117 lines
3.9 KiB
Python
117 lines
3.9 KiB
Python
"""LangGraph agent with reflection and rewrite.
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This implementation follows the assignment requirements:
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- Draft answer node
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- Reflect node that critiques the draft
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- Rewrite node that updates draft based on critique
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- max_rounds default 2
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- CLI entry point
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"""
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from typing import TypedDict, Dict
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import os
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from langgraph.graph import StateGraph, END
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from langchain_openai import ChatOpenAI
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# --- State definition -----------------------------------------------------
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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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# --- LLM setup ------------------------------------------------------------
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# Use environment variable for API key; fallback to dummy for local testing
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llm = ChatOpenAI(model_name="gpt-4o-mini", temperature=0.2)
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# --- Node definitions -----------------------------------------------------
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def draft_answer(state: ReflectState) -> Dict:
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"""Generate initial draft answer to the question."""
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question = state["question"]
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prompt = f"Write a concise answer (5–10 sentences) to the following question: {question}"
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response = llm.invoke(prompt)
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return {"draft": response.content}
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def reflect(state: ReflectState) -> Dict:
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"""Critique the draft.
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The node returns a verdict ('ok' or 'needs_revision') and 2–3 concise points of improvement.
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"""
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draft = state["draft"]
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question = state["question"]
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prompt = (
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f"You are a critical reviewer. Evaluate the following draft answer to the question '{question}'. "
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"Provide a verdict ('ok' or 'needs_revision') and 2–3 concise points of improvement. "
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"Respond in JSON with keys 'verdict' and 'critique'."
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)
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response = llm.invoke(prompt)
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# Expect JSON; simple parse
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import json
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data = json.loads(response.content)
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verdict = data.get("verdict", "needs_revision")
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critique = data.get("critique", "")
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return {"verdict": verdict, "critique": critique}
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def rewrite(state: ReflectState) -> Dict:
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"""Rewrite draft based on critique and increment round."""
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draft = state["draft"]
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critique = state["critique"]
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round_num = state["round"] + 1
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prompt = (
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f"Rewrite the following draft answer to improve it based on these points: {critique}. "
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f"Keep the answer concise (5–10 sentences)."
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)
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response = llm.invoke(prompt)
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return {"draft": response.content, "round": round_num}
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# --- Graph construction ---------------------------------------------------
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builder = StateGraph(ReflectState)
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builder.add_node("draft_answer", draft_answer)
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builder.add_node("reflect", reflect)
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builder.add_node("rewrite", rewrite)
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# Connections
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builder.set_entry_point("draft_answer")
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builder.add_edge("draft_answer", "reflect")
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# Conditional after reflect: if ok -> END, else if round < max_rounds -> rewrite, else -> END
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builder.add_conditional_edges(
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"reflect",
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lambda x: END if x["verdict"] == "ok" else "rewrite" if x["round"] < x["max_rounds"] else END,
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)
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builder.add_edge("rewrite", "reflect")
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graph = builder.compile()
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# --- CLI ---------------------------------------------------------------
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="LangGraph reflection demo")
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parser.add_argument("question", type=str, help="Question to answer")
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parser.add_argument("--max_rounds", type=int, default=2, help="Maximum rewrite rounds")
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args = parser.parse_args()
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initial_state: ReflectState = {
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"question": args.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": args.max_rounds,
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}
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# Run graph
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result = graph.invoke(initial_state)
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print("\nFinal answer:\n", result["draft"])
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print("\nCritique:\n", result["critique"])
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print("\nVerdict:\n", result["verdict"])
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print("\nRounds used:\n", result["round"])
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