75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
import os
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import asyncio
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from langchain.tools import tool
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from deepagents import create_deep_agent
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from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
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from pydantic import BaseModel, Field
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from langchain_core.output_parsers import PydanticOutputParser
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# LLM configuration – OpenRouter
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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# Backend for file operations and shell execution (not used directly but required by deepagents)
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backend = CompositeBackend([
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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])
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# Structured output model for the AI fluency plan
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class FluencyPlan(BaseModel):
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goal: str = Field(description="Overall goal of the AI fluency plan")
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timeline: str = Field(description="High‑level timeline (e.g., 3 months, 6 months)")
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milestones: list[str] = Field(description="Key milestones to achieve along the way")
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resources: list[str] = Field(description="Recommended courses, books, tools, and communities")
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assessment: str = Field(description="How to assess progress and adjust the plan")
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parser = PydanticOutputParser(pydantic_object=FluencyPlan)
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# Simple validation tool to ensure the plan is not empty
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@tool
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def validate_plan(plan: str) -> str:
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"""Validate that the plan contains at least one milestone and resources."""
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if "milestones" not in plan.lower() or "resources" not in plan.lower():
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return "Plan is missing essential sections."
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return "OK"
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# Create the deep agent
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agent = create_deep_agent(
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model=llm,
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tools=[validate_plan],
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backend=backend,
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system_prompt="You are an expert educational planner. Your task is to create a detailed personal AI fluency plan based on the course "
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"Ai Fluency and the Build a personal AI fluency plan assignment. The output must be a JSON object that matches the FluencyPlan schema."
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)
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async def main():
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# Human message with the assignment description
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human_msg = HumanMessage(content="Create a personal AI fluency plan for completing the Ai Fluency course and the Build a personal AI fluency plan assignment. Use the provided schema.")
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# Invoke the agent
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result = await agent.ainvoke(
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{"messages": [human_msg]},
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{"configurable": {"thread_id": "ai-fluency-plan"}},
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)
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# Extract the assistant message
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assistant_msg = result["messages"][-1].content
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# Parse the JSON using the Pydantic parser to ensure correctness
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try:
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plan_obj = parser.parse(assistant_msg)
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print("✅ Generated AI Fluency Plan:\n", plan_obj.json(indent=2))
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except Exception as e:
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print("❌ Failed to parse plan:", e)
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print("Raw output:", assistant_msg)
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if __name__ == "__main__":
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asyncio.run(main())
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