151 lines
5.0 KiB
Python
151 lines
5.0 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# DESIGN DECISION: deepagents is required by the course assignment to build the agent.
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# NECESSITY: The assignment explicitly requires using create_deep_agent from deepagents; without it the agent cannot be instantiated.
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# OPTIMALITY: Using deepagents ensures consistent agent behavior and simplifies tool integration; alternative frameworks would violate the course constraints.
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# ALTERNATIVES CONSIDERED: Using plain langgraph without deepagents would miss the required framework; manually handling tool calls would increase boilerplate.
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import os
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import asyncio
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import argparse
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from typing import TypedDict, Annotated
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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 FilesystemBackend, LocalShellBackend, CompositeBackend
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from langgraph.graph import StateGraph, START, END
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from langchain_core.output_parsers import PydanticOutputParser
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from pydantic import BaseModel, Field
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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 deepagents
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backend = CompositeBackend(
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[
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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]
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)
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# ---------- LangGraph components ----------
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class ReflectState(TypedDict):
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question: str
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draft: str
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critique: str
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verdict: str # ok | needs_revision
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round: int
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max_rounds: int
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class CritiqueOutput(BaseModel):
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verdict: str = Field(description="ok or needs_revision")
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critique: str = Field(description="2-3 bullet points of critique")
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critique_parser = PydanticOutputParser(pydantic_object=CritiqueOutput)
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def draft_answer(state: dict) -> dict:
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prompt = f"Write a brief answer (5-10 sentences) to the following question:\n\n{state['question']}"
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result = llm.invoke([HumanMessage(content=prompt)])
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state["draft"] = result.content.strip()
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return state
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def reflect(state: dict) -> dict:
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prompt = (
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f"You are a critic. Evaluate the following draft answer for completeness, specificity, and lack of fluff.\n\nDraft:\n{state['draft']}\n\n"
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"Respond with JSON containing 'verdict' ('ok' or 'needs_revision') and 'critique' (2-3 bullet points)."
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)
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result = llm.invoke([HumanMessage(content=prompt)])
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critique = critique_parser.parse(result.content)
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state["verdict"] = critique.verdict
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state["critique"] = critique.critique
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return state
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def rewrite(state: dict) -> dict:
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prompt = (
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f"Rewrite the draft answer to address the following critique:\n\n{state['critique']}\n\n"
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"Keep the answer brief (5-10 sentences)."
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)
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result = llm.invoke([HumanMessage(content=prompt)])
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state["draft"] = result.content.strip()
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state["round"] += 1
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return state
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def build_graph() -> StateGraph:
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graph = StateGraph(ReflectState)
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graph.add_node("draft_answer", draft_answer)
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graph.add_node("reflect", reflect)
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graph.add_node("rewrite", rewrite)
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graph.add_edge(START, "draft_answer")
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graph.add_edge("draft_answer", "reflect")
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def decide_next(state: dict) -> str:
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if state["verdict"] == "needs_revision" and state["round"] < state["max_rounds"]:
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return "rewrite"
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return END
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graph.add_conditional_edges("reflect", decide_next, {"rewrite": "rewrite", "END": END})
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graph.add_edge("rewrite", "reflect")
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return graph.compile()
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def run_graph(question: str) -> str:
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graph = build_graph()
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initial_state: ReflectState = {
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"question": question,
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"draft": "",
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"critique": "",
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"verdict": "",
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"round": 0,
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"max_rounds": 2,
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}
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final_state = graph.run(initial_state)
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return final_state["draft"]
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# ---------- DeepAgents tool ----------
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@tool
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def run_graph_tool(question: str) -> str:
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"""Run the LangGraph to produce a refined answer."""
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return run_graph(question)
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# ---------- Agent ----------
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agent = create_deep_agent(
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model=llm,
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tools=[run_graph_tool],
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backend=backend,
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system_prompt="You are a helpful agent that answers questions by running the run_graph tool.",
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)
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# ---------- CLI ----------
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async def main():
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parser = argparse.ArgumentParser(description="Answer a question with self-reflection.")
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parser.add_argument("question", nargs="*", help="The question to answer.")
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args = parser.parse_args()
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if not args.question:
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question = input("Enter your question: ").strip()
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else:
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question = " ".join(args.question).strip()
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=question)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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# The agent will return the final answer in the last message
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final_message = result["messages"][-1].content
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print("\nAnswer:\n")
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print(final_message)
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if __name__ == "__main__":
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asyncio.run(main()) |