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Quickstart Guide
This tutorial gives you a quick walkthrough about use DB-GPT with you environment and data.
Installation
To get started, install DB-GPT with the following steps.
1. Hardware Requirements
As our project has the ability to achieve ChatGPT performance of over 85%, there are certain hardware requirements. However, overall, the project can be deployed and used on consumer-grade graphics cards. The specific hardware requirements for deployment are as follows:
| GPU | VRAM Size | Performance |
|---|---|---|
| RTX 4090 | 24 GB | Smooth conversation inference |
| RTX 3090 | 24 GB | Smooth conversation inference, better than V100 |
| V100 | 16 GB | Conversation inference possible, noticeable stutter |
2. Install
This project relies on a local MySQL database service, which you need to install locally. We recommend using Docker for installation.
$ docker run --name=mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=aa12345678 -dit mysql:latest
We use Chroma embedding database as the default for our vector database, so there is no need for special installation. If you choose to connect to other databases, you can follow our tutorial for installation and configuration. For the entire installation process of DB-GPT, we use the miniconda3 virtual environment. Create a virtual environment and install the Python dependencies.
python>=3.10
conda create -n dbgpt_env python=3.10
conda activate dbgpt_env
pip install -r requirements.txt
3. Run
You can refer to this document to obtain the Vicuna weights: Vicuna .
If you have difficulty with this step, you can also directly use the model from this link as a replacement.
- Run server
$ python pilot/server/llmserver.py
Run gradio webui
$ python pilot/server/webserver.py
Notice: the webserver need to connect llmserver, so you need change the .env file. change the MODEL_SERVER = "http://127.0.0.1:8000" to your address. It's very important.