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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
)
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