Revised the postgres implementation, to use attributes(node_id) rather than nodes to identify an entity. Which significantly reduced the table counts.

README.md

@@ -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