├── .env ├── requirements.txt ├── README.md ├── openai_api.py └── LICENSE /.env: -------------------------------------------------------------------------------- 1 | OPENAI_BASEURL= 2 | OPENAI_API_KEY= -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | openai 2 | orjson 3 | fastapi 4 | pydantic 5 | loguru 6 | uvicorn 7 | python-dotenv 8 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Qwen OpenAI API Tools Middleware 3 | 4 | ## Introduction 5 | 6 | This is an OpenAI API-compatible server based on the Qwen model, implementing tool calling functionality using the ReAct strategy. Qwen, a large language model developed by Alibaba Cloud, is equipped with the capability to call tools. However, common inference engines such as vLLM and sglang do not support tools APIs. 7 | 8 | The purpose of this project is to provide a middleware enabling developers to easily integrate Qwen's tool calling capabilities into their applications. 9 | 10 | ## Installation 11 | 12 | 1. Clone or download this project to your local machine. 13 | 2. Install the required libraries using `pip`: 14 | 15 | ```bash 16 | pip install -r requirements.txt 17 | ``` 18 | 19 | ## Running the Server 20 | 21 | Start the server: 22 | 23 | ```bash 24 | python openai_api.py 25 | ``` 26 | 27 | By default, the server will run on `http://localhost:8600`. 28 | 29 | ## Usage 30 | 31 | You can call the API using `curl` or any HTTP client (such as Postman). For example: 32 | 33 | ```bash 34 | curl -X 'POST' \ 35 | 'http://localhost:8600/v1/chat/completions' \ 36 | -H 'accept: application/json' \ 37 | -H 'Content-Type: application/json' \ 38 | -d '{ 39 | "model": "gpt-4o", 40 | "messages": [ 41 | { 42 | "role": "user", 43 | "content": "What\'s the weather like in Boston today?" 44 | } 45 | ], 46 | "tools": [ 47 | { 48 | "type": "function", 49 | "function": { 50 | "name": "get_current_weather", 51 | "description": "Get the current weather in a given location", 52 | "parameters": { 53 | "type": "object", 54 | "properties": { 55 | "location": { 56 | "type": "string", 57 | "description": "The city and state, e.g. San Francisco, CA" 58 | }, 59 | "unit": { 60 | "type": "string", 61 | "enum": ["celsius", "fahrenheit"] 62 | } 63 | }, 64 | "required": ["location"] 65 | } 66 | } 67 | } 68 | ], 69 | "tool_choice": "auto", 70 | "stream":true 71 | }' 72 | ``` 73 | 74 | --- 75 | 76 | ## 简介 77 | 78 | 这是一个基于Qwen模型的OpenAI API格式的服务器，用 ReAct 策略实现了（流式）工具调用功能。 79 | 80 | Qwen是阿里云开发的大型语言模型，具备工具调用能力。但是常见的推理引擎，如 vLLM，sglang 均不支持 tools API。 81 | 82 | 本项目旨在使得开发者可以轻松地集成Qwen的工具调用能力。 83 | 84 | ## 安装 85 | 86 | 1. 克隆或下载本项目到本地。 87 | 2. 使用`pip`安装依赖库： 88 | 89 | ```bash 90 | pip install -r requirements.txt 91 | ``` 92 | 93 | ## 运行 94 | 95 | 启动服务器： 96 | 97 | ```bash 98 | python openai_api.py 99 | ``` 100 | 101 | 默认情况下，服务器将在本地的`http://localhost:8600`上运行。 102 | 103 | ## 使用 104 | 105 | 你可以使用`curl`或任何HTTP客户端（如Postman）来调用API。例如： 106 | 107 | ```bash 108 | curl -X 'POST' \ 109 | 'http://localhost:8600/v1/chat/completions' \ 110 | -H 'accept: application/json' \ 111 | -H 'Content-Type: application/json' \ 112 | -d '{ 113 | "model": "gpt-4o", 114 | "messages": [ 115 | { 116 | "role": "user", 117 | "content": "What'\''s the weather like in Boston today?" 118 | } 119 | ], 120 | "tools": [ 121 | { 122 | "type": "function", 123 | "function": { 124 | "name": "get_current_weather", 125 | "description": "Get the current weather in a given location", 126 | "parameters": { 127 | "type": "object", 128 | "properties": { 129 | "location": { 130 | "type": "string", 131 | "description": "The city and state, e.g. San Francisco, CA" 132 | }, 133 | "unit": { 134 | "type": "string", 135 | "enum": ["celsius", "fahrenheit"] 136 | } 137 | }, 138 | "required": ["location"] 139 | } 140 | } 141 | } 142 | ], 143 | "tool_choice": "auto", 144 | "stream":true 145 | }' 146 | ``` 147 | 148 | -------------------------------------------------------------------------------- /openai_api.