From 725c72298d2339b5ced5bff8819e721b2641a302 Mon Sep 17 00:00:00 2001 From: Danil Parunin 5f1b81b8-4f5d-11e8-9c2d-fa7ae01bbebc Date: Wed, 17 Jun 2026 12:27:04 +0000 Subject: [PATCH] Add main.py --- main.py | 170 +++++++++++++++++++++++++------------------------------- 1 file changed, 77 insertions(+), 93 deletions(-) diff --git a/main.py b/main.py index 5baa944..8726d05 100644 --- a/main.py +++ b/main.py @@ -1,109 +1,96 @@ -""" -# main.py -# LangGraph agent with reflection and rewrite loop -# Author: ChatGPT -# Requirements: langgraph, langchain-openai, deepagents - import os -import asyncio -from typing import TypedDict - +from typing import TypedDict, Dict, Any +from langgraph.graph import StateGraph, END from langchain_openai import ChatOpenAI -from langgraph.graph import StateGraph, START, END -from deepagents import create_deep_agent -from deepagents.backends import CompositeBackend, LocalShellBackend - -# LLM setup -llm = ChatOpenAI( - model="openai/gpt-oss-20b:free", - base_url="https://openrouter.ai/api/v1", - api_key=os.getenv("OPENAI_API_KEY"), - temperature=0.0, -) - -backend = CompositeBackend( - default=LocalShellBackend(root_dir="./workspace", virtual_mode=True, inherit_env=True), - routes={}, -) -agent = create_deep_agent( - model=llm, - tools=[], - backend=backend, - system_prompt="You are a helpful assistant.", -) +# 1. State definition class ReflectState(TypedDict): question: str draft: str critique: str - verdict: str + verdict: str # "ok" | "needs_revision" round: int max_rounds: int -async def draft_answer(state: ReflectState) -> ReflectState: - prompt = f"Write a concise answer (5–10 sentences) to the following question:\n\n{state['question']}" - response = await agent.ainvoke({"messages": ["Human: " + prompt]}, {"configurable": {"thread_id": "draft"}}) - draft = response["messages"][-1].content - state["draft"] = draft - return state +# 2. LLM instance +llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.2) -async def reflect(state: ReflectState) -> ReflectState: +# 3. Nodes + +def draft_answer(state: ReflectState) -> Dict[str, Any]: prompt = ( - "You are a critical reviewer.\n" - "Evaluate the following draft answer for completeness, specificity, and lack of filler.\n" - "Provide a verdict: 'ok' if the answer is satisfactory, otherwise 'needs_revision'.\n" - "If revision is needed, give 2–3 concrete points for improvement.\n" - "Respond in JSON with keys 'verdict' and 'critique'.\n" - f"Draft: {state['draft']}" + "Write a concise answer (5–10 sentences) to the following question:\n" + f"Question: {state['question']}\n" + "Answer:" ) - response = await agent.ainvoke({"messages": ["Human: " + prompt]}, {"configurable": {"thread_id": "reflect"}}) - import json - try: - data = json.loads(response["messages"][-1].content) - verdict = data.get("verdict", "needs_revision") - critique = data.get("critique", "") - except Exception: - verdict = "needs_revision" - critique = "Could not parse critique." + response = llm.invoke(prompt) + state["draft"] = response.content.strip() + return {"draft": state["draft"]} + + +def reflect(state: ReflectState) -> Dict[str, Any]: + prompt = ( + "You are a critical reviewer of the draft answer.\n" + "Evaluate the draft for completeness, specificity, and lack of filler.\n" + "Provide a verdict: 'ok' if the answer is satisfactory, otherwise 'needs_revision'.\n" + "If revision is needed, give 2–3 concise points for improvement.\n" + f"Draft: {state['draft']}\n" + "Verdict and critique:" + ) + response = llm.invoke(prompt) + text = response.content.strip() + lines = text.splitlines() + verdict_line = lines[0].lower().strip() + verdict = "ok" if "ok" in verdict_line else "needs_revision" + critique = "\n".join(lines[1:]).strip() state["verdict"] = verdict state["critique"] = critique - return state + return {"verdict": verdict, "critique": critique} -async def rewrite(state: ReflectState) -> ReflectState: + +def rewrite(state: ReflectState) -> Dict[str, Any]: prompt = ( - "You are revising the following draft answer based on the critique.\n" - "Make the answer clearer, more specific, and remove any filler.\n" - "Do not add new information beyond what is already in the draft.\n" - f"Draft: {state['draft']}\n" - f"Critique: {state['critique']}" + "Rewrite the draft answer incorporating the following critique points.\n" + "Keep the answer concise (5–10 sentences).\n" + f"Critique: {state['critique']}\n" + f"Original Draft: {state['draft']}\n" + "Revised Answer:" ) - response = await agent.ainvoke({"messages": ["Human: " + prompt]}, {"configurable": {"thread_id": "rewrite"}}) - new_draft = response["messages"][-1].content - state["draft"] = new_draft + response = llm.invoke(prompt) + state["draft"] = response.content.strip() state["round"] += 1 + return {"draft": state["draft"], "round": state["round"]} + +# 4. Graph construction +builder = StateGraph(ReflectState) +builder.add_node("draft_answer", draft_answer) +builder.add_node("reflect", reflect) +builder.add_node("rewrite", rewrite) + +# Edges +builder.set_entry_point("draft_answer") +builder.add_edge("draft_answer", "reflect") +builder.add_conditional_edges( + "reflect", + lambda x: x["verdict"], + { + "ok": END, + "needs_revision": "rewrite" + } +) +builder.add_edge("rewrite", "reflect") + +# Max rounds guard +@builder.before_node("rewrite") +def check_rounds(state: ReflectState) -> ReflectState: + if state["round"] >= state["max_rounds"]: + state["verdict"] = "ok" return state +graph = builder.compile() -def build_graph() -> StateGraph[ReflectState]: - graph = StateGraph(ReflectState) - graph.add_node("draft_answer", draft_answer) - graph.add_node("reflect", reflect) - graph.add_node("rewrite", rewrite) - graph.add_edge(START, "draft_answer") - graph.add_edge("draft_answer", "reflect") - graph.add_conditional_edges( - "reflect", - lambda state: state["verdict"], - {"ok": END, "needs_revision": "rewrite"}, - ) - graph.add_conditional_edges( - "rewrite", - lambda state: "rewrite" if state["round"] < state["max_rounds"] else END, - {"rewrite": "reflect", END: END}, - ) - return graph - -async def main(): +# 5. Demo execution +if __name__ == "__main__": question = "Объясни студенту разницу между tool и resource в MCP" initial_state: ReflectState = { "question": question, @@ -111,15 +98,12 @@ async def main(): "critique": "", "verdict": "", "round": 0, - "max_rounds": 2, + "max_rounds": 2 } - graph = build_graph() - result = await graph.ainvoke(initial_state) - print("\n--- Final Answer ---") + result = graph.invoke(initial_state) + print("\n--- Final Draft ---\n") print(result["draft"]) - print("\n--- Final Critique ---") + print("\n--- Critique ---\n") print(result["critique"]) - -if __name__ == "__main__": - asyncio.run(main()) -""" + print("\n--- Verdict ---\n") + print(result["verdict"])