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 .

OceanBase 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