from fastapi import Body, File, Form, UploadFile from sse_starlette.sse import EventSourceResponse from configs import (LLM_MODELS, VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD, TEMPERATURE, CHUNK_SIZE, OVERLAP_SIZE, ZH_TITLE_ENHANCE) from server.utils import (wrap_done, get_ChatOpenAI, BaseResponse, get_prompt_template, get_temp_dir, run_in_thread_pool) from server.knowledge_base.kb_cache.faiss_cache import memo_faiss_pool from langchain.chains import LLMChain from langchain.callbacks import AsyncIteratorCallbackHandler from typing import AsyncIterable, List, Optional import asyncio from langchain.prompts.chat import ChatPromptTemplate from server.chat.utils import History from server.knowledge_base.kb_service.base import EmbeddingsFunAdapter from server.knowledge_base.utils import KnowledgeFile import json import os from pathlib import Path def _parse_files_in_thread( files: List[UploadFile], dir: str, zh_title_enhance: bool, chunk_size: int, chunk_overlap: int, ): """ 通过多线程将上传的文件保存到对应目录内。 生成器返回保存结果：[success or error, filename, msg, docs] """ def parse_file(file: UploadFile) -> dict: ''' 保存单个文件。 ''' try: filename = file.filename file_path = os.path.join(dir, filename) file_content = file.file.read() # 读取上传文件的内容 if not os.path.isdir(os.path.dirname(file_path)): os.makedirs(os.path.dirname(file_path)) with open(file_path, "wb") as f: f.write(file_content) kb_file = KnowledgeFile(filename=filename, knowledge_base_name="temp") kb_file.filepath = file_path docs = kb_file.file2text(zh_title_enhance=zh_title_enhance, chunk_size=chunk_size, chunk_overlap=chunk_overlap) return True, filename, f"成功上传文件 {filename}", docs except Exception as e: msg = f"{filename} 文件上传失败，报错信息为: {e}" return False, filename, msg, [] params = [{"file": file} for file in files] for result in run_in_thread_pool(parse_file, params=params): yield result def upload_temp_docs( files: List[UploadFile] = File(..., description="上传文件，支持多文件"), prev_id: str = Form(None, description="前知识库ID"), chunk_size: int = Form(CHUNK_SIZE, description="知识库中单段文本最大长度"), chunk_overlap: int = Form(OVERLAP_SIZE, description="知识库中相邻文本重合长度"), zh_title_enhance: bool = Form(ZH_TITLE_ENHANCE, description="是否开启中文标题加强"), ) -> BaseResponse: ''' 将文件保存到临时目录，并进行向量化。 返回临时目录名称作为ID，同时也是临时向量库的ID。 ''' if prev_id is not None: memo_faiss_pool.pop(prev_id) failed_files = [] documents = [] path, id = get_temp_dir(prev_id) for success, file, msg, docs in _parse_files_in_thread(files=files, dir=path, zh_title_enhance=zh_title_enhance, chunk_size=chunk_size, chunk_overlap=chunk_overlap): if success: documents += docs else: failed_files.append({file: msg}) with memo_faiss_pool.load_vector_store(id).acquire() as vs: vs.add_documents(documents) return BaseResponse(data={"id": id, "failed_files": failed_files}) async def file_chat(query: str = Body(..., description="用户输入", examples=["你好"]), knowledge_id: str = Body(..., description="临时知识库ID"), top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"), score_threshold: float = Body(SCORE_THRESHOLD, 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=2), 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"}, {"role": "assistant", "content": "虎头虎脑"}]] ), stream: bool = Body(False, description="流式输出"), model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"), prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), ): if knowledge_id not in memo_faiss_pool.keys(): return BaseResponse(code=404, msg=f"未找到临时知识库 {knowledge_id}，请先上传文件") history = [History.from_data(h) for h in history] async def knowledge_base_chat_iterator() -> AsyncIterable[str]: nonlocal max_tokens callback = AsyncIteratorCallbackHandler() if isinstance(max_tokens, int) and max_tokens <= 0: max_tokens = None model = get_ChatOpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, callbacks=[callback], ) embed_func = EmbeddingsFunAdapter() embeddings = await embed_func.aembed_query(query) with memo_faiss_pool.acquire(knowledge_id) as vs: docs = vs.similarity_search_with_score_by_vector(embeddings, k=top_k, score_threshold=score_threshold) docs = [x[0] for x in docs] context = "\n".join([doc.page_content for doc in docs]) if len(docs) == 0: ## 如果没有找到相关文档，使用Empty模板 prompt_template = get_prompt_template("knowledge_base_chat", "empty") else: prompt_template = get_prompt_template("knowledge_base_chat", prompt_name) input_msg = History(role="user", content=prompt_template).to_msg_template(False) chat_prompt = ChatPromptTemplate.from_messages( [i.to_msg_template() for i in history] + [input_msg]) chain = LLMChain(prompt=chat_prompt, llm=model) # Begin a task that runs in the background. task = asyncio.create_task(wrap_done( chain.acall({"context": context, "question": query}), callback.done), ) source_documents = [] for inum, doc in enumerate(docs): filename = doc.metadata.get("source") text = f"""出处 [{inum + 1}] [{filename}] \n\n{doc.page_content}\n\n""" source_documents.append(text) if len(source_documents) == 0: # 没有找到相关文档 source_documents.append(f"""未找到相关文档,该回答为大模型自身能力解答！""") if stream: async for token in callback.aiter(): # Use server-sent-events to stream the response yield json.dumps({"answer": token}, ensure_ascii=False) yield json.dumps({"docs": source_documents}, ensure_ascii=False) else: answer = "" async for token in callback.aiter(): answer += token yield json.dumps({"answer": answer, "docs": source_documents}, ensure_ascii=False) await task return EventSourceResponse(knowledge_base_chat_iterator())