From 441cb55377953a43d462e3c62469b348c13852ab Mon Sep 17 00:00:00 2001
From: Aries-ckt <916701291@qq.com>
Date: Mon, 14 Jul 2025 17:18:10 +0800
Subject: [PATCH] doc: fix ob rag doc (#2840)

---
 .../integrations/oceanbase_rag_install.md        | 16 ++++++++--------
 .../storage/vector_store/oceanbase_store.py      |  2 +-
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/docs/installation/integrations/oceanbase_rag_install.md b/docs/docs/installation/integrations/oceanbase_rag_install.md
index cf75d9374..05f2273fe 100644
--- a/docs/docs/installation/integrations/oceanbase_rag_install.md
+++ b/docs/docs/installation/integrations/oceanbase_rag_install.md
@@ -1,12 +1,12 @@
-# Oceanbase Vector RAG
+# OceanBase Vector RAG
 
 
-In this example, we will show how to use the Oceanbase Vector as in DB-GPT RAG Storage. Using a graph database to implement RAG can, to some extent, alleviate the uncertainty and interpretability issues brought about by vector database retrieval.
+In this example, we will show how to use the OceanBase Vector as in DB-GPT RAG Storage. Using a graph database to implement RAG can, to some extent, alleviate the uncertainty and interpretability issues brought about by vector database retrieval.
 
 
 ### Install Dependencies
 
-First, you need to install the `dbgpt Oceanbase Vector storage` library.
+First, you need to install the `dbgpt OceanBase Vector storage` library.
 
 ```bash
 uv sync --all-packages \
@@ -17,19 +17,19 @@ uv sync --all-packages \
 --extra "dbgpts"
 ````
 
-### Prepare Oceanbase Vector
+### Prepare OceanBase Vector
 
-Prepare Oceanbase Vector database service, reference[Oceanbase Vector](https://open.oceanbase.com/) .
+Prepare OceanBase Vector database service, reference[OceanBase Vector](https://open.oceanbase.com/) .
 
 
-### TuGraph Configuration
+### OceanBase Configuration
 
-Set rag storage variables below in `configs/dbgpt-proxy-openai.toml` file, let DB-GPT know how to connect to Oceanbase Vector.
+Set rag storage variables below in `configs/dbgpt-proxy-openai.toml` file, let DB-GPT know how to connect to OceanBase Vector.
 
 ```
 [rag.storage]
 [rag.storage.vector]
-type = "Oceanbase"
+type = "oceanbase"
 uri = "127.0.0.1"
 port = "19530"
 #username="dbgpt"
diff --git a/packages/dbgpt-ext/src/dbgpt_ext/storage/vector_store/oceanbase_store.py b/packages/dbgpt-ext/src/dbgpt_ext/storage/vector_store/oceanbase_store.py
index 261655972..13a86094b 100644
--- a/packages/dbgpt-ext/src/dbgpt_ext/storage/vector_store/oceanbase_store.py
+++ b/packages/dbgpt-ext/src/dbgpt_ext/storage/vector_store/oceanbase_store.py
@@ -204,7 +204,7 @@ class OceanBaseStore(VectorStoreBase):
                 "Please install it with `pip install pyobvector`."
             )
 
-        if vector_store_config.embedding_fn is None:
+        if embedding_fn is None:
             raise ValueError("embedding_fn is required for OceanBaseStore")
 
         super().__init__(