[全量] 初始化项目代码、配置、文档及Agent协同harness
This commit is contained in:
591
langchain-chat/server/chat/search_engine_chat.py
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591
langchain-chat/server/chat/search_engine_chat.py
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from concurrent.futures import ThreadPoolExecutor
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from datetime import datetime
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import itertools
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import random
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from bs4 import BeautifulSoup
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from selenium import webdriver
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from selenium.webdriver.chrome.service import Service
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from selenium.webdriver.chrome.options import Options
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from selenium.webdriver.common.by import By
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from langchain.utilities.bing_search import BingSearchAPIWrapper
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import requests
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from fake_useragent import UserAgent
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# from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
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from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY, METAPHOR_API_KEY,
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LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE,
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TEXT_SPLITTER_NAME, OVERLAP_SIZE)
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from fastapi import Body
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from sse_starlette import EventSourceResponse
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from fastapi.concurrency import run_in_threadpool
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from configs.kb_config import CHROME_DIR, EN_BASE_NAME
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from server.agent.tools.duckduckgo_search import DuckduckgoInput
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from server.chat import utils
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from server.chat.ZhipuSearchAPI import ZhipuSearchAPIWrapper
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from server.utils import wrap_done, get_ChatOpenAI
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from server.utils import BaseResponse, get_prompt_template
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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
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import asyncio
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from langchain.prompts.chat import ChatPromptTemplate
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from typing import List, Optional, Dict
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from server.chat.utils import History, get_similar_documents
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from langchain.docstore.document import Document
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import json
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from strsimpy.normalized_levenshtein import NormalizedLevenshtein
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from markdownify import markdownify
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from .KgoSearchAPIWrapper import KgoSearchAPIWrapper
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from server.chat.policy_fun_iast import get_llm_model_response
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import re
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from configs.basic_config import *
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def bing_search(text, result_len=SEARCH_ENGINE_TOP_K, **kwargs):
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if not (BING_SEARCH_URL and BING_SUBSCRIPTION_KEY):
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return [{"snippet": "please set BING_SUBSCRIPTION_KEY and BING_SEARCH_URL in os ENV",
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"title": "env info is not found",
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"link": "https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html"}]
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search = BingSearchAPIWrapper(bing_subscription_key=BING_SUBSCRIPTION_KEY,
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bing_search_url=BING_SEARCH_URL)
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return search.results(text, result_len)
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# def duckduckgo_search(text, result_len=SEARCH_ENGINE_TOP_K, **kwargs):
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# search = DuckDuckGoSearchAPIWrapper()
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# return search.results(text, result_len)
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def duckduckgo_search(text, result_len=SEARCH_ENGINE_TOP_K, **kwargs):
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search = DuckduckgoInput()
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return search.results(text, result_len)
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def kgo_search(text,origin_query, kgo_search_type="1000",**kwargs):
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search = KgoSearchAPIWrapper()
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return search.results(text, kgo_search_type,origin_query=origin_query)
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def metaphor_search(
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text: str,
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result_len: int = SEARCH_ENGINE_TOP_K,
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split_result: bool = False,
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chunk_size: int = 500,
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chunk_overlap: int = OVERLAP_SIZE,
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) -> List[Dict]:
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from metaphor_python import Metaphor
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if not METAPHOR_API_KEY:
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return []
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client = Metaphor(METAPHOR_API_KEY)
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search = client.search(text, num_results=result_len, use_autoprompt=True)
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contents = search.get_contents().contents
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for x in contents:
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x.extract = markdownify(x.extract)
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# metaphor 返回的内容都是长文本，需要分词再检索
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if split_result:
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docs = [Document(page_content=x.extract,
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metadata={"link": x.url, "title": x.title})
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for x in contents]
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text_splitter = RecursiveCharacterTextSplitter(["\n\n", "\n", ".", " "],
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap)
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splitted_docs = text_splitter.split_documents(docs)
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# 将切分好的文档放入临时向量库，重新筛选出TOP_K个文档
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if len(splitted_docs) > result_len:
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normal = NormalizedLevenshtein()
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for x in splitted_docs:
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x.metadata["score"] = normal.similarity(text, x.page_content)
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splitted_docs.sort(key=lambda x: x.metadata["score"], reverse=True)
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splitted_docs = splitted_docs[:result_len]
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docs = [{"snippet": x.page_content,
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"link": x.metadata["link"],
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"title": x.metadata["title"]}
