DB-GPT 最新0.7.0版本Windows 部署

文档：官方文档

Data Agents

我这里就说下细节，我是

小兵我是在在服务器里部署的，不建议小的服务器部署，会GG了

1. 前置环境

因为DB-GPT 越来越强大了，只用pip来管理依赖包的话不太优雅，因此使用 uv 来管理。我最近写的项目也再慢慢转 uv 了，也推荐大家慢慢转过来。本教程讲解以 代理模型 为例，也就是 openai-proxy

1.1 uv 安装

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powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

当然我这里用到的是conda来进行虚拟环境

1.2项目拉取

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

弄好后，记得把sqlite3 的数据也弄好哦，特别提醒

拉取完后，我是本地部署的

1.3Local（本地模型）:

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uv sync --all-packages --extra "base" --extra "cuda121" --extra "hf" --extra "rag" --extra "storage_chromadb" --extra "quant_bnb" --extra "dbgpts"

安装完包后

可以提前把模型下载

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git clone https://www.modelscope.cn/Qwen/Qwen2.5-14B-Instruct.git

git clone https://hf-mirror.com/BAAI/bge-large-zh-v1.5.git

这里大概33G+，请耐心等待

2.最后

1步骤

cd E:\DB-GPT\

2进入环境

conda activate dbgpt_env_7

3.执行系统运行

uv run dbgpt start webserver --config .\configs\dbgpt-local-qwen.toml

这里提醒一下，local本地一定运行对应的配置文件

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[system]
# Load language from environment variable(It is set by the hook)
language = "${env:DBGPT_LANG:-zh}"
api_keys = []
encrypt_key = "your_secret_key"

# Server Configurations
[service.web]
host = "0.0.0.0"
port = 5670

[service.web.database]
type = "sqlite"
path = "pilot/meta_data/dbgpt.db"

[rag.storage]
[rag.storage.vector]
type = "chroma"
persist_path = "pilot/data"

# Model Configurations
[models]
[[models.llms]]
name = "Qwen2.5-0.5B-Instruct"
provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "models/Qwen2.5-0.5B-Instruct"

[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"
provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "models/BAAI/bge-large-zh-v1.5"

配置文件根据实际的情况来改

特别注意

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# Model Configurations
[models]
[[models.llms]]
name = "Qwen2.5-0.5B-Instruct"
provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "models/Qwen2.5-0.5B-Instruct"

[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"
provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "models/BAAI/bge-large-zh-v1.5"

这里的路径和模型名称一定要对上，否则无法加载

提示如果本地部署，内容不够建议32G以上，这是最少的。