openai-forward 0.6.3

🚀 OpenAI-Forward 是一个专为大型语言模型设计的高级转发代理，提供包括用户请求速率控制、Token速率限制和自定义API密钥等增强功能。该服务可用于代理本地模型和云端模型。OpenAI API Reverse Proxy

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OpenAI Forward

Features | Deployment Guide | User Guide | Configuration | Conversation Logs

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OpenAI-Forward is an efficient forwarding service designed for large language models. Its core features include user request rate control, Token rate limits, intelligent prediction caching, log management, and API key management, aiming to provide a fast and convenient model forwarding service. Whether proxying local language models or cloud-based language models, such as LocalAI or OpenAI, OpenAI Forward facilitates easy implementation. With the support of libraries like uvicorn, aiohttp, and asyncio, OpenAI-Forward achieves impressive asynchronous performance.

Key Features

OpenAI-Forward offers the following capabilities:

• Universal Forwarding: Supports forwarding of almost all types of requests.
• Performance First: Boasts outstanding asynchronous performance.
• Cache AI Predictions: Caches AI predictions, accelerating service access and saving costs.
• User Traffic Control: Customize request and Token rates.
• Real-time Response Logs: Enhances observability of the call chain.
• Custom Secret Keys: Replaces the original API keys.
• Multi-target Routing: Forwards to multiple service addresses under a single service to different routes.
• Automatic Retries: Ensures service stability; will automatically retry on failed requests.
• Quick Deployment: Supports fast deployment locally or on the cloud via pip and docker.

Proxy services set up by this project include:

• Original OpenAI Service Address:

  https://api.openai-forward.com
  https://render.openai-forward.com

• Cached Service Address (User request results will be saved for some time):

  https://smart.openai-forward.com

_{Note: The proxy services deployed here are for personal learning and research purposes only and should not be used for any commercial purposes.}

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Deployment Guide

👉 Deployment Documentation

User Guide

Quick Start

Installation

pip install openai-forward

Starting the Service

aifd run

If the configuration from the .env file at the root path is read, you will see the following startup information.

❯ aifd run
╭────── 🤗 openai-forward is ready to serve!  ───────╮
│                                                    │
│  base url         https://api.openai.com           │
│  route prefix     /                                │
│  api keys         False                            │
│  forward keys     False                            │
│  cache_backend    MEMORY                           │
╰────────────────────────────────────────────────────╯
╭──────────── ⏱️ Rate Limit configuration ───────────╮
│                                                    │
│  backend                memory                     │
│  strategy               moving-window              │
│  global rate limit      100/minute (req)           │
│  /v1/chat/completions   100/2minutes (req)         │
│  /v1/completions        60/minute;600/hour (req)   │
│  /v1/chat/completions   60/second (token)          │
│  /v1/completions        60/second (token)          │
╰────────────────────────────────────────────────────╯
INFO:     Started server process [191471]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

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Proxy OpenAI Model:

The default option for aifd run is to proxy https://api.openai.com.

The following uses the set up service address https://api.openai-forward.com as an example.

Click to expand

Use in Third-party Applications

Integrate within the open-source project ChatGPT-Next-Web: Replace the BASE_URL in the Docker startup command with the address of your self-hosted proxy service.

docker run -d \
    -p 3000:3000 \
    -e OPENAI_API_KEY="sk-******" \
    -e BASE_URL="https://api.openai-forward.com" \
    -e CODE="******" \
    yidadaa/chatgpt-next-web 

Integrate within Code

---

Python

  import openai
+ openai.api_base = "https://api.openai-forward.com/v1"
  openai.api_key = "sk-******"

JS/TS

  import { Configuration } from "openai";
  
  const configuration = new Configuration({
+ basePath: "https://api.openai-forward.com/v1",
  apiKey: "sk-******",
  });

gpt-3.5-turbo

curl https://api.openai-forward.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-******" \
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Image Generation (DALL-E)

curl --location 'https://api.openai-forward.com/v1/images/generations' \
--header 'Authorization: Bearer sk-******' \
--header 'Content-Type: application/json' \
--data '{
    "prompt": "A photo of a cat",
    "n": 1,
    "size": "512x512"
}'

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Proxy Local Model

• Applicable scenarios: To be used in conjunction with projects such as LocalAI and api-for-open-llm.

• How to operate: Using LocalAI as an example, if the LocalAI service has been deployed at http://localhost:8080, you only need to set OPENAI_BASE_URL=http://localhost:8080 in the environment variable or in the .env file. After that, you can access LocalAI through http://localhost:8000.

(More)

Proxy Other Cloud Models

• Applicable scenarios: For instance, through LiteLLM, you can convert the API format of many cloud models to the OpenAI API format and then use this service as a proxy.

