7960158ee8
This PR updates the LangChain Arcade integration to v1.0.0, making the following key changes: • Bumped the package version in pyproject.toml from 0.2.0 to 1.0.0. • Changed the default parameter in ArcadeToolManager from langgraph=False to langgraph=True. • Updated dependencies to require langgraph≥0.2.67,<0.3.0 and simplified extras. • Adjusted example scripts to remove explicit authorization_url references in favor of a unified URL field. • Updated docs and environment references to align with new usage patterns and emphasize environment variables. These changes unify and streamline the LangGraph-based tooling while ensuring compatibility with the latest 1.0.0 release.
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import os
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# Import necessary modules and classes
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from langchain_arcade import ArcadeToolManager
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from langchain_core.messages import HumanMessage
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from langchain_openai import ChatOpenAI
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from langgraph.prebuilt import create_react_agent
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"""
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Example showing how to use pre-auth'd tokens for tools
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this will not wait for the user to authorize the tool
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if the tool is not authorized, it will return an error
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to have the user authorize the tool, you can see the
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example in langgraph_with_user_auth.py
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"""
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arcade_api_key = os.environ["ARCADE_API_KEY"]
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openai_api_key = os.environ["OPENAI_API_KEY"]
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# Initialize the tool manager that fetches
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# tools from arcade and wraps them as langgraph tools
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tool_manager = ArcadeToolManager(api_key=arcade_api_key)
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tools = tool_manager.get_tools()
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# Create an instance of the AI language model
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model = ChatOpenAI(model="gpt-4o", api_key=openai_api_key)
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# Init a prebuilt agent that can use tools
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# in a REACT style langgraph
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graph = create_react_agent(model, tools=tools)
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# Define the initial input message from the user
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inputs = {
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"messages": [HumanMessage(content="Check and see if I have any important emails in my inbox")],
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}
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# Configuration parameters for the agent and tools
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config = {
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"configurable": {
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"thread_id": "2",
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"user_id": "user@example.com",
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}
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}
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# Stream the assistant's responses by executing the graph
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for chunk in graph.stream(inputs, stream_mode="values", config=config):
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# Access the latest message from the conversation
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last_message = chunk["messages"][-1]
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# Print the assistant's message content
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if last_message.content:
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print(last_message.content)
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