fix: main.py — Повторный экзамен #2: Граф с рефлексией на код
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@@ -1,34 +1,44 @@
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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 typing import TypedDict, Dict, 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_core.messages import HumanMessage, SystemMessage
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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 langgraph.graph import StateGraph, START, END, add_conditional_edges
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from pydantic import BaseModel, Field
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from langchain_core.output_parsers import PydanticOutputParser
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# DESIGN DECISION: Add deepagents to requirements.txt
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# NECESSITY: deepagents is required for create_deep_agent usage
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# OPTIMALITY: ensures reproducible installation
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# ALTERNATIVES CONSIDERED: manual installation or alternative agent framework, but violates course requirement
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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 configuration using OpenRouter
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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model="openai/gpt-oss-20b",
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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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# Backend for deepagents (not heavily used 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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# Deepagents agent used for drafting and rewriting reviews
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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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# State definition
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@@ -41,100 +51,82 @@ class CodeReviewState(TypedDict):
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round: int
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max_rounds: int
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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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# Structured output for the reflect node
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class CritiqueOutput(BaseModel):
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scores: Dict[str, int] = Field(description="Scores 0-10 for each criterion")
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verdict: str = Field(description="ok or needs_revision")
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weakest_criterion: str = Field(description="Criterion with lowest score")
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review_parser = PydanticOutputParser(pydantic_object=ReviewScores)
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parser = PydanticOutputParser(pydantic_object=CritiqueOutput)
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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(state["draft_review"])
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# Node: draft_review
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async def draft_review_node(state: CodeReviewState) -> CodeReviewState:
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system_msg = SystemMessage(content="You are a code reviewer. Provide a concise review (3-6 points) of the following Python function.")
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user_msg = HumanMessage(content=state["code"])
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result = await agent.ainvoke(
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{"messages": [system_msg, user_msg]},
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{"configurable": {"thread_id": "draft-review"}},
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)
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review = result["messages"][-1].content
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state["draft_review"] = review
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return state
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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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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: {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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# Node: reflect
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async def reflect_node(state: CodeReviewState) -> CodeReviewState:
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system_msg = SystemMessage(content="You are a code critic. Score the following review on four criteria: pep8, type_hints, edge_cases, naming. Return a JSON with scores, verdict, and weakest_criterion.")
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user_msg = HumanMessage(content=state["draft_review"])
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raw_output = await llm.invoke([system_msg, user_msg])
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critique = parser.parse(raw_output.content)
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state["criteria_scores"] = critique.scores
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state["weakest_criterion"] = critique.weakest_criterion
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state["verdict"] = critique.verdict
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return state
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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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# Node: rewrite
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async def rewrite_node(state: CodeReviewState) -> CodeReviewState:
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criterion = state["weakest_criterion"]
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system_msg = SystemMessage(content=f"You are a code reviewer. Rewrite the review to improve the section about {criterion}. Keep the overall structure.")
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user_msg = HumanMessage(content=state["draft_review"])
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result = await agent.ainvoke(
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{"messages": [system_msg, user_msg]},
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{"configurable": {"thread_id": "rewrite"}},
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)
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new_review = result["messages"][-1].content
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state["draft_review"] = new_review
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state["round"] += 1
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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 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.set_entry_point("draft_review")
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graph.add_edge("draft_review", "reflect")
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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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# Conditional edge function
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def decide_next(state: CodeReviewState) -> str:
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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", condition, ["rewrite", "END"])
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graph.add_edge("rewrite", "reflect")
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# Build the graph
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graph = StateGraph(CodeReviewState)
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graph.add_node("draft_review", draft_review_node)
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graph.add_node("reflect", reflect_node)
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graph.add_node("rewrite", rewrite_node)
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graph.add_conditional_edges("reflect", decide_next, {
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"rewrite": "rewrite",
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"END": END,
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})
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graph.add_edge(START, "draft_review")
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graph.add_edge("draft_review", "reflect")
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graph.add_edge("rewrite", "reflect")
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app = graph.compile()
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return graph
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# Demo function
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def demo_code() -> str:
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return """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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async def main():
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code_str = demo_code()
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initial_state: CodeReviewState = {
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"code": code,
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"code": code_str,
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"draft_review": "",
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"criteria_scores": {},
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"weakest_criterion": "",
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@@ -142,32 +134,20 @@ def run_review(code: str) -> str:
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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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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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# 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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final_state = await app.ainvoke(initial_state)
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print("\n=== Initial Draft Review ===")
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print(initial_state["draft_review"])
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print("\n=== Scores ===")
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print(final_state["criteria_scores"])
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print("\n=== Weakest Criterion ===")
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print(final_state["weakest_criterion"])
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if final_state["verdict"] == "needs_revision":
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print("\n=== Revised Review ===")
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print(final_state["draft_review"])
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print("\n=== Updated Scores ===")
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print(final_state["criteria_scores"])
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else:
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print("\nReview is satisfactory. No rewrite needed.")
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if __name__ == "__main__":
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asyncio.run(main())
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