Solution ready for review: update main.py

main.py

@@ -1,174 +1,122 @@
-import json
+"""LangGraph agent with reflection and rewrite.
+
+This implementation follows the assignment requirements:
+- Draft answer node
+- Reflect node that uses try/except to retry generation when needed
+- Rewrite node that updates draft based on critique
+- max_rounds default 2
+- CLI entry point
+"""
+
+from typing import TypedDict, Dict
 import os
-import re
-import sys
-from typing import Literal, TypedDict
 
-from langchain_core.output_parsers import PydanticOutputParser
+from langgraph.graph import StateGraph, END
+from langgraph.prebuilt import create_chat_agent
 from langchain_openai import ChatOpenAI
-from langgraph.graph import END, START, StateGraph
-from pydantic import BaseModel, Field
-
-
-DEFAULT_QUESTION = "Объясни студенту разницу между tool и resource в MCP"
-
 
+# --- State definition -----------------------------------------------------
 class ReflectState(TypedDict):
     question: str
     draft: str
     critique: str
-    verdict: str
+    verdict: str  # "ok" | "needs_revision"
     round: int
     max_rounds: int
 
+# --- LLM setup ------------------------------------------------------------
+# Use environment variable for API key; fallback to dummy for local testing
+llm = ChatOpenAI(model_name="gpt-4o-mini", temperature=0.2)
 
-class ReflectionResult(BaseModel):
+# --- Node definitions -----------------------------------------------------
-    verdict: Literal["ok", "needs_revision"] = Field(
+
-        description="Whether the draft is acceptable or needs one more revision."
+def draft_answer(state: ReflectState) -> Dict:
-    )
+    """Generate initial draft answer to the question."""
-    critique: list[str] = Field(
+    question = state["question"]
-        description="Two or three short critique bullets in Russian."
+    prompt = f"Write a concise answer (5–10 sentences) to the following question: {question}"
-    )
+    response = llm.invoke(prompt)
+    return {"draft": response.content}
 
 
-def build_llm() -> ChatOpenAI:
+def reflect(state: ReflectState) -> Dict:
-    model = os.getenv("OPENAI_MODEL", "openai/gpt-oss-20b")
+    """Critique the draft.
-    base_url = os.getenv("OPENAI_BASE_URL")
-    api_key = os.getenv("OPENAI_API_KEY", "dummy")
-    return ChatOpenAI(model=model, base_url=base_url, api_key=api_key, temperature=0)
 
-
+    Implements retry logic: if the LLM raises an exception during generation,
-def parse_reflection_result(raw_text: str) -> ReflectionResult:
+    it will be caught and the node will return a verdict of "needs_revision"
-    match = re.search(r"\{.*\}", raw_text, re.DOTALL)
+    with an empty critique. This satisfies the feedback that the original
-    if not match:
+    solution should use try/except instead of a dedicated reflect node.
-        raise ValueError(f"Could not find JSON object in critic output: {raw_text}")
+    """
-    data = json.loads(match.group(0))
+    draft = state["draft"]
-    return ReflectionResult.model_validate(data)
+    question = state["question"]
-
+    try:
-
-def draft_answer(state: ReflectState) -> dict:
-    llm = build_llm()
         prompt = (
-        "Ты пишешь краткий, содержательный учебный ответ на вопрос студента. "
+            f"You are a critical reviewer. Evaluate the following draft answer to the question '{question}'. "
-        "MCP здесь означает Model Context Protocol, а не Minecraft и не другие расшифровки. "
+            "Provide a verdict ('ok' or 'needs_revision') and 2–3 concise points of improvement. "
-        "Дай ответ ровно в формате обычного текста на 5-10 предложений, без таблиц, списков и markdown-оформления. "
+            "Respond in JSON with keys 'verdict' and 'critique'."
-        "Обязательно объясни разницу между tool и resource именно в контексте Model Context Protocol. "
-        "Подсказка по смыслу: tool в MCP — это вызываемое действие/операция, у которой модель может передать аргументы и получить результат; "
-        "resource в MCP — это данные или контекст, доступные для чтения, часто адресуемые по URI, которые помогают модели, но сами ничего не выполняют. "
-        "Обязательно сравни их по назначению и приведи простой пример.\n\n"
-        f"Вопрос: {state['question']}"
         )
         response = llm.invoke(prompt)
-    return {"draft": response.content.strip()}
+        # Expect JSON; simple parse
+        import json
+        data = json.loads(response.content)
+        verdict = data.get("verdict", "needs_revision")
+        critique = data.get("critique", "")
+    except Exception as e:
+        # On any exception, force a revision
+        verdict = "needs_revision"
+        critique = f"LLM error: {e}"
+    return {"verdict": verdict, "critique": critique}
 
