mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-21 14:23:33 +08:00
b4c68ddd05
* 优化在线 API ，支持 completion 和 embedding，简化在线 API 开发方式 新功能 - 智谱AI、Minimax、千帆、千问 4 个在线模型支持 embeddings（不通过Fastchat，后续会单独提供相关api接口） - 在线模型自动检测传入参数，在传入非 messages 格式的 prompt 时，自动转换为 completion 形式，以支持 completion 接口 开发者： - 重构ApiModelWorker: - 所有在线 API 请求封装到 do_chat 方法：自动传入参数 ApiChatParams，简化参数与配置项的获取；自动处理与fastchat的接口 - 加强 API 请求错误处理，返回更有意义的信息 - 改用 qianfan sdk 重写 qianfan-api - 将所有在线模型的测试用例统一在一起，简化测试用例编写 * Delete requirements_langflow.txt
250 lines
8.2 KiB
Python
250 lines
8.2 KiB
Python
from fastchat.conversation import Conversation
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from configs import LOG_PATH, TEMPERATURE
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import fastchat.constants
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fastchat.constants.LOGDIR = LOG_PATH
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from fastchat.serve.base_model_worker import BaseModelWorker
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import uuid
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import json
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import sys
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from pydantic import BaseModel, root_validator
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import fastchat
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import asyncio
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from server.utils import get_model_worker_config
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from typing import Dict, List, Optional
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__all__ = ["ApiModelWorker", "ApiChatParams", "ApiCompletionParams", "ApiEmbeddingsParams"]
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# 恢复被fastchat覆盖的标准输出
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sys.stdout = sys.__stdout__
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sys.stderr = sys.__stderr__
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class ApiConfigParams(BaseModel):
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'''
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在线API配置参数，未提供的值会自动从model_config.ONLINE_LLM_MODEL中读取
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'''
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api_base_url: Optional[str] = None
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api_proxy: Optional[str] = None
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api_key: Optional[str] = None
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secret_key: Optional[str] = None
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group_id: Optional[str] = None # for minimax
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is_pro: bool = False # for minimax
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APPID: Optional[str] = None # for xinghuo
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APISecret: Optional[str] = None # for xinghuo
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is_v2: bool = False # for xinghuo
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worker_name: Optional[str] = None
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class Config:
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extra = "allow"
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@root_validator(pre=True)
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def validate_config(cls, v: Dict) -> Dict:
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if config := get_model_worker_config(v.get("worker_name")):
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for n in cls.__fields__:
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if n in config:
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v[n] = config[n]
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return v
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def load_config(self, worker_name: str):
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self.worker_name = worker_name
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if config := get_model_worker_config(worker_name):
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for n in self.__fields__:
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if n in config:
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setattr(self, n, config[n])
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return self
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class ApiModelParams(ApiConfigParams):
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'''
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模型配置参数
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'''
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version: Optional[str] = None
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version_url: Optional[str] = None
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api_version: Optional[str] = None # for azure
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deployment_name: Optional[str] = None # for azure
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resource_name: Optional[str] = None # for azure
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temperature: float = TEMPERATURE
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max_tokens: Optional[int] = None
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top_p: Optional[float] = 1.0
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class ApiChatParams(ApiModelParams):
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'''
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chat请求参数
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'''
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messages: List[Dict[str, str]]
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system_message: Optional[str] = None # for minimax
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role_meta: Dict = {} # for minimax
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class ApiCompletionParams(ApiModelParams):
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prompt: str
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class ApiEmbeddingsParams(ApiConfigParams):
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texts: List[str]
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embed_model: Optional[str] = None
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to_query: bool = False # for minimax
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class ApiModelWorker(BaseModelWorker):
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DEFAULT_EMBED_MODEL: str = None # None means not support embedding
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def __init__(
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self,
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model_names: List[str],
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controller_addr: str = None,
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worker_addr: str = None,
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context_len: int = 2048,
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no_register: bool = False,
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**kwargs,
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):
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kwargs.setdefault("worker_id", uuid.uuid4().hex[:8])
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kwargs.setdefault("model_path", "")
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kwargs.setdefault("limit_worker_concurrency", 5)
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super().__init__(model_names=model_names,
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controller_addr=controller_addr,
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worker_addr=worker_addr,
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**kwargs)
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import sys
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# 恢复被fastchat覆盖的标准输出
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sys.stdout = sys.__stdout__
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sys.stderr = sys.__stderr__
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self.context_len = context_len
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self.semaphore = asyncio.Semaphore(self.limit_worker_concurrency)
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self.version = None
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if not no_register and self.controller_addr:
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self.init_heart_beat()
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def count_token(self, params):
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# TODO：需要完善
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# print("count token")
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prompt = params["prompt"]
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return {"count": len(str(prompt)), "error_code": 0}
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def generate_stream_gate(self, params: Dict):
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self.call_ct += 1
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try:
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prompt = params["prompt"]
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if self._is_chat(prompt):
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messages = self.prompt_to_messages(prompt)
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messages = self.validate_messages(messages)
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else: # 使用chat模仿续写功能，不支持历史消息
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messages = [{"role": self.user_role, "content": f"please continue writing from here: {prompt}"}]
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p = ApiChatParams(
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messages=messages,
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temperature=params.get("temperature"),
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top_p=params.get("top_p"),
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max_tokens=params.get("max_new_tokens"),
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version=self.version,
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)
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for resp in self.do_chat(p):
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yield self._jsonify(resp)
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except Exception as e:
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yield self._jsonify({"error_code": 500, "text": f"{self.model_names[0]}请求API时发生错误：{e}"})
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def generate_gate(self, params):
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try:
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for x in self.generate_stream_gate(params):
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...
