├── .dockerignore ├── .github └── workflows │ └── docker-image.yml ├── .gitignore ├── Dockerfile ├── config.yaml.example ├── docker-compose.yaml ├── logging_config ├── main.py ├── poe_api ├── __init__.py ├── config.py ├── const.py ├── exceptions.py ├── logger.py ├── middlewares │ ├── __init__.py │ └── asgi_logger │ │ ├── __init__.py │ │ ├── middleware.py │ │ └── utils.py ├── models.py ├── response.py ├── router.py └── utils.py ├── poetry.lock ├── pyproject.toml └── requirements.txt /.dockerignore: -------------------------------------------------------------------------------- 1 | .idea 2 | __pycache__ 3 | config.yaml 4 | logs -------------------------------------------------------------------------------- /.github/workflows/docker-image.yml: -------------------------------------------------------------------------------- 1 | name: Build and publish Docker image 2 | 3 | on: 4 | push: 5 | workflow_dispatch: 6 | 7 | env: 8 | IMAGE_NAME: ${{ github.repository }} 9 | 10 | jobs: 11 | build-and-push-x64-image: 12 | runs-on: ubuntu-latest 13 | permissions: 14 | contents: read 15 | packages: write 16 | 17 | steps: 18 | - name: Checkout repository 19 | uses: actions/checkout@v3 20 | 21 | - name: Docker meta 22 | id: meta 23 | uses: docker/metadata-action@v4 24 | with: 25 | images: | 26 | ghcr.io/${{ env.IMAGE_NAME }} 27 | tags: | 28 | type=raw,value=latest 29 | 30 | - name: Set up Docker Buildx 31 | uses: docker/setup-buildx-action@v2 32 | 33 | - name: Log in to the Container registry 34 | uses: docker/login-action@v2 35 | with: 36 | registry: ghcr.io 37 | username: ${{ github.actor }} 38 | password: ${{ secrets.GITHUB_TOKEN }} 39 | 40 | - name: Build and push Docker image 41 | uses: docker/build-push-action@v4 42 | with: 43 | context: . 44 | platforms: linux/amd64 45 | push: true 46 | tags: ${{ steps.meta.outputs.tags }} 47 | labels: ${{ steps.meta.outputs.labels }} 48 | cache-from: type=gha 49 | cache-to: type=gha,mode=max -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .idea 2 | __pycache__ 3 | config.yaml 4 | logs -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM python:3.11-alpine AS base 2 | 3 | RUN apk add --no-cache build-base 4 | 5 | WORKDIR /app 6 | COPY requirements.txt /tmp/requirements.txt 7 | RUN pip install --no-cache-dir -r /tmp/requirements.txt 8 | 9 | FROM base AS runner 10 | 11 | COPY . /app 12 | WORKDIR /app 13 | 14 | CMD ["sh", "-c", "python main.py"] 15 | -------------------------------------------------------------------------------- /config.yaml.example: -------------------------------------------------------------------------------- 1 | poe_api_key: YOUR_API_KEY 2 | log_dir: logs 3 | log_level: INFO 4 | host: 0.0.0.0 5 | port: 8800 6 | auth_secret: 7 | -------------------------------------------------------------------------------- /docker-compose.yaml: -------------------------------------------------------------------------------- 1 | version: "3.9" 2 | services: 3 | poe-api: 4 | # platform: "linux/amd64" 5 | container_name: poe-api 6 | ports: 7 | - "8810:8800" 8 | build: 9 | context: . 10 | dockerfile: Dockerfile 11 | volumes: 12 | - ./logs:/app/logs 13 | - ./config.yaml:/app/config.yaml 14 | working_dir: /app 15 | image: poekmon/poe-api 16 | -------------------------------------------------------------------------------- /logging_config: -------------------------------------------------------------------------------- 1 | version: 1 2 | formatters: 3 | simple: 4 | format: "%(asctime)s.%(msecs)03d %(levelname)8s: [%(name)s]\t%(message)s" 5 | datefmt: '%Y/%m/%d %H:%M:%S' 6 | proxy-output: 7 | format: "%(message)s" 8 | datefmt: '%Y/%m/%d %H:%M:%S' 9 | colored: 10 | (): colorlog.ColoredFormatter 11 | format: "%(asctime)s.%(msecs)03d %(log_color)s%(levelname)8s%(reset)s: %(cyan)s[%(name)s]%(reset)s %(message)s" 12 | datefmt: '%Y/%m/%d %H:%M:%S' 13 | handlers: 14 | file_handler: 15 | class: logging.handlers.RotatingFileHandler 16 | formatter: simple 17 | encoding: utf-8 18 | level: DEBUG 19 | filename: log.txt 20 | maxBytes: 10485760 # 10MB 21 | console_handler: 22 | class: logging.StreamHandler 23 | formatter: colored 24 | level: DEBUG 25 | 26 | root: 27 | level: DEBUG 28 | handlers: [] 29 | loggers: 30 | uvicorn.error: 31 | level: INFO 32 | handlers: [console_handler, file_handler] 33 | uvicorn.access: 34 | level: INFO 35 | handlers: [] # disable uvicorn access log 36 | poe_api: 37 | level: DEBUG 38 | handlers: [console_handler, file_handler] 39 | sqlalchemy: 40 | level: WARN 41 | handlers: [console_handler, file_handler] 42 | alembic: 43 | level: INFO 44 | handlers: [console_handler, file_handler] 45 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import logging 3 | from fastapi import FastAPI 4 | from fastapi.exceptions import RequestValidationError 5 | from fastapi.middleware.cors import CORSMiddleware 6 | from poe_api.config import CONFIG, load_config 7 | from poe_api.exceptions import SelfDefinedException 8 | from poe_api.logger import get_log_config, setup_logger 9 | from poe_api.middlewares.asgi_logger.middleware import