134 lines
4.3 KiB
Python
134 lines
4.3 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 FilesystemBackend, LocalShellBackend, CompositeBackend
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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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# ---------- 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 for deepagents (not used directly in graph but required by create_deep_agent) ----------
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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 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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# ---------- Nodes ----------
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async def draft_answer(state: ReflectState) -> ReflectState:
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prompt = f"Write a concise answer (5–10 sentences) to the following question:\n\n{state['question']}"
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response = await llm.ainvoke([HumanMessage(content=prompt)])
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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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prompt = (
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f"You are a critic. Evaluate the following draft answer for completeness, specificity, and lack of filler.\n\nDraft:\n{state['draft']}\n\nProvide a verdict (ok or needs_revision) and 2–3 bullet points of critique."
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)
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response = await llm.ainvoke([HumanMessage(content=prompt)])
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text = response.content.strip()
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# Simple parsing: first line verdict, rest critique
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lines = text.splitlines()
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verdict_line = lines[0].lower()
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verdict = "ok" if "ok" in verdict_line else "needs_revision"
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critique = "\n".join(lines[1:]).strip()
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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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prompt = (
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f"Rewrite the draft answer taking into account the following critique. Keep the answer concise (5–10 sentences).\n\nCritique:\n{state['critique']}\n\nOriginal Draft:\n{state['draft']}"
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)
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response = await llm.ainvoke([HumanMessage(content=prompt)])
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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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# Transition logic
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# After draft_answer -> 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, {
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"rewrite": "rewrite",
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"END": "END",
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})
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# After rewrite -> reflect
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graph.add_edge("rewrite", "reflect")
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app = graph.compile()
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# ---------- DeepAgent wrapper ----------
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# The deepagent will simply forward the human message to the graph and return the final draft.
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@tool
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def run_graph(question: str) -> str:
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"""Run the reflection graph for a given question."""
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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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result = app.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_graph],
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backend=backend,
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system_prompt="You are an assistant that can answer questions using a self‑checking process.",
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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=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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