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import uuid 4 | from typing import Any, List, Optional, Union 5 | 6 | import openai 7 | import orjson 8 | from dotenv import load_dotenv 9 | from fastapi import FastAPI, HTTPException 10 | from fastapi.responses import JSONResponse, StreamingResponse 11 | from loguru import logger 12 | from openai.types.chat import ChatCompletionMessageParam, ChatCompletionToolParam 13 | from pydantic import BaseModel 14 | 15 | app = FastAPI() 16 | 17 | # 设置Qwen OpenAI API的URL和密钥 18 | load_dotenv(override=True) 19 | OPENAI_BASEURL = os.getenv("OPENAI_BASEURL") 20 | OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 21 | CLIENT = openai.AsyncOpenAI(base_url=OPENAI_BASEURL, api_key=OPENAI_API_KEY) 22 | 23 | 24 | class ChatCompletionRequest(BaseModel): 25 | class Config: 26 | extra = "allow" 27 | 28 | messages: list[dict[str, Any]] 29 | stream: bool = False 30 | temperature: float = 1.0 31 | top_p: float = 1.0 32 | 33 | tools: Optional[list[ChatCompletionToolParam]] = None 34 | stop: Union[str, List[str], None] = None 35 | 36 | 37 | TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters} Format the arguments as a JSON object.""" 38 | REACT_INSTRUCTION = """Answer the following questions as best you can. You have access to the following APIs: 39 | 40 | {tools_text} 41 | 42 | Use the following format: 43 | 44 | Question: the input question you must answer 45 | Thought: you should always think about what to do 46 | Action: the action to take, should be one of [{tools_name_text}] 47 | Action Input: the input to the action 48 | Observation: the result of the action 49 | ... (this Thought/Action/Action Input/Observation can be repeated zero or more times) 50 | Thought: I now know the final answer 51 | Final Answer: the final answer to the original input question 52 | 53 | Begin!""" 54 | 55 | 56 | def process_tools_from_prompt( 57 | messages: list[ChatCompletionMessageParam], 58 | tools: list[ChatCompletionToolParam] | None, 59 | ) -> list[ChatCompletionMessageParam]: 60 | if tools: 61 | tools_text = [] 62 | tools_name_text = [] 63 | for func_info in tools: 64 | parameters = [] 65 | fp = func_info["function"].get("parameters", {}) 66 | if fp: 67 | required_parameters = fp.get("required", []) 68 | for name, p in fp["properties"].items(): # type: ignore 69 | param = dict({"name": name}, **p) 70 | if name in required_parameters: 71 | param["required"] = True 72 | parameters.append(param) 73 | 74 | name = func_info["function"]["name"] 75 | desc = func_info["function"].get("description", "") 76 | tool_string = TOOL_DESC.format( 77 | name_for_model=name, 78 | name_for_human=name, 79 | description_for_model=desc, 80 | parameters=json.dumps(parameters, ensure_ascii=False), 81 | ) 82 | tools_text.append(tool_string) 83 | tools_name_text.append(name) 84 | tools_text_string = "\n\n".join(tools_text) 85 | tools_name_text_string = ", ".join(tools_name_text) 86 | tool_system = REACT_INSTRUCTION.format( 87 | tools_text=tools_text_string, 88 | tools_name_text=tools_name_text_string, 89 | ) 90 | else: 91 | tool_system = "" 92 | 93 | new_messages = [] 94 | for message in messages: 95 | role = message["role"] 96 | content = message.get("content") 97 | if tools: 98 | if role == "user": 99 | if tool_system: 100 | content = tool_system + f"\n\nQuestion: {content}" 101 | tool_system = "" 102 | else: 103 | content = f"Question: {content}" 104 | elif role == "assistant": 105 | tool_calls = message.get("tool_calls") 106 | if tool_calls: 107 | func_call = tool_calls[0]["function"] 108 | f_name, f_args = ( 109 | func_call["name"], 110 | func_call["arguments"], 111 | ) 112 | content = f"Thought: I can use {f_name}.\nAction: {f_name}\nAction Input: {f_args}" 113 | elif content: 114 | content = f"Thought: I now know the final answer.\nFinal Answer: {content}" 115 | elif role == "tool": 116 | content = f"Observation: {content}" 117 | if content: 118 | content = content.lstrip("\n").rstrip() 119 | new_messages.append({"role": role, "content": content}) 120 | 121 | return new_messages 122 | 123 | 124 | def eval_qwen_tools_arguments(text: str, tool_names: set[str]): 125 | """ 126 | 解析文本以提取函数名、函数参数和内容。 127 | """ 128 | try: 129 | func_name, func_args, content = "", "", "" 130 | i = text.rfind("\nAction:") 131 | j = text.rfind("\nAction Input:") 132 | k = text.rfind("\nObservation:") 133 | t = max(text.rfind("\nThought:", 0, i), text.rfind("Thought:", 0, i)) 134 | if 0 <= i < j: 135 | if k < j: 136 | text = text.rstrip() + "\nObservation:" 137 | k = text.rfind("\nObservation:") 138 | if 0 <= t < i < j < k: 139 | func_name = text[i + len("\nAction:") : j].strip() 140 | func_args = text[j + len("\nAction Input:") : k].strip() 141 | content = text[t + len("\nThought:") : i].strip() 142 | if func_name in tool_names: 143 | return content, func_name, func_args 144 | except Exception as e: 145 | logger.error("Eval tool calls completion failed:", e) 146 | t = max(text.rfind("\nThought:"), text.rfind("Thought:")) 147 | z = max(text.rfind("\nFinal Answer:"), text.rfind("Final Answer:")) 148 | if z >= 0: 149 | text = text[z + len("\nFinal Answer:") :] 150 | else: 151 | text = text[t + len("\nThought:") :] 152 | return text, None, None 153 | 154 | 155 | def get_tokens_filter(): 156 | found = False 157 | 158 | def process_tokens(tokens: str, delta: str): 159 | nonlocal found 160 | # Once "Final Answer:" is found, future tokens are allowed. 