AccessLoggerMiddleware 10 | from poe_api.response import handle_exception_response 11 | from poe_api.router import api_router 12 | 13 | setup_logger() 14 | 15 | app = FastAPI() 16 | 17 | app.add_middleware( 18 | CORSMiddleware, 19 | allow_origins=["*"], 20 | allow_credentials=True, 21 | allow_methods=["*"], 22 | allow_headers=["*"], 23 | ) 24 | 25 | app.add_middleware( 26 | AccessLoggerMiddleware, format="%(client_addr)s | %(request_line)s | %(status_code)s | %(M)s ms", logger=logging.getLogger("poe_api.access") 27 | ) 28 | 29 | app.include_router(api_router) 30 | 31 | 32 | @app.exception_handler(RequestValidationError) 33 | async def validation_exception_handler(request, exc): 34 | return handle_exception_response(exc) 35 | 36 | 37 | @app.exception_handler(SelfDefinedException) 38 | async def self_defined_exception_handler(request, exc): 39 | return handle_exception_response(exc) 40 | 41 | 42 | if __name__ == "__main__": 43 | import uvicorn 44 | 45 | uvicorn.run( 46 | app, 47 | host=CONFIG.host, 48 | port=CONFIG.port, 49 | proxy_headers=True, 50 | forwarded_allow_ips="*", 51 | log_config=get_log_config(), 52 | ) 53 | -------------------------------------------------------------------------------- /poe_api/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/poekmon/poe-api/9d4ce927101666835f952b877087b64034b2fc21/poe_api/__init__.py -------------------------------------------------------------------------------- /poe_api/config.py: -------------------------------------------------------------------------------- 1 | import os 2 | from typing import Literal, Optional 3 | from pydantic import BaseModel, Field, field_validator 4 | from ruamel.yaml import YAML 5 | 6 | 7 | LOG_LEVEL = Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] 8 | 9 | 10 | class ConfigModel(BaseModel): 11 | poe_api_key: str = Field(default="YOUR_API_KEY", description="POE API key") 12 | log_dir: str = Field(default="logs", description="Log directory") 13 | log_level: LOG_LEVEL = Field(default="INFO", description="Log level") 14 | 15 | host: str = Field(default="0.0.0.0", description="Host") 16 | port: int = Field(default=8800, description="Port") 17 | 18 | auth_secret: str | None = Field(default=None, description="Auth secret") 19 | 20 | 21 | def load_config(): 22 | config_path = os.environ.get("POE_API_CONFIG_PATH", "config.yaml") 23 | if not os.path.exists(config_path): 24 | raise ValueError(f"Config file not found: {config_path}") 25 | with open(config_path, mode="r", encoding="utf-8") as f: 26 | yaml = YAML() 27 | config_dict = yaml.load(f) or {} 28 | model = ConfigModel.model_validate(config_dict) 29 | return model 30 | 31 | 32 | def dump_config(model: ConfigModel, output_path): 33 | with open(output_path, mode="w", encoding="utf-8") as f: 34 | yaml = YAML() 35 | yaml.dump(model.model_dump(mode="json"), f) 36 | 37 | 38 | CONFIG = load_config() 39 | 40 | 41 | if __name__ == "__main__": 42 | config = ConfigModel() 43 | dump_config(config, "config.yaml.example") 44 | print("Example config file written.") 45 | -------------------------------------------------------------------------------- /poe_api/const.py: -------------------------------------------------------------------------------- 1 | # | | botId | handle | displayName | description | 2 | # | ---- | ------- | ---------------------- | ---------------------- | ------------------------------------------------------------ | 3 | # | 0 | 3002 | Assistant | Assistant | 通用助手机器人，擅长编程相关任务和非英语语言。由gpt-3.5-turbo和Claude 3 Haiku提供技术支持。 | 4 | # | 1 | 2254704 | Web-Search | Web-Search | General-purpose assistant bot capable of conducting web search as necessary to inform its responses. Particularly good for queries regarding up-to-date information or specific facts. Powered by gpt-3.5-turbo. (Currently in beta release) Web-Search is powered by search APIs to ground its outputs, which may include Bing (Microsoft Privacy Statement: https://privacy.microsoft.com/en-us/privacystatement). | 5 | # | 2 | 1011 | Claude-3-Sonnet | Claude-3-Sonnet | Anthropic的Claude-3-Sonnet实现了智能和速度的平衡，并缩短了上下文窗口，用以优化速度和成本。对于更长的上下文信息，请尝试Claude-3-Sonnet-200k。计算积分值可能会变化。 | 6 | # | 3 | 1017 | Claude-3-Haiku | Claude-3-Haiku | Anthropic的Claude 3 Haiku在性能、速度和成本方面超越了同类智能模型，无需专门微调。缩短了上下文窗口，用以优化速度和成本。对于更长的上下文消息，请尝试Claude-3-Haiku-200k。计算积分值可能会变化。 | 7 | # | 4 | 1012 | Claude-3-Opus | Claude-3-Opus | Anthropic最智能的模型，能够处理复杂分析、多步骤的长任务以及高阶数学和编码任务。缩短了上下文窗口，用以优化速度和成本。对于更长的上下文信息，请尝试Claude-3-Opus-200k。计算积分值可能会变化。 | 8 | # | 5 | 3015 | GPT-4o | GPT-4o | OpenAI最强大的模型。在数量问题（数学和物理）、创意写作以及许多其他挑战性任务中比ChatGPT更强。由GPT-4o提供支持。 | 9 | # | 6 | 3007 | GPT-4 | GPT-4 | OpenAI的最强大模型。在定量问题（数学和物理）、创意写作以及许多其他挑战性任务中，表现比ChatGPT更强。由GPT-4 Turbo with Vision提供技术支持。 | 10 | # | 7 | 3089135 | Playground-v2.5 | Playground-v2.5 | Generates high quality images based on the user's most recent prompt. Allows users to specify elements to avoid in the image using the "--no" parameter at the end of the prompt (e.g. "Tall trees, daylight --no rain"). Optionally, specify the aspect ratio using the `--aspect` parameter (e.g. "Tall trees, daylight --aspect 1:2"). Powered by Playground_v2.5 from playground.com. | 11 | # | 8 | 6008 | Gemini-1.5-Pro | Gemini-1.5-Pro | Google的Gemini系列多模态模型实现了性能和速度的平。