||
history_tokens = len(tokenizer.encode(history_context)) if history_context else 0
|
||||
|
||||
# Calculate system prompt template tokens (excluding content_data)
|
||||
user_prompt = query_param.user_prompt if query_param.user_prompt else ""
|
||||
response_type = (
|
||||
query_param.response_type
|
||||
if query_param.response_type
|
||||
else "Multiple Paragraphs"
|
||||
)
|
||||
|
||||
# Use the provided system prompt or default
|
||||
sys_prompt_template = (
|
||||
system_prompt if system_prompt else PROMPTS["naive_rag_response"]
|
||||
)
|
||||
|
||||
# Create a sample system prompt with empty content_data to calculate overhead
|
||||
sample_sys_prompt = sys_prompt_template.format(
|
||||
content_data="", # Empty for overhead calculation
|
||||
response_type=response_type,
|
||||
history=history_context,
|
||||
user_prompt=user_prompt,
|
||||
)
|
||||
sys_prompt_template_tokens = len(tokenizer.encode(sample_sys_prompt))
|
||||
|
||||
# Total system prompt overhead = template + query tokens
|
||||
query_tokens = len(tokenizer.encode(query))
|
||||
sys_prompt_overhead = sys_prompt_template_tokens + query_tokens
|
||||
|
||||
buffer_tokens = 100 # Safety buffer
|
||||
|
||||
# Calculate available tokens for chunks
|
||||
used_tokens = sys_prompt_overhead + buffer_tokens
|
||||
available_chunk_tokens = max_total_tokens - used_tokens
|
||||
|
||||
logger.debug(
|
||||
f"Naive query token allocation - Total: {max_total_tokens}, History: {history_tokens}, SysPrompt: {sys_prompt_overhead}, Buffer: {buffer_tokens}, Available for chunks: {available_chunk_tokens}"
|
||||
)
|
||||
|
||||
# Process chunks using unified processing with dynamic token limit
|
||||
processed_chunks = await process_chunks_unified(
|
||||
query=query,
|
||||
chunks=chunks,
|
||||
query_param=query_param,
|
||||
global_config=global_config,
|
||||
source_type="vector",
|
||||
chunk_token_limit=available_chunk_tokens, # Pass dynamic limit
|
||||
)
|
||||
|
||||
logger.info(f"Final context: {len(processed_chunks)} chunks")
|
||||
|
|
@ -2622,7 +2862,9 @@ async def naive_query(
|
|||
return sys_prompt
|
||||
|
||||
len_of_prompts = len(tokenizer.encode(query + sys_prompt))
|
||||
logger.debug(f"[naive_query]Prompt Tokens: {len_of_prompts}")
|
||||
logger.debug(
|
||||
f"[naive_query] Sending to LLM: {len_of_prompts:,} tokens (Query: {len(tokenizer.encode(query))}, System: {len(tokenizer.encode(sys_prompt))})"
|
||||
)
|
||||
|
||||
response = await use_model_func(
|
||||
query,
|
||||
|
|
@ -2746,7 +2988,9 @@ async def kg_query_with_keywords(
|
|||
|
||||
tokenizer: Tokenizer = global_config["tokenizer"]
|
||||
len_of_prompts = len(tokenizer.encode(query + sys_prompt))
|
||||
logger.debug(f"[kg_query_with_keywords]Prompt Tokens: {len_of_prompts}")
|
||||
logger.debug(
|
||||
f"[kg_query_with_keywords] Sending to LLM: {len_of_prompts:,} tokens (Query: {len(tokenizer.encode(query))}, System: {len(tokenizer.encode(sys_prompt))})"
|
||||
)
|
||||
|
||||
# 6. Generate response
|
||||
response = await use_model_func(
|
||||
|
|
@ -2866,6 +3110,7 @@ async def apply_rerank_if_enabled(
|
|||
query: str,
|
||||
retrieved_docs: list[dict],
|
||||
global_config: dict,
|
||||
enable_rerank: bool = True,
|
||||
top_k: int = None,
|
||||
) -> list[dict]:
|
||||
"""
|
||||
|
|
@ -2875,18 +3120,19 @@ async def apply_rerank_if_enabled(
|
|||
query: The search query
|
||||
retrieved_docs: List of retrieved documents
|
||||
global_config: Global configuration containing rerank settings
|
||||
enable_rerank: Whether to enable reranking from query parameter
|
||||
top_k: Number of top documents to return after reranking
|
||||
|
||||
Returns:
|
||||
Reranked documents if rerank is enabled, otherwise original documents
|
||||
"""
|
||||
if not global_config.get("enable_rerank", False) or not retrieved_docs:
|
||||
if not enable_rerank or not retrieved_docs:
|
||||
return retrieved_docs
|
||||
|
||||
rerank_func = global_config.get("rerank_model_func")
|
||||
if not rerank_func:
|
||||
logger.debug(
|
||||
"Rerank is enabled but no rerank function provided, skipping rerank"
|
||||
logger.warning(
|
||||
"Rerank is enabled but no rerank model is configured. Please set up a rerank model or set enable_rerank=False in query parameters."
|
||||
)
|
||||
return retrieved_docs
|
||||
|
||||
|
|
@ -2923,6 +3169,7 @@ async def process_chunks_unified(
|
|||
query_param: QueryParam,
|
||||
global_config: dict,
|
||||
source_type: str = "mixed",
|
||||
chunk_token_limit: int = None, # Add parameter for dynamic token limit
|
||||
) -> list[dict]:
|
||||
"""
|
||||
Unified processing for text chunks: deduplication, chunk_top_k limiting, reranking, and token truncation.
