147 lines
4.4 KiB
Python
147 lines
4.4 KiB
Python
import os
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import json
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from typing import TypedDict, Dict
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from langgraph.graph import StateGraph, END
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from langgraph.prebuilt import create_agent
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage, AIMessage
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# ---------------------
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# 1. State definition
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# ---------------------
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class CodeReviewState(TypedDict):
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code: str
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draft_review: str
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criteria_scores: Dict[str, int] # {"pep8": 0-10, "type_hints": 0-10, "edge_cases": 0-10, "naming": 0-10}
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weakest_criterion: 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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# ---------------------
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# 2. LLM setup
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# ---------------------
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# Use OpenAI or Ollama based on env variable
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if os.getenv("USE_OLLAMA", "false").lower() == "true":
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from langchain_ollama import ChatOllama
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llm = ChatOllama(model="llama3", temperature=0.2)
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else:
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llm = ChatOpenAI(temperature=0.2, model_name="gpt-4o-mini")
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# ---------------------
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# 3. Node definitions
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# ---------------------
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def draft_review_fn(state: CodeReviewState) -> Dict:
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code = state["code"]
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prompt = f"""
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You are a senior Python developer. Provide a concise code review (3-6 bullet points) for the following function. Focus on style, correctness, and potential improvements.
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Function:
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{code}
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Review:
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"""
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response = llm.invoke([HumanMessage(content=prompt)])
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review = response.content.strip()
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return {"draft_review": review}
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def reflect_fn(state: CodeReviewState) -> Dict:
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review = state["draft_review"]
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prompt = f"""
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You are an automated code review critic. Evaluate the following code review on four criteria: PEP8 compliance, type hints usage, edge case handling, and naming conventions. Assign each a score from 0 to 10. Also determine the weakest criterion and a verdict: "ok" if all scores are 7 or higher, otherwise "needs_revision".
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Review:
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{review}
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Respond in JSON with keys: "pep8", "type_hints", "edge_cases", "naming", "weakest_criterion", "verdict".
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"""
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response = llm.invoke([HumanMessage(content=prompt)])
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try:
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data = json.loads(response.content)
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except Exception:
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# Fallback: simple parsing
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data = {
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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": int(data.get("pep8", 0)),
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"type_hints": int(data.get("type_hints", 0)),
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"edge_cases": int(data.get("edge_cases", 0)),
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"naming": int(data.get("naming", 0))
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},
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"weakest_criterion": data.get("weakest_criterion", "pep8"),
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"verdict": data.get("verdict", "needs_revision")
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}
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def rewrite_fn(state: CodeReviewState) -> Dict:
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weakest = state["weakest_criterion"]
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review = state["draft_review"]
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prompt = f"""
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You are a senior Python developer. The following code review has been identified as weak in the "{weakest}" criterion. Rewrite only the part of the review that addresses this criterion, improving it significantly. Keep the rest of the review unchanged.
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Original Review:
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{review}
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Rewritten Review:
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"""
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response = llm.invoke([HumanMessage(content=prompt)])
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new_review = response.content.strip()
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return {"draft_review": new_review, "round": state["round"] + 1}
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# ---------------------
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# 4. Graph construction
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# ---------------------
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builder = StateGraph(CodeReviewState)
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builder.add_node("draft_review", draft_review_fn)
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builder.add_node("reflect", reflect_fn)
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builder.add_node("rewrite", rewrite_fn)
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# Entry point
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builder.set_entry_point("draft_review")
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# Transitions
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builder.add_edge("draft_review", "reflect")
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builder.add_conditional_edges(
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"reflect",
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lambda x: x["verdict"],
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{
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"ok": END,
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"needs_revision": "rewrite"
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}
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)
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builder.add_edge("rewrite", "reflect")
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# Final graph
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graph = builder.compile()
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# ---------------------
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# 5. Demo CLI
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# ---------------------
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if __name__ == "__main__":
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sample_code = """
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def sort_numbers(arr):
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return sorted(arr)
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"""
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initial_state: CodeReviewState = {
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"code": sample_code.strip(),
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"draft_review": "",
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"criteria_scores": {},
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"weakest_criterion": "",
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"verdict": "",
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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("\n--- Final State ---")
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print(json.dumps(result, indent=2))
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