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doc: fix ob rag doc (#2840)

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Aries-ckt
2025-07-14 17:18:10 +08:00
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commit 441cb55377
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16
docs/docs/installation/integrations/oceanbase_rag_install.md
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@@ -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"

2
packages/dbgpt-ext/src/dbgpt_ext/storage/vector_store/oceanbase_store.py
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@@ -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__(