add main.py
This commit is contained in:
@@ -0,0 +1,198 @@
|
||||
"""
|
||||
LangGraph Code Review Agent
|
||||
===========================
|
||||
|
||||
This repository contains a minimal LangGraph implementation that
|
||||
performs a code review on a Python function. The graph consists of
|
||||
three nodes:
|
||||
|
||||
* ``draft_review`` – generates an initial review.
|
||||
* ``reflect`` – a critic that scores the review on four criteria
|
||||
(PEP8, type hints, edge cases, naming) and decides whether a
|
||||
rewrite is required.
|
||||
* ``rewrite`` – rewrites the weakest part of the review.
|
||||
|
||||
The graph runs for a maximum of ``max_rounds`` (default 2). The
|
||||
demo can be executed with ``python -m main``.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import TypedDict, Dict
|
||||
|
||||
from langgraph.graph import StateGraph, END
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# State definition
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class CodeReviewState(TypedDict):
|
||||
code: str
|
||||
draft_review: str
|
||||
criteria_scores: Dict[str, int]
|
||||
weakest_criterion: str
|
||||
verdict: str # "ok" | "needs_revision"
|
||||
round: int
|
||||
max_rounds: int
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# LLM configuration
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
# The user must set the OPENAI_API_KEY environment variable.
|
||||
llm = ChatOpenAI(temperature=0.0, model="gpt-4o-mini")
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Node implementations
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
async def draft_review(state: CodeReviewState) -> CodeReviewState:
|
||||
"""Generate an initial review of the provided code.
|
||||
|
||||
The review is a short list of 3–6 bullet points.
|
||||
"""
|
||||
code = state["code"]
|
||||
prompt = (
|
||||
"You are a senior Python developer.\n"
|
||||
"Review the following function and provide a concise list of 3–6 points\n"
|
||||
"highlighting what is good and what could be improved.\n"
|
||||
"Do not mention the criteria – just give the review.\n"
|
||||
f"Function:\n{code}\n"
|
||||
"Review:" # LLM will continue after this
|
||||
)
|
||||
review = await llm.ainvoke([HumanMessage(content=prompt)])
|
||||
state["draft_review"] = review.content.strip()
|
||||
return state
|
||||
|
||||
async def reflect(state: CodeReviewState) -> CodeReviewState:
|
||||
"""Critic that scores the draft review on four criteria.
|
||||
|
||||
The output is a JSON object with keys:
|
||||
* pep8, type_hints, edge_cases, naming – integers 0‑10
|
||||
* weakest_criterion – one of the four keys
|
||||
* verdict – "ok" or "needs_revision"
|
||||
"""
|
||||
review = state["draft_review"]
|
||||
code = state["code"]
|
||||
prompt = (
|
||||
"You are a code quality critic.\n"
|
||||
"Given the following code and its draft review, score the review on\n"
|
||||
"four criteria: PEP8, type hints, edge cases, naming.\n"
|
||||
"Return a JSON object with keys: pep8, type_hints, edge_cases, naming,\n"
|
||||
"weakest_criterion, verdict.\n"
|
||||
f"Code:\n{code}\n"
|
||||
f"Draft review:\n{review}\n"
|
||||
"Answer in JSON only."
|
||||
)
|
||||
response = await llm.ainvoke([HumanMessage(content=prompt)])
|
||||
import json
|
||||
try:
|
||||
scores = json.loads(response.content)
|
||||
except Exception as e:
|
||||
# Fallback: if parsing fails, treat as needs_revision
|
||||
scores = {
|
||||
"pep8": 0,
|
||||
"type_hints": 0,
|
||||
"edge_cases": 0,
|
||||
"naming": 0,
|
||||
"weakest_criterion": "pep8",
|
||||
"verdict": "needs_revision",
|
||||
}
|
||||
state["criteria_scores"] = {
|
||||
"pep8": int(scores.get("pep8", 0)),
|
||||
"type_hints": int(scores.get("type_hints", 0)),
|
||||
"edge_cases": int(scores.get("edge_cases", 0)),
|
||||
"naming": int(scores.get("naming", 0)),
|
||||
}
|
||||
state["weakest_criterion"] = scores.get("weakest_criterion", "pep8")
|
||||
state["verdict"] = scores.get("verdict", "needs_revision")
|
||||
return state
|
||||
|
||||
async def rewrite(state: CodeReviewState) -> CodeReviewState:
|
||||
"""Rewrite the weakest part of the review.
|
||||
|
||||
The node receives the current state and the weakest criterion.
|
||||
It generates a new review that specifically addresses that criterion.
|
||||
"""
|
||||
code = state["code"]
|
||||
weakest = state["weakest_criterion"]
|
||||
prompt = (
|
||||
"You are a senior Python developer.\n"
|
||||
"Rewrite the draft review to improve the part related to the following criterion: "
|
||||
f"{weakest}.\n"
|
||||
"Keep the rest of the review unchanged.\n"
|
||||
"Output only the updated review.\n"
|
||||
f"Current draft review:\n{state['draft_review']}\n"
|
||||
)
|
||||
new_review = await llm.ainvoke([HumanMessage(content=prompt)])
|
||||
state["draft_review"] = new_review.content.strip()
|
||||
state["round"] += 1
|
||||
return state
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Graph construction
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def build_graph() -> StateGraph[CodeReviewState]:
|
||||
graph = StateGraph(CodeReviewState)
|
||||
graph.add_node("draft_review", draft_review)
|
||||
graph.add_node("reflect", reflect)
|
||||
graph.add_node("rewrite", rewrite)
|
||||
|
||||
# Entry point
|
||||
graph.set_entry_point("draft_review")
|
||||
|
||||
# Transitions
|
||||
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
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Demo execution
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
import textwrap
|
||||
|
||||
parser = argparse.ArgumentParser(description="Run the code review graph on a demo function.")
|
||||
parser.add_argument("--max-rounds", type=int, default=2, help="Maximum number of rewrite rounds")
|
||||
args = parser.parse_args()
|
||||
|
||||
# Demo function – can be replaced by any user code
|
||||
demo_code = textwrap.dedent(
|
||||
"""
|
||||
def sort_numbers(arr):
|
||||
return sorted(arr)
|
||||
"""
|
||||
).strip()
|
||||
|
||||
initial_state: CodeReviewState = {
|
||||
"code": demo_code,
|
||||
"draft_review": "",
|
||||
"criteria_scores": {},
|
||||
"weakest_criterion": "",
|
||||
"verdict": "",
|
||||
"round": 0,
|
||||
"max_rounds": args.max_rounds,
|
||||
}
|
||||
|
||||
graph = build_graph()
|
||||
final_state = graph.invoke(initial_state)
|
||||
|
||||
print("\n=== Final Review ===")
|
||||
print(final_state["draft_review"])
|
||||
print("\n=== Scores ===")
|
||||
for k, v in final_state["criteria_scores"].items():
|
||||
print(f"{k}: {v}")
|
||||
print(f"Verdict: {final_state['verdict']}")
|
||||
print(f"Rounds: {final_state['round']}")
|
||||
Reference in New Issue
Block a user