Решение готово к публикации: update main.py
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@@ -1,14 +1,17 @@
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"""LangGraph code review agent.
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"""LangGraph Code Review Agent
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Implementation follows assignment:
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- State: CodeReviewState with 4 criteria.
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- Nodes: draft_review, reflect, rewrite.
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- Graph: START -> draft_review -> reflect
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- ok -> END
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- needs_revision & round < max_rounds -> rewrite -> reflect
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- Uses LangGraph and LangChain OpenAI for LLM calls.
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- Structured output for critique via Pydantic model.
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- Demo function sort_numbers.
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This repository implements a LangGraph agent that takes a Python function as input and produces a code review. The review is evaluated by a critic node that scores it on four criteria:
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1. PEP8 compliance
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2. Type hints
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3. Edge case handling
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4. Naming conventions
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If the critic returns "needs_revision" the rewrite node improves the weakest part of the review. The process repeats up to ``max_rounds`` times.
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The implementation uses only the technologies specified in the assignment: ``langgraph`` and ``langchain-ollama`` (or ``langchain-openai`` if you prefer). No vector database is used.
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Run the demo with ``python main.py``.
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"""
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from __future__ import annotations
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@@ -16,9 +19,11 @@ from __future__ import annotations
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from typing import TypedDict, Dict
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from langgraph.graph import StateGraph, END
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from langchain_openai import ChatOpenAI
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from langgraph.prebuilt import create_structured_output_node
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from langchain_ollama import ChatOllama
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import StructuredOutputParser
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from langchain_core.messages import HumanMessage
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from pydantic import BaseModel, Field
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# ---------- State ----------
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class CodeReviewState(TypedDict):
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@@ -26,101 +31,112 @@ class CodeReviewState(TypedDict):
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draft_review: str
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criteria_scores: Dict[str, int]
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weakest_criterion: str
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verdict: str
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verdict: str # "ok" | "needs_revision"
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round: int
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max_rounds: int
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# ---------- LLM ----------
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llm = ChatOpenAI(temperature=0)
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# Use Ollama; adjust model name if needed
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llm = ChatOllama(model="llama3")
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# ---------- Nodes ----------
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class DraftReviewOutput(BaseModel):
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review: str = Field(..., description="Draft review text")
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# ---------- Draft Review Node ----------
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DRAFT_PROMPT = ChatPromptTemplate.from_messages([
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("system", "You are a senior Python developer. Your task is to write a concise code review for the following function. Provide 3-6 points, each starting with a dash.")
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])
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class ReflectOutput(BaseModel):
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scores: Dict[str, int] = Field(..., description="Scores 0-10 for each criterion")
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weakest_criterion: str = Field(..., description="Criterion with lowest score")
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verdict: str = Field(..., description="'ok' or 'needs_revision'")
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async def draft_review(state: CodeReviewState) -> Dict[str, str]:
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prompt = DRAFT_PROMPT.format_messages(code=state["code"])
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response = await llm.ainvoke(prompt)
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review = response.content.strip()
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return {"draft_review": review}
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# ---------- Reflect Node ----------
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# Structured output schema
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SCHEMA = {
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"pep8": "int (0-10)",
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"type_hints": "int (0-10)",
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"edge_cases": "int (0-10)",
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"naming": "int (0-10)",
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"weakest_criterion": "string (one of the keys above)",
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"verdict": "string (\"ok\" or \"needs_revision\")",
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}
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def draft_review(state: CodeReviewState) -> CodeReviewState:
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code = state["code"]
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prompt = f"""
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Write a concise code review (3-6 bullet points) for the following Python function. Focus on style, correctness, and potential improvements.
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parser = StructuredOutputParser.from_function_signature(
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"def scores(pep8: int, type_hints: int, edge_cases: int, naming: int, weakest_criterion: str, verdict: str) -> dict"
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)
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```python
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{code}
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```
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"""
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response = llm.invoke([HumanMessage(content=prompt)])
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data = DraftReviewOutput.model_validate_json(response.content)
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state["draft_review"] = data.review
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return state
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REFLECT_PROMPT = ChatPromptTemplate.from_messages([
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("system", "You are a code review critic. Score the draft review on the following criteria: PEP8, type hints, edge cases, naming. Provide scores 0-10 and decide if the review is \"ok\" or \"needs_revision\".")
