github/DB-GPT
1
0
Fork 0
mirror of https://github.com/csunny/DB-GPT.git synced 2025-09-06 11:31:12 +00:00
Code Issues Packages Projects Releases Wiki Activity

docs: add module description

Browse Source
This commit is contained in:
csunny
2023-05-24 22:21:16 +08:00
parent 4021e9a597
commit 340069aee6
10 changed files with 70 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
docs/getting_started/concepts.md
View File

@@ -1,3 +1,2 @@
# Concepts

46
docs/getting_started/getting_started.md
View File

@@ -4,4 +4,48 @@ This tutorial gives you a quick walkthrough about use DB-GPT with you environmen
## Installation
To get started, install DB-GPT with the following command.
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.
```bash
$ docker run --name=mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=aa12345678 -dit mysql:latest
```
We use [Chroma embedding database](https://github.com/chroma-core/chroma) 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](https://github.com/lm-sys/FastChat/blob/main/README.md#model-weights) .
If you have difficulty with this step, you can also directly use the model from [this link](https://huggingface.co/Tribbiani/vicuna-7b) as a replacement.
1. Run server
```bash
$ python pilot/server/llmserver.py
```
Run gradio webui
```bash
$ 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.