fix: main.py — Повторный экзамен #2: Граф с рефлексией на код
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@@ -1,20 +1,37 @@
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import os
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import asyncio
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from typing import TypedDict, Annotated, Dict
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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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from pydantic import BaseModel, Field
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from langchain_core.output_parsers import PydanticOutputParser
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# ---------- State definition ----------
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# Load environment variables
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from dotenv import load_dotenv
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load_dotenv()
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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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# Backend for deepagents
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backend = CompositeBackend(
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[
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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]
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)
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# State definition
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class CodeReviewState(TypedDict):
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code: str
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draft_review: str
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@@ -24,129 +41,100 @@ class CodeReviewState(TypedDict):
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round: int
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max_rounds: int
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# ---------- Structured output for critic ----------
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class CriticOutput(BaseModel):
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pep8: int = Field(description="Score for PEP8 compliance (0-10)")
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type_hints: int = Field(description="Score for type hints (0-10)")
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edge_cases: int = Field(description="Score for edge case handling (0-10)")
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naming: int = Field(description="Score for naming conventions (0-10)")
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verdict: str = Field(description='Verdict: "ok" or "needs_revision"')
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# Structured output for reflect node
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class ReviewScores(BaseModel):
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pep8: int = Field(description="Score 0-10 for PEP8 compliance")
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type_hints: int = Field(description="Score 0-10 for type hints usage")
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edge_cases: int = Field(description="Score 0-10 for edge case handling")
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naming: int = Field(description="Score 0-10 for naming conventions")
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verdict: str = Field(description='\"ok\" or \"needs_revision\"')
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critic_parser = PydanticOutputParser(pydantic_object=CriticOutput)
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review_parser = PydanticOutputParser(pydantic_object=ReviewScores)
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# ---------- LLM and backend ----------
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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 = CompositeBackend(
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[
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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]
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)
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agent = create_deep_agent(
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model=llm,
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tools=[],
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backend=backend,
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system_prompt="You are a helpful agent.",
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)
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# ---------- Node functions ----------
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async def draft_review(state: CodeReviewState) -> CodeReviewState:
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prompt = f"Please provide a concise code review (3-6 points) for the following Python function:\n\n{state['code']}"
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result = await agent.ainvoke([HumanMessage(content=prompt)])
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review = result["messages"][-1].content.strip()
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state["draft_review"] = review
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# Draft review node
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def draft_review(state: CodeReviewState) -> CodeReviewState:
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prompt = f"""Write a concise code review for the following Python function. Provide 3-6 bullet points. Do not include any code blocks. The function is:
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```python
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{state["code"]}
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```"""
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response = llm.invoke(prompt)
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state["draft_review"] = response.content.strip()
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print("\n--- Draft Review ---")
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print(review)
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print(state["draft_review"])
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return state
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async def reflect(state: CodeReviewState) -> CodeReviewState:
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prompt = (
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f"Evaluate the following draft review:\n\n{state['draft_review']}\n\n"
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"Score each of the following criteria on a scale of 0-10:\n"
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"- pep8\n- type_hints\n- edge_cases\n- naming\n\n"
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"Return the scores and a verdict ('ok' or 'needs_revision') in the following JSON format:\n"
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"{\n \"pep8\": int,\n \"type_hints\": int,\n \"edge_cases\": int,\n \"naming\": int,\n \"verdict\": \"ok\" | \"needs_revision\"\n}"
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)
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result = await agent.ainvoke([HumanMessage(content=prompt)])
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raw_output = result["messages"][-1].content.strip()
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try:
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parsed = critic_parser.parse(raw_output)
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except Exception as e:
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# Fallback: simple parsing if LLM output is not perfectly formatted
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parsed = CriticOutput(
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pep8=0,
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type_hints=0,
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edge_cases=0,
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naming=0,
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verdict="needs_revision",
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)
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scores = {
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# Reflect node
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def reflect(state: CodeReviewState) -> CodeReviewState:
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prompt = f"""You are a code review critic. Evaluate the following draft review for the given code. Provide scores 0-10 for each criterion and a verdict. Return JSON matching the schema:
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{review_parser.get_format_instructions()}
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Draft review:
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{state["draft_review"]}
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Code:
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```python
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{state["code"]}
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```"""
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response = llm.invoke(prompt)
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parsed = review_parser.parse(response.content)
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state["criteria_scores"] = {
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"pep8": parsed.pep8,
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"type_hints": parsed.type_hints,
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"edge_cases": parsed.edge_cases,
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"naming": parsed.naming,
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}
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weakest = min(scores, key=scores.get)
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state["criteria_scores"] = scores
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state["weakest_criterion"] = weakest
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state["verdict"] = parsed.verdict
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state["weakest_criterion"] = min(state["criteria_scores"], key=state["criteria_scores"].get)
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print("\n--- Reflection ---")
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print(f"Scores: {scores}")
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print(f"Weakest criterion: {weakest}")
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print(f"Verdict: {parsed.verdict}")
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print(f"Scores: {state['criteria_scores']}")
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print(f"Weakest criterion: {state['weakest_criterion']}")
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print(f"Verdict: {state['verdict']}")
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return state
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async def rewrite(state: CodeReviewState) -> CodeReviewState:
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prompt = (
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f"Rewrite the section of the draft review that addresses the weakest criterion "
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f"('{state['weakest_criterion']}') to improve it. Keep all other parts unchanged.\n\n"
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f"Original draft review:\n\n{state['draft_review']}"
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)
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result = await agent.ainvoke([HumanMessage(content=prompt)])
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new_review = result["messages"][-1].content.strip()
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state["draft_review"] = new_review
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# Rewrite node
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def rewrite(state: CodeReviewState) -> CodeReviewState:
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prompt = f"""Rewrite the section of the draft review that addresses the weakest criterion ({state['weakest_criterion']}) to improve it. Keep other parts unchanged. Return only the updated draft review.
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Original draft review:
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{state['draft_review']}
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Code:
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```python
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{state['code']}
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```"""
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response = llm.invoke(prompt)
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state["draft_review"] = response.content.strip()
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state["round"] += 1
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print("\n--- Rewritten Review ---")
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print(new_review)
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print("\n--- Rewrite ---")
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print(state["draft_review"])
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return state
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# ---------- Graph ----------
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# Graph definition
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def build_graph() -> StateGraph:
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graph = StateGraph(CodeReviewState)
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graph.add_node("draft_review", draft_review)
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graph.add_node("reflect", reflect)
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graph.add_node("rewrite", rewrite)
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graph.add_edge(START, "draft_review")
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graph.set_entry_point("draft_review")
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graph.add_edge("draft_review", "reflect")
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def reflect_cond(state: CodeReviewState):
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def condition(state: CodeReviewState):
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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 "END"
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if state["round"] >= state["max_rounds"]:
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return "END"
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return "rewrite"
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return END
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graph.add_conditional_edges("reflect", reflect_cond, {"rewrite": "rewrite", END: END})
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graph.add_conditional_edges("reflect", condition, ["rewrite", "END"])
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graph.add_edge("rewrite", "reflect")
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return graph
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# ---------- Demo ----------
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async def main():
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# Sample function to review
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code_str = """def sort_numbers(arr):
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return sorted(arr)"""
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# Tool that runs the graph
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@tool
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def run_review(code: str) -> str:
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"""Run a code review cycle on the provided Python function."""
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initial_state: CodeReviewState = {
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"code": code_str,
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"code": code,
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"draft_review": "",
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"criteria_scores": {},
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"weakest_criterion": "",
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@@ -154,16 +142,32 @@ async def main():
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"round": 0,
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"max_rounds": 2,
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}
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graph = build_graph()
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app = graph.compile()
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final_state = await app.ainvoke(initial_state)
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final_state = graph.invoke(initial_state)
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output = f"Final review after {final_state['round']} round(s):\n{final_state['draft_review']}\n\nScores: {final_state['criteria_scores']}\nVerdict: {final_state['verdict']}"
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return output
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print("\n=== Final State ===")
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print(f"Round: {final_state['round']}")
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print(f"Verdict: {final_state['verdict']}")
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print(f"Draft Review:\n{final_state['draft_review']}")
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print(f"Scores: {final_state['criteria_scores']}")
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# Deepagents agent
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agent = create_deep_agent(
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model=llm,
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tools=[run_review],
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backend=backend,
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system_prompt="You are a helpful code review agent.",
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)
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# Demo function
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demo_code = """
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def sort_numbers(arr):
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return sorted(arr)
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"""
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async def main():
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=demo_code)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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print("\n=== Final Output ===")
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print(result["messages"][-1].content)
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if __name__ == "__main__":
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asyncio.run(main())
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