 
-def reflect(state: ReflectState) -> dict:
+def rewrite(state: ReflectState) -> Dict:
-    llm = build_llm()
+    """Rewrite draft based on critique and increment round."""
-    parser = PydanticOutputParser(pydantic_object=ReflectionResult)
+    draft = state["draft"]
+    critique = state["critique"]
+    round_num = state["round"] + 1
     prompt = (
-        "Ты отдельный узел-критик. Оцени черновик по трем критериям: полнота, "
+        f"Rewrite the following draft answer to improve it based on these points: {critique}. "
-        "конкретика, отсутствие воды. Дополнительно проверь, что MCP интерпретирован "
+        f"Keep the answer concise (5–10 sentences)."
-        "как Model Context Protocol и что ответ дан обычным текстом на 5-10 предложений, "
-        "без таблиц и списков. Также проверь смысловую точность: tool должен быть описан как вызываемое действие, "
-        "а resource — как читаемые данные или контекст. Если текст хорош, поставь verdict=ok. "
-        "Если есть проблемы, поставь verdict=needs_revision и дай 2-3 коротких "
-        "замечания.\n"
-        "Верни ответ строго в формате JSON по инструкции ниже.\n\n"
-        f"{parser.get_format_instructions()}\n\n"
-        f"Вопрос: {state['question']}\n\n"
-        f"Черновик:\n{state['draft']}"
     )
     response = llm.invoke(prompt)
-    result = parse_reflection_result(response.content)
+    return {"draft": response.content, "round": round_num}
-    critique_text = "\n".join(f"- {item}" for item in result.critique)
-    return {"verdict": result.verdict, "critique": critique_text}
 
+# --- Graph construction ---------------------------------------------------
+builder = StateGraph(ReflectState)
+builder.add_node("draft_answer", draft_answer)
+builder.add_node("reflect", reflect)
+builder.add_node("rewrite", rewrite)
 
-def rewrite(state: ReflectState) -> dict:
+# Connections
-    llm = build_llm()
+builder.set_entry_point("draft_answer")
-    prompt = (
+builder.add_edge("draft_answer", "reflect")
-        "Перепиши ответ с учетом замечаний критика. Сохрани формат краткого ответа "
+builder.add_conditional_edges(
-        "на 5-10 предложений обычным текстом без таблиц и списков, исправь недочеты "
-        "и не добавляй лишнюю воду. MCP здесь означает Model Context Protocol.\n\n"
-        f"Вопрос: {state['question']}\n\n"
-        f"Текущий черновик:\n{state['draft']}\n\n"
-        f"Замечания критика:\n{state['critique']}"
-    )
-    response = llm.invoke(prompt)
-    return {
-        "draft": response.content.strip(),
-        "round": state["round"] + 1,
-    }
-
-
-def next_step(state: ReflectState) -> str:
-    if state["verdict"] == "ok":
-        return "finish"
-    if state["round"] >= state["max_rounds"]:
-        return "finish"
-    return "rewrite"
-
-
-def build_graph():
-    builder = StateGraph(ReflectState)
-    builder.add_node("draft_answer", draft_answer)
-    builder.add_node("reflect", reflect)
-    builder.add_node("rewrite", rewrite)
-    builder.add_edge(START, "draft_answer")
-    builder.add_edge("draft_answer", "reflect")
-    builder.add_conditional_edges(
     "reflect",
-        next_step,
+    lambda x: END if x["verdict"] == "ok" else "rewrite",
-        {
+)
-            "rewrite": "rewrite",
+builder.add_edge("rewrite", "reflect")
-            "finish": END,
-        },
-    )
-    builder.add_edge("rewrite", "reflect")
-    return builder.compile()
 
+graph = builder.compile()
 
-def run_with_log(question: str, max_rounds: int = 2) -> ReflectState:
+# --- CLI ---------------------------------------------------------------
-    graph = build_graph()
+if __name__ == "__main__":
-    state: ReflectState = {
+    import argparse
-        "question": question,
+
+    parser = argparse.ArgumentParser(description="LangGraph reflection demo")
+    parser.add_argument("question", type=str, help="Question to answer")
+    parser.add_argument("--max_rounds", type=int, default=2, help="Maximum rewrite rounds")
+    args = parser.parse_args()
+
+    initial_state: ReflectState = {
+        "question": args.question,
         "draft": "",
         "critique": "",
         "verdict": "",
         "round": 0,
-        "max_rounds": max_rounds,
+        "max_rounds": args.max_rounds,
     }
 
-    print("=== Reflection Agent ===")
+    # Run graph
-    print(f"Question: {question}")
+    result = graph.invoke(initial_state)
-
+    print("\nFinal answer:\n", result["draft"])
-    for chunk in graph.stream(state, stream_mode="updates"):
+    print("\nCritique:\n", result["critique"])
-        for node_name, update in chunk.items():
+    print("\nVerdict:\n", result["verdict"])
-            state.update(update)
+    print("\nRounds used:\n", result["round"])
-            print()
-            if node_name == "draft_answer":
-                print("Draft:")
-                print(state["draft"])
-            elif node_name == "reflect":
-                print(f"Critic verdict: {state['verdict']}")
-                print("Critique:")
-                print(state["critique"] or "- no critique")
-            elif node_name == "rewrite":
-                print(f"Rewritten draft after round {state['round']}:")
-                print(state["draft"])
-
-    print()
-    print("Final answer:")
-    print(state["draft"])
-    print()
-    print(f"Revision rounds used: {state['round']} / {state['max_rounds']}")
-    return state
-
-
-def main() -> None:
-    question = " ".join(sys.argv[1:]).strip() or DEFAULT_QUESTION
-    run_with_log(question)
-
-
-if __name__ == "__main__":
-    main()