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.
104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
import os
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# Import necessary classes and modules
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from langchain_arcade import ArcadeToolManager
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from langchain_openai import ChatOpenAI
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.graph import END, START, MessagesState, StateGraph
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from langgraph.prebuilt import ToolNode
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arcade_api_key = os.environ["ARCADE_API_KEY"]
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# Initialize the tool manager and fetch tools compatible with langgraph
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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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toolkits=["Google"], langgraph=True
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) # use langgraph-specific behavior
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tool_node = ToolNode(tools)
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# Create a language model instance and bind it with the tools
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model = ChatOpenAI(model="gpt-4o")
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model_with_tools = model.bind_tools(tools)
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#### Workflow ####
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# Function to invoke the model and get a response
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def call_agent(state: MessagesState):
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messages = state["messages"]
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response = model_with_tools.invoke(messages)
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# Return the updated message history
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return {"messages": [response]}
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# Function to determine the next step in the workflow based on the last message
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def should_continue(state: MessagesState):
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if state["messages"][-1].tool_calls:
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for tool_call in state["messages"][-1].tool_calls:
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if tool_manager.requires_auth(tool_call["name"]):
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return "authorization"
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return "tools" # Proceed to tool execution if no authorization is needed
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return END # End the workflow if no tool calls are present
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# Function to handle authorization for tools that require it
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def authorize(state: MessagesState, config: dict):
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user_id = config["configurable"].get("user_id")
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for tool_call in state["messages"][-1].tool_calls:
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tool_name = tool_call["name"]
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if not tool_manager.requires_auth(tool_name):
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continue
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auth_response = tool_manager.authorize(tool_name, user_id)
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if auth_response.status != "completed":
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# Prompt the user to visit the authorization URL
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print(f"Visit the following URL to authorize: {auth_response.url}")
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# wait for the user to complete the authorization
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# and then check the authorization status again
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tool_manager.wait_for_auth(auth_response.id)
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if not tool_manager.is_authorized(auth_response.id):
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# node interrupt?
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raise ValueError("Authorization failed")
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return {"messages": []}
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if __name__ == "__main__":
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# Build the workflow graph using StateGraph
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workflow = StateGraph(MessagesState)
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# Add nodes (steps) to the graph
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workflow.add_node("agent", call_agent)
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workflow.add_node("tools", tool_node)
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workflow.add_node("authorization", authorize)
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# Define the edges and control flow between nodes
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workflow.add_edge(START, "agent")
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workflow.add_conditional_edges("agent", should_continue, ["authorization", "tools", END])
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workflow.add_edge("authorization", "tools")
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workflow.add_edge("tools", "agent")
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# Set up memory for checkpointing the state
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memory = MemorySaver()
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# Compile the graph with the checkpointer
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graph = workflow.compile(checkpointer=memory)
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# Define the input messages from the user
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inputs = {
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"messages": [
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{
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"role": "user",
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"content": "Check and see if I have any important emails in my inbox",
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}
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],
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}
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# Configuration with thread and user IDs for authorization purposes
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config = {"configurable": {"thread_id": "4", "user_id": "user@example.com"}}
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# Run the graph and stream the outputs
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for chunk in graph.stream(inputs, config=config, stream_mode="values"):
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# Pretty-print the last message in the chunk
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chunk["messages"][-1].pretty_print()
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