161 | if found: 162 | return delta 163 | # Check if the token ends with "\nFinal Answer:" and update `found`. 164 | final_answer_idx = tokens.lower().rfind("\nfinal answer:") 165 | if final_answer_idx != -1: 166 | found = True 167 | return tokens[final_answer_idx + len("\nfinal answer:") :] 168 | return "" 169 | 170 | return process_tokens 171 | 172 | 173 | @app.post("/v1/chat/completions") 174 | async def proxy_completions(request: ChatCompletionRequest): 175 | logger.info(request) 176 | request.temperature = min(max(request.temperature, 0.1), 2) 177 | tools = request.tools 178 | stream = request.stream 179 | request.messages = process_tools_from_prompt(request.messages, tools) 180 | 181 | body = request.model_dump(exclude_none=True) 182 | tool_names = set() 183 | if tools: 184 | del body["tools"] 185 | tool_names = set(func["function"]["name"] for func in tools) 186 | # FIXME: 有的推理引擎不支持设置 stop 187 | add_stop_words(request, body) 188 | try: 189 | 190 | if stream: 191 | response_stream = await CLIENT.chat.completions.create(**body) 192 | 193 | async def stream_generator(): 194 | previous_text: str = "" 195 | tools_token_filter = get_tokens_filter() 196 | async for i in response_stream: 197 | chunk = i.model_dump(exclude_none=True) 198 | 199 | if tools: 200 | choice = chunk["choices"][0] 201 | delta = choice["delta"] 202 | delta_content = delta.get("content", "") 203 | previous_text += delta_content 204 | if ( 205 | previous_text.endswith("Observation:") 206 | or choice.get("finish_reason") is not None 207 | ): 208 | _, func, args = eval_qwen_tools_arguments( 209 | previous_text, tool_names 210 | ) 211 | delta["content"] = tools_token_filter( 212 | tokens=previous_text, delta=delta_content 213 | ) 214 | if func is not None: 215 | _id = str(uuid.uuid4()) 216 | delta["content"] = None 217 | choice["finish_reason"] = "tool_calls" 218 | delta["tool_calls"] = [ 219 | { 220 | "id": _id, 221 | "function": { 222 | "name": func, 223 | "arguments": args, 224 | }, 225 | "type": "function", 226 | } 227 | ] 228 | yield "data: " + orjson.dumps(chunk).decode() + "\n" 229 | break 230 | else: 231 | # 过滤掉不需要的内容 232 | delta["content"] = tools_token_filter( 233 | tokens=previous_text, delta=delta_content 234 | ) 235 | if not delta["content"]: 236 | continue 237 | 238 | yield "data: " + orjson.dumps(chunk).decode() + "\n" 239 | yield "data: [DONE]\n" 240 | 241 | # 在上下文中返回流式响应 242 | return StreamingResponse(stream_generator(), media_type="text/event-stream") 243 | else: 244 | response = await CLIENT.chat.completions.create(**body) 245 | ret = response.model_dump(exclude_none=True) 246 | 247 | # 处理返回的响应 248 | 249 | if tools: 250 | choice = ret["choices"][0] 251 | text = choice["message"]["content"] 252 | content, func, args = eval_qwen_tools_arguments(text, tool_names) 253 | choice["message"]["content"] = content 254 | if func and args: 255 | _id = str(uuid.uuid4()) 256 | 257 | choice["message"]["tool_calls"] = [ 258 | { 259 | "id": _id, 260 | "function": {"name": func, "arguments": args}, 261 | "type": "function", 262 | } 263 | ] 264 | choice["finish_reason"] = "tool_calls" 265 | return JSONResponse(ret) 266 | 267 | except Exception as e: 268 | # 捕获HTTP异常并返回错误信息 269 | raise HTTPException(status_code=500, detail=str(e)) 270 | 271 | 272 | def add_stop_words(request, body): 273 | stop = request.stop 274 | if isinstance(stop, str): 275 | body["stop"] = [stop, "Observation:"] 276 | elif isinstance(stop, list): 277 | body["stop"] = list(stop) + ["Observation:"] 278 | else: 279 | body["stop"] = "Observation:" 280 | 281 | 282 | if __name__ == "__main__": 283 | import uvicorn 284 | 285 | uvicorn.run(app, host="0.0.0.0", port=8600) 286 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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