358 lines
17 KiB
Python
358 lines
17 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.chat.agent_chat_test import run_sync
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from server.chat.policy_fun_iast import get_llm_model_response
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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, split_questions
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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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from langchain.chains.question_answering import load_qa_chain
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from langchain.memory import ConversationBufferMemory,ConversationSummaryMemory, ConversationBufferWindowMemory
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from langchain.docstore.document import Document
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from langchain_core.prompts import PromptTemplate
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from datetime import datetime
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from server.knowledge_base.kb_service.base import TextRank
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from configs.basic_config import *
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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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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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for doc in docs:
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if isinstance(doc, Document):
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# 去除分词处理多余换行符
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doc.page_content = doc.page_content.replace('\n', '')
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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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context = ""
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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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if result[0]: # success
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for doc in result[3]: # docs
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context += doc.page_content + "\n"
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return context
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def generate_summary(text: str) -> str:
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# 根据文本长度，每 100 字生成一句摘要，最多生成 300 句
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num_sentences = min(len(text) // 50, 300)
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# num_sentences = 80
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# 使用 TextRank 算法生成摘要
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summary = TextRank(text, num_sentences=num_sentences)
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return summary
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@ timing_decorator
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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，并返回临时目录名称作为ID，同时返回文件的全文。
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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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context = ""
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summary = ""
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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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for doc in docs:
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context += doc.page_content + "\n"
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if len(context) > 30000:
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summary = generate_summary(context)
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else:
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failed_files.append({file: msg})
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return BaseResponse(data={"id": id, "context": context, "summary": summary})
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async def file_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
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file_name: str = Body("", description="文件名称", examples=["123.txt"]),
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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": "现在开始针对我上传的文件回答我的问题"},
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{"role": "assistant",
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"content": "好的，让我们开始看吧"}]]
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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("0.5", 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("file_chat", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
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context: str = Body("", description="文件内容")):
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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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# # 调用模型进行总结
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# def llm_summary(
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# llm_model_name: "str",
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# prompt_template: str,
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# temperature: float,
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# max_tokens: int,
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# ) -> str:
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# '''调用大模型进行总结'''
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# # 读取指定的大模型，这里不能加入callback，否则会把这部分模型响应加入最终的回答
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# model = get_ChatOpenAI(
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# model_name=llm_model_name,
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# temperature=temperature,
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# max_tokens=max_tokens,
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# callbacks=[],
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# )
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# # 获取prompt
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# prompt_template = get_prompt_template("knowledge_base_chat", "file_summary")
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# input_msg = History(role="system", content=prompt_template).to_msg_template(False)
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# prompt = ChatPromptTemplate.from_messages([input_msg])
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# # 获取模型响应
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# llm_chain = LLMChain(prompt=prompt, llm=model)
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# summary = llm_chain.run(prompt_param_dict)
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# return summary
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async def knowledge_base_chat_iterator() -> AsyncIterable[str]:
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nonlocal history, context, max_tokens
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callback = AsyncIteratorCallbackHandler()
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memory = None
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# 获取当前时间并格式化为YYYYMMDD
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time = datetime.now().strftime("%Y%m%d")
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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([summary])
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# if len(kdocs) == 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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if history:
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history = [History.from_data(h) for h in history]
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prompt_template = get_prompt_template("knowledge_base_chat", "file_chat_history")
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# input_prompt = History(role="system", content=prompt_template).to_msg_template(False)
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# input_msg = History(role="user", content=query).to_msg_template(False)
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# chat_prompt = ChatPromptTemplate.from_messages([input_prompt] + [input_msg])
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chat_prompt = PromptTemplate.from_template(prompt_template)
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# 把history转成memory
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memory = ConversationBufferWindowMemory(k=1, input_key="question")
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# memory = ConversationSummaryMemory(llm=model)
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for message in history:
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# 检查消息的角色
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if message.role == 'user':
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# 添加用户消息
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memory.chat_memory.add_user_message(message.content)
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elif message.role == 'assistant':
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# 添加AI消息
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memory.chat_memory.add_ai_message(message.content)
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else:
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prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
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input_prompt = History(role="system", content=prompt_template).to_msg_template(False)
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input_msg = History(role="user", content=query).to_msg_template(False)
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chat_prompt = ChatPromptTemplate.from_messages([input_prompt] + [input_msg])
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# chain = LLMChain(prompt=chat_prompt, llm=model, memory=memory)
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chain = load_qa_chain(model, chain_type="stuff", memory=memory, prompt=chat_prompt, verbose=True)
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# print("file memory:>>",memory)
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# print("file chat_prompt:>>",chat_prompt)
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# source_documents = []
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# for inum, doc in enumerate(docs):
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# source = doc.metadata.get("source")
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# print("file source: \n", source)
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# file = source.split('/')[-1]
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# title = file.split('.')[0]
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# text = f"""出处 [{inum + 1}] [{title}] \n\n{doc.page_content}\n\n"""
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# source_documents.append(text)
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# knowledgeFile = KnowledgeFile(
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# filename=file_name,
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# knowledge_base_name="temp"
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# )
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# summary = knowledgeFile.file2docs()
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# print("file summary: \n", summary)
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# summary = ''
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# print("file summary: \n", summary)
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# Begin a task that runs in the background.
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# summary = llm_summary()
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# docs =
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# 确保input_documents是Document对象列表
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context = [Document(page_content=context)]
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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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task = asyncio.create_task(wrap_done(
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chain.ainvoke({"input_documents": context,"question": query, "title": file_name, "time":time}, return_only_outputs=True),
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callback.done),
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)
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# print("file_chain:\n", chain)
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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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history_summary = ""
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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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history_summary += token
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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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ensure_ascii=False)
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question_history = [
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{"role": "user", "content": query},
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{"role": "assistant", "content": history_summary}
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]
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question = (await run_sync(
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get_llm_model_response,
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strategy_name="question_recommend",
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llm_model_name=LLM_MODELS[0],
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template_prompt_name="question_recommend",
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prompt_param_dict={"history": question_history},
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temperature=0.3,
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max_tokens=512,
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)).strip()
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formatted = split_questions(question)
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logger.info(f"推荐问题: \n{formatted}")
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yield json.dumps({"question": formatted}, ensure_ascii=False)
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await task
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return EventSourceResponse(knowledge_base_chat_iterator())
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