Milvus RAG

In this example, we will show how to use the Milvus 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 milvus storage library.

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

Prepare Milvus

Prepare Milvus database service, reference-Milvus Installation .

TuGraph Configuration

Set rag storage variables below in configs/dbgpt-proxy-openai.toml file, let DB-GPT know how to connect to Milvus.

[rag.storage]
[rag.storage.vector]
type = "Milvus"
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