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Update postgreSQL docker image link

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yangdx
2025-11-20 12:34:49 +08:00
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README-zh.md
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@@ -881,7 +881,7 @@ rag = LightRAG(
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储解解决方案，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。
* 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
* 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（默认帐号密码:rag/rag）：https://hub.docker.com/r/gzdaniel/postgres-for-rag
* Apache AGE的性能不如Neo4j。追求高性能的图数据库请使用Noe4j。
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README.md
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@@ -845,7 +845,7 @@ 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 version 16.6 or higher is supported.
* 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.
* 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
* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (Default user password:rag/rag): https://hub.docker.com/r/gzdaniel/postgres-for-rag
* How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
* For high-performance graph database requirements, Neo4j is recommended as Apache AGE's performance is not as competitive.