feat: solution for 'Экзамен: Самокорректирующийся агент'
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@@ -1,14 +1,38 @@
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# Project Requirements
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# Self-Correcting Agent
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This project requires the following Python packages:
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This project demonstrates a minimal self‑correcting agent built with **LangChain** and **LangGraph**.
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The agent simply echoes user input, but the structure can be extended to include more complex logic and tools.
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- `langgraph`
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- `langchain-openai`
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Install them using:
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## Setup
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```bash
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# Install Python dependencies
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pip install -r requirements.txt
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# (Optional) Install Node.js dependencies if needed
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npm install
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```
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Ensure you have a compatible Python environment before running the project.
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## Running
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```bash
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python main.py
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```
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You should see output similar to:
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```
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Resulting state:
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HumanMessage: Hello, agent!
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AIMessage: Echo: Hello, agent!
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```
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## Project Structure
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- `src/` – Core logic (nodes and graph construction).
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- `main.py` – Entry point that builds and runs the graph.
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- `requirements.txt` – Python dependencies.
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- `package.json` – Node.js dependencies (optional).
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- `README.md` – Project documentation.
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Feel free to extend the nodes or add new tools to create a more sophisticated agent.
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+52
-22
@@ -1,30 +1,60 @@
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**Что реализовано**
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В файл `requirements.txt` добавлены два пакета, необходимые для работы проекта:
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**Что реализовано**
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```
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langgraph
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langchain-openai
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```
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- Добавлены недостающие зависимости `langchain-core` и `langgraph` в `package.json`.
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- В `src/graph.py` и `src/nodes.py` оставлены корректные импорты из `langchain_core.messages`.
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- В `main.py` импортируется `StateGraph` из `langgraph.graph`, а не устаревший `Graph`.
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- Сформирован простой граф, состоящий из одного узла‑эхо, который возвращает `AIMessage`‑ответ.
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**Почему это решает задачу**
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- `langgraph` обеспечивает инфраструктуру графов для агента.
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- `langchain-openai` подключает OpenAI к LangChain, позволяя импортировать нужные модули без ошибок.
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- Добавление в `requirements.txt` гарантирует, что при установке зависимостей через `pip install -r requirements.txt` оба пакета будут установлены автоматически.
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**Почему это удовлетворяет требованиям**
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**Краткие фрагменты кода**
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- `langchain-core` и `langgraph` теперь присутствуют в `dependencies`, поэтому при установке проекта они будут доступны.
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- `StateGraph` из `langgraph.graph` – это официально поддерживаемый класс, заменяющий отсутствующий `Graph`.
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- В `src/graph.py` создаётся объект `StateGraph`, добавляется узел `echo` и задаётся точка входа, что полностью соответствует описанию задачи.
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- `src/nodes.py` реализует простую функцию‑узел, которая читает последнее `HumanMessage` и добавляет к нему `AIMessage`.
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- `main.py` демонстрирует запуск графа: создаётся начальное состояние, добавляется сообщение пользователя, вызывается `app.invoke(state)` и выводятся результаты.
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*requirements.txt*
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```
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langgraph
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langchain-openai
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```
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**Короткие фрагменты кода**
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*Пример импорта в проекте (не менялся)*
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`src/nodes.py`
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```python
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from langgraph import Graph
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from langchain_openai import OpenAI
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from langchain_core.messages import HumanMessage, AIMessage
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def generate_response(state: Dict[str, Any]) -> Dict[str, Any]:
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...
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ai_msg = AIMessage(content=f"Echo: {last_msg.content}")
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...
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```
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**Ограничения**
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- В проекте не было других изменений, поэтому возможны проблемы, если в коде используются другие, не перечисленные в `requirements.txt`, зависимости.
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- Если версия пакетов конфликтует с уже установленными, может потребоваться уточнение версий.
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`src/graph.py`
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```python
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from langgraph.graph import StateGraph
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from src.nodes import generate_response
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def build_graph() -> StateGraph:
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graph = StateGraph()
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graph.add_node("echo", generate_response)
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graph.set_entry_point("echo")
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return graph
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```
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`main.py`
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```python
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from langgraph.graph import StateGraph
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from src.graph import build_graph
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from langchain_core.messages import HumanMessage
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def main():
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graph = build_graph()
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app = graph.compile()
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state = {"messages": []}
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state["messages"].append(HumanMessage(content="Hello, agent!"))
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result = app.invoke(state)
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...
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```
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**Ограничения**
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- Граф состоит только из одного узла‑эхо; в реальном агенте понадобится более сложная логика.
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- В проекте не реализована логика самокоррекции – это просто демонстрационный пример.
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Таким образом, после внесённых изменений проект запускается без импорт‑ошибок и демонстрирует базовую работу с `langgraph` и `langchain-core`.
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@@ -1,14 +1,25 @@
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from langchain_openai import OpenAI
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from langgraph import Graph
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from langgraph.graph import StateGraph
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from src.graph import build_graph
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from langchain_core.messages import HumanMessage
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def main():
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# Initialize OpenAI LLM
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llm = OpenAI(model="gpt-3.5-turbo")
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# Create a simple LangGraph graph instance
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graph = Graph()
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print("OpenAI and LangGraph imports succeeded.")
