mirror of
https://github.com/hwchase17/langchain
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97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
import os
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from typing import List, Tuple
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from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_to_openai_function_messages
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from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
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from langchain.tools.render import format_tool_to_openai_function
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.tools.gmail import (
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GmailCreateDraft,
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GmailGetMessage,
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GmailGetThread,
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GmailSearch,
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GmailSendMessage,
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)
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from langchain_community.tools.gmail.utils import build_resource_service
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from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
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from langchain_core.messages import AIMessage, HumanMessage
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain_core.tools import tool
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@tool
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def search_engine(query: str, max_results: int = 5) -> str:
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""""A search engine optimized for comprehensive, accurate, \
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and trusted results. Useful for when you need to answer questions \
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about current events or about recent information. \
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Input should be a search query. \
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If the user is asking about something that you don't know about, \
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you should probably use this tool to see if that can provide any information."""
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return TavilySearchAPIWrapper().results(query, max_results=max_results)
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# Create the tools
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tools = [
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GmailCreateDraft(),
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GmailGetMessage(),
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GmailGetThread(),
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GmailSearch(),
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search_engine,
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]
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if os.environ.get("GMAIL_AGENT_ENABLE_SEND") == "true":
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tools.append(GmailSendMessage())
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current_user = (
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build_resource_service().users().getProfile(userId="me").execute()["emailAddress"]
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)
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assistant_system_message = """You are a helpful assistant aiding a user with their \
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emails. Use tools (only if necessary) to best answer \
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the users questions.\n\nCurrent user: {user}"""
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", assistant_system_message),
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MessagesPlaceholder(variable_name="chat_history"),
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("user", "{input}"),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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).partial(user=current_user)
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llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0)
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llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
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def _format_chat_history(chat_history: List[Tuple[str, str]]):
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buffer = []
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for human, ai in chat_history:
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buffer.append(HumanMessage(content=human))
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buffer.append(AIMessage(content=ai))
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return buffer
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agent = (
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{
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"input": lambda x: x["input"],
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"chat_history": lambda x: _format_chat_history(x["chat_history"]),
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"agent_scratchpad": lambda x: format_to_openai_function_messages(
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x["intermediate_steps"]
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),
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}
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| prompt
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| llm_with_tools
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| OpenAIFunctionsAgentOutputParser()
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)
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class AgentInput(BaseModel):
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input: str
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chat_history: List[Tuple[str, str]] = Field(
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..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
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)
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
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input_type=AgentInput
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)
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