from langchain_community.vectorstores import FAISS from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain.tools.retriever import create_retriever_tool from langchain_community.tools.tavily_search import TavilySearchResults from langchain import hub from langchain.agents import cre...
from dotenv import load_dotenv import os load_dotenv() os.environ['TAVILY_API_KEY'] = os.getenv('TAVILY_API_KEY') from langchain_community.tools.tavily_search import TavilySearchResults search = TavilySearchResults(max_results=2) res = search.invoke("what is the weather in SF") print...
from langchain_community.vectorstores import FAISS from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain.tools.retriever import create_retriever_tool from langchain_community.tools.tavily_search import TavilySearchResults from langchain import hub from langchain.agents import cre...
我们将使用tavily来实现这一点。 代码语言:javascript 代码运行次数:0 复制 Cloud Studio代码运行 importos os.environ['TAVILY_API_KEY']="<Your Tavily API Key here>"from langchain_core.toolsimporttool from langchain_community.tools.tavily_searchimportTavilySearchResults tavily_tool=TavilySearchResults...
from langchain_community.tools.tavily_search import TavilySearchResults from langchain_community.document_loaders import WebBaseLoader from langchain_community.vectorstores import FAISS from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter ...
from langchain_community.tools.tavily_search import TavilySearchResults from langchain_core.prompts import ChatPromptTemplate ## 设置Tavily搜索工具 tools = [TavilySearchResults(max_results=1)] ## 创建聊天提示模板 prompt = ChatPromptTemplate.from_messages([ ...
Tavily :https://tavily.com/。创建API密钥后,您需要将其导出为: export TAVILY_API_KEY="..." from langchain_community.tools.tavily_search import TavilySearchResults search = TavilySearchResults(max_results=1) result = search.invoke("今天重庆的天气预报") ...
from langchain_community.tools.tavily_search import TavilySearchResults from langchain_community.document_loaders import WebBaseLoader from langchain_community.vectorstores import FAISS from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter from langcha...
from langchain_community.chat_models import ChatZhipuAI import os from langchain import hub from langchain.agents import AgentExecutor, create_react_agent from langchain_community.tools.tavily_search import TavilySearchResults os.environ["ZHIPUAI_API_KEY"] = "28ad7bb6200e2942eb52a5cff8dd1ba8...
fromlangchain.agentsimportAgentExecutor,create_openai_tools_agentfromlangchain_community.tools.tavily_searchimportTavilySearchResultsfromlangchain_openaiimportChatOpenAI# 使用Tavily搜索工具tools=[TavilySearchResults(max_results=1)]# 获取要使用的提示prompt=hub.pull("hwchase17/openai-tools-agent")# 初始...