|
||||
|
|
@ -2933,6 +3180,7 @@ async def process_chunks_unified(
|
|||
query_param: Query parameters containing configuration
|
||||
global_config: Global configuration dictionary
|
||||
source_type: Source type for logging ("vector", "entity", "relationship", "mixed")
|
||||
chunk_token_limit: Dynamic token limit for chunks (if None, uses default)
|
||||
|
||||
Returns:
|
||||
Processed and filtered list of text chunks
|
||||
|
|
@ -2954,12 +3202,13 @@ async def process_chunks_unified(
|
|||
)
|
||||
|
||||
# 2. Apply reranking if enabled and query is provided
|
||||
if global_config.get("enable_rerank", False) and query and unique_chunks:
|
||||
rerank_top_k = query_param.chunk_rerank_top_k or len(unique_chunks)
|
||||
if query_param.enable_rerank and query and unique_chunks:
|
||||
rerank_top_k = query_param.chunk_top_k or len(unique_chunks)
|
||||
unique_chunks = await apply_rerank_if_enabled(
|
||||
query=query,
|
||||
retrieved_docs=unique_chunks,
|
||||
global_config=global_config,
|
||||
enable_rerank=query_param.enable_rerank,
|
||||
top_k=rerank_top_k,
|
||||
)
|
||||
logger.debug(f"Rerank: {len(unique_chunks)} chunks (source: {source_type})")
|
||||
|
|
@ -2975,16 +3224,25 @@ async def process_chunks_unified(
|
|||
# 4. Token-based final truncation
|
||||
tokenizer = global_config.get("tokenizer")
|
||||
if tokenizer and unique_chunks:
|
||||
# Set default chunk_token_limit if not provided
|
||||
if chunk_token_limit is None:
|
||||
# Get default from query_param or global_config
|
||||
chunk_token_limit = getattr(
|
||||
query_param,
|
||||
"max_total_tokens",
|
||||
global_config.get("MAX_TOTAL_TOKENS", 32000),
|
||||
)
|
||||
|
||||
original_count = len(unique_chunks)
|
||||
unique_chunks = truncate_list_by_token_size(
|
||||
unique_chunks,
|
||||
key=lambda x: x.get("content", ""),
|
||||
max_token_size=query_param.max_token_for_text_unit,
|
||||
max_token_size=chunk_token_limit,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
logger.debug(
|
||||
f"Token truncation: {len(unique_chunks)} chunks from {original_count} "
|
||||
f"(max tokens: {query_param.max_token_for_text_unit}, source: {source_type})"
|
||||
f"(chunk available tokens: {chunk_token_limit}, source: {source_type})"
|
||||
)
|
||||
|
||||
return unique_chunks
|
||||
|
|
|
|||
|
|
@ -10,55 +10,58 @@ from .utils import logger
|
|||
|
||||
class RerankModel(BaseModel):
|
||||
"""
|
||||
Pydantic model class for defining a custom rerank model.
|
||||
|
||||
This class provides a convenient wrapper for rerank functions, allowing you to
|
||||
encapsulate all rerank configurations (API keys, model settings, etc.) in one place.
|
||||
|
||||
Attributes:
|
||||
rerank_func (Callable[[Any], List[Dict]]): A callable function that reranks documents.
|
||||
The function should take query and documents as input and return reranked results.
|
||||
kwargs (Dict[str, Any]): A dictionary that contains the arguments to pass to the callable function.
|
||||
This should include all necessary configurations such as model name, API key, base_url, etc.
|
||||
Wrapper for rerank functions that can be used with LightRAG.
|
||||
|
||||
Example usage:
|
||||
Rerank model example with Jina:
|
||||
```python
|
||||
rerank_model = RerankModel(
|
||||
rerank_func=jina_rerank,
|
||||
kwargs={
|
||||
"model": "BAAI/bge-reranker-v2-m3",
|
||||
"api_key": "your_api_key_here",
|
||||
"base_url": "https://api.jina.ai/v1/rerank"
|
||||
}
|
||||
```python
|
||||
from lightrag.rerank import RerankModel, jina_rerank
|
||||
|
||||
# Create rerank model
|
||||
rerank_model = RerankModel(
|
||||
rerank_func=jina_rerank,
|
||||
kwargs={
|
||||
"model": "BAAI/bge-reranker-v2-m3",
|
||||
"api_key": "your_api_key_here",
|
||||
"base_url": "https://api.jina.ai/v1/rerank"
|
||||
}
|
||||
)
|
||||
|
||||
# Use in LightRAG
|
||||
rag = LightRAG(
|
||||
rerank_model_func=rerank_model.rerank,
|
||||
# ... other configurations
|
||||
)
|
||||
|
||||
# Query with rerank enabled (default)
|
||||
result = await rag.aquery(
|
||||
"your query",
|
||||
param=QueryParam(enable_rerank=True)
|
||||
)
|
||||
```
|
||||
|
||||
Or define a custom function directly:
|
||||
```python
|
||||
async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
|
||||
return await jina_rerank(
|
||||
query=query,
|
||||
documents=documents,
|
||||
model="BAAI/bge-reranker-v2-m3",
|
||||
api_key="your_api_key_here",
|
||||
top_k=top_k or 10,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
# Use in LightRAG
|
||||
rag = LightRAG(
