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https://github.com/laoshubaby/liulianmao-fastapi
A wrapper that can imitate forward gateway and make call use liulianmao
https://github.com/laoshubaby/liulianmao-fastapi
Last synced: about 2 months ago
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A wrapper that can imitate forward gateway and make call use liulianmao
- Host: GitHub
- URL: https://github.com/laoshubaby/liulianmao-fastapi
- Owner: LaoshuBaby
- License: mit
- Created: 2024-08-17T13:07:13.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-08-30T12:35:47.000Z (4 months ago)
- Last Synced: 2024-08-30T12:47:15.575Z (4 months ago)
- Language: Python
- Size: 92.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# liulianmao-fastapi
A wrapper that can imitate forward gateway and make call use liulianmao
## 芝士神马？
这玩意是配合[liulianmao](https://github.com/LaoshuBaby/liulianmao)使用的。
大部分时候当您需要转发API的时候，只需要用[one-api](https://github.com/songquanpeng/one-api)就可以了，剩下的时候用[new-api](https://github.com/Calcium-Ion/new-api)就可以了。
本项目主要适合如下场景
1. 为了更好的debug，需要更详细的日志
2. 为了对内容进行审计，需要更详细的日志
3. 需要对模型的名称或请求地址进行映射
4. 需要对模型的请求方法进行处理
## 使用说明
### 设置和运行FastAPI服务器
1. 安装所需的依赖项:
```bash
pip install fastapi liulianmao
```
2. 运行FastAPI服务器:
```bash
uvicorn main:app --reload
```
或
py main.py
### 示例请求和响应
#### 请求的接口：/hello
**请求**
请求体：以curl表达：
```json
curl -X GET http://localhost:9000/
```
**响应:**
```json
{
"os": "posix",
"platform": "linux",
"version": "3.11.0",
"environment_variables": {
"PATH": "/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin",
...
}
}
```
#### 请求的接口：/logs
**请求**
请求体：以curl表达：
```json
curl -X GET http://localhost:9000/logs
```
**响应:**
```json
{
"logs": [
"log1.txt",
"log2.txt",
...
]
}
```
#### 请求的接口：/logs/{filename}
**请求**
请求体：以curl表达：
```json
curl -X GET http://localhost:9000/logs/log1.txt
```
**响应:**
```text
Log file content here...
```
#### 请求的接口：/paas/v1/chat/completions
**请求**
请求体：以curl表达：
```json
curl -X POST http://localhost:9000/paas/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "gpt-3-turbo",
"messages": [
{
"role": "user",
"content": "你好"
}
]
}'
```
**响应:**
```json
{
"model": "glm-4",
"data": [
{
"embedding": [
-0.02675454691052437,
0.019060475751757622,
......
-0.005519774276763201,
0.014949671924114227
],
"index": 0,
"object": "embedding"
}
],
"object": "list",
"usage": {
"completion_tokens": 0,
"prompt_tokens": 100,
"total_tokens": 100
}
}
```
#### 请求的接口：/paas/v4/chat/completions
**请求**
请求体：以curl表达：
```json
curl -X POST http://localhost:9000/paas/v4/chat/completions -H "Content-Type: application/json" -d '{
"model": "gpt-3-turbo",
"messages": [
{
"role": "user",
"content": "你好"
}
]
}'
```
**响应:**
```json
{
"model": "glm-4",
"data": [
{
"embedding": [
-0.02675454691052437,
0.019060475751757622,
......
-0.005519774276763201,
0.014949671924114227
],
"index": 0,
"object": "embedding"
}
],
"object": "list",
"usage": {
"completion_tokens": 0,
"prompt_tokens": 100,
"total_tokens": 100
}
}
```
#### 请求的接口：/embedding
**请求**
请求体：以curl表达：
```json
curl -X POST http://localhost:9000/embedding -H "Content-Type: application/json" -d '{
"model": "text-embedding-ada-002",
"input": "Hello, world!"
}'
```
**响应:**
```json
{
"model": "embedding-3",
"data": [
{
"embedding": [
-0.02675454691052437,
0.019060475751757622,
......
-0.005519774276763201,
0.014949671924114227
],
"index": 0,
"object": "embedding"
}
],
"object": "list",
"usage": {
"completion_tokens": 0,
"prompt_tokens": 100,
"total_tokens": 100
}
}
```
## 部署方法
### 在Zeabur上部署
1. 创建一个新的Zeabur项目。
2. 将代码库连接到Zeabur项目。
3. 配置Zeabur以使用Python 3.11环境。
4. 设置启动命令为 `uvicorn main:app --host 0.0.0.0 --port $PORT`。
5. 部署项目。
### 在AWS Lambda上部署
1. 创建一个新的AWS Lambda函数。
2. 将代码打包为zip文件并上传到Lambda。
3. 配置Lambda函数以使用Python 3.11运行时。
4. 设置Lambda函数的入口点为 `main.app`。
5. 配置API Gateway以触发Lambda函数。
6. 部署API Gateway。