9d00295e33
Closes: https://app.clickup.com/t/86b2k2962 --------- Co-authored-by: sdreyer <sterling@arcade-ai.com>
65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
from typing import cast
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from arcadepy import NOT_GIVEN, Arcade
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from arcadepy.types.auth_authorize_params import AuthRequirement, AuthRequirementOauth2
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from google.oauth2.credentials import Credentials
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from langchain_google_community import GmailToolkit
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from langchain_google_community.gmail.utils import (
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build_resource_service,
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)
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from langchain_openai import ChatOpenAI
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from langgraph.prebuilt import create_react_agent
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# Step 1: Install required packages
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# Run the following in your terminal:
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# %pip install -qU langchain-google-community[gmail]
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# %pip install -qU langchain-openai
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# %pip install -qU langgraph
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client = Arcade()
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# Start the authorization process for the tool "ListEmails"
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auth_response = client.auth.authorize(
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auth_requirement=AuthRequirement(
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provider_id="google",
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oauth2=AuthRequirementOauth2(
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scopes=["https://www.googleapis.com/auth/gmail.readonly"],
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),
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),
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user_id="sam@arcade-ai.com",
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)
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# If authorization is not completed, prompt the user and poll for status
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if auth_response.status != "completed":
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print("Please complete the authorization challenge in your browser before continuing:")
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print(auth_response.authorization_url)
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input("Press Enter to continue...")
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# Poll for authorization status using the auth polling method
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while auth_response.status != "completed":
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auth_response = client.auth.status(
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authorization_id=cast(str, auth_response.authorization_id),
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scopes=" ".join(auth_response.scopes) if auth_response.scopes else NOT_GIVEN,
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wait=30, # Long poll
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)
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# Authorization is completed; proceed with obtaining credentials
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creds = Credentials(auth_response.context.token)
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api_resource = build_resource_service(credentials=creds)
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toolkit = GmailToolkit(api_resource=api_resource)
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# Step 4: Get available tools
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tools = toolkit.get_tools()
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# Step 5: Initialize the LLM and create an agent
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llm = ChatOpenAI(model="gpt-4o")
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agent_executor = create_react_agent(llm, tools)
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# Step 6: Draft an email using the agent
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example_query = "Read my latest emails to me and summarize them."
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events = agent_executor.stream(
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{"messages": [("user", example_query)]},
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stream_mode="values",
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)
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for event in events:
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event["messages"][-1].pretty_print()
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