|
||||
enable_rerank=True,
|
||||
rerank_model_func=rerank_model.rerank,
|
||||
# ... other configurations
|
||||
)
|
||||
```
|
||||
rag = LightRAG(
|
||||
rerank_model_func=my_rerank_func,
|
||||
# ... other configurations
|
||||
)
|
||||
|
||||
Or define a custom function directly:
|
||||
```python
|
||||
async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
|
||||
return await jina_rerank(
|
||||
query=query,
|
||||
documents=documents,
|
||||
model="BAAI/bge-reranker-v2-m3",
|
||||
api_key="your_api_key_here",
|
||||
top_k=top_k or 10,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
rag = LightRAG(
|
||||
enable_rerank=True,
|
||||
rerank_model_func=my_rerank_func,
|
||||
# ... other configurations
|
||||
)
|
||||
```
|
||||
# Control rerank per query
|
||||
result = await rag.aquery(
|
||||
"your query",
|
||||
param=QueryParam(enable_rerank=True) # Enable rerank for this query
|
||||
)
|
||||
```
|
||||
"""
|
||||
|
||||
rerank_func: Callable[[Any], List[Dict]]
|
||||
|
|
|
|||
|
|
@ -795,7 +795,9 @@ def process_combine_contexts(*context_lists):
|
|||
if not context_list: # Skip empty lists
|
||||
continue
|
||||
for item in context_list:
|
||||
content_dict = {k: v for k, v in item.items() if k != "id"}
|
||||
content_dict = {
|
||||
k: v for k, v in item.items() if k != "id" and k != "created_at"
|
||||
}
|
||||
content_key = tuple(sorted(content_dict.items()))
|
||||
if content_key not in seen_content:
|
||||
seen_content[content_key] = item
|
||||
|
|
|
|||
|
|
@ -106,12 +106,14 @@ export type QueryRequest = {
|
|||
stream?: boolean
|
||||
/** Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode. */
|
||||
top_k?: number
|
||||
/** Maximum number of tokens allowed for each retrieved text chunk. */
|
||||
max_token_for_text_unit?: number
|
||||
/** Maximum number of tokens allocated for relationship descriptions in global retrieval. */
|
||||
max_token_for_global_context?: number
|
||||
/** Maximum number of tokens allocated for entity descriptions in local retrieval. */
|
||||
max_token_for_local_context?: number
|
||||
/** Maximum number of text chunks to retrieve and keep after reranking. */
|
||||
chunk_top_k?: number
|
||||
/** Maximum number of tokens allocated for entity context in unified token control system. */
|
||||
max_entity_tokens?: number
|
||||
/** Maximum number of tokens allocated for relationship context in unified token control system. */
|
||||
max_relation_tokens?: number
|
||||
/** Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt). */
|
||||
max_total_tokens?: number
|
||||
/**
|
||||
* Stores past conversation history to maintain context.
|
||||
* Format: [{"role": "user/assistant", "content": "message"}].
|
||||
|
|
@ -121,6 +123,8 @@ export type QueryRequest = {
|
|||
history_turns?: number
|
||||
/** User-provided prompt for the query. If provided, this will be used instead of the default value from prompt template. */
|
||||
user_prompt?: string
|
||||
/** Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued. Default is True. */
|
||||
enable_rerank?: boolean
|
||||
}
|
||||
|
||||
export type QueryResponse = {
|
||||
|
|
|
|||
|
|
@ -119,7 +119,6 @@ export default function QuerySettings() {
|
|||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
<div>
|
||||
{/* Removed sr-only label */}
|
||||
<Input
|
||||
id="top_k"
|
||||
type="number"
|
||||
|
|
@ -136,120 +135,148 @@ export default function QuerySettings() {
|
|||
}}
|
||||
min={1}
|
||||
placeholder={t('retrievePanel.querySettings.topKPlaceholder')}
|
||||
className="h-9"
|
||||
/>
|
||||
</div>
|
||||
</>
|
||||
|
||||
{/* Max Tokens */}
|
||||
{/* Chunk Top K */}
|
||||
<>
|
||||
<>
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
<TooltipTrigger asChild>
|
||||
<label htmlFor="max_token_for_text_unit" className="ml-1 cursor-help">
|
||||
{t('retrievePanel.querySettings.maxTokensTextUnit')}
|
||||
</label>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent side="left">
|
||||
<p>{t('retrievePanel.querySettings.maxTokensTextUnitTooltip')}</p>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
<div>
|
||||
{/* Removed sr-only label */}
|
||||
<Input
|
||||
id="max_token_for_text_unit"
|
||||
type="number"
|
||||
value={querySettings.max_token_for_text_unit ?? ''}
|
||||
onChange={(e) => {
|
||||
const value = e.target.value
|
||||
handleChange('max_token_for_text_unit', value === '' ? '' : parseInt(value) || 0)
|
||||
}}
|
||||
onBlur={(e) => {
|
||||
const value = e.target.value
|
||||