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for x in splitted_docs]
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else:
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docs = [{"snippet": x.extract,
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"link": x.url,
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"title": x.title}
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for x in contents]
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return docs
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def zhipu_search(origin_query, **kwargs):
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search = ZhipuSearchAPIWrapper()
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return search.zhipu_search(origin_query)
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SEARCH_ENGINES = {"bing": bing_search,
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"duckduckgo": duckduckgo_search,
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"metaphor": metaphor_search,
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"kgo": kgo_search,
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"zhipu_search": zhipu_search,
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}
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def search_result2docs(search_results):
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docs = []
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for result in search_results:
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# 提取信息
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title = result.get("title", "")
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keywords = result.get("keywords", "")
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snippet = result.get("snippet", "")
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author = result.get("author", "")
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resource_type = result.get("resource_type", "")
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publish_year = result.get("publish_year", "")
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# 创建page_content
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page_content = f"【本资料的标题为：{title}】\n"
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page_content += f"【本资料的关键词为：{keywords}】\n"
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page_content += f"【本资料的摘要为：{snippet}】\n"
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page_content += f"【本资料的作者为：{author}】\n"
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page_content += f"【本资料的发布时间为：{publish_year}】\n"
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page_content += f"【本资料的资源类型为：{resource_type}】\n"
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# 创建metadata
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metadata = {
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"source": result.get("link", ""),
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"filename": title,
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"author": author,
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"keywords": keywords,
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"publish_year": publish_year,
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"resource_type": resource_type,
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}
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doc = Document(page_content=page_content, metadata=metadata)
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docs.append(doc)
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return docs
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# 定义替换函数
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# def replace_ref(match):
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# num = match.group(1)
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# return f'^[{num}]^'
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def ZhipuSearch_result2docs(search_results):
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docs = []
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for result in search_results:
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# 提取信息
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title = result.get("title", "")
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cleaned_title = re.sub(r'（发布时间：.*?）', '', title) # 展示的参考资料名称需过滤掉发布时间
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link = result.get("link", "")
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content = result.get("content", "")
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# refer = result.get("refer", "")
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# refer = re.sub(r'ref_(\d+)', replace_ref, refer)
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# icon = result.get("icon", "")
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media = result.get("media", "")
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# 创建 page_content
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page_content = (
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f"【资料：{cleaned_title}\n"
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f"内容如下：{content}\n"
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f"资料链接：{link}\n"
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f"发布媒体为：{media}】\n"
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)
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# 创建metadata
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metadata = {
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"link": link,
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"title": cleaned_title,
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# "refer": refer,
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"content": content,
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# "icon": icon,
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"media": media,
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}
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doc = Document(page_content=page_content, metadata=metadata)
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docs.append(doc)
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logger.info(f"Zhipu搜索召回资料:{doc.metadata}")
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return docs
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def Duckduckgo_result2docs(search_results):
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docs = []
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for result in search_results:
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# 提取信息
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title = result.get("title", "")
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url = result.get("url", "")
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content = result.get("body", "")
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date = result.get("date", "")
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image = result.get("image", "")
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source = result.get("source", "")
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# 创建page_content
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page_content = f"【本资料的标题为：{title}\n"
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page_content += f"链接为：{url}\n"
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page_content += f"内容为：{content}\n"
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page_content += f"发布时间为：{date}\n"
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page_content += f"封面图为：{image}\n"
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page_content += f"发布媒体为：{source}】\n"
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# 创建metadata
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metadata = {
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"url": url,
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"title": title,
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"date": date,
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"content": content,
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"image": image,
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"source": source,
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}
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doc = Document(page_content=page_content, metadata=metadata)
