147 lines
4.5 KiB
Python
147 lines
4.5 KiB
Python
import os
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import asyncio
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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 CompositeBackend, LocalShellBackend, FilesystemBackend
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# ---------- LLM ----------
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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 ----------
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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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# ---------- State ----------
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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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# ---------- Nodes ----------
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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# Helper to format the prompt for each node
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DRAFT_PROMPT = """Write a concise answer (5–10 sentences) to the following question:
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{question}
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"""
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REFLECT_PROMPT = """You are a critic. Given the draft answer below, evaluate its completeness, specificity, and absence of filler. Respond with:
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1. verdict: either "ok" or "needs_revision"
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2. critique: 2–3 bullet points explaining what to improve (if any)
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Draft:
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{draft}
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"""
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REWRITE_PROMPT = """You are revising the draft answer based on the critique. Produce a new draft that addresses the points. Keep the answer concise (5–10 sentences).
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Critique:
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{critique}
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Previous draft:
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{draft}
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"""
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# Node functions
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async def draft_answer(state: ReflectState) -> ReflectState:
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response = await llm.ainvoke([HumanMessage(content=DRAFT_PROMPT.format(question=state["question"]))])
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state["draft"] = response.content.strip()
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return state
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async def reflect(state: ReflectState) -> ReflectState:
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response = await llm.ainvoke([HumanMessage(content=REFLECT_PROMPT.format(draft=state["draft"]))])
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# Parse verdict and critique
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text = response.content.strip()
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verdict_line = next((l for l in text.splitlines() if l.lower().startswith("verdict:")), "")
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critique_lines = [l for l in text.splitlines() if l.startswith("-") or l.startswith("•")]
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verdict = verdict_line.split(":",1)[1].strip().lower() if verdict_line else "needs_revision"
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critique = "\n".join(critique_lines) if critique_lines else ""
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state["verdict"] = verdict
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state["critique"] = critique
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return state
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async def rewrite(state: ReflectState) -> ReflectState:
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response = await llm.ainvoke([HumanMessage(content=REWRITE_PROMPT.format(critique=state["critique"], draft=state["draft"]))])
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state["draft"] = response.content.strip()
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state["round"] += 1
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return state
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# ---------- Graph ----------
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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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# Entry point
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graph.set_entry_point("draft_answer")
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# Transitions
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# After draft -> reflect
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graph.add_edge("draft_answer", "reflect")
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# After reflect
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# if ok -> END
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# if needs_revision and round < max_rounds -> rewrite
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# else -> END
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def reflect_conditional(state: ReflectState):
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if state["verdict"] == "ok":
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return "END"
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if 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", reflect_conditional, {"rewrite": "rewrite", "END": "END"})
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# After rewrite -> reflect
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graph.add_edge("rewrite", "reflect")
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graph.compile()
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# ---------- DeepAgent wrapper ----------
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@tool
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def run_reflect_graph(question: str, max_rounds: int = 2) -> str:
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"""Run the reflection graph and return the final draft."""
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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": max_rounds,
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}
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result = graph.invoke(initial_state)
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return result["draft"]
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agent = create_deep_agent(
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model=llm,
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tools=[run_reflect_graph],
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backend=backend,
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system_prompt="You are an assistant that can answer questions and self‑critique using the provided tool.",
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)
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async def main():
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question = "Объясни студенту разницу между tool и resource в MCP."
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response = await agent.ainvoke(
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{"messages": [HumanMessage(content=f"Please answer: {question}")]},
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{"configurable": {"thread_id": "session-1"}},
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)
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print("Final answer:\n", response["messages"][-1].content)
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if __name__ == "__main__":
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asyncio.run(main())
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