if (value === '' || isNaN(parseInt(value))) {
|
||||
handleChange('max_token_for_text_unit', 10000)
|
||||
}
|
||||
}}
|
||||
min={1}
|
||||
placeholder={t('retrievePanel.querySettings.maxTokensTextUnit')}
|
||||
className="h-9"
|
||||
/>
|
||||
</div>
|
||||
</>
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
<TooltipTrigger asChild>
|
||||
<label htmlFor="chunk_top_k" className="ml-1 cursor-help">
|
||||
{t('retrievePanel.querySettings.chunkTopK')}
|
||||
</label>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent side="left">
|
||||
<p>{t('retrievePanel.querySettings.chunkTopKTooltip')}</p>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
<div>
|
||||
<Input
|
||||
id="chunk_top_k"
|
||||
type="number"
|
||||
value={querySettings.chunk_top_k ?? ''}
|
||||
onChange={(e) => {
|
||||
const value = e.target.value
|
||||
handleChange('chunk_top_k', value === '' ? '' : parseInt(value) || 0)
|
||||
}}
|
||||
onBlur={(e) => {
|
||||
const value = e.target.value
|
||||
if (value === '' || isNaN(parseInt(value))) {
|
||||
handleChange('chunk_top_k', 1)
|
||||
}
|
||||
}}
|
||||
min={1}
|
||||
placeholder={t('retrievePanel.querySettings.chunkTopKPlaceholder')}
|
||||
/>
|
||||
</div>
|
||||
</>
|
||||
|
||||
<>
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
<TooltipTrigger asChild>
|
||||
<label htmlFor="max_token_for_global_context" className="ml-1 cursor-help">
|
||||
{t('retrievePanel.querySettings.maxTokensGlobalContext')}
|
||||
</label>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent side="left">
|
||||
<p>{t('retrievePanel.querySettings.maxTokensGlobalContextTooltip')}</p>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
<div>
|
||||
{/* Removed sr-only label */}
|
||||
<Input
|
||||
id="max_token_for_global_context"
|
||||
type="number"
|
||||
value={querySettings.max_token_for_global_context ?? ''}
|
||||
onChange={(e) => {
|
||||
const value = e.target.value
|
||||
handleChange('max_token_for_global_context', value === '' ? '' : parseInt(value) || 0)
|
||||
}}
|
||||
onBlur={(e) => {
|
||||
const value = e.target.value
|
||||
if (value === '' || isNaN(parseInt(value))) {
|
||||
handleChange('max_token_for_global_context', 4000)
|
||||
}
|
||||
}}
|
||||
min={1}
|
||||
placeholder={t('retrievePanel.querySettings.maxTokensGlobalContext')}
|
||||
className="h-9"
|
||||
/>
|
||||
</div>
|
||||
</>
|
||||
{/* Max Entity Tokens */}
|
||||
<>
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
<TooltipTrigger asChild>
|
||||
<label htmlFor="max_entity_tokens" className="ml-1 cursor-help">
|
||||
{t('retrievePanel.querySettings.maxEntityTokens')}
|
||||
</label>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent side="left">
|
||||
<p>{t('retrievePanel.querySettings.maxEntityTokensTooltip')}</p>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
<div>
|
||||
<Input
|
||||
id="max_entity_tokens"
|
||||
type="number"
|
||||
value={querySettings.max_entity_tokens ?? ''}
|
||||
onChange={(e) => {
|
||||
const value = e.target.value
|
||||
handleChange('max_entity_tokens', value === '' ? '' : parseInt(value) || 0)
|
||||
}}
|
||||
onBlur={(e) => {
|
||||
const value = e.target.value
|
||||
if (value === '' || isNaN(parseInt(value))) {
|
||||
handleChange('max_entity_tokens', 1000)
|
||||
}
|
||||
}}
|
||||
min={1}
|
||||
placeholder={t('retrievePanel.querySettings.maxEntityTokensPlaceholder')}
|
||||
/>
|
||||
</div>
|
||||
</>
|
||||
|
||||
<>
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
<TooltipTrigger asChild>
|
||||
<label htmlFor="max_token_for_local_context" className="ml-1 cursor-help">
|
||||
{t('retrievePanel.querySettings.maxTokensLocalContext')}
|
||||
</label>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent side="left">
|
||||
<p>{t('retrievePanel.querySettings.maxTokensLocalContextTooltip')}</p>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
<div>
|
||||
{/* Removed sr-only label */}
|
||||
<Input
|
||||
id="max_token_for_local_context"
|
||||
type="number"
|
||||
value={querySettings.max_token_for_local_context ?? ''}
|
||||
onChange={(e) => {
|
||||
const value = e.target.value
|
||||
handleChange('max_token_for_local_context', value === '' ? '' : parseInt(value) || 0)
|
||||
}}
|
||||
onBlur={(e) => {
|
||||
const value = e.target.value
|
||||
if (value === '' || isNaN(parseInt(value))) {
|
||||
handleChange('max_token_for_local_context', 4000)
|
||||
}
|
||||
}}
|
||||
min={1}
|
||||
placeholder={t('retrievePanel.querySettings.maxTokensLocalContext')}
|
||||
className="h-9"
|
||||
/>
