from fastapi import Body, Request from fastapi.responses import StreamingResponse import asyncio, json, os from typing import List, AsyncIterable from langchain import LLMChain from langchain.callbacks import AsyncIteratorCallbackHandler from langchain.prompts.chat import ChatPromptTemplate from coagent.llm_models import getChatModel, getChatModelFromConfig from coagent.chat.utils import History, wrap_done from coagent.llm_models.llm_config import LLMConfig, EmbedConfig # from configs.model_config import (llm_model_dict, LLM_MODEL, VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD) from coagent.utils import BaseResponse from loguru import logger class Chat: def __init__( self, engine_name: str = "", top_k: int = 1, stream: bool = False, ) -> None: self.engine_name = engine_name self.top_k = top_k self.stream = stream def check_service_status(self, ) -> BaseResponse: return BaseResponse(code=200, msg=f"okok") def chat( self, query: str = Body(..., description="用户输入", examples=["hello"]), history: List[History] = Body( [], description="历史对话", examples=[[{"role": "user", "content": "我们来玩成语接龙，我先来，生龙活虎"}]] ), engine_name: str = Body(..., description="知识库名称", examples=["samples"]), top_k: int = Body(5, description="匹配向量数"), score_threshold: float = Body(1, description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右", ge=0, le=1), stream: bool = Body(False, description="流式输出"), local_doc_url: bool = Body(False, description="知识文件返回本地路径(true)或URL(false)"), request: Request = None, api_key: str = Body(os.environ.get("OPENAI_API_KEY")), api_base_url: str = Body(os.environ.get("API_BASE_URL")), embed_model: str = Body("", ), embed_model_path: str = Body("", ), embed_engine: str = Body("", ), model_name: str = Body("", ), temperature: float = Body(0.5, ), model_device: str = Body("", ), **kargs ): params = locals() params.pop("self", None) llm_config: LLMConfig = LLMConfig(**params) embed_config: EmbedConfig = EmbedConfig(**params) self.engine_name = engine_name if isinstance(engine_name, str) else engine_name.default self.top_k = top_k if isinstance(top_k, int) else top_k.default self.score_threshold = score_threshold if isinstance(score_threshold, float) else score_threshold.default self.stream = stream if isinstance(stream, bool) else stream.default self.local_doc_url = local_doc_url if isinstance(local_doc_url, bool) else local_doc_url.default self.request = request return self._chat(query, history, llm_config, embed_config, **kargs) def _chat(self, query: str, history: List[History], llm_config: LLMConfig, embed_config: EmbedConfig, **kargs): history = [History(**h) if isinstance(h, dict) else h for h in history] ## check service dependcy is ok service_status = self.check_service_status() if service_status.code!=200: return service_status def chat_iterator(query: str, history: List[History]): # model = getChatModel() model = getChatModelFromConfig(llm_config) result, content = self.create_task(query, history, model, llm_config, embed_config, **kargs) logger.info('result={}'.format(result)) logger.info('content={}'.format(content)) if self.stream: for token in content["text"]: result["answer"] = token yield json.dumps(result, ensure_ascii=False) else: for token in content["text"]: result["answer"] += token yield json.dumps(result, ensure_ascii=False) return StreamingResponse(chat_iterator(query, history), media_type="text/event-stream") def achat( self, query: str = Body(..., description="用户输入", examples=["hello"]), history: List[History] = Body( [], description="历史对话", examples=[[{"role": "user", "content": "我们来玩成语接龙，我先来，生龙活虎"}]] ), engine_name: str = Body(..., description="知识库名称", examples=["samples"]), top_k: int = Body(5, description="匹配向量数"), score_threshold: float = Body(1, description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右", ge=0, le=1), stream: bool = Body(False, description="流式输出"), local_doc_url: bool = Body(False, description="知识文件返回本地路径(true)或URL(false)"), request: Request = None, api_key: str = Body(os.environ.get("OPENAI_API_KEY")), api_base_url: str = Body(os.environ.get("API_BASE_URL")), embed_model: str = Body("", ), embed_model_path: str = Body("", ), embed_engine: str = Body("", ), model_name: str = Body("", ), temperature: float = Body(0.5, ), model_device: str = Body("", ), ): # params = locals() params.pop("self", None) llm_config: LLMConfig = LLMConfig(**params) embed_config: EmbedConfig = EmbedConfig(**params) self.engine_name = engine_name if isinstance(engine_name, str) else engine_name.default self.top_k = top_k if isinstance(top_k, int) else top_k.default self.score_threshold = score_threshold if isinstance(score_threshold, float) else score_threshold.default self.stream = stream if isinstance(stream, bool) else stream.default self.local_doc_url = local_doc_url if isinstance(local_doc_url, bool) else local_doc_url.default self.request = request return self._achat(query, history, llm_config, embed_config) def _achat(self, query: str, history: List[History], llm_config: LLMConfig, embed_config: EmbedConfig): history = [History(**h) if isinstance(h, dict) else h for h in history] ## check service dependcy is ok service_status = self.check_service_status() if service_status.code!=200: return service_status async def chat_iterator(query, history): callback = AsyncIteratorCallbackHandler() # model = getChatModel() model = getChatModelFromConfig(llm_config) task, result = self.create_atask(query, history, model, llm_config, embed_config, callback) if self.stream: for token in callback["text"]: result["answer"] = token yield json.dumps(result, ensure_ascii=False) else: for token in callback["text"]: result["answer"] += token yield json.dumps(result, ensure_ascii=False) await task return StreamingResponse(chat_iterator(query, history), media_type="text/event-stream") def create_task(self, query: str, history: List[History], model, llm_config: LLMConfig, embed_config: EmbedConfig, **kargs): '''构建 llm 生成任务''' chat_prompt = ChatPromptTemplate.from_messages( [i.to_msg_tuple() for i in history] + [("human", "{input}")] ) chain = LLMChain(prompt=chat_prompt, llm=model) content = chain({"input": query}) return {"answer": "", "docs": ""}, content def create_atask(self, query, history, model, llm_config: LLMConfig, embed_config: EmbedConfig, callback: AsyncIteratorCallbackHandler): chat_prompt = ChatPromptTemplate.from_messages( [i.to_msg_tuple() for i in history] + [("human", "{input}")] ) chain = LLMChain(prompt=chat_prompt, llm=model) task = asyncio.create_task(wrap_done( chain.acall({"input": query}), callback.done )) return task, {"answer": "", "docs": ""}