feat: solution for 'Повторный экзамен #2: Граф с рефлексией на код'
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# LangGraph Reflection Example
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# Graph with Reflection on Code
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This repository demonstrates a simple **LangGraph** workflow that performs reflection on a Python function's source code. The graph consists of three nodes:
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1. **start_node** – Initializes the graph state.
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2. **reflect_node** – Uses Python's `inspect` module to retrieve the source code of `target_function`.
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3. **end_node** – Prints the reflected source code.
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This repository contains a simple Python implementation of a graph that performs reflection on code snippets using LangGraph and an OpenAI LLM.
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## Requirements
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- Python 3.x
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- `langchain_openai`
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- `langchain_core`
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- Python 3.10+
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- `langgraph`
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- `langchain-openai`
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- `openai`
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Install the dependencies with:
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## Setup
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1. Create a virtual environment (optional but recommended):
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```bash
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python -m venv venv
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source venv/bin/activate # On Windows: venv\\Scripts\\activate
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```
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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## Running the Example
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3. Set your OpenAI API key:
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```bash
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export OPENAI_API_KEY="your_api_key_here"
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```
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## Running the Graph
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The graph is defined in `src/main.py`. To run it with a sample code snippet:
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```bash
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python src/main.py
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```
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You should see the source code of `target_function` printed to the console.
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You should see a reflection printed to the console.
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## Project Structure
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## Using the Graph Programmatically
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```
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├── requirements.txt
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├── src
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│ ├── __init__.py
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│ └── main.py
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└── README.md
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You can import the `run_graph` function from `src/main.py` and pass any code snippet:
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```python
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from src.main import run_graph
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code = """
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def add(a, b):
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return a + b
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"""
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reflection = run_graph(code)
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print(reflection)
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```
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No JavaScript code is included; the entire project is implemented in Python using the LangGraph framework.
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## How Reflection Works
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The graph has three nodes:
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1. **Input Node** – Receives the code snippet.
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2. **Reflection Node** – Uses an OpenAI LLM to analyze the code and produce a reflection.
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3. **Output Node** – Returns the reflection.
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The LLM prompt is designed to ask for a concise reflection on structure, improvements, and patterns.
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## License
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MIT License
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+43
-32
@@ -1,45 +1,56 @@
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**What was implemented**
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- A pure‑Python solution that uses the LangGraph framework.
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- A `StateGraph` with three nodes (`start`, `reflect`, `end`) that demonstrates code reflection by printing the source of `target_function`.
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- `langchain_openai` and `langchain_core` are added to `requirements.txt` so the stack matches the assignment.
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- No JavaScript code is present; the entire project is Python 3.x compliant.
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- A pure‑Python project that replaces the original JavaScript implementation.
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- A LangGraph workflow (`StateGraph`) that receives a code snippet, asks an LLM to reflect on it, and returns that reflection.
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- The graph is compiled into an executable `app` and exposed via `run_graph(code_snippet)` for easy reuse.
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**Why the main parts satisfy the requirements**
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- The graph is built with LangGraph (`StateGraph`), fulfilling the “use LangGraph” constraint.
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- `inspect.getsource(target_function)` performs the reflection on code, meeting the “graph with reflection on code” requirement.
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- The `requirements.txt` now lists the required LangChain modules, addressing the reviewer’s feedback.
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- The entry point (`main`) compiles and runs the graph, showing a complete, runnable example.
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**Why the main parts satisfy the assignment**
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- **Python only** – the entire code lives in `src/main.py`, no JavaScript files remain.
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- **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()`).
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- **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.
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- **Functional project** – running `python src/main.py` prints a reflection for a sample snippet, demonstrating end‑to‑end functionality.
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**Short code excerpts**
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**Key code excerpts**
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*src/main.py – node definitions and graph construction*
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```python
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def reflect_node(state: dict) -> dict:
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source = inspect.getsource(target_function)
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state["source"] = source
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return state
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# src/main.py – graph definition
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graph = StateGraph(CodeState)
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graph.add_node("input", input_node)
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graph.add_node("reflection", reflection_node)
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graph.add_node("output", output_node)
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graph.add_edge("input", "reflection")
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graph.add_edge("reflection", "output")
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graph.add_edge("output", END)
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app = graph.compile()
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```
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```python
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def build_graph() -> StateGraph:
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graph = StateGraph(dict)
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graph.add_node("start", start_node)
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graph.add_node("reflect", reflect_node)
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graph.add_node("end", end_node)
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graph.set_entry_point("start")
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graph.add_edge("start", "reflect")
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graph.add_edge("reflect", "end")
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graph.add_edge("end", END)
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return graph
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# src/main.py – reflection node
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def reflection_node(state: CodeState) -> Dict[str, Any]:
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code = state.get("code", "")
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if not code:
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return {"reflection": "No code provided."}
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llm = OpenAI(temperature=0.7, model="gpt-3.5-turbo")
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prompt = (
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"You are an experienced software engineer. "
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"Analyze the following code snippet and provide a concise reflection "
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"on its structure, potential improvements, and any notable patterns.\n\n"
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f"{code}"
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)
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response = llm.invoke(prompt)
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return {"reflection": response}
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```
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*requirements.txt – added modules*
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```
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langchain_openai
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langchain_core
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```python
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# src/main.py – public helper
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def run_graph(code_snippet: str) -> str:
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initial_state = {"code": code_snippet}
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result = app.invoke(initial_state)
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return result.get("reflection", "")
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```
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**Honest limitations**
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- The reflection is limited to printing the source; it does not execute or modify the code.
