mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-27 17:23:19 +08:00
42aa900566
- 修复：
- Qwen Agent 的 OutputParser 不再抛出异常，遇到非 COT 文本直接返回
- CallbackHandler 正确处理工具调用信息
- 重写 tool 定义方式：
- 添加 regist_tool 简化 tool 定义:
- 可以指定一个用户友好的名称
- 自动将函数的 __doc__ 作为 tool.description
- 支持用 Field 定义参数，不再需要额外定义 ModelSchema
- 添加 BaseToolOutput 封装 tool 返回结果，以便同时获取原始值、给LLM的字符串值
- 支持工具热加载（有待测试）
- 增加 openai 兼容的统一 chat 接口，通过 tools/tool_choice/extra_body 不同参数组合支持：
- Agent 对话
- 指定工具调用（如知识库RAG）
- LLM 对话
- 根据后端功能更新 webui
103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
from typing import List, Dict
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from langchain.utilities.bing_search import BingSearchAPIWrapper
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from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.docstore.document import Document
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from markdownify import markdownify
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from strsimpy.normalized_levenshtein import NormalizedLevenshtein
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from chatchat.server.utils import get_tool_config
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from chatchat.server.pydantic_v1 import Field
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from .tools_registry import regist_tool, BaseToolOutput
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def bing_search(text, config):
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search = BingSearchAPIWrapper(bing_subscription_key=config["bing_key"],
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bing_search_url=config["bing_search_url"])
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return search.results(text, config["result_len"])
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def duckduckgo_search(text, config):
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search = DuckDuckGoSearchAPIWrapper()
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return search.results(text, config["result_len"])
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def metaphor_search(
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text: str,
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config: dict,
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) -> List[Dict]:
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from metaphor_python import Metaphor
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client = Metaphor(config["metaphor_api_key"])
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search = client.search(text, num_results=config["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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if config["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=config["chunk_size"],
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chunk_overlap=config["chunk_overlap"])
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splitted_docs = text_splitter.split_documents(docs)
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if len(splitted_docs) > config["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[:config["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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SEARCH_ENGINES = {"bing": bing_search,
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"duckduckgo": duckduckgo_search,
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"metaphor": metaphor_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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doc = Document(page_content=result["snippet"] if "snippet" in result.keys() else "",
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metadata={"source": result["link"] if "link" in result.keys() else "",
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"filename": result["title"] if "title" in result.keys() else ""})
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docs.append(doc)
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return docs
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def search_engine(query: str,
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config: dict):
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search_engine_use = SEARCH_ENGINES[config["search_engine_name"]]
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results = search_engine_use(text=query,
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config=config["search_engine_config"][
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config["search_engine_name"]])
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docs = search_result2docs(results)
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context = ""
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docs = [
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f"""出处 [{inum + 1}] [{doc.metadata["source"]}]({doc.metadata["source"]}) \n\n{doc.page_content}\n\n"""
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for inum, doc in enumerate(docs)
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]
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for doc in docs:
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context += doc + "\n"
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return context
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@regist_tool(title="互联网搜索")
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def search_internet(query: str = Field(description="query for Internet search")):
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'''Use this tool to use bing search engine to search the internet and get information.'''
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tool_config = get_tool_config("search_internet")
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return BaseToolOutput(search_engine(query=query, config=tool_config))
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