Getting Started

Goal: go from zero to a first working chat with minimal setup.

Fastest path

Use an API proxy (OpenAI or DeepSeek) — no GPU required. You will have a working DB-GPT chat in under 5 minutes.

What you need

• Python 3.10 or newer
• uv package manager

tip

Check your versions with python --version and uv --version. Full requirements: Prerequisites.

Quick setup

Step 1 — Clone the repository

git clone https://github.com/eosphoros-ai/DB-GPT.git
cd DB-GPT

Step 2 — Install dependencies

OpenAI (proxy)

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

DeepSeek (proxy)

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

Ollama (local)

uv sync --all-packages \
  --extra "base" \
  --extra "proxy_ollama" \
  --extra "rag" \
  --extra "storage_chromadb" \
  --extra "dbgpts"

Step 3 — Configure your model

OpenAI

Edit configs/dbgpt-proxy-openai.toml and set your API key:

[models]
[[models.llms]]
name = "chatgpt_proxyllm"
provider = "proxy/openai"
api_key = "your-openai-api-key"    # <-- replace this

[[models.embeddings]]
name = "text-embedding-3-small"
provider = "proxy/openai"
api_key = "your-openai-api-key"    # <-- replace this

DeepSeek

Edit configs/dbgpt-proxy-deepseek.toml and set your API key:

[models]
[[models.llms]]
name = "deepseek-reasoner"
provider = "proxy/deepseek"
api_key = "your-deepseek-api-key"  # <-- replace this

[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"
provider = "hf"

info

The default embedding model is BAAI/bge-large-zh-v1.5. If using a HuggingFace embedding, also add --extra "hf" and --extra "cpu" to the install command.

Ollama

Make sure Ollama is running, then edit configs/dbgpt-proxy-ollama.toml:

[models]
[[models.llms]]
name = "qwen2.5:latest"
provider = "proxy/ollama"
api_base = "http://localhost:11434"

[[models.embeddings]]
name = "nomic-embed-text:latest"
provider = "proxy/ollama"
api_base = "http://localhost:11434"

Step 4 — Start the server

OpenAI

uv run dbgpt start webserver --config configs/dbgpt-proxy-openai.toml

DeepSeek

uv run dbgpt start webserver --config configs/dbgpt-proxy-deepseek.toml

Ollama

uv run dbgpt start webserver --config configs/dbgpt-proxy-ollama.toml

Step 5 — Open the Web UI

Open your browser and visit http://localhost:5670.

Verify it works

If the Web UI loads and you can start a chat conversation, your DB-GPT is ready for use.

Verify

• The webserver is running
• Your model config loads without errors
• The Web UI opens at http://localhost:5670
• SQLite is available as the default metadata store

Common first-run issues

• uv: command not found
  • Install uv first: Prerequisites
• Model key/auth errors
  • Re-check the provider config under configs/
  • Start here: Model Providers
• Web UI does not load
  • Confirm the server is listening on port 5670
  • Check the server logs in the terminal where you started DB-GPT
• Local model does not respond
  • Confirm Ollama or your local inference backend is already running

If you need more

• Run the web front-end separately

  cd web && npm install
  cp .env.template .env
  # Edit .env — set API_BASE_URL=http://localhost:5670
  npm run dev

  Then open http://localhost:3000.

• Use the install helper

  uv run install_help.py install-cmd --interactive
  uv run install_help.py list

• Use a different database

  • Default is SQLite
  • For MySQL, PostgreSQL, and others, see Data Sources

• Useful environment variables

  • UV_INDEX_URL — PyPI mirror URL
  • OPENAI_API_KEY — alternative to storing the key in TOML
  • CUDA_VISIBLE_DEVICES — GPU device selection
  • Full reference: Config Reference

Go deeper

Topic	Link
Full architecture overview	Architecture
Connect more model providers	Model Providers
Docker deployment	Docker
Knowledge base setup	Knowledge Base

Next steps

• Configure model providers: Model Providers
• Deploy with Docker: Docker Deployment
• Explore the Web UI: Web UI Guide
• Build your first AWEL workflow: AWEL Quickstart