该模型接受整个对话中的文本、图像和视频输入，并提供文本输出。每条信息仅限一个视频。缩短了上下文窗口，用以优化速度和成本。对于更长的上下文信息，请尝试Gemini-1.5-Pro-128k和Gemini-1.5-Pro-1M。计算积分值可能会变化。 | 12 | # | 9 | 6011 | Gemini-1.5-Pro-Search | Gemini-1.5-Pro-Search | Gemini 1.5 Pro由Grounding和Google搜索提供功能增强支持，可以提供最新信息，并实现了模型性能与速度的平衡。Grounding模型目前仅支持文本。 | 13 | # | 10 | 6004 | Gemini-1.0-Pro | Gemini-1.0-Pro | The multi-modal model from Google's Gemini family that balances model performance and speed. Exhibits strong generalist capabilities and excels particularly in cross-modal reasoning. The model accepts text, image, and video input and provides text output. It considers only the images and videos from the latest user message, with a restriction of one video per message. | 14 | # | 11 | 6007 | Gemini-1.0-Pro-Search | Gemini-1.0-Pro-Search | Gemini Pro由Grounding和Google搜索提供功能增强支持，可以提供最新信息，并实现了模型性能与速度的平衡。Grounding模型目前仅支持文本。 | 15 | # | 12 | 2828029 | DALL-E-3 | DALL-E-3 | OpenAI最强大的图像生成模型。可根据用户最近的提示词生成具有复杂细节的高质量图像。 | 16 | # | 13 | 4041989 | StableDiffusion3 | StableDiffusion3 | Stable Diffusion 3, the newest image generation model from Stability AI, which is equal to or outperforms state-of-the-art text-to-image generation systems such as DALL-E 3 and Midjourney v6 in typography and prompt adherence, based on human preference evaluations. Stability AI has partnered with Fireworks AI, the fastest and most reliable API platform in the market, to deliver Stable Diffusion 3 and Stable Diffusion 3 Turbo. | 17 | # | 14 | 4042183 | SD3-Turbo | SD3-Turbo | Distilled, few-step version of Stable Diffusion 3, the newest image generation model from Stability AI, which is equal to or outperforms state-of-the-art text-to-image generation systems such as DALL-E 3 and Midjourney v6 in typography and prompt adherence, based on human preference evaluations. Stability AI has partnered with Fireworks AI, the fastest and most reliable API platform in the market, to deliver Stable Diffusion 3 and Stable Diffusion 3 Turbo. | 18 | # | 15 | 1579852 | StableDiffusionXL | StableDiffusionXL | 根据用户最近的提示词生成高质量的图像。 允许用户在提示词末尾使用“--无”参数指定图像中要避免的元素（例如“高大的树木，白天，--无雨”）。 由Stable Diffusion XL提供技术支持。 | 19 | # | 16 | 4064124 | Llama-3-70b-Groq | Llama-3-70b-Groq | Llama 3 70b由Groq LPU™推理引擎提供技术支持 | 20 | # | 17 | 4035297 | Llama-3-70B-T | Llama-3-70B-T | Llama 3 70B Instruct from Meta. Hosted by Together.ai: https://api.together.xyz/playground/chat/meta-llama/Llama-3-70b-chat-hf The points price is subject to change. | 21 | # | 18 | 4036115 | Llama-3-70b-Inst-FW | Llama-3-70b-Inst-FW | Meta's Llama-3-70B-Instruct hosted by Fireworks AI. | 22 | # | 19 | 3993736 | Mixtral8x22b-Inst-FW | Mixtral8x22b-Inst-FW | Mixtral 8x22B Mixture-of-Experts instruct model from Mistral hosted by Fireworks. | 23 | # | 20 | 3843197 | Command-R | Command-R | I can search the web for up to date information and respond in over 10 languages! | 24 | # | 21 | 7010 | Mistral-Large | Mistral-Large | Mistral AI的最强大模型。支持32k个Token（约24,000字）的上下文窗口，并且在全面的基准测试中比Mistral-Medium、Mixtral-8x7b和Mistral-7b更强大。 | 25 | # | 22 | 7009 | Mistral-Medium | Mistral-Medium | Mistral AI的中等大小模型。支持32k个Token（约24,000字）的上下文窗口，并且在全面的基准测试中比Mixtral-8x7b和Mistral-7b更强大。 | 26 | # | 23 | 3900408 | dbrx-instruct-fw | dbrx-instruct-fw | DBRX Instruct is a mixture-of-experts (MoE) large language model trained from scratch by Databricks. DBRX Instruct specializes in few-turn interactions. Dbrx is hosted as an experimental model. Fireworks only guarantees that it will be hosted through April 2024. Future availability will depend on overall usage. | 27 | # | 24 | 3999947 | RekaCore | RekaCore | Reka's largest and most capable multimodal language model. Works with text, images, and video inputs. 8k context length. | 28 | # | 25 | 3607788 | RekaFlash | RekaFlash | Reka's efficient and capable 21B multimodal model optimized for fast workloads and amazing quality. Works with text, images and video inputs. | 29 | # | 26 | 6009 | Gemini-1.5-Pro-128k | Gemini-1.5-Pro-128k | Google的Gemini系列多模态模型实现了性能和速度的平。该模型接受整个对话中的文本、图像和视频输入，并提供文本输出。每条信息仅限一个视频。计算积分值可能会变化。 | 30 | # | 27 | 3975312 | Command-R-Plus | Command-R-Plus | A supercharged version of Command R. I can search the web for up to date information and respond in over 10 languages! | 31 | # | 28 | 3004 | ChatGPT | ChatGPT | 由gpt-3.5-turbo提供技术支持。 | 32 | # | 29 | 3009 | ChatGPT-16k | ChatGPT-16k | 由gpt-3.5-turbo-16k提供技术支持。 | 33 | # | 30 | 3010 | GPT-4-128k | GPT-4-128k | 由GPT-4 Turbo with Vision提供技术支持。 | 34 | # | 31 | 1013 | Claude-3-Sonnet-200k | Claude-3-Sonnet-200k | Anthropic的Claude-3-Sonnet实现了智能和速度的平衡。支持200k个上下文Token。计算积分值可能会变化。 | 35 | # | 32 | 1018 | Claude-3-Haiku-200k | Claude-3-Haiku-200k | Anthropic的Claude 3 Haiku在性能、速度和成本方面超越了同类智能模型，无需专门微调。计算积分值可能会变化。 | 36 | # | 33 | 1014 | Claude-3-Opus-200k | Claude-3-Opus-200k | Anthropic最智能的模型，能够处理复杂分析、多步骤的长任务以及高阶数学和编码任务。支持200k个上下文Token。计算积分值可能会变化。 | 37 | # | 34 | 6010 | Gemini-1.5-Pro-1M | Gemini-1.5-Pro-1M | Google的Gemini系列多模态模型实现了性能和速度的平。