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Version: dev

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.

Install Dependencies​

First, you need to install the dbgpt Oceanbase Vector storage library.

uv sync --all-packages \
--extra "base" \
--extra "proxy_openai" \
--extra "rag" \
--extra "storage_obvector" \
--extra "dbgpts"

Prepare Oceanbase Vector​

Prepare Oceanbase Vector database service, referenceOceanbase Vector .

TuGraph Configuration​

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"
uri = "127.0.0.1"
port = "19530"
#username="dbgpt"
#password=19530

Then run the following command to start the webserver:

uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml

Optionally, you can also use the following command to start the webserver:

uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml