125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
import os
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from typing import TypedDict, Annotated
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from langgraph.graph import StateGraph, START, END
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage, SystemMessage
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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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# ---------- 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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# ---------- Structured output models ----------
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class CritiqueModel(BaseModel):
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verdict: str = Field(..., description="ok or needs_revision")
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remarks: list[str] = Field(..., description="2-3 bullet points of critique")
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class RewriteModel(BaseModel):
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draft: str = Field(..., description="Rewritten draft answer")
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critique_parser = PydanticOutputParser(pydantic_object=CritiqueModel)
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rewrite_parser = PydanticOutputParser(pydantic_object=RewriteModel)
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# ---------- Nodes ----------
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async def draft_answer(state: ReflectState) -> ReflectState:
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prompt = (
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f"Write a concise answer (5–10 sentences) to the following question:\n\n"
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f"Question: {state['question']}"
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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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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 evaluating the draft answer for completeness, concreteness, and absence of filler.\n"
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f"Draft: {state['draft']}\n"
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f"Provide a verdict ("ok" or "needs_revision") and 2–3 bullet points of critique.\n"
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f"Return the result in JSON format: {{\"verdict\": "...", \"remarks\": ["...", ...]}}"
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)
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response = await llm.ainvoke([HumanMessage(content=prompt)])
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parsed = critique_parser.parse(response.content)
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state['critique'] = "\n".join(parsed.remarks)
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state['verdict'] = parsed.verdict
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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 to address the following critique:\n"
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f"Critique: {state['critique']}\n"
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f"Original draft: {state['draft']}\n"
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f"Provide the rewritten draft only.\n"
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f"Return JSON: {{\"draft\": "..."}}"
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)
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response = await llm.ainvoke([HumanMessage(content=prompt)])
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parsed = rewrite_parser.parse(response.content)
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state['draft'] = parsed.draft
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state['round'] += 1
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return state
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# ---------- Graph ----------
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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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builder.add_edge(START, "draft_answer")
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builder.add_edge("draft_answer", "reflect")
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# Conditional edges after reflect
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builder.add_conditional_edges(
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"reflect",
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lambda x: x['verdict'] == "ok",
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{"ok": END, "needs_revision": "rewrite"},
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)
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# After rewrite go back to reflect
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builder.add_edge("rewrite", "reflect")
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# If max rounds exceeded, end
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builder.add_conditional_edges(
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"reflect",
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lambda x: x['round'] >= x['max_rounds'] and x['verdict'] == "needs_revision",
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{True: END, False: END}, # both lead to END, but loop already handled
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)
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graph = builder.compile()
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# ---------- Demo ----------
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async def main():
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initial_state: ReflectState = {
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"question": "Объясни студенту разницу между tool и resource в MCP",
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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 = await graph.ainvoke(initial_state)
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print("\n--- Final Draft ---")
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print(final_state['draft'])
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print("\n--- Critique ---")
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print(final_state['critique'])
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print("\n--- Verdict ---")
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print(final_state['verdict'])
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print("\n--- Rounds Used ---")
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print(final_state['round'])
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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