140f4eca17
- **New Class Structure**: Introduced `ToolManager` and `AsyncToolManager` classes (`ArcadeToolManager` is deprecated) - **Async Support**: Full async implementation for modern LangChain applications - **Better Tool Management**: New methods for adding individual tools and toolkits - **CI/CD**: for langchain_arcade ## Upgrade Changes ```python # Old pattern manager = ArcadeToolManager(api_key="...") tools = manager.get_tools(toolkits=["Google"]) # New pattern manager = ToolManager(api_key="...") manager.init_tools(toolkits=["Google"]) tools = manager.to_langchain() ``` Now supports underscores vs dots in tool names for better model compatibility.
98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
import os
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from datetime import datetime
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from configuration import AgentConfigurable
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from langchain_arcade import ToolManager
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_openai import ChatOpenAI
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from langgraph.errors import NodeInterrupt
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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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# Initialize the Arcade Tool Manager with your API key
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arcade_api_key = os.getenv("ARCADE_API_KEY")
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openai_api_key = os.getenv("OPENAI_API_KEY")
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manager = ToolManager(api_key=arcade_api_key)
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manager.init_tools(toolkits=["Github"])
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tools = manager.to_langchain(use_interrupts=True)
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tool_node = ToolNode(tools)
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PROMPT_TEMPLATE = f"""
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You are a helpful assistant who can use tools to help users with tasks
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Today's date is {datetime.now().strftime("%Y-%m-%d")}
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ALL RESPONSES should be in plain text and not markdown.
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"""
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# prompt for the main agent
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prompt = ChatPromptTemplate.from_messages([
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("system", PROMPT_TEMPLATE),
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("placeholder", "{messages}"),
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])
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# Initialize the language model with your OpenAI API key
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model = ChatOpenAI(model="gpt-4o", api_key=openai_api_key).bind_tools(tools)
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prompted_model = prompt | model
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class AgentState(MessagesState):
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auth_url: str | None = None
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def call_agent(state):
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"""Define the agent function that invokes the model"""
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messages = state["messages"]
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# replace placeholder with messages from state
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response = prompted_model.invoke({"messages": messages})
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return {"messages": [response]}
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def should_continue(state: AgentState, config: dict):
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"""Function to determine the next step based on the model's response"""
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last_message = state["messages"][-1]
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if last_message.tool_calls:
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return "check_auth"
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# If no tool calls are present, end the workflow
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return END
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def check_auth(state: AgentState, config: dict):
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user_id = config["configurable"].get("user_id")
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tool_name = state["messages"][-1].tool_calls[0]["name"]
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auth_response = manager.authorize(tool_name, user_id)
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if auth_response.status != "completed":
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return {"auth_url": auth_response.url}
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else:
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return {"auth_url": None}
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def authorize(state: AgentState, config: dict):
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"""Function to handle tool authorization"""
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user_id = config["configurable"].get("user_id")
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tool_name = state["messages"][-1].tool_calls[0]["name"]
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auth_response = manager.authorize(tool_name, user_id)
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if auth_response.status != "completed":
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auth_message = (
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f"Please authorize the application in your browser:\n\n {state.get('auth_url')}"
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)
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raise NodeInterrupt(auth_message)
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# Build the workflow graph
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workflow = StateGraph(AgentState, AgentConfigurable)
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# Add nodes 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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workflow.add_node("check_auth", check_auth)
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# Define the edges and control flow
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workflow.add_edge(START, "agent")
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workflow.add_conditional_edges("agent", should_continue, ["check_auth", END])
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workflow.add_edge("check_auth", "authorization")
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workflow.add_edge("authorization", "tools")
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workflow.add_edge("tools", "agent")
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# Compile the graph with an interrupt after the authorization node
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# so that we can prompt the user to authorize the application
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graph = workflow.compile()
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