|
||||
</div>
|
||||
</>
|
||||
{/* Max Relation Tokens */}
|
||||
<>
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
<TooltipTrigger asChild>
|
||||
<label htmlFor="max_relation_tokens" className="ml-1 cursor-help">
|
||||
{t('retrievePanel.querySettings.maxRelationTokens')}
|
||||
</label>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent side="left">
|
||||
<p>{t('retrievePanel.querySettings.maxRelationTokensTooltip')}</p>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
<div>
|
||||
<Input
|
||||
id="max_relation_tokens"
|
||||
type="number"
|
||||
value={querySettings.max_relation_tokens ?? ''}
|
||||
onChange={(e) => {
|
||||
const value = e.target.value
|
||||
handleChange('max_relation_tokens', value === '' ? '' : parseInt(value) || 0)
|
||||
}}
|
||||
onBlur={(e) => {
|
||||
const value = e.target.value
|
||||
if (value === '' || isNaN(parseInt(value))) {
|
||||
handleChange('max_relation_tokens', 1000)
|
||||
}
|
||||
}}
|
||||
min={1}
|
||||
placeholder={t('retrievePanel.querySettings.maxRelationTokensPlaceholder')}
|
||||
/>
|
||||
</div>
|
||||
</>
|
||||
|
||||
{/* Max Total Tokens */}
|
||||
<>
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
<TooltipTrigger asChild>
|
||||
<label htmlFor="max_total_tokens" className="ml-1 cursor-help">
|
||||
{t('retrievePanel.querySettings.maxTotalTokens')}
|
||||
</label>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent side="left">
|
||||
<p>{t('retrievePanel.querySettings.maxTotalTokensTooltip')}</p>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
<div>
|
||||
<Input
|
||||
id="max_total_tokens"
|
||||
type="number"
|
||||
value={querySettings.max_total_tokens ?? ''}
|
||||
onChange={(e) => {
|
||||
const value = e.target.value
|
||||
handleChange('max_total_tokens', value === '' ? '' : parseInt(value) || 0)
|
||||
}}
|
||||
onBlur={(e) => {
|
||||
const value = e.target.value
|
||||
if (value === '' || isNaN(parseInt(value))) {
|
||||
handleChange('max_total_tokens', 1000)
|
||||
}
|
||||
}}
|
||||
min={1}
|
||||
placeholder={t('retrievePanel.querySettings.maxTotalTokensPlaceholder')}
|
||||
/>
|
||||
</div>
|
||||
</>
|
||||
|
||||
{/* History Turns */}
|
||||
|
|
@ -267,7 +294,6 @@ export default function QuerySettings() {
|
|||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
<div>
|
||||
{/* Removed sr-only label */}
|
||||
<Input
|
||||
id="history_turns"
|
||||
type="number"
|
||||
|
|
@ -316,6 +342,27 @@ export default function QuerySettings() {
|
|||
|
||||
{/* Toggle Options */}
|
||||
<>
|
||||
<div className="flex items-center gap-2">
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
<TooltipTrigger asChild>
|
||||
<label htmlFor="enable_rerank" className="flex-1 ml-1 cursor-help">
|
||||
{t('retrievePanel.querySettings.enableRerank')}
|
||||
</label>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent side="left">
|
||||
<p>{t('retrievePanel.querySettings.enableRerankTooltip')}</p>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
<Checkbox
|
||||
className="mr-1 cursor-pointer"
|
||||
id="enable_rerank"
|
||||
checked={querySettings.enable_rerank}
|
||||
onCheckedChange={(checked) => handleChange('enable_rerank', checked)}
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="flex items-center gap-2">
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
|
|
@ -379,6 +426,7 @@ export default function QuerySettings() {
|
|||
/>
|
||||
</div>
|
||||
</>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</CardContent>
|
||||
|
|
|
|||
|
|
@ -363,16 +363,22 @@
|
|||
"singleParagraph": "فقرة واحدة",
|
||||
"bulletPoints": "نقاط نقطية"
|
||||
},
|
||||
"topK": "أعلى K نتائج",
|
||||
"topKTooltip": "عدد العناصر العلوية للاسترجاع. يمثل الكيانات في وضع 'محلي' والعلاقات في وضع 'عالمي'",
|
||||
"topKPlaceholder": "عدد النتائج",
|
||||
"maxTokensTextUnit": "أقصى عدد من الرموز لوحدة النص",
|
||||
"maxTokensTextUnitTooltip": "الحد الأقصى لعدد الرموز المسموح به لكل جزء نصي مسترجع",
|
||||
"maxTokensGlobalContext": "أقصى عدد من الرموز للسياق العالمي",
|
||||
"maxTokensGlobalContextTooltip": "الحد الأقصى لعدد الرموز المخصص لأوصاف العلاقات في الاسترجاع العالمي",
|
||||
"maxTokensLocalContext": "أقصى عدد من الرموز للسياق المحلي",
|
||||
"maxTokensLocalContextTooltip": "الحد الأقصى لعدد الرموز المخصص لأوصاف الكيانات في الاسترجاع المحلي",
|
||||
"historyTurns": "دورات التاريخ",
|
||||
"topK": "أعلى K",
|
||||
"topKTooltip": "عدد العناصر العلوية للاسترداد. يمثل الكيانات في الوضع 'المحلي' والعلاقات في الوضع 'العالمي'.",
|
||||