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docs.append(doc)
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logger.info(f"Duckduckgo搜索召回资料:{doc.metadata}")
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return docs
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def extract_html(url,proxy):
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try:
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# url = url.replace("https", "http")
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urlss = "http://%(ip)s:%(port)s" % {
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"ip": proxy["sever"],
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"port": proxy["port"],
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}
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proxies = {
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"http": urlss,
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"https": urlss,
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}
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headers = {'User-Agent': UserAgent().random}
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response = requests.get(url,proxies=proxies, timeout=3,headers=headers)
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response.raise_for_status()
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soup = BeautifulSoup(response.content, 'html.parser')
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res = {}
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res ["html"] = soup.prettify()
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res["title"] = soup.title.string if soup.title else ""
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if res["title"]==None:
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res["title"] = ""
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return res
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except requests.exceptions.RequestException as e:
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res = {}
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res ["html"] = ""
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res["title"] = ""
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return res
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chrome_options = Options()
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chrome_options.add_argument("--headless") # 无头模式
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chrome_options.add_argument("--disable-gpu") # 禁用 GPU
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chrome_options.add_argument("--ignore-certificate-errors") # 忽略 SSL 证书错误
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chrome_options.add_argument("--no-sandbox") # 解决DevToolsActivePort文件不存在的报错
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chrome_options.add_argument("--disable-dev-shm-usage") # 解决资源限制问题
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chrome_options.binary_location = f"{CHROME_DIR}/chrome-linux64/chrome"#chrome可执行文件
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service = Service(f'{CHROME_DIR}/chromedriver-linux64/chromedriver') # ChromeDriver
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async def lookup_search_engine(
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uid: str,
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search_query: str,
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origin_query: str,
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search_engine_name: str,
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top_k: int = SEARCH_ENGINE_TOP_K,
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split_result: bool = False,
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kgo_search_type: str = "1000"
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):
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search_engine = SEARCH_ENGINES.get(search_engine_name)
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if not search_engine:
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raise ValueError(f"Unsupported search engine: {search_engine_name}")
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if search_engine_name == "zhipu_search":
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# 对于 zhipu_search，只需要 origin_query
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results = await run_in_threadpool(search_engine, search_query)
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if len(results) > 0:
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try:
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sentences = [doc["title"] for doc in results]
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sentences_page_content = [str(i+1)+":【"+doc["title"]+doc["content"]+"】" for i,doc in enumerate(results)]
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except Exception as e:
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sentences = [doc["source"] for doc in results]
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sentences_page_content = [str(i+1)+":【"+doc["source"]+doc["content"]+"】" for i,doc in enumerate(results)]
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res = get_llm_model_response(
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strategy_name="default_similar",
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llm_model_name=LLM_MODELS[0],
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template_prompt_name="default_similar",
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prompt_param_dict={"input": origin_query, "title": f"{sentences_page_content}", "time": datetime.now().strftime("%Y%m%d")},
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temperature=0.01,
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max_tokens=512
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)
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try:
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index =[]
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if res == "无":
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index = []
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else:
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index = res.split(",")
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index = [int(i)-1 for i in index]
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results = get_similar_documents(index=index,sentences=sentences,query=origin_query, docs=results, top_k=top_k)
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except Exception as e:
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print(e)
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results = get_similar_documents(index=[],sentences=sentences,query=origin_query, docs=results, top_k=top_k)
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# 从中选择几个不同的代理
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# 例如，选择 2 个不同的代理
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if len(results)>0:
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try:
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url = f"https://sch.shanchendaili.com/api.html?action=get_ip&key=HU301b55318830279250RjH2&time=10&count={len(results)}&type=txt&province=215&only=0"
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ip_list = requests.get(url=url).json()["list"]
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with ThreadPoolExecutor() as executor:
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html_contents = list(executor.map(extract_html, [result.get("link", "") for result in results],ip_list))
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except Exception as e:
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print(e)
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html_contents = [{"html": "","title":""} for _ in range(len(results))]
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info = {}
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res_list = []
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for result, html_content in zip(results, html_contents):
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info.clear()
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info["content"] = result.get("content", "")
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info["icon"] = "无"
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info["index"] = 0
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info["link"] = result.get("link", "")
