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| 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"""<span style='color:red'>未找到相关文档,该回答为大模型自身能力解答！</span>""") | |
| 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()) | |