Merge pull request #570 from ShanGor/main

Revise the AGE usage for postgres_impl

README.md

@@ -26,7 +26,7 @@ This repository hosts the code of LightRAG. The structure of this code is based
 </div>
 
 ## 🎉 News
-- [x] [2025.01.06]🎯📢LightRAG now supports [PostgreSQL for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-postgres-for-storage).
+- [x] [2025.01.06]🎯📢You can now [use PostgreSQL for Storage](#using-postgresql-for-storage).
 - [x] [2024.12.31]🎯📢LightRAG now supports [deletion by document ID](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
 - [x] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise.
 - [x] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
@@ -361,6 +361,11 @@ see test_neo4j.py for a working example.
 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 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.
 * How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
+* Create index for AGE example: (Change below `dickens` to your graph name if necessary)
+  ```
+  SET search_path = ag_catalog, "$user", public;
+  CREATE INDEX idx_entity ON dickens."Entity" USING gin (agtype_access_operator(properties, '"node_id"'));
+  ```
 
 ### Insert Custom KG