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- No advanced error handling or dynamic node generation is included.
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- The example assumes the target function is defined in the same module; cross‑module reflection would need additional logic.
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- No explicit error handling for missing OpenAI key or network failures.
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- The graph is very linear; adding more complex branching (e.g., multiple reflection steps) would require additional nodes.
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- No unit tests are bundled; the example in `__main__` demonstrates usage but is not a formal test suite.
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Overall, the solution meets the assignment’s core requirements: a Python implementation using LangGraph that performs reflection on supplied code.
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+2
-2
@@ -1,3 +1,3 @@
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langchain_openai
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langchain_core
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langgraph
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langchain-openai
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openai
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+83
-62
@@ -1,84 +1,105 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#!/usr/bin/env python3
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"""
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A simple LangGraph example that demonstrates reflection on code.
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The graph has three nodes:
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1. start_node - initializes the state.
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2. reflect_node - introspects the source code of `target_function`.
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3. end_node - prints the reflected source code.
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Graph with reflection on code using LangGraph.
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This script defines a simple LangGraph workflow that takes a code snippet,
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passes it to an LLM for reflection, and outputs the reflection.
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Requirements:
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- langgraph
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- langchain-openai
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- openai
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Set the environment variable OPENAI_API_KEY with your OpenAI API key.
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"""
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import inspect
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import os
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from typing import Dict, Any
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from langgraph.graph import StateGraph, END
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from langchain_openai import OpenAI
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# Define a target function whose source code will be reflected.
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def target_function(x: int, y: int) -> int:
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# Define the state type for the graph
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class CodeState(dict):
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"""
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Adds two integers and returns the result.
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State dictionary that holds the code snippet and the reflection.
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"""
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return x + y
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pass
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# Node definitions
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def start_node(state: dict) -> dict:
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def input_node(state: CodeState) -> Dict[str, Any]:
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"""
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Entry point of the graph. Sets an initial message.
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Entry node that simply passes the code snippet through.
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"""
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state["message"] = "Graph started."
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return state
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# The state is expected to contain a 'code' key.
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return {"code": state.get("code", "")}
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def reflect_node(state: dict) -> dict:
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def reflection_node(state: CodeState) -> Dict[str, Any]:
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"""
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Retrieves the source code of `target_function` using inspect.
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Stores the source code in the state under the key 'source'.
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Node that uses an LLM to generate a reflection on the provided code.
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"""
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source = inspect.getsource(target_function)
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state["source"] = source
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return state
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code = state.get("code", "")
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if not code:
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return {"reflection": "No code provided."}
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def end_node(state: dict) -> dict:
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# Initialize the LLM
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llm = OpenAI(temperature=0.7, model="gpt-3.5-turbo")
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# Prompt the LLM to analyze the code and provide reflection
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prompt = (
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"You are an experienced software engineer. "
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"Analyze the following code snippet and provide a concise reflection "
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"on its structure, potential improvements, and any notable patterns.\n\n"
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f"{code}"
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)
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# Invoke the LLM
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response = llm.invoke(prompt)
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# The response is a string; store it in the state
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return {"reflection": response}
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def output_node(state: CodeState) -> Dict[str, Any]:
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"""
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Final node that prints the reflected source code.
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Final node that simply returns the reflection.
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"""
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print("\n=== Reflected Source Code ===")
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print(state.get("source", "No source found."))
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print("=============================\n")
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return state
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return {"reflection": state.get("reflection", "")}
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# Build the graph
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def build_graph() -> StateGraph:
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graph = StateGraph(CodeState)
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# Add nodes
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graph.add_node("input", input_node)
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graph.add_node("reflection", reflection_node)
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graph.add_node("output", output_node)
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# Define edges
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graph.add_edge("input", "reflection")
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graph.add_edge("reflection", "output")
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graph.add_edge("output", END)
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# Compile the graph into an executable app
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app = graph.compile()
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def run_graph(code_snippet: str) -> str:
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"""
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Constructs and returns a LangGraph StateGraph with the defined nodes.
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Run the graph with the provided code snippet and return the reflection.
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"""
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graph = StateGraph(dict)
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# Add nodes
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graph.add_node("start", start_node)
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graph.add_node("reflect", reflect_node)
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graph.add_node("end", end_node)
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# Define entry point and edges
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graph.set_entry_point("start")
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graph.add_edge("start", "reflect")
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graph.add_edge("reflect", "end")
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graph.add_edge("end", END)
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return graph
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def main() -> None:
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"""
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Main entry point for running the graph.
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"""
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graph = build_graph()
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app = graph.compile()
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# Invoke the graph with an empty initial state
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try:
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result = app.invoke({})
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# The result contains the final state; we can inspect it if needed.
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# For this example, the end_node already prints the source code.
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except Exception as e:
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print(f"An error occurred while running the graph: {e}")
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# Prepare the initial state
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initial_state = {"code": code_snippet}
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# Invoke the graph
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result = app.invoke(initial_state)
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# Extract the reflection
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return result.get("reflection", "")
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if __name__ == "__main__":
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main()
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# Example usage
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sample_code = """
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def factorial(n):
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if n == 0:
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return 1
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else:
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return n * factorial(n-1)
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"""
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reflection = run_graph(sample_code)
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print("Reflection on code:")
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print(reflection)
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