该模型接受整个对话中的文本、图像和视频输入，并提供文本输出。每条信息仅限一个视频。计算积分值可能会变化。 | 38 | # | 35 | 3129602 | Mixtral-8x7B-Chat | Mixtral-8x7B-Chat | Mixtral 8x7B Mixture-of-Experts model from Mistral AI fine-tuned for instruction following Hosted by Fireworks.ai: https://app.fireworks.ai/models/fireworks/mixtral-8x7b-instruct | 39 | # | 36 | 3741558 | DeepSeek-Coder-33B-T | DeepSeek-Coder-33B-T | Deepseek Coder Instruct (33B) from Deepseek. Hosted by Together.ai: https://api.together.xyz/playground/chat/deepseek-ai/deepseek-coder-33b-instruct The points price is subject to change. | 40 | # | 37 | 3512418 | Code-Llama-70B-FW | Code-Llama-70B-FW | Llama 70b Code Instruct bot hosted by Fireworks. This model is designed for general code synthesis and understanding. Model card: https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf | 41 | # | 38 | 3697906 | CodeLlama-70B-T | CodeLlama-70B-T | Code Llama Instruct (70B) from Meta. Hosted by Together.ai: https://api.together.xyz/playground/chat/codellama/CodeLlama-70b-Instruct-hf The points price is subject to change. | 42 | # | 39 | 4160791 | Qwen-1.5-110B-T | Qwen-1.5-110B-T | Qwen1.5 (通义千问1.5) 110B，基于阿里巴巴自研大模型的AI助手，尤其擅长中文对话。 Alibaba's general-purpose model which excels particularly in Chinese-language queries. Hosted by Together.ai: https://api.together.xyz/playground/chat/Qwen/Qwen1.5-110B-Chat The points price is subject to change. | 43 | # | 40 | 3154259 | Qwen-72b-Chat | Qwen-72b-Chat | 通义千问，基于阿里巴巴自研大模型的AI助手， 尤其擅长中文对话。 Alibaba's general-purpose model which excels particularly in Chinese-language queries. | 44 | # | 41 | 3697829 | Qwen-72B-T | Qwen-72B-T | Qwen1.5 (通义千问1.5) 72B，基于阿里巴巴自研大模型的AI助手，尤其擅长中文对话。 Alibaba's general-purpose model which excels particularly in Chinese-language queries. Hosted by Together.ai: https://api.together.xyz/playground/chat/qwen/qwen1.5-72B-Chat The points price is subject to change. | 45 | # | 42 | 1015 | Claude-2 | Claude-2 | Anthropic的Claude 2模型。缩短了上下文窗口，用以优化速度和成本。对于更长的上下文信息，请尝试Claude-2-100k。 | 46 | # | 43 | 1008 | Claude-2-100k | Claude-2-100k | Anthropic的Claude 2模型，支持100k个Token（约75,000个词）的上下文窗口。特别擅长创意写作。 | 47 | # | 44 | 3014 | GPT-4-Classic | GPT-4-Classic | OpenAI的GPT-4模型。文本输入由gpt-4-0613（非Turbo）提供技术支持，图像输入由gpt-4-1106-vision-preview提供技术支持。 | 48 | # | 45 | 6000 | Google-PaLM | Google-PaLM | 由Google的PaLM 2 chat-bison@002模型提供技术支持。支持8k个Token的上下文窗口。 | 49 | # | 46 | 3377302 | Llama-3-8b-Groq | Llama-3-8b-Groq | Llama 3 8b由Groq LPU™推理引擎提供技术支持 | 50 | # | 47 | 4035261 | Llama-3-8B-T | Llama-3-8B-T | Llama 3 8B Instruct from Meta. Hosted by Together.ai: https://api.together.xyz/playground/chat/meta-llama/Llama-3-8b-chat-hf The points price is subject to change. | 51 | # | 48 | 3697694 | Gemma-Instruct-7B-T | Gemma-Instruct-7B-T | Gemma Instruct 7B from Google. Hosted by Together.ai: https://api.together.xyz/playground/chat/google/gemma-7b-it The points price is subject to change. | 52 | # | 49 | 3660235 | Gemma-7b-FW | Gemma-7b-FW | A lightweight open model from Google built from the same research and technology used to create the Gemini models. Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms | 53 | # | 50 | 2506542 | fw-mistral-7b | fw-mistral-7b | Bot powered by Fireworks.ai's hosted Mistral-7b-instruct model https://app.fireworks.ai/models/fireworks/mistral-7b-instruct-4k | 54 | # | 51 | 3618254 | MythoMax-L2-13B | MythoMax-L2-13B | 这个模型由Gryphe基于LLama-2-13B创建，擅长角色扮演和故事编写。 | 55 | # | 52 | 7000 | Llama-2-70b | Llama-2-70b | Meta的Llama-2-70b-chat。 | 56 | # | 53 | 7005 | Code-Llama-34b | Code-Llama-34b | Meta的Code-Llama-34b-instruct。擅长生成和讨论代码，并支持16k个Token的上下文窗口。 | 57 | # | 54 | 7001 | Llama-2-13b | Llama-2-13b | Meta的Llama-2-13b-chat。 | 58 | # | 55 | 7002 | Llama-2-7b | Llama-2-7b | Meta的Llama-2-7b-chat。 | 59 | # | 56 | 7004 | Code-Llama-13b | Code-Llama-13b | Meta的Code-Llama-13b-instruct。擅长生成和讨论代码，并支持16k个Token的上下文窗口。 | 60 | # | 57 | 7003 | Code-Llama-7b | Code-Llama-7b | Meta的Code-Llama-7b-instruct。擅长生成和讨论代码，并支持16k个Token的上下文窗口。 | 61 | # | 58 | 7006 | Solar-Mini | Solar-Mini | 相比其前身Solar-0-70b，Solar Mini体积更小，但速度更快、功能更强。 | 62 | # | 59 | 3011 | GPT-3.5-Turbo-Instruct | GPT-3.5-Turbo-Instruct | 由gpt-3.5-turbo-instruct提供技术支持。 | 63 | # | 60 | 3012 | GPT-3.5-Turbo | GPT-3.5-Turbo | 由gpt-3.5-turbo提供技术支持，无系统提示词。 | 64 | # | 61 | 1006 | Claude-instant | Claude-instant | Anthropic的最快模型，擅长创意类任务。支持9k个Token（约7,000个词）的上下文窗口。 | 65 | # | 62 | 1009 | Claude-instant-100k | Claude-instant-100k | Anthropic的最快速模型，支持高达100k个Token（约75,000个词）的上下文窗口。可以分析非常长的文档、代码等。 | 66 | # | 63 | 1016 | Claude-2.1-200k | Claude-2.1-200k | Anthropic的Claude 2.1在性能上有所提升，相较于Claude 2，其上下文大小增加到了200k个令牌。 | 67 | # | 64 | 3462638 | Mixtral-8x7b-Groq | Mixtral-8x7b-Groq | Enjoy Mixtral 8x7B running on the Groq LPU™ Inference Engine. API access available, email api@groq.com. | 68 | 69 | 70 | bot_handles = [ 71 | "Assistant", 72 | "Web-Search", 73 | "Claude-3-Sonnet", 74 | "Claude-3-Haiku", 75 | "Claude-3-Opus", 76 | "GPT-4o", 77 | "GPT-4", 78 | "Playground-v2.5", 79 | "Gemini-1.5-Pro", 80 | "Gemini-1.5-Pro-Search", 81 | "Gemini-1.0-Pro", 82 | "Gemini-1.0-Pro-Search", 83 | "DALL-E-3", 84 | "StableDiffusion3", 85 | "SD3-Turbo", 86 | "StableDiffusionXL", 87 | "Llama-3-70b-Groq", 88 | "Llama-3-70B-T", 89 | "Llama-3-70b-Inst-FW", 90 | "Mixtral8x22b-Inst-FW", 91 | "Command-R", 92 | "Mistral-Large", 93 | "Mistral-Medium", 94 | "dbrx-instruct-fw", 95 | "RekaCore", 96 | "RekaFlash", 97 | "Gemini-1.5-Pro-128k", 98 | "Command-R-Plus", 99 | "ChatGPT", 100 | "ChatGPT-16k", 101 | "GPT-4-128k", 102 | "Claude-3-Sonnet-200k", 103 | "Claude-3-Haiku-200k", 104 | "Claude-3-Opus-200k", 105 | "Gemini-1.5-Pro-1M", 106 | "Mixtral-8x7B-Chat", 107 | "DeepSeek-Coder-33B-T", 108 | "Code-Llama-70B-FW", 109 | "CodeLlama-70B-T", 110 | "Qwen-1.5-110B-T", 111 | "Qwen-72b-Chat", 112 | "Qwen-72B-T", 113 | "Claude-2", 114 | "Claude-2-100k", 115 | "GPT-4-Classic", 116 | "Google-PaLM", 117 | "Llama-3-8b-Groq", 118 | "Llama-3-8B-T", 119 | "Gemma-Instruct-7B-T", 120 | "Gemma-7b-FW", 121 | "fw-mistral-7b", 122 | "MythoMax-L2-13B", 123 | "Llama-2-70b", 124 | "Code-Llama-34b", 125 | "Llama-2-13b", 126 | "Llama-2-7b", 127 | "Code-Llama-13b", 128 | "Code-Llama-7b", 129 | "Solar-Mini", 130 | "GPT-3.5-Turbo-Instruct", 131 | "GPT-3.5-Turbo", 132 | "Claude-instant", 133 | "Claude-instant-100k", 134 | "Claude-2.1-200k", 135 | "Mixtral-8x7b-Groq", 136 | ] 137 | 138 | bot_handles_lower = [handle.lower() for handle in bot_handles] 139 | 140 | openai_model_map = {"gpt-4-turbo": "GPT-4"} 141 | openai_model_map.update({handle.lower(): handle for handle in bot_handles}) 142 | 143 | -------------------------------------------------------------------------------- /poe_api/exceptions.py: -------------------------------------------------------------------------------- 1 | from typing import Any 2 | 3 | 4 | class SelfDefinedException(Exception): 5 | def __init__(self, reason: Any = None, message: str = "", code: int = -1) -> None: 6 | self.reason = reason # 异常主要原因 7 | self.message = message # 更细节的描述 8 | self.code = code # 错误码：-1 为默认；0～1000 以内正数为 http 错误码；10000 以上为自定义错误码 9 | 10 | def __str__(self): 11 | return f"{self.__class__.__name__}: [{self.code}] {self.reason} {self.message}" 12 | 13 | 14 | class AuthenticationFailedException(SelfDefinedException): 15 | def __init__(self, message: str = ""): 16 | super().__init__(reason="errors.authenticationFailed", message=message, code=401) 17 | 18 | 19 | class AuthorityDenyException(SelfDefinedException): 20 | def __init__(self, message: str = ""): 21 | super().__init__(reason="errors.authorityDeny", message=message, code=403) 22 | 23 | 24 | class ResourceNotFoundException(SelfDefinedException): 25 | def __init__(self, message: str = ""): 26 | super().__init__(reason="errors.resourceNotFound", message=message, code=404) 27 | 28 | 29 | class InvalidRequestException(SelfDefinedException): 30 | def __init__(self, message: str = ""): 31 | super().__init__(reason="errors.invalidRequest", message=message, code=400) 32 | 33 | 34 | class InternalException(SelfDefinedException): 35 | def __init__(self, message: str = ""): 36 | super().__init__(reason="errors.internal", message=message) 37 | 38 | 39 | class PoeBotException(SelfDefinedException): 40 | def __init__(self, message: str = ""): 41 | super().__init__(reason="errors.poeBot", message=message, code=400) -------------------------------------------------------------------------------- /poe_api/logger.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import logging.config 3 | import os 4 | import yaml 5 | 6 | from poe_api.config import CONFIG 7 | 8 | server_log_filename = None 9 | console_log_level = None 10 | 11 | 12 | def get_log_config(): 13 | global server_log_filename, console_log_level 14 | with open("logging_config", "r") as f: 15 | log_config = yaml.safe_load(f.read()) 16 | log_config["handlers"]["file_handler"]["filename"] = server_log_filename 17 | log_config["handlers"]["console_handler"]["level"] = console_log_level 18 | return log_config 19 | 20 | 21 | def setup_logger(): 22 | global server_log_filename, console_log_level 23 | 24 | log_dir = CONFIG.log_dir 25 | 26 | os.makedirs(log_dir, exist_ok=True) 27 | server_log_filename = os.path.join(log_dir, f"latest.log") 28 | console_log_level = CONFIG.log_level 29 | 30 | log_config = get_log_config() 31 | logging.config.dictConfig(log_config) 32 | -------------------------------------------------------------------------------- /poe_api/middlewares/__init__.py: -------------------------------------------------------------------------------- 1 | from .asgi_logger import AccessLoggerMiddleware 2 | -------------------------------------------------------------------------------- /poe_api/middlewares/asgi_logger/__init__.py: -------------------------------------------------------------------------------- 1 | from .middleware import AccessLoggerMiddleware -------------------------------------------------------------------------------- /poe_api/middlewares/asgi_logger/middleware.