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info["media"] = "未知"
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info["refer"] = result.get("positions", [0])[0]
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info["title"] = html_content["title"] if ((result["title"].replace("...","")) in html_content["title"]) else result.get("title", "")
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res_list.append(info.copy())
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results = res_list
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else:
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return []
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if "￥" in uid:
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res =utils.get_shared_variable(uid.replace("￥",""))
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results,titles=utils.remove_docs(res["title"], results)
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res["title"].extend(titles)
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utils.set_shared_variable(uid.replace("￥",""),res)
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else:
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res =utils.get_shared_variable(uid)
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results,titles=utils.remove_docs(res["title"], results)
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res["title"].extend(titles)
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utils.set_shared_variable(uid,res)
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docs = ZhipuSearch_result2docs(results)
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else:
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# 其他搜索引擎需要更多参数
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results = await run_in_threadpool(
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search_engine,
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search_query,
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origin_query=origin_query,
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result_len=top_k,
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split_result=split_result,
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kgo_search_type=kgo_search_type
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)
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docs = search_result2docs(results)
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return docs
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async def search_engine_chat(uid: Optional[str]=Body(None, description="userID"),
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query: str = Body(..., description="用户输入", examples=["你好"]),
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search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]),
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top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
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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[1], 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",
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description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
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split_result: bool = Body(False, description="是否对搜索结果进行拆分（主要用于metaphor搜索引擎）"),
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kgo_search_type: str = Body("1000", description="kgo搜索引擎搜索类型，默认1000"),
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):
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if search_engine_name == "bing" and not BING_SUBSCRIPTION_KEY:
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return BaseResponse(code=404, msg=f"要使用Bing搜索引擎，需要设置 `BING_SUBSCRIPTION_KEY`")
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if search_engine_name not in SEARCH_ENGINES.keys():
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return BaseResponse(code=404, msg=f"未支持搜索引擎 {search_engine_name}")
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if search_engine_name == "bing" and not BING_SUBSCRIPTION_KEY:
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return BaseResponse(code=404, msg=f"要使用Bing搜索引擎，需要设置 `BING_SUBSCRIPTION_KEY`")
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history = [History.from_data(h) for h in history]
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user_queries = []
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# 遍历历史消息并收集用户消息
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for message in history:
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if message.role == 'user':
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user_queries.append(message.content)
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if search_engine_name == "kgo":
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metal_search_entity = get_llm_model_response(
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strategy_name="KGO_NER",
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llm_model_name=LLM_MODELS[0],
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template_prompt_name="KGO_NER",
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prompt_param_dict={"input": query, "history": user_queries},
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temperature=TEMPERATURE,
|
||||
max_tokens=512
|
||||
)
|
||||
logger.info(f"KGO_NER体识别结果：{metal_search_entity}")
|
||||
# 使用正则表达式来匹配和拆分多个 JSON 对象
|
||||
json_strings = re.findall(r'\{.*?\}', metal_search_entity)
|
||||
# 初始化搜索查询和类型
|
||||
search_query = []
|
||||
kgo_search_type = []
|
||||
|
||||
# 处理每个 JSON 对象
|
||||
for json_string in json_strings:
|
||||
try:
|
||||
# 尝试解析 JSON
|
||||
data = json.loads(json_string)
|
||||
|
||||
# 提取键值对
|
||||
for key, value in data.items():
|
||||
search_query.append(key)
|
||||
if value in [" ", ""]: # 如果类型为空，则使用默认值
|
||||
kgo_search_type = "10000"
|
||||
else:
|
||||
kgo_search_type.extend(value)
|
||||
|
||||
except json.JSONDecodeError:
|
||||
# 处理 JSON 解码错误的情况，继续使用原始查询和默认类型
|
||||
search_query = ','.join(search_query.append(query))
|
||||
kgo_search_type = ','.join(kgo_search_type.append("1000,1002,1002,1003,1004,1005,1006,1007,1010,1011,1013,1015,3001,4001,4004,4005,6001"))
|
||||
# 输出处理结果
|
||||
search_query = ','.join(search_query)
|
||||
kgo_search_type_list = ','.join(kgo_search_type).split(',')
|
||||
search_type_map = {
|
||||
"全部": "1000",
|
||||
"期刊论文": "1002",
|
||||
"学位论文": "1003",
|
||||
"会议论文": "1004",
|
||||
"政策": "1005",
|
||||
"成果": "1006",
|
||||
"科技成果": "1006",
|
||||
"项目": "1007",
|
||||
"报告": "1010",
|
||||
"图书": "1011",
|
||||
"外文期刊论文": "1013",
|
||||
"外文资料": "1013",
|
||||
"期刊": "1015",
|
||||
"专利": "3001",
|
||||
"新闻": "4001",
|
||||
"数据集": "4004",
|
||||
"视频": "4005",
|
||||
"统计数据": "6001"
|
||||
}
|
||||
# 通过搜索类型映射，找到多个对应的值
|
||||
matched_types = []
|
||||
|
||||
# 确保 search_type_map 不为空
|
||||
if not search_type_map:
|
||||
raise ValueError("search_type_map 不能为空")
|
||||
|
||||
for search_type in kgo_search_type_list:
|
||||
if search_type in search_type_map:
|
||||
matched_types.append(search_type_map[search_type])
|
||||
|
||||
# 如果没有匹配项，默认使用 "1000"
|
||||
if not matched_types:
|
||||
matched_types.append("1000")
|
||||
|
||||
# 处理 search_query 为空的情况
|
||||
if search_query == '':
|
||||
search_query = query
|
||||
|
||||
# 移除重复的类型并转换为字符串
|
||||
kgo_search_type = ','.join(set(matched_types))
|
||||
|
||||
logger.info(f"KGO检索关键词:{search_query}，KGO检索类型:{kgo_search_type}")
|
||||
elif search_engine_name == "zhipu_search":
|
||||
# search_query = get_llm_model_response(
|
||||
# strategy_name="zhipu_search_rewrite",
|
||||
# llm_model_name=LLM_MODELS[0],
|
||||
# template_prompt_name="zhipu_search_rewrite",
|
||||
# prompt_param_dict={"input": query, "year": datetime.now().strftime("%Y")},
|
||||
# temperature=TEMPERATURE,
|
||||
# max_tokens=512
|
||||
# )
|
||||
search_query = query
|
||||
async def search_engine_chat_iterator(
|
||||
uid: str,
|
||||
search_query: str,
|
||||
origin_query: str,
|
||||
search_engine_name: str,
|
||||
top_k: int,
|
||||
history: Optional[List[History]],
|
||||
model_name: str = LLM_MODELS[1],
|
||||
prompt_name: str = prompt_name,
|
||||
kgo_search_type: str = kgo_search_type
|
||||
) -> 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]
|
||||
)
|
||||
|
||||
docs = await lookup_search_engine(uid,search_query,origin_query,search_engine_name, top_k, split_result=split_result,
|
||||
kgo_search_type=kgo_search_type)
|
||||
|
||||
context = []