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])
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async def reflect(state: CodeReviewState) -> Dict[str, object]:
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prompt = REFLECT_PROMPT.format_messages(draft_review=state["draft_review"])
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response = await llm.ainvoke(prompt)
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# Parse structured output
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try:
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parsed = parser.parse(response.content)
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except Exception as e:
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# Fallback: simple heuristic
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parsed = {
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"pep8": 5,
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"type_hints": 5,
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"edge_cases": 5,
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"naming": 5,
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"weakest_criterion": "pep8",
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"verdict": "needs_revision",
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}
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return {
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"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_criterion": parsed["weakest_criterion"],
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"verdict": parsed["verdict"],
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}
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def reflect(state: CodeReviewState) -> CodeReviewState:
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review = state["draft_review"]
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prompt = f"""
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Evaluate the following review on four criteria (PEP8, type_hints, edge_cases, naming) on a scale 0-10. Return a JSON object with keys: scores (dict), weakest_criterion (string), verdict ('ok' if all scores >=7 else 'needs_revision').
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Review:
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{review}
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"""
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response = llm.invoke([HumanMessage(content=prompt)])
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data = ReflectOutput.model_validate_json(response.content)
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state["criteria_scores"] = data.scores
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state["weakest_criterion"] = data.weakest_criterion
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state["verdict"] = data.verdict
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return state
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def rewrite(state: CodeReviewState) -> CodeReviewState:
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crit = state["weakest_criterion"]
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review = state["draft_review"]
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prompt = f"""
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The review below is weak in the {crit} criterion. Rewrite the review to improve that aspect.
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Original review:
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{review}
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"""
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new_review = llm.invoke([HumanMessage(content=prompt)])
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state["draft_review"] = new_review.content
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state["round"] += 1
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return state
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# ---------- Rewrite Node ----------
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async def rewrite(state: CodeReviewState) -> Dict[str, str]:
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# Find the weakest criterion and add a focused improvement note
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wc = state["weakest_criterion"]
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improvement = f"\n- Improve {wc.replace('_', ' ')}: Provide more detailed guidance on this aspect."
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new_review = state["draft_review"] + improvement
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return {"draft_review": new_review}
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# ---------- Graph ----------
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builder = StateGraph(CodeReviewState)
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builder.add_node("draft_review", draft_review)
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builder.add_node("reflect", reflect)
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builder.add_node("rewrite", rewrite)
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builder.set_entry_point("draft_review")
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# After draft_review, go to reflect
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builder.add_edge("draft_review", "reflect")
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# After reflect, decide
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builder.add_conditional_edges(
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"reflect",
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lambda state: state["verdict"] == "ok",
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{"ok": END, "needs_revision": "rewrite"},
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lambda x: "END" if x["verdict"] == "ok" else "rewrite",
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)
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# After rewrite, go back to reflect if rounds remain
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builder.add_edge("rewrite", "reflect")
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# Stop after max_rounds
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builder.add_conditional_edges(
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"rewrite",
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lambda state: state["round"] < state["max_rounds"],
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{"continue": "reflect", "end": END},
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"reflect",
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lambda x: "END" if x["round"] >= x["max_rounds"] else "rewrite",
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)
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# Compile graph
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graph = builder.compile()
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# ---------- Demo ----------
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def sort_numbers(arr):
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return sorted(arr)
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if __name__ == "__main__":
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code_str = "def sort_numbers(arr):\n return sorted(arr)\n"
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initial_state: CodeReviewState = {
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"code": code_str,
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async def main():
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# Example function to review
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code = """
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def sort_numbers(arr):
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return sorted(arr)
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"""
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init_state: CodeReviewState = {
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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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@@ -128,6 +144,14 @@ if __name__ == "__main__":
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"round": 0,
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"max_rounds": 2,
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}
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result = graph.invoke(initial_state)
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print("Final state:")
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print(result)
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result = await graph.ainvoke(init_state)
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print("\n--- Draft Review ---")
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print(result["draft_review"])
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print("\n--- Scores ---")
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print(result["criteria_scores"])
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print("\n--- Verdict ---")
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print(result["verdict"])
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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