DB-GPT/docs/docs/quickstart.md

sidebar_position

sidebar_position
0

Quickstart

DB-GPT supports the installation and use of a variety of open source and closed models. Different models have different requirements for environment and resources. If localized model deployment is required, GPU resources are required for deployment. The API proxy model requires relatively few resources and can be deployed and started on a CPU machine.

:::info note

• Detailed installation and deployment tutorials can be found in Installation.
• This page only introduces deployment based on ChatGPT proxy and local glm model. :::

Environmental preparation

Download source code

:::tip Download DB-GPT :::

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

Miniconda environment installation

• The default database uses SQLite, so there is no need to install a database in the default startup mode. If you need to use other databases, you can read the advanced tutorials below. We recommend installing the Python virtual environment through the conda virtual environment. For the installation of Miniconda environment, please refer to the Miniconda installation tutorial.

:::tip Create a Python virtual environment :::

python >= 3.10
conda create -n dbgpt_env python=3.10
conda activate dbgpt_env

# it will take some minutes
pip install -e ".[default]"

:::tip Copy environment variables :::

cp .env.template  .env

Model deployment

:::info note

Provide two deployment methods to quickly start experiencing DB-GPT.

::: import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

<Tabs defaultValue="openai" values={[ {label: 'Open AI(Proxy LLM)', value: 'openai'}, {label: 'glm-4(Local LLM)', value: 'glm-4'}, ]}>

:::info note

⚠️ You need to ensure that git-lfs is installed

● CentOS installation: yum install git-lfs
● Ubuntu installation: apt-get install git-lfs
● MacOS installation: brew install git-lfs

:::

Install dependencies

pip install  -e ".[openai]"

Download embedding model

cd DB-GPT
mkdir models and cd models
git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese

Configure the proxy and modify LLM_MODEL, PROXY_API_URL and API_KEY in the .envfile

# .env
LLM_MODEL=chatgpt_proxyllm
PROXY_API_KEY={your-openai-sk}
PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions

Hardware requirements description

Model	GPU VRAM Size
glm-4-9b	16GB

Download LLM

cd DB-GPT
mkdir models and cd models

# embedding model
git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese
# also you can use m3e-large model, you can choose one of them according to your needs
# git clone https://huggingface.co/moka-ai/m3e-large

# LLM model, if you use openai or Azure or tongyi llm api service, you don't need to download llm model
git clone https://huggingface.co/THUDM/glm-4-9b-chat

Environment variable configuration, configure the LLM_MODEL parameter in the .env file

# .env
LLM_MODEL=glm-4-9b-chat

Test data (optional)

Load default test data into SQLite database

• Linux

bash ./scripts/examples/load_examples.sh

• Windows

.\scripts\examples\load_examples.bat

Run service

python dbgpt/app/dbgpt_server.py

:::info NOTE

Run old service

If you are running version v0.4.3 or earlier, please start with the following command:

python pilot/server/dbgpt_server.py

Run DB-GPT with command dbgpt

If you want to run DB-GPT with the command dbgpt:

dbgpt start webserver

:::

Visit website

Open the browser and visit http://localhost:5670

(Optional) Run web front-end separately

On the other hand, you can also run the web front-end separately.

cd web & npm install
cp .env.template .env
// set the API_BASE_URL to your DB-GPT server address, it usually is http://localhost:5670
npm run dev

Open the browser and visit http://localhost:3000