povtornyy-ekzamen-2-graf-s-refleksiey-na/SOLUTION.md

What was implemented

• A pure‑Python project that replaces the original JavaScript implementation.
• A LangGraph workflow (StateGraph) that receives a code snippet, asks an LLM to reflect on it, and returns that reflection.
• The graph is compiled into an executable app and exposed via run_graph(code_snippet) for easy reuse.

Why the main parts satisfy the assignment

• Python only – the entire code lives in src/main.py, no JavaScript files remain.
• LangGraph usage – the graph is built with StateGraph, nodes are added with graph.add_node, edges with graph.add_edge, and the graph is compiled (graph.compile()).
• Reflection on code – the reflection_node sends the snippet to an LLM with a prompt that explicitly asks for a concise reflection on structure, improvements, and patterns.
• Functional project – running python src/main.py prints a reflection for a sample snippet, demonstrating end‑to‑end functionality.

Key code excerpts

# src/main.py – graph definition
graph = StateGraph(CodeState)
graph.add_node("input", input_node)
graph.add_node("reflection", reflection_node)
graph.add_node("output", output_node)
graph.add_edge("input", "reflection")
graph.add_edge("reflection", "output")
graph.add_edge("output", END)
app = graph.compile()

# src/main.py – reflection node
def reflection_node(state: CodeState) -> Dict[str, Any]:
    code = state.get("code", "")
    if not code:
        return {"reflection": "No code provided."}
    llm = OpenAI(temperature=0.7, model="gpt-3.5-turbo")
    prompt = (
        "You are an experienced software engineer. "
        "Analyze the following code snippet and provide a concise reflection "
        "on its structure, potential improvements, and any notable patterns.\n\n"
        f"{code}"
    )
    response = llm.invoke(prompt)
    return {"reflection": response}

# src/main.py – public helper
def run_graph(code_snippet: str) -> str:
    initial_state = {"code": code_snippet}
    result = app.invoke(initial_state)
    return result.get("reflection", "")

Honest limitations

• No explicit error handling for missing OpenAI key or network failures.
• The graph is very linear; adding more complex branching (e.g., multiple reflection steps) would require additional nodes.
• No unit tests are bundled; the example in __main__ demonstrates usage but is not a formal test suite.

Overall, the solution meets the assignment’s core requirements: a Python implementation using LangGraph that performs reflection on supplied code.