Update README
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README-zh.md
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README-zh.md
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@ -781,7 +781,6 @@ async def initialize_rag():
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<summary> <b>使用Faiss进行存储</b> </summary>
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在使用Faiss向量数据库之前必须手工安装`faiss-cpu`或`faiss-gpu`。
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- 安装所需依赖：
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```
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@ -818,51 +817,13 @@ rag = LightRAG(
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<details>
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<summary> <b>使用PostgreSQL进行存储</b> </summary>
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对于生产级场景，您很可能想要利用企业级解决方案。PostgreSQL可以为您提供一站式解决方案，作为KV存储、向量数据库（pgvector）和图数据库（apache AGE）。
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对于生产级场景，您很可能想要利用企业级解决方案。PostgreSQL可以为您提供一站式解决方案，作为KV存储、向量数据库（pgvector）和图数据库（apache AGE）。支持 PostgreSQL 版本为16.6或以上。
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* PostgreSQL很轻量，整个二进制发行版包括所有必要的插件可以压缩到40MB：参考[Windows发布版](https://github.com/ShanGor/apache-age-windows/releases/tag/PG17%2Fv1.5.0-rc0)，它在Linux/Mac上也很容易安装。
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* 如果您是初学者并想避免麻烦，推荐使用docker，请从这个镜像开始（请务必阅读概述）：https://hub.docker.com/r/shangor/postgres-for-rag
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* 如何开始？参考：[examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
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* 为AGE创建索引示例：（如有必要，将下面的`dickens`改为您的图名）
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```sql
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load 'age';
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SET search_path = ag_catalog, "$user", public;
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CREATE INDEX CONCURRENTLY entity_p_idx ON dickens."Entity" (id);
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CREATE INDEX CONCURRENTLY vertex_p_idx ON dickens."_ag_label_vertex" (id);
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CREATE INDEX CONCURRENTLY directed_p_idx ON dickens."DIRECTED" (id);
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CREATE INDEX CONCURRENTLY directed_eid_idx ON dickens."DIRECTED" (end_id);
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CREATE INDEX CONCURRENTLY directed_sid_idx ON dickens."DIRECTED" (start_id);
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CREATE INDEX CONCURRENTLY directed_seid_idx ON dickens."DIRECTED" (start_id,end_id);
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CREATE INDEX CONCURRENTLY edge_p_idx ON dickens."_ag_label_edge" (id);
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CREATE INDEX CONCURRENTLY edge_sid_idx ON dickens."_ag_label_edge" (start_id);
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CREATE INDEX CONCURRENTLY edge_eid_idx ON dickens."_ag_label_edge" (end_id);
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CREATE INDEX CONCURRENTLY edge_seid_idx ON dickens."_ag_label_edge" (start_id,end_id);
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create INDEX CONCURRENTLY vertex_idx_node_id ON dickens."_ag_label_vertex" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
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create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
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CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties);
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ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx;
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-- 如有必要可以删除
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drop INDEX entity_p_idx;
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drop INDEX vertex_p_idx;
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drop INDEX directed_p_idx;
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drop INDEX directed_eid_idx;
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drop INDEX directed_sid_idx;
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drop INDEX directed_seid_idx;
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drop INDEX edge_p_idx;
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drop INDEX edge_sid_idx;
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drop INDEX edge_eid_idx;
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drop INDEX edge_seid_idx;
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drop INDEX vertex_idx_node_id;
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drop INDEX entity_idx_node_id;
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drop INDEX entity_node_id_gin_idx;
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```
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* Apache AGE的已知问题：发布版本存在以下问题：
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> 您可能会发现节点/边的属性是空的。
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> 这是发布版本的已知问题：https://github.com/apache/age/pull/1721
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>
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> 您可以从源代码编译AGE来修复它。
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>
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* Apache AGE的性能不如Neo4j。最求高性能的图数据库请使用Noe4j。
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</details>
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50
README.md
50
README.md
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@ -792,58 +792,18 @@ see test_neo4j.py for a working example.
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<details>
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<summary> <b>Using PostgreSQL for Storage</b> </summary>
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For production level scenarios you will most likely want to leverage an enterprise solution. PostgreSQL can provide a one-stop solution for you as KV store, VectorDB (pgvector) and GraphDB (apache AGE).
