Add Gmail Agent Example (#14567)

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

templates/openai-functions-agent-gmail/openai_functions_agent/__init__.py

@@ -0,0 +1,3 @@
+from openai_functions_agent.agent import agent_executor
+
+__all__ = ["agent_executor"]

templates/openai-functions-agent-gmail/openai_functions_agent/agent.py

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+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.tools.render import format_tool_to_openai_function
+from langchain_community.chat_models import ChatOpenAI
+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(),
+    GmailSendMessage(),
+    search_engine,
+]
+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
+)