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