from typing import List, Tuple from langchain.agents import AgentExecutor from langchain.agents.format_scratchpad import format_to_openai_function_messages from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser from langchain_community.tools.tavily_search import TavilySearchResults from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper from langchain_core.messages import AIMessage, HumanMessage from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_core.pydantic_v1 import BaseModel, Field from langchain_google_genai import ChatGoogleGenerativeAI # Create the tool search = TavilySearchAPIWrapper() description = """"A search engine optimized for comprehensive, accurate, \ and trusted results. Useful for when you need to answer questions \ about current events or about recent information. \ Input should be a search query. \ If the user is asking about something that you don't know about, \ you should probably use this tool to see if that can provide any information.""" tavily_tool = TavilySearchResults(api_wrapper=search, description=description) tools = [tavily_tool] llm = ChatGoogleGenerativeAI(temperature=0, model="gemini-pro") prompt = ChatPromptTemplate.from_messages( [ MessagesPlaceholder(variable_name="chat_history"), ("user", "{input}"), MessagesPlaceholder(variable_name="agent_scratchpad"), ] ) llm_with_tools = llm.bind(functions=tools) def _format_chat_history(chat_history: List[Tuple[str, str]]): buffer = [] for human, ai in chat_history: buffer.append(HumanMessage(content=human)) buffer.append(AIMessage(content=ai)) return buffer agent = ( { "input": lambda x: x["input"], "chat_history": lambda x: _format_chat_history(x["chat_history"]), "agent_scratchpad": lambda x: format_to_openai_function_messages( x["intermediate_steps"] ), } | prompt | llm_with_tools | OpenAIFunctionsAgentOutputParser() ) class AgentInput(BaseModel): input: str chat_history: List[Tuple[str, str]] = Field( ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}} ) agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types( input_type=AgentInput )