|
||||
|
||||
# prompt_template = get_prompt_template("search_engine_chat", prompt_name)
|
||||
# input_msg = History(role="system", content=prompt_template).to_msg_template(False)
|
||||
# chat_prompt = ChatPromptTemplate.from_messages([input_msg])
|
||||
|
||||
# chain = LLMChain(prompt=chat_prompt, llm=model, verbose = True)
|
||||
|
||||
# # Begin a task that runs in the background.
|
||||
# task = asyncio.create_task(wrap_done(
|
||||
# chain.acall({"context": context, "question": query}),
|
||||
# callback.done),
|
||||
# )
|
||||
index = utils.get_shared_variable(uid)["num"]
|
||||
if uid and "source_docs" in utils.get_shared_variable(uid):
|
||||
for knowledge_name in EN_BASE_NAME:
|
||||
if knowledge_name in utils.get_shared_variable(uid)["source_docs"]:
|
||||
index += len(utils.get_shared_variable(uid)["source_docs"][knowledge_name])
|
||||
|
||||
k = 0
|
||||
for doc in docs:
|
||||
context.append(f"""\n\n资料[{index + k + 1}]内容：{doc.page_content} ) \n\n""")
|
||||
k +=1
|
||||
del k
|
||||
# 搜索到的资料
|
||||
if search_engine_name == "zhipu_search":
|
||||
source_documents = [
|
||||
f"""[{index + inum + 1}] [{doc.metadata["title"]}]({doc.metadata["link"]}) \n"""
|
||||
if doc.metadata.get("link") else
|
||||
f"""[{index + inum + 1}] [{doc.metadata["title"]}]\n"""
|
||||
for inum, doc in enumerate(docs)
|
||||
]
|
||||
if len(source_documents) == 0: # 没有找到相关资料（不太可能）
|
||||
# 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>""")
|
||||
source_documents = []
|
||||
elif search_engine_name == "duckduckgo":
|
||||
source_documents = [
|
||||
f"""[{index + inum + 1}] [{doc.metadata["title"]}]({doc.metadata["url"]}) \n"""
|
||||
if doc.metadata.get("url") else
|
||||
f"""[{index + inum + 1}] [{doc.metadata["title"]}]\n"""
|
||||
for inum, doc in enumerate(docs)
|
||||
]
|
||||
if len(source_documents) == 0: # 没有找到相关资料（不太可能）
|
||||
source_documents.append(f"""未找到相关资料""")
|
||||
else:
|
||||
source_documents = [
|
||||
f"""[{index+inum + 1}] [{doc.metadata["filename"]}]({doc.metadata["source"]}) \n"""
|
||||
for inum, doc in enumerate(docs)
|
||||
]
|
||||
# if len(source_documents) == 1:
|
||||
# source_documents = [f"""暂未找到相关资料。"""]
|
||||
if len(source_documents) == 0: # 没有找到相关资料（不太可能）
|
||||
# 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>""")
|
||||
source_documents = []
|
||||
try:
|
||||
res = utils.get_shared_variable(uid)
|
||||
res["num"] = index + len(source_documents)
|
||||
res["source_docs"].extend(source_documents)
|
||||
# res["END"]="ok"
|
||||
utils.set_shared_variable(uid, res)
|
||||
del index
|
||||
except:
|
||||
pass
|
||||
# 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({"answer": context,"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(search_engine_chat_iterator(uid=uid,
|
||||
search_query=search_query,
|
||||
origin_query=query,
|
||||
search_engine_name=search_engine_name,
|
||||
top_k=top_k,
|
||||
history=history,
|
||||
model_name=model_name,
|
||||
prompt_name=prompt_name,
|
||||
kgo_search_type=kgo_search_type),
|
||||
)
|
||||
Reference in New Issue
Block a user