"topKPlaceholder": "أدخل قيمة أعلى k",
|
||||
"chunkTopK": "أعلى K للقطع",
|
||||
"chunkTopKTooltip": "العدد الأقصى لقطع النص المراد استردادها ومعالجتها.",
|
||||
"chunkTopKPlaceholder": "أدخل قيمة أعلى k للقطع",
|
||||
"chunkRerankTopK": "أعلى K لإعادة الترتيب",
|
||||
"chunkRerankTopKTooltip": "عدد قطع النص المراد الاحتفاظ بها بعد إعادة الترتيب.",
|
||||
"chunkRerankTopKPlaceholder": "أدخل قيمة أعلى k لإعادة الترتيب",
|
||||
"maxEntityTokens": "الحد الأقصى لرموز الكيان",
|
||||
"maxEntityTokensTooltip": "الحد الأقصى لعدد الرموز المخصصة لسياق الكيان في نظام التحكم الموحد في الرموز",
|
||||
"maxRelationTokens": "الحد الأقصى لرموز العلاقة",
|
||||
"maxRelationTokensTooltip": "الحد الأقصى لعدد الرموز المخصصة لسياق العلاقة في نظام التحكم الموحد في الرموز",
|
||||
"maxTotalTokens": "إجمالي الحد الأقصى للرموز",
|
||||
"maxTotalTokensTooltip": "الحد الأقصى الإجمالي لميزانية الرموز لسياق الاستعلام بالكامل (الكيانات + العلاقات + الأجزاء + موجه النظام)",
|
||||
"historyTurns": "أدوار التاريخ",
|
||||
"historyTurnsTooltip": "عدد الدورات الكاملة للمحادثة (أزواج المستخدم-المساعد) التي يجب مراعاتها في سياق الرد",
|
||||
"historyTurnsPlaceholder": "عدد دورات التاريخ",
|
||||
"onlyNeedContext": "تحتاج فقط إلى السياق",
|
||||
|
|
@ -383,7 +389,9 @@
|
|||
"streamResponseTooltip": "إذا كان صحيحًا، يتيح إخراج التدفق للردود في الوقت الفعلي",
|
||||
"userPrompt": "مطالبة مخصصة",
|
||||
"userPromptTooltip": "تقديم متطلبات استجابة إضافية إلى نموذج اللغة الكبير (غير متعلقة بمحتوى الاستعلام، فقط لمعالجة المخرجات).",
|
||||
"userPromptPlaceholder": "أدخل مطالبة مخصصة (اختياري)"
|
||||
"userPromptPlaceholder": "أدخل مطالبة مخصصة (اختياري)",
|
||||
"enableRerank": "تمكين إعادة الترتيب",
|
||||
"enableRerankTooltip": "تمكين إعادة ترتيب أجزاء النص المسترجعة. إذا كان True ولكن لم يتم تكوين نموذج إعادة الترتيب، فسيتم إصدار تحذير. افتراضي True."
|
||||
}
|
||||
},
|
||||
"apiSite": {
|
||||
|
|
|
|||
|
|
@ -363,15 +363,21 @@
|
|||
"singleParagraph": "Single Paragraph",
|
||||
"bulletPoints": "Bullet Points"
|
||||
},
|
||||
"topK": "Top K Results",
|
||||
"topKTooltip": "Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode",
|
||||
"topKPlaceholder": "Number of results",
|
||||
"maxTokensTextUnit": "Max Tokens for Text Unit",
|
||||
"maxTokensTextUnitTooltip": "Maximum number of tokens allowed for each retrieved text chunk",
|
||||
"maxTokensGlobalContext": "Max Tokens for Global Context",
|
||||
"maxTokensGlobalContextTooltip": "Maximum number of tokens allocated for relationship descriptions in global retrieval",
|
||||
"maxTokensLocalContext": "Max Tokens for Local Context",
|
||||
"maxTokensLocalContextTooltip": "Maximum number of tokens allocated for entity descriptions in local retrieval",
|
||||
"topK": "Top K",
|
||||
"topKTooltip": "Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode.",
|
||||
"topKPlaceholder": "Enter top k value",
|
||||
"chunkTopK": "Chunk Top K",
|
||||
"chunkTopKTooltip": "Maximum number of text chunks to retrieve and process.",
|
||||
"chunkTopKPlaceholder": "Enter chunk top k value",
|
||||
"chunkRerankTopK": "Chunk Rerank Top K",
|
||||
"chunkRerankTopKTooltip": "Number of text chunks to keep after reranking.",
|
||||
"chunkRerankTopKPlaceholder": "Enter rerank top k value",
|
||||
"maxEntityTokens": "Max Entity Tokens",
|
||||
"maxEntityTokensTooltip": "Maximum number of tokens allocated for entity context in unified token control system",
|
||||
"maxRelationTokens": "Max Relation Tokens",
|
||||
"maxRelationTokensTooltip": "Maximum number of tokens allocated for relationship context in unified token control system",
|
||||
"maxTotalTokens": "Max Total Tokens",
|
||||
"maxTotalTokensTooltip": "Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt)",
|
||||
"historyTurns": "History Turns",
|
||||
"historyTurnsTooltip": "Number of complete conversation turns (user-assistant pairs) to consider in the response context",
|
||||
"historyTurnsPlaceholder": "Number of history turns",
|
||||
|
|
@ -383,7 +389,9 @@
|
|||
"streamResponseTooltip": "If True, enables streaming output for real-time responses",
|
||||
"userPrompt": "User Prompt",
|
||||
"userPromptTooltip": "Provide additional response requirements to the LLM (unrelated to query content, only for output processing).",
|
||||
"userPromptPlaceholder": "Enter custom prompt (optional)"
|
||||
"userPromptPlaceholder": "Enter custom prompt (optional)",
|
||||
"enableRerank": "Enable Rerank",
|
||||
"enableRerankTooltip": "Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued. Default is True."