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | import http 4 | import logging 5 | import os 6 | import sys 7 | import time 8 | from typing import TypedDict 9 | 10 | from asgiref.typing import ASGI3Application, ASGIReceiveCallable, ASGISendCallable 11 | from asgiref.typing import ASGISendEvent, HTTPScope 12 | 13 | from .utils import get_client_addr, get_path_with_query_string 14 | 15 | 16 | class AccessInfo(TypedDict, total=False): 17 | response: ASGISendEvent 18 | start_time: float 19 | end_time: float 20 | 21 | 22 | class AccessLoggerMiddleware: 23 | DEFAULT_FORMAT = '%(client_addr)s - "%(request_line)s" %(status_code)s' 24 | 25 | def __init__( 26 | self, 27 | app: ASGI3Application, 28 | format: str | None = None, 29 | logger: logging.Logger | None = None, 30 | ) -> None: 31 | self.app = app 32 | self.format = format or self.DEFAULT_FORMAT 33 | if logger is None: 34 | self.logger = logging.getLogger("access") 35 | self.logger.setLevel(logging.INFO) 36 | handler = logging.StreamHandler(sys.stdout) 37 | handler.setLevel(logging.INFO) 38 | handler.setFormatter(logging.Formatter("%(message)s")) 39 | self.logger.addHandler(handler) 40 | else: 41 | self.logger = logger 42 | 43 | async def __call__( 44 | self, scope: HTTPScope, receive: ASGIReceiveCallable, send: ASGISendCallable 45 | ) -> None: 46 | if scope["type"] != "http": 47 | return await self.app(scope, receive, send) # pragma: no cover 48 | 49 | info = AccessInfo(response={}) 50 | 51 | async def inner_send(message: ASGISendEvent) -> None: 52 | if message["type"] == "http.response.start": 53 | info["response"] = message 54 | await send(message) 55 | 56 | try: 57 | info["start_time"] = time.time() 58 | await self.app(scope, receive, inner_send) 59 | except Exception as exc: 60 | info["response"]["status"] = 500 61 | raise exc 62 | finally: 63 | info["end_time"] = time.time() 64 | self.log(scope, info) 65 | 66 | def log(self, scope: HTTPScope, info: AccessInfo) -> None: 67 | self.logger.info(self.format, AccessLogAtoms(scope, info)) 68 | 69 | 70 | class AccessLogAtoms(dict): 71 | def __init__(self, scope: HTTPScope, info: AccessInfo) -> None: 72 | for name, value in scope["headers"]: 73 | self[f"{{{name.decode('latin1').lower()}}}i"] = value.decode("latin1") 74 | for name, value in info["response"].get("headers", []): 75 | self[f"{{{name.decode('latin1').lower()}}}o"] = value.decode("latin1") 76 | for name, value in os.environ.items(): 77 | self[f"{{{name.lower()!r}}}e"] = value 78 | 79 | protocol = f"HTTP/{scope['http_version']}" 80 | 81 | status = info["response"].get("status", None) 82 | try: 83 | if status: 84 | status_phrase = http.HTTPStatus(status).phrase 85 | else: 86 | status_phrase = "-" 87 | except ValueError: 88 | status_phrase = "-" 89 | 90 | path = scope["root_path"] + scope["path"] 91 | full_path = get_path_with_query_string(scope) 92 | request_line = f"{scope['method']} {path} {protocol}" 93 | full_request_line = f"{scope['method']} {full_path} {protocol}" 94 | 95 | request_time = info["end_time"] - info["start_time"] 96 | client_addr = get_client_addr(scope) 97 | self.update( 98 | { 99 | "h": client_addr, 100 | "client_addr": client_addr, 101 | "l": "-", 102 | "u": "-", # Not available on ASGI. 103 | "t": time.strftime("[%d/%b/%Y:%H:%M:%S %z]"), 104 | "r": request_line, 105 | "request_line": full_request_line, 106 | "R": full_request_line, 107 | "m": scope["method"], 108 | "U": scope["path"], 109 | "q": scope["query_string"].decode(), 110 | "H": protocol, 111 | "s": status, 112 | "status_code": f"{status} {status_phrase}", 113 | "st": status_phrase, 114 | "B": self["{Content-Length}o"], 115 | "b": self.get("{Content-Length}o", "-"), 116 | "f": self["{Referer}i"], 117 | "a": self["{User-Agent}i"], 118 | "T": str(round(request_time)), 119 | "M": str(round(request_time * 1_000)), 120 | "D": str(round(request_time * 1_000_000)), 121 | "L": f"{request_time:.6f}", 122 | "p": f"<{os.getpid()}>", 123 | } 124 | ) 125 | 126 | def __getitem__(self, key: str) -> str: 127 | try: 128 | if key.startswith("{"): 129 | return super().__getitem__(key.lower()) 130 | else: 131 | return super().__getitem__(key) 132 | except KeyError: 133 | return "-" -------------------------------------------------------------------------------- /poe_api/middlewares/asgi_logger/utils.py: -------------------------------------------------------------------------------- 1 | from urllib.parse import quote 2 | 3 | from asgiref.typing import HTTPScope 4 | 5 | 6 | def get_client_addr(scope: HTTPScope): 7 | if scope["client"] is None: 8 | return "-" # pragma: no cover 9 | return f"{scope['client'][0]}:{scope['client'][1]}" 10 | 11 | 12 | def get_path_with_query_string(scope: HTTPScope) -> str: 13 | path_with_query_string = quote(scope.get("root_path", "") + scope["path"]) 14 | if scope["query_string"]: # pragma: no cover 15 | return f"{path_with_query_string}?{scope['query_string'].decode('ascii')}" 16 | return path_with_query_string -------------------------------------------------------------------------------- /poe_api/models.