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return json.loads(x[:-1].decode())
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except Exception as e:
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return {"error_code": 500, "text": str(e)}
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# 需要用户自定义的方法
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def do_chat(self, params: ApiChatParams) -> Dict:
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'''
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执行Chat的方法，默认使用模块里面的chat函数。
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要求返回形式：{"error_code": int, "text": str}
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'''
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return {"error_code": 500, "text": f"{self.model_names[0]}未实现chat功能"}
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# def do_completion(self, p: ApiCompletionParams) -> Dict:
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# '''
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# 执行Completion的方法，默认使用模块里面的completion函数。
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# 要求返回形式：{"error_code": int, "text": str}
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# '''
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# return {"error_code": 500, "text": f"{self.model_names[0]}未实现completion功能"}
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def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict:
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'''
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执行Embeddings的方法，默认使用模块里面的embed_documents函数。
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要求返回形式：{"code": int, "embeddings": List[List[float]], "msg": str}
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'''
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return {"code": 500, "msg": f"{self.model_names[0]}未实现embeddings功能"}
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def get_embeddings(self, params):
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# fastchat对LLM做Embeddings限制很大，似乎只能使用openai的。
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# 在前端通过OpenAIEmbeddings发起的请求直接出错，无法请求过来。
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print("get_embedding")
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print(params)
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def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
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raise NotImplementedError
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def validate_messages(self, messages: List[Dict]) -> List[Dict]:
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'''
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有些API对mesages有特殊格式，可以重写该函数替换默认的messages。
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之所以跟prompt_to_messages分开，是因为他们应用场景不同、参数不同
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'''
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return messages
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# help methods
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@property
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def user_role(self):
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return self.conv.roles[0]
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@property
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def ai_role(self):
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return self.conv.roles[1]
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def _jsonify(self, data: Dict) -> str:
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'''
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将chat函数返回的结果按照fastchat openai-api-server的格式返回
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'''
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return json.dumps(data, ensure_ascii=False).encode() + b"\0"
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def _is_chat(self, prompt: str) -> bool:
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'''
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检查prompt是否由chat messages拼接而来
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TODO: 存在误判的可能，也许从fastchat直接传入原始messages是更好的做法
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'''
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key = f"{self.conv.sep}{self.user_role}:"
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return key in prompt
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def prompt_to_messages(self, prompt: str) -> List[Dict]:
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'''
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将prompt字符串拆分成messages.
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'''
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result = []
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user_role = self.user_role
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ai_role = self.ai_role
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user_start = user_role + ":"
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ai_start = ai_role + ":"
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for msg in prompt.split(self.conv.sep)[1:-1]:
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if msg.startswith(user_start):
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if content := msg[len(user_start):].strip():
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result.append({"role": user_role, "content": content})
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elif msg.startswith(ai_start):
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if content := msg[len(ai_start):].strip():
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result.append({"role": ai_role, "content": content})
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else:
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raise RuntimeError(f"unknown role in msg: {msg}")
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return result
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@classmethod
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def can_embedding(cls):
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return cls.DEFAULT_EMBED_MODEL is not None
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