|
||||
}
|
||||
},
|
||||
"apiSite": {
|
||||
|
|
|
|||
|
|
@ -363,15 +363,21 @@
|
|||
"singleParagraph": "Paragraphe unique",
|
||||
"bulletPoints": "Points à puces"
|
||||
},
|
||||
"topK": "Top K résultats",
|
||||
"topKTooltip": "Nombre d'éléments supérieurs à récupérer. Représente les entités en mode 'local' et les relations en mode 'global'",
|
||||
"topKPlaceholder": "Nombre de résultats",
|
||||
"maxTokensTextUnit": "Nombre maximum de jetons pour l'unité de texte",
|
||||
"maxTokensTextUnitTooltip": "Nombre maximum de jetons autorisés pour chaque fragment de texte récupéré",
|
||||
"maxTokensGlobalContext": "Nombre maximum de jetons pour le contexte global",
|
||||
"maxTokensGlobalContextTooltip": "Nombre maximum de jetons alloués pour les descriptions des relations dans la récupération globale",
|
||||
"maxTokensLocalContext": "Nombre maximum de jetons pour le contexte local",
|
||||
"maxTokensLocalContextTooltip": "Nombre maximum de jetons alloués pour les descriptions des entités dans la récupération locale",
|
||||
"topK": "Top K",
|
||||
"topKTooltip": "Nombre d'éléments principaux à récupérer. Représente les entités en mode 'local' et les relations en mode 'global'.",
|
||||
"topKPlaceholder": "Entrez la valeur top k",
|
||||
"chunkTopK": "Top K des Chunks",
|
||||
"chunkTopKTooltip": "Nombre maximum de chunks de texte à récupérer et traiter.",
|
||||
"chunkTopKPlaceholder": "Entrez la valeur top k des chunks",
|
||||
"chunkRerankTopK": "Top K du Reclassement",
|
||||
"chunkRerankTopKTooltip": "Nombre de chunks de texte à conserver après reclassement.",
|
||||
"chunkRerankTopKPlaceholder": "Entrez la valeur top k du reclassement",
|
||||
"maxEntityTokens": "Limite de jetons d'entité",
|
||||
"maxEntityTokensTooltip": "Nombre maximum de jetons alloués au contexte d'entité dans le système de contrôle de jetons unifié",
|
||||
"maxRelationTokens": "Limite de jetons de relation",
|
||||
"maxRelationTokensTooltip": "Nombre maximum de jetons alloués au contexte de relation dans le système de contrôle de jetons unifié",
|
||||
"maxTotalTokens": "Limite totale de jetons",
|
||||
"maxTotalTokensTooltip": "Budget total maximum de jetons pour l'ensemble du contexte de requête (entités + relations + blocs + prompt système)",
|
||||
"historyTurns": "Tours d'historique",
|
||||
"historyTurnsTooltip": "Nombre de tours complets de conversation (paires utilisateur-assistant) à prendre en compte dans le contexte de la réponse",
|
||||
"historyTurnsPlaceholder": "Nombre de tours d'historique",
|
||||
|
|
@ -383,7 +389,9 @@
|
|||
"streamResponseTooltip": "Si vrai, active la sortie en flux pour des réponses en temps réel",
|
||||
"userPrompt": "Invite personnalisée",
|
||||
"userPromptTooltip": "Fournir des exigences de réponse supplémentaires au LLM (sans rapport avec le contenu de la requête, uniquement pour le traitement de sortie).",
|
||||
"userPromptPlaceholder": "Entrez une invite personnalisée (facultatif)"
|
||||
"userPromptPlaceholder": "Entrez une invite personnalisée (facultatif)",
|
||||
"enableRerank": "Activer le Reclassement",
|
||||
"enableRerankTooltip": "Active le reclassement pour les fragments de texte récupérés. Si True mais qu'aucun modèle de reclassement n'est configuré, un avertissement sera émis. True par défaut."
|
||||
}
|
||||
},
|
||||
"apiSite": {
|
||||
|
|
|
|||
|
|
@ -363,15 +363,21 @@
|
|||
"singleParagraph": "单段落",
|
||||
"bulletPoints": "要点"
|
||||
},
|
||||
"topK": "Top K结果",
|
||||
"topKTooltip": "检索的顶部项目数。在'local'模式下表示实体，在'global'模式下表示关系",
|
||||
"topKPlaceholder": "结果数量",
|
||||
"maxTokensTextUnit": "文本单元最大令牌数",
|
||||
"maxTokensTextUnitTooltip": "每个检索文本块允许的最大令牌数",
|
||||
"maxTokensGlobalContext": "全局上下文最大令牌数",
|
||||
"maxTokensGlobalContextTooltip": "全局检索中关系描述的最大令牌数",
|
||||
"maxTokensLocalContext": "本地上下文最大令牌数",
|
||||
"maxTokensLocalContextTooltip": "本地检索中实体描述的最大令牌数",
|
||||
"topK": "Top K",
|
||||
"topKTooltip": "检索的顶部条目数量。在'local'模式下表示实体，在'global'模式下表示关系。",
|
||||
"topKPlaceholder": "输入top k值",
|
||||
"chunkTopK": "文本块 Top K",
|
||||
"chunkTopKTooltip": "检索和处理的最大文本块数量。",
|
||||
"chunkTopKPlaceholder": "输入文本块top k值",
|
||||