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, Any, Literal, Optional 2 | from pydantic import BaseModel 3 | 4 | 5 | class OpenAIMessageRequest(BaseModel): 6 | role: Literal["user", "system", "assistant", "tool"] 7 | content: str 8 | name: Optional[str] = None 9 | 10 | 11 | class OpenAIMessageResponse(BaseModel): 12 | role: Optional[Literal["user", "system", "assistant", "tool"]] = None 13 | content: Optional[str] = None 14 | 15 | 16 | class OpenAIChatCompletionRequest(BaseModel): 17 | model: str 18 | messages: List[OpenAIMessageRequest] 19 | stream: Optional[bool] = False 20 | logit_bias: Optional[Dict[str, Any]] = None 21 | temperature: Optional[float] = None 22 | 23 | 24 | class OpenAIChatCompletionResponseChoice(BaseModel): 25 | index: int 26 | message: OpenAIMessageResponse 27 | finish_reason: Optional[Literal["stop", "length", "content_filter", "tool_calls", "function_call"]] = None 28 | 29 | 30 | class OpenAIChatCompletionResponseUsage(BaseModel): 31 | prompt_tokens: int 32 | completion_tokens: int 33 | total_tokens: int 34 | 35 | 36 | class OpenAIChatCompletionResponse(BaseModel): 37 | id: str 38 | choices: List[OpenAIChatCompletionResponseChoice] 39 | created: int # Unix timestamp (in seconds) 40 | model: str 41 | system_fingerprint: Optional[str] = None 42 | object: Literal["chat.completion"] = "chat.completion" 43 | usage: OpenAIChatCompletionResponseUsage 44 | 45 | 46 | class OpenAIChatCompletionResponseChunkChoice(BaseModel): 47 | index: int 48 | delta: OpenAIMessageResponse 49 | finish_reason: Optional[Literal["stop", "length", "content_filter", "tool_calls", "function_call"]] = None 50 | 51 | 52 | class OpenAIChatCompletionResponseChunk(BaseModel): 53 | id: str 54 | choices: List[OpenAIChatCompletionResponseChunkChoice] 55 | object: Literal["chat.completion.chunk"] = "chat.completion.chunk" 56 | created: int # Unix timestamp (in seconds) 57 | model: str 58 | usage: Optional[OpenAIChatCompletionResponseUsage] = None 59 | -------------------------------------------------------------------------------- /poe_api/response.py: -------------------------------------------------------------------------------- 1 | import json 2 | import logging 3 | import typing 4 | from typing import Optional, Any, Generic, TypeVar, Dict 5 | 6 | import httpx 7 | from fastapi import Response 8 | from fastapi.encoders import jsonable_encoder 9 | from fastapi.exceptions import RequestValidationError 10 | from pydantic import BaseModel, ValidationError 11 | from starlette.background import BackgroundTask 12 | from starlette.exceptions import HTTPException as StarletteHTTPException 13 | 14 | from poe_api.exceptions import SelfDefinedException 15 | 16 | logger = logging.getLogger(__name__) 17 | 18 | 19 | def response(code, content): 20 | return Response(content, code) 21 | 22 | 23 | def handle_exception_response(e: Exception): 24 | if isinstance(e, ValidationError): 25 | return response(400, f"errors.validationError") 26 | elif isinstance(e, SelfDefinedException): 27 | return response(e.code, f"{e.reason}: {e.message}") 28 | # logger.error(f"Unhandled exception: {e}") 29 | return response(500, "errors.internal") 30 | -------------------------------------------------------------------------------- /poe_api/router.py: -------------------------------------------------------------------------------- 1 | import time 2 | from typing import Annotated 3 | from fastapi import APIRouter, Header, Response 4 | from fastapi.responses import StreamingResponse 5 | from starlette.exceptions import HTTPException as StarletteHTTPException 6 | import fastapi_poe as fp 7 | from poe_api.config import CONFIG 8 | from poe_api.exceptions import AuthenticationFailedException, PoeBotException 9 | from poe_api.models import ( 10 | OpenAIChatCompletionRequest, 11 | OpenAIChatCompletionResponse, 12 | OpenAIChatCompletionResponseChoice, 13 | OpenAIChatCompletionResponseChunk, 14 | OpenAIChatCompletionResponseChunkChoice, 15 | OpenAIMessageResponse, 16 | ) 17 | from poe_api.utils import convert_role_to_poe, gen_id, get_bot_name, calculate_usage 18 | 19 | 20 | api_router = APIRouter() 21 | 22 | 23 | def authenticate(auth_key): 24 | if CONFIG.auth_secret: 25 | if auth_key is None: 26 | raise AuthenticationFailedException("Missing authentication header") 27 | if "Bearer " in auth_key: 28 | auth_key = auth_key.split("Bearer ")[1] 29 | if auth_key.strip() != CONFIG.auth_secret.strip(): 30 | raise AuthenticationFailedException() 31 | 32 | 33 | @api_router.get("/") 34 | async def root(): 35 | return Response("running") 36 | 37 | 38 | @api_router.get("/status") 39 | async def status(authorization: Annotated[str | None, Header()] = None): 40 | authenticate(auth_key=authorization) 41 | return {"status": "ok"} 42 | 43 | 44 | @api_router.post("/v1/chat/completions") 45 | async def chat_completions(request: OpenAIChatCompletionRequest, authorization: Annotated[str | None, Header()] = None): 46 | bot_name = get_bot_name(request.model) 47 | 48 | API_KEY = CONFIG.poe_api_key 49 | 50 | authenticate(auth_key=authorization) 51 | 52 | messages = [fp.ProtocolMessage(role=convert_role_to_poe(msg.role), content=msg.content) for msg in request.messages] 53 | 54 | response_id = gen_id() 55 | 56 | extra_kwargs = {} 57 | if request.logit_bias: 58 | extra_kwargs["logit_bias"] = request.logit_bias 59 | if request.temperature: 60 | extra_kwargs["temperature"] = request.temperature 61 | 62 | try: 63 | if request.stream: 64 | 65 | async def generate(): 66 | response_text = "" 67 | async for partial in fp.get_bot_response(messages=messages, bot_name=bot_name, api_key=API_KEY, **extra_kwargs): 68 | chunk = OpenAIChatCompletionResponseChunk( 69 | id=response_id, 70 | object="chat.completion.chunk", 71 | choices=[ 72 | OpenAIChatCompletionResponseChunkChoice( 73 | index=0, finish_reason=None, delta=OpenAIMessageResponse(role="assistant", content=partial.text) 74 | ) 75 | ], 76 | created=int(time.time()), 77 | model=request.model, 78 | ) 79 | response_text += partial.text 80 | yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n" 81 | 82 | chunk = OpenAIChatCompletionResponseChunk( 83 | id=response_id, 84 | object="chat.completion.chunk", 85 | choices=[OpenAIChatCompletionResponseChunkChoice(index=0, finish_reason="stop", delta=OpenAIMessageResponse())], 86 | created=int(time.time()), 87 | model=request.model, 88 | ) 89 | yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n" 90 | 91 | # Send the final chunk with usage 92 | usage = calculate_usage(request.messages, response_text) 93 | chunk = OpenAIChatCompletionResponseChunk( 94 | id=response_id, 95 | object="chat.completion.chunk", 96 | choices=[], 97 | created=int(time.time()), 98 | model=request.model, 99 | usage=usage, 100 | ) 101 | yield f"data: {chunk.model_dump_json()}\n\n" 102 | 103 | yield "data: [DONE]\n\n" 104 | 105 | return StreamingResponse(generate(), media_type="text/event-stream") 106 | 107 | else: 108 | response_text = "" 109 | async for partial in fp.get_bot_response(messages=messages, bot_name=bot_name, api_key=API_KEY, **extra_kwargs): 110 | response_text += partial.text 111 | 112 | usage = calculate_usage(request.messages, response_text) 113 | 114 | response = OpenAIChatCompletionResponse( 115 | id=gen_id(), 116 | object="chat.completion", 117 | choices=[ 118 | OpenAIChatCompletionResponseChoice( 119 | index=0, finish_reason="stop", message=OpenAIMessageResponse(role="assistant", content=response_text) 120 | ) 121 | ], 122 | created=int(time.time()), 123 | model=request.model, 124 | usage=usage, 125 | ) 126 | return response 127 | 128 | except fp.client.BotError as e: 129 | raise PoeBotException(str(e)) 130 | -------------------------------------------------------------------------------- /poe_api/utils.py: -------------------------------------------------------------------------------- 1 | import uuid 2 | from typing import Literal, List 3 | 4 | import fastapi_poe as fp 5 | import tiktoken 6 | 7 | from poe_api.models import OpenAIMessageRequest, OpenAIChatCompletionResponseUsage 8 | from poe_api.const import openai_model_map 9 | 10 | 11 | def convert_role_to_poe(role: Literal["system", "user", "assistant", "tool"]) -> str: 12 | if role == "system": 13 | return "system" 14 | elif role == "user": 15 | return "user" 16 | elif role == "assistant": 17 | return "bot" 18 | elif role == "tool": 19 | return "bot" 20 | else: 21 | raise ValueError(f"Unsupported role: {role}") 22 | 23 | 24 | def convert_role_to_openai(role: Literal["system", "user", "bot"]) -> str: 25 | if role == "system": 26 | return "system" 27 | elif role == "user": 28 | return "user" 29 | elif role == "bot": 30 | return "assistant" 31 | else: 32 | raise ValueError(f"Unsupported role: {role}") 33 | 34 | 35 | def gen_id() -> str: 36 | return str(uuid.uuid4()) 37 | 38 | 39 | def get_bot_name(model: str) -> str: 40 | bot_name = openai_model_map.get(model, model) 41 | return bot_name 42 | 43 | 44 | UsageTargetType = str | OpenAIMessageRequest | fp.PartialResponse | List[str] | List[OpenAIMessageRequest] | List[fp.PartialResponse] 45 | 46 | 47 | def convert_usage_target_to_str(target: UsageTargetType) -> str: 48 | def convert_msg_to_str(msg: str | OpenAIMessageRequest | fp.PartialResponse) -> str: 49 | if isinstance(msg, str): 50 | return msg 51 | elif isinstance(msg, OpenAIMessageRequest): 52 | return msg.content 53 | elif isinstance(msg, fp.PartialResponse): 54 | return msg.text 55 | else: 56 | raise TypeError(f"Unsupported type: {type(msg)}") 57 | 58 | if isinstance(target, list): 59 | return "".join([convert_msg_to_str(msg) for msg in target]) 60 | else: 61 | return convert_msg_to_str(target) 62 | 63 | 64 | def calculate_usage(prompts: UsageTargetType, response: UsageTargetType) -> OpenAIChatCompletionResponseUsage: 65 | encoding = tiktoken.get_encoding("cl100k_base") 66 | prompt_tokens = len(encoding.encode(convert_usage_target_to_str(prompts))) 67 | completion_tokens = len(encoding.encode(convert_usage_target_to_str(response))) 68 | 69 | total_tokens = prompt_tokens + completion_tokens 70 | 71 | return OpenAIChatCompletionResponseUsage(prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens) 72 | -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | # 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