import os 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.chat_models import ChatOpenAI from langchain_community.tools.convert_to_openai import format_tool_to_openai_function from langchain_community.tools.gmail import ( GmailCreateDraft, GmailGetMessage, GmailGetThread, GmailSearch, GmailSendMessage, ) from langchain_community.tools.gmail.utils import build_resource_service 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_core.tools import tool @tool def search_engine(query: str, max_results: int = 5) -> str: """"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.""" return TavilySearchAPIWrapper().results(query, max_results=max_results) # Create the tools tools = [ GmailCreateDraft(), GmailGetMessage(), GmailGetThread(), GmailSearch(), search_engine, ] if os.environ.get("GMAIL_AGENT_ENABLE_SEND") == "true": tools.append(GmailSendMessage()) current_user = ( build_resource_service().users().getProfile(userId="me").execute()["emailAddress"] ) assistant_system_message = """You are a helpful assistant aiding a user with their \ emails. Use tools (only if necessary) to best answer \ the users questions.\n\nCurrent user: {user}""" prompt = ChatPromptTemplate.from_messages( [ ("system", assistant_system_message), MessagesPlaceholder(variable_name="chat_history"), ("user", "{input}"), MessagesPlaceholder(variable_name="agent_scratchpad"), ] ).partial(user=current_user) llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0) llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in 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 )