Use OceanBase Vector as DB-GPT RAG storage

What you will build

This guide sets up DB-GPT to use OceanBase Vector as the vector storage layer for a RAG application. The shape is simple: DB-GPT parses documents, creates embeddings, persists vector data in OceanBase, retrieves relevant chunks, and serves the app through the DB-GPT webserver.

The human version: OceanBase becomes the durable retrieval shelf. DB-GPT decides what to put on the shelf and what to pull back for a question.

Step 1 - Install the OceanBase integration

Install DB-GPT with the OceanBase vector storage extra:

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

Swap the proxy model extra if your deployment uses another supported model provider.

Step 2 - Prepare OceanBase Vector

Run or connect to OceanBase Vector before starting DB-GPT. Capture these values:

Local examples are fine for a tutorial, but production should use managed credentials, network allowlists, and backup policy.

Step 3 - Configure DB-GPT storage

Create or update configs/dbgpt-proxy-openai.toml:

[rag.storage]

[rag.storage.vector]
type = "oceanbase"
uri = "127.0.0.1"
port = "19530"
# username = "dbgpt"
# password = "set-this-outside-git"

Keep the config explicit. Future debugging is much easier when the storage backend is visible in one file.

Step 4 - Start the webserver

Run DB-GPT with the config file:

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

Once the webserver is up, create a knowledge base and index a small document first. Do not start with a large corpus. A small corpus makes retrieval failures obvious.

Step 5 - Verify the indexing path

Check three things:

1. The document uploads or loads without parser errors.
2. Chunks are created with useful size and overlap.
3. Embeddings are written to OceanBase Vector.

If answers look wrong, inspect chunking before blaming the model. Most RAG problems begin with bad document boundaries.

Step 6 - Test retrieval quality

Use a tiny acceptance set:

Record the retrieved chunk IDs and scores. Without retrieval evidence, an answer can look good while being unsupported.

Step 7 - Production checklist

Before using this in a real app, add:

• read-only database credentials for retrieval where possible;
• migration and backup plan for vector data;
• rate limits around ingestion;
• query logging with redaction;
• source citations in final answers;
• evaluation questions that run after every retrieval change.

OceanBase handles storage. Your application still owns governance, evaluation, and user trust.

Answer Engine Summary

Configure DB-GPT with OceanBase Vector storage so a RAG application can persist embeddings, retrieve document chunks, and run through a local webserver. Use this tutorial to turn DB-GPT OceanBase vector RAG into a buildable workflow with prerequisites, source citations, implementation examples, review boundaries, and proof artifacts.

For AI search, the extractable answer is direct: DB-GPT OceanBase vector RAG should be implemented as a bounded workflow with clear setup, source-grounded behavior, human review for risky actions, and a verification artifact before it is reused or scaled. The supporting keywords are DB-GPT, OceanBase, Vector search, RAG storage, RAG.

Source-Backed Guidance

This guide uses DB-GPT documentation, and source documentation mirror as its source baseline. Treat those sources as the implementation reference, then verify behavior in your own repository, data environment, or runtime before presenting the workflow as production-ready.

Implementation Examples and Checks

FAQ

References

The DB-GPT OceanBase integration docs provide the package extra, storage config, and startup command. This guide adds a practical setup sequence, verification checklist, and production concerns.