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For production level scenarios you will most likely want to leverage an enterprise solution. PostgreSQL can provide a one-stop solution for you as KV store, VectorDB (pgvector) and GraphDB (apache AGE). PostgreSQL version 16.6 or higher is supported.
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* PostgreSQL is lightweight,the whole binary distribution including all necessary plugins can be zipped to 40MB: Ref to [Windows Release](https://github.com/ShanGor/apache-age-windows/releases/tag/PG17%2Fv1.5.0-rc0) as it is easy to install for Linux/Mac.
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* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (DO read the overview): https://hub.docker.com/r/shangor/postgres-for-rag
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* How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
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* Create index for AGE example: (Change below `dickens` to your graph name if necessary)
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```sql
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load 'age';
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SET search_path = ag_catalog, "$user", public;
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CREATE INDEX CONCURRENTLY entity_p_idx ON dickens."Entity" (id);
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CREATE INDEX CONCURRENTLY vertex_p_idx ON dickens."_ag_label_vertex" (id);
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CREATE INDEX CONCURRENTLY directed_p_idx ON dickens."DIRECTED" (id);
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CREATE INDEX CONCURRENTLY directed_eid_idx ON dickens."DIRECTED" (end_id);
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CREATE INDEX CONCURRENTLY directed_sid_idx ON dickens."DIRECTED" (start_id);
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CREATE INDEX CONCURRENTLY directed_seid_idx ON dickens."DIRECTED" (start_id,end_id);
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CREATE INDEX CONCURRENTLY edge_p_idx ON dickens."_ag_label_edge" (id);
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CREATE INDEX CONCURRENTLY edge_sid_idx ON dickens."_ag_label_edge" (start_id);
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CREATE INDEX CONCURRENTLY edge_eid_idx ON dickens."_ag_label_edge" (end_id);
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CREATE INDEX CONCURRENTLY edge_seid_idx ON dickens."_ag_label_edge" (start_id,end_id);
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create INDEX CONCURRENTLY vertex_idx_node_id ON dickens."_ag_label_vertex" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
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create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
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CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties);
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ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx;
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-- drop if necessary
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drop INDEX entity_p_idx;
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drop INDEX vertex_p_idx;
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drop INDEX directed_p_idx;
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drop INDEX directed_eid_idx;
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drop INDEX directed_sid_idx;
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drop INDEX directed_seid_idx;
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drop INDEX edge_p_idx;
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drop INDEX edge_sid_idx;
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drop INDEX edge_eid_idx;
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drop INDEX edge_seid_idx;
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drop INDEX vertex_idx_node_id;
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drop INDEX entity_idx_node_id;
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drop INDEX entity_node_id_gin_idx;
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```
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* Known issue of the Apache AGE: The released versions got below issue:
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> You might find that the properties of the nodes/edges are empty.
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> It is a known issue of the release version: https://github.com/apache/age/pull/1721
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>
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> You can Compile the AGE from source code and fix it.
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>
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* For high-performance graph database requirements, Neo4j is recommended as Apache AGE's performance is not as competitive.
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</details>
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<details>
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<summary> <b>Using Faiss for Storage</b> </summary>
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You must manually install faiss-cpu or faiss-gpu before using FAISS vector db.
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Manually install `faiss-cpu` or `faiss-gpu` before using FAISS vector db.
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Before using Faiss vector database, you must manually install `faiss-cpu` or `faiss-gpu`.
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- Install the required dependencies:
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@ -1280,10 +1240,8 @@ LightRAG now seamlessly integrates with [RAG-Anything](https://github.com/HKUDS/
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),
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)
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)
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# Initialize storage (this will load existing data if available)
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await lightrag_instance.initialize_storages()
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# Now initialize RAGAnything with the existing LightRAG instance
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rag = RAGAnything(
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lightrag=lightrag_instance, # Pass the existing LightRAG instance
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@ -1312,14 +1270,12 @@ LightRAG now seamlessly integrates with [RAG-Anything](https://github.com/HKUDS/
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)
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# Note: working_dir, llm_model_func, embedding_func, etc. are inherited from lightrag_instance
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)
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# Query the existing knowledge base
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result = await rag.query_with_multimodal(
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"What data has been processed in this LightRAG instance?",
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mode="hybrid"
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)
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print("Query result:", result)
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# Add new multimodal documents to the existing LightRAG instance
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await rag.process_document_complete(
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file_path="path/to/new/multimodal_document.pdf",
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