(More)

Configuration

Command Line Arguments

Execute aifd run --help to get details on arguments.

Click for more details

Configuration	Description	Default Value
--port	Service port	8000
--workers	Number of working processes	1

Environment Variable Details

You can create a .env file in the project's run directory to customize configurations. For a reference configuration, see the .env.example file in the root directory.

Environment Variable	Description	Default Value
OPENAI_BASE_URL	Set base address for OpenAI-style API	https://api.openai.com
OPENAI_ROUTE_PREFIX	Define a route prefix for the OPENAI_BASE_URL interface address	/
OPENAI_API_KEY	Configure API key in OpenAI style, supports using multiple keys separated by commas	None
FORWARD_KEY	Set a custom key for proxying, multiple keys can be separated by commas. If not set (not recommended), it will directly use OPENAI_API_KEY	None
EXTRA_BASE_URL	Configure the base URL for additional proxy services	None
EXTRA_ROUTE_PREFIX	Define the route prefix for additional proxy services	None
REQ_RATE_LIMIT	Set the user request rate limit for specific routes (user distinguished)	None
GLOBAL_RATE_LIMIT	Configure a global request rate limit applicable to routes not specified in REQ_RATE_LIMIT	None
RATE_LIMIT_STRATEGY	Choose a rate limit strategy, options include: fixed-window, fixed-window-elastic-expiry, moving-window	None
TOKEN_RATE_LIMIT	Limit the output rate of each token (or SSE chunk) in a streaming response	None
PROXY	Set HTTP proxy address	None
LOG_CHAT	Toggle chat content logging for debugging and monitoring	false
CACHE_BACKEND	Cache backend, supports memory backend and database backend. By default, it's memory backend, optional database backends are lmdb, rocksdb, and leveldb	MEMORY
CACHE_CHAT_COMPLETION	Whether to cache /v1/chat/completions results	false

Detailed configuration descriptions can be seen in the .env.example file. (To be completed)

Note: If you set OPENAI_API_KEY but did not set FORWARD_KEY, clients will not need to provide a key when calling. As this may pose a security risk, it's not recommended to leave FORWARD_KEY unset unless there's a specific need.

Caching

By default, caching uses a memory backend. You can choose a database backend but need to install the corresponding environment:

pip install openai-forward[lmdb] # lmdb backend
pip install openai-forward[leveldb] # leveldb backend
pip install openai-forward[rocksdb] # rocksdb backend

• Configure CACHE_BACKEND in the environment variable to use the respective database backend for storage. Options are MEMORY, LMDB, ROCKSDB, and LEVELDB.
• Set CACHE_CHAT_COMPLETION to true to cache /v1/chat/completions results.

  import openai
  openai.api_base = "https://smart.openai-forward.com/v1"
  openai.api_key = "sk-******"

  completion = openai.ChatCompletion.create(
+   caching=False, # Cache by default, can be set to not cache
    model="gpt-3.5-turbo",
    messages=[
      {"role": "user", "content": "Hello!"}
    ]
)

Custom Keys

Click for more details

Configure OPENAI_API_KEY and FORWARD_KEY, for example:

OPENAI_API_KEY=sk-*******
FORWARD_KEY=fk-****** # Here, the fk-token is customized

Use case:

  import openai
+ openai.api_base = "https://api.openai-forward.com/v1"
- openai.api_key = "sk-******"
+ openai.api_key = "fk-******"

Multi-Target Service Forwarding

Supports forwarding services from different addresses to different routes under the same port. Refer to the .env.example for examples.

Conversation Logs

Chat logs are not recorded by default. If you wish to enable it, set the LOG_CHAT=true environment variable.

Click for more details

Logs are saved in the current directory under Log/openai/chat/chat.log. The recording format is:

{'messages': [{'role': 'user', 'content': 'hi'}], 'model': 'gpt-3.5-turbo', 'stream': True, 'max_tokens': None, 'n': 1, 'temperature': 1, 'top_p': 1, 'logit_bias': None, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None, 'user': None, 'ip': '127.0.0.1', 'uid': '2155fe1580e6aed626aa1ad74c1ce54e', 'datetime': '2023-10-17 15:27:12'}
{'assistant': 'Hello! How can I assist you today?', 'is_function_call': False, 'uid': '2155fe1580e6aed626aa1ad74c1ce54e'}

To convert to json format:

aifd convert

You'll get chat_openai.json:

[
  {
    "datetime": "2023-10-17 15:27:12",
    "ip": "127.0.0.1",
    "model": "gpt-3.5-turbo",
    "temperature": 1,
    "messages": [
      {
        "user": "hi"
      }
    ],
    "functions": null,
    "is_function_call": false,
    "assistant": "Hello! How can I assist you today?"
  }
]

Backer and Sponsor

License

OpenAI-Forward is licensed under the MIT license.