"chunkRerankTopK": "重排序 Top K",
|
||||
"chunkRerankTopKTooltip": "重排序后保留的文本块数量。",
|
||||
"chunkRerankTopKPlaceholder": "输入重排序top k值",
|
||||
"maxEntityTokens": "实体令牌数上限",
|
||||
"maxEntityTokensTooltip": "统一令牌控制系统中分配给实体上下文的最大令牌数",
|
||||
"maxRelationTokens": "关系令牌数上限",
|
||||
"maxRelationTokensTooltip": "统一令牌控制系统中分配给关系上下文的最大令牌数",
|
||||
"maxTotalTokens": "总令牌数上限",
|
||||
"maxTotalTokensTooltip": "整个查询上下文的最大总令牌预算（实体+关系+文档块+系统提示）",
|
||||
"historyTurns": "历史轮次",
|
||||
"historyTurnsTooltip": "响应上下文中考虑的完整对话轮次（用户-助手对）数量",
|
||||
"historyTurnsPlaceholder": "历史轮次数",
|
||||
|
|
@ -383,7 +389,9 @@
|
|||
"streamResponseTooltip": "如果为True，启用实时流式输出响应",
|
||||
"userPrompt": "用户提示词",
|
||||
"userPromptTooltip": "向LLM提供额外的响应要求（与查询内容无关，仅用于处理输出）。",
|
||||
"userPromptPlaceholder": "输入自定义提示词（可选）"
|
||||
"userPromptPlaceholder": "输入自定义提示词（可选）",
|
||||
"enableRerank": "启用重排",
|
||||
"enableRerankTooltip": "为检索到的文本块启用重排。如果为True但未配置重排模型，将发出警告。默认为True。"
|
||||
}
|
||||
},
|
||||
"apiSite": {
|
||||
|
|
|
|||
|
|
@ -304,7 +304,7 @@
|
|||
"file_path": "來源",
|
||||
"keywords": "Keys",
|
||||
"weight": "權重"
|
||||
}
|
||||
}
|
||||
},
|
||||
"edge": {
|
||||
"title": "關係",
|
||||
|
|
@ -363,15 +363,15 @@
|
|||
"singleParagraph": "單段落",
|
||||
"bulletPoints": "重點"
|
||||
},
|
||||
"topK": "Top 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",
|
||||
"topK": "Top K",
|
||||
"topKTooltip": "檢索的頂部條目數量。在'local'模式下表示實體，在'global'模式下表示關係。",
|
||||
"topKPlaceholder": "輸入top k值",
|
||||
"chunkTopK": "文字區塊 Top K",
|
||||
"chunkTopKTooltip": "檢索和處理的最大文字區塊數量。",
|
||||
"chunkTopKPlaceholder": "輸入文字區塊top k值",
|
||||
"chunkRerankTopK": "重新排序 Top K",
|
||||
"chunkRerankTopKTooltip": "重新排序後保留的文字區塊數量。",
|
||||
"chunkRerankTopKPlaceholder": "輸入重新排序top k值",
|
||||
"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",
|
||||
|
|
@ -383,7 +383,15 @@
|
|||
"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）"
|
||||
"userPromptPlaceholder": "輸入自定義提示詞（可選）",
|
||||
"enableRerank": "啟用重排",
|
||||
"enableRerankTooltip": "為檢索到的文本塊啟用重排。如果為True但未配置重排模型，將發出警告。默認為True。",
|
||||
"maxEntityTokens": "實體令牌數上限",
|
||||
"maxEntityTokensTooltip": "統一令牌控制系統中分配給實體上下文的最大令牌數",
|
||||
"maxRelationTokens": "關係令牌數上限",
|
||||
"maxRelationTokensTooltip": "統一令牌控制系統中分配給關係上下文的最大令牌數",
|
||||
"maxTotalTokens": "總令牌數上限",
|
||||
"maxTotalTokensTooltip": "整個查詢上下文的最大總令牌預算（實體+關係+文檔塊+系統提示）"
|
||||
}
|
||||
},
|
||||
"apiSite": {
|
||||
|
|
|
|||
|
|
@ -110,17 +110,17 @@ const useSettingsStoreBase = create<SettingsState>()(
|
|||
querySettings: {
|
||||
mode: 'global',
|
||||
response_type: 'Multiple Paragraphs',
|
||||
top_k: 10,
|
||||
max_token_for_text_unit: 6000,
|
||||
max_token_for_global_context: 4000,
|
||||
max_token_for_local_context: 4000,
|
||||
top_k: 40,
|
||||
chunk_top_k: 10,
|
||||
max_entity_tokens: 10000,
|
||||
max_relation_tokens: 10000,
|
||||
max_total_tokens: 32000,
|
||||
only_need_context: false,
|
||||
only_need_prompt: false,
|
||||
stream: true,
|
||||
history_turns: 3,
|
||||
hl_keywords: [],
|
||||
ll_keywords: [],
|
||||
user_prompt: ''
|
||||
user_prompt: '',
|
||||
enable_rerank: true
|
||||
},
|
||||
|
||||
setTheme: (theme: Theme) => set({ theme }),
|
||||
|
|
@ -192,7 +192,7 @@ const useSettingsStoreBase = create<SettingsState>()(
|
|||
{
|
||||
name: 'settings-storage',
|
||||
storage: createJSONStorage(() => localStorage),
|
||||
version: 14,
|
||||
version: 15,
|
||||
migrate: (state: any, version: number) => {
|
||||
if (version < 2) {
|
||||
state.showEdgeLabel = false
|
||||
|
|
@ -260,6 +260,20 @@ const useSettingsStoreBase = create<SettingsState>()(
|
|||
// Add backendMaxGraphNodes field for older versions
|
||||
state.backendMaxGraphNodes = null
|
||||
}
|
||||
if (version < 15) {
|
||||
// Add new querySettings
|
||||
state.querySettings = {
|
||||
...state.querySettings,
|
||||
mode: 'mix',
|
||||
response_type: 'Multiple Paragraphs',
|
||||
top_k: 40,
|
||||
chunk_top_k: 10,
|
||||
max_entity_tokens: 10000,
|
||||
max_relation_tokens: 10000,
|
||||
max_total_tokens: 32000,
|
||||
enable_rerank: true
|
||||
}
|
||||
}
|
||||
return state
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue