feat: solution for 'Повторный экзамен #2: Граф с рефлексией на код'

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2026-06-29 17:40:07 +03:00
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commit 805fb9546a
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EN
Повторный экзамен #2: Граф с рефлексией на код
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Версия 1
Версия 2
Дедлайн сдачи: 31.08.2026
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В работе не обнаружено использования ключевых технологий, указанных в условии задания. Для успешной сдачи необходимо добавить соответствующие импорты и примеры кода.
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Задание
Практическое задание: LangGraph с рефлексией на код
Цель
Реализовать LangGraph-агента, который берёт функцию на Python и п
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langgraph
langchain-openai
langchain-core
python-dotenv
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import os
from src.graph import build_graph
from src.state import CodeReviewState
def main():
# Example function to review
def sort_numbers(arr):
return sorted(arr)
# Get source code as string
import inspect
code_str = inspect.getsource(sort_numbers)
# Initial state
state: CodeReviewState = {
"code": code_str,
"draft_review": None,
"criteria_scores": None,
"weakest_criterion": None,
"verdict": None,
"round": 0,
"max_rounds": 2,
}
graph = build_graph()
# Run the graph
final_state = graph.invoke(state)
# Print results
print("\n=== Initial Draft Review ===")
print(final_state["draft_review"])
print("\n=== Scores ===")
for k, v in final_state["criteria_scores"].items():
print(f"{k}: {v}")
print("\n=== Verdict ===")
print(final_state["verdict"])
print("\n=== Final Review (after rewrites if any) ===")
print(final_state["draft_review"])
if __name__ == "__main__":
main()
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from langgraph.graph import StateGraph, END, START
from src.state import CodeReviewState
from src.nodes import draft_review, reflect, rewrite
def build_graph() -> StateGraph:
graph = StateGraph(CodeReviewState)
# Add nodes
graph.add_node("draft_review", draft_review)
graph.add_node("reflect", reflect)
graph.add_node("rewrite", rewrite)
# Define transitions
graph.set_entry_point("draft_review")
graph.add_edge("draft_review", "reflect")
graph.add_conditional_edges(
"reflect",
lambda state: (
"rewrite" if state["verdict"] == "needs_revision" and state["round"] < state["max_rounds"] else END
),
)
graph.add_edge("rewrite", "reflect")
return graph
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from src.cli import main
if __name__ == "__main__":
main()
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from typing import Dict, Any
from langgraph.graph import StateGraph, END, START
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.output_parsers import JsonOutputParser
from src.state import CodeReviewState
# LLM instance
llm = ChatOpenAI(temperature=0, model="gpt-4o-mini")
# Prompt for draft review
draft_prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a senior code reviewer. Provide a concise review with 3-6 bullet points."),
("human", "Here is the Python function:\n\n{code}\n\nWrite your review:"),
]
)
# Prompt for reflection (scoring)
reflect_prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a code quality critic. Evaluate the following review on four criteria: PEP8, type hints, edge cases, naming. Provide scores 0-10 and a verdict 'ok' or 'needs_revision'. Output JSON with keys: pep8, type_hints, edge_cases, naming, verdict."),
("human", "Review:\n\n{draft_review}\n\nScores:"),
]
)
# Prompt for rewrite
rewrite_prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a code reviewer tasked with improving the weakest part of the review. Keep other points unchanged."),
("human", "Weakest criterion: {weakest_criterion}\n\nOriginal review:\n\n{draft_review}\n\nRewrite the section addressing the weakest criterion:"),
]
)
# Output parser for reflection
json_parser = JsonOutputParser()
def draft_review(state: CodeReviewState) -> CodeReviewState:
"""Generate initial draft review."""
messages = draft_prompt.format_messages(code=state["code"])
review = llm.invoke(messages).content
state["draft_review"] = review.strip()
state["round"] = 0
return state
def reflect(state: CodeReviewState) -> CodeReviewState:
"""Score the draft review."""
messages = reflect_prompt.format_messages(draft_review=state["draft_review"])
raw_output = llm.invoke(messages).content
try:
scores = json_parser.parse(raw_output)
except Exception as e:
# Fallback: if parsing fails, set default scores
scores = {
"pep8": 0,
"type_hints": 0,
"edge_cases": 0,
"naming": 0,
"verdict": "needs_revision",
}
# Determine weakest criterion
criteria = ["pep8", "type_hints", "edge_cases", "naming"]
weakest = min(criteria, key=lambda c: scores[c])
state["criteria_scores"] = {c: int(scores[c]) for c in criteria}
state["weakest_criterion"] = weakest
state["verdict"] = scores["verdict"]
return state
def rewrite(state: CodeReviewState) -> CodeReviewState:
"""Rewrite the weakest part of the review."""
messages = rewrite_prompt.format_messages(
weakest_criterion=state["weakest_criterion"],
draft_review=state["draft_review"],
)
rewritten = llm.invoke(messages).content
# Replace the section related to weakest criterion
# For simplicity, we just append the rewritten part to the original review
state["draft_review"] = f"{state['draft_review']}\n\nImproved {state['weakest_criterion']} section:\n{rewritten.strip()}"
state["round"] += 1
return state
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from typing import TypedDict, Dict
from typing import TypedDict, Dict, Any
class CodeReviewState(TypedDict):
code: str
draft_review: str
criteria_scores: Dict[str, int]
weakest_criterion: str
verdict: str
draft_review: str | None
criteria_scores: Dict[str, int] | None
weakest_criterion: str | None
verdict: str | None # "ok" | "needs_revision"
round: int
max_rounds: int