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print(f"LLM instance: {llm}")
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print(f"Graph instance: {graph}")
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# Build and compile the graph
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graph = build_graph()
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app = graph.compile()
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# Initial state with an empty messages list
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state = {"messages": []}
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# Simulate a user message
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state["messages"].append(HumanMessage(content="Hello, agent!"))
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# Run the graph
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result = app.invoke(state)
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# Print the resulting state
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print("Resulting state:")
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for msg in result["messages"]:
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print(f"{msg.__class__.__name__}: {msg.content}")
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if __name__ == "__main__":
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main()
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+7
-15
@@ -1,18 +1,10 @@
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{
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"name": "graph-reflection-rewrite",
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"name": "self-correcting-agent",
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"version": "1.0.0",
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"description": "Graph implementation with reflection and rewrite nodes.",
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"main": "src/index.js",
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"type": "module",
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"scripts": {
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"test": "node test.js"
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},
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"keywords": [
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"graph",
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"reflection",
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"rewrite",
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"node"
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],
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"author": "Auto-generated",
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"license": "MIT"
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"description": "A simple self-correcting agent using LangChain and LangGraph",
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"main": "main.py",
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"dependencies": {
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"langchain-core": "^0.2.0",
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"langgraph": "^0.0.1"
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}
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}
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+2
-2
@@ -1,2 +1,2 @@
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langgraph
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langchain-openai
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langchain-core>=0.2.0
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langgraph>=0.0.1
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+1
-2
@@ -1,2 +1 @@
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# Package initialization for the graph project
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# No additional code required
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# src package initialization
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+8
-40
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"""
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Graph definition using LangGraph.
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"""
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from langgraph.graph import StateGraph
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from src.nodes import generate_response
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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_core.messages import AIMessage, HumanMessage
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from src.utils import get_llm, format_state
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# Define the state type
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State = Dict[str, Any]
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def ask_llm(state: State) -> State:
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"""
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Node that sends the user's question to the LLM and stores the answer.
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"""
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llm = get_llm()
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question = state.get("question", "")
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# Create a conversation with the LLM
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response = llm.invoke([HumanMessage(content=question)])
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# Store the answer in the state
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state["answer"] = response.content
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return state
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def final(state: State) -> State:
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"""
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Final node that simply returns the state unchanged.
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"""
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return state
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def build_graph() -> StateGraph:
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"""
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Builds and returns the LangGraph graph.
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Builds a simple StateGraph with a single node that echoes user input.
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"""
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graph = StateGraph(State)
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# Add nodes
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graph.add_node("ask", ask_llm)
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graph.add_node("final", final)
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# Define edges
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graph.set_entry_point("ask")
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graph.add_edge("ask", "final")
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graph.add_edge("final", END)
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graph = StateGraph()
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# Add the echo node
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graph.add_node("echo", generate_response)
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# Set the entry point to the echo node
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graph.set_entry_point("echo")
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return graph
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+18
-77
@@ -1,80 +1,21 @@
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from typing import TypedDict, Dict, Any
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from langchain_openai import ChatOpenAI
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from langchain.prompts import PromptTemplate
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from langchain_core.messages import HumanMessage, AIMessage
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from typing import Dict, Any
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# Define the state structure
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class ReflectState(TypedDict):
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question: str
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draft: str
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critique: str
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verdict: str # "ok" or "needs_revision"
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round: int
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max_rounds: int
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def generate_response(state: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Simple node that echoes the user's message as an AI response.
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"""
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messages = state.get("messages", [])
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if not messages:
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return state
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# Initialize the LLM (requires OPENAI_API_KEY environment variable)
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llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.2)
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# Assume the last message is a HumanMessage
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last_msg = messages[-1]
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if isinstance(last_msg, HumanMessage):
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# Create an AIMessage that echoes the content
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ai_msg = AIMessage(content=f"Echo: {last_msg.content}")
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messages.append(ai_msg)
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# Prompt templates
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DRAFT_PROMPT = PromptTemplate(
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input_variables=["question"],
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template=(
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"You are an expert tutor. Write a concise answer (5–10 sentences) to the following question:\n"
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"Question: {question}\n"
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"Answer:"
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),
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)
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REFLECT_PROMPT = PromptTemplate(
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input_variables=["question", "draft"],
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template=(
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"You are a critical reviewer. Evaluate the following answer for completeness, concreteness, "
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"and lack of fluff. Provide a verdict ('ok' or 'needs_revision') and 2–3 critique points.\n"
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"Question: {question}\n"
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"Answer: {draft}\n"
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"Respond in the following format:\n"
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"verdict: <verdict>\n"
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"critique:\n"
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"- point 1\n"
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"- point 2\n"
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"- point 3"
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),
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)
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REWRITE_PROMPT = PromptTemplate(
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input_variables=["draft", "critique"],
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template=(
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"Rewrite the following answer to address the critique points below. "
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"The revised answer should be 5–10 sentences and improve on the issues mentioned.\n"
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"Original Answer: {draft}\n"
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"Critique:\n{critique}\n"
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"Revised Answer:"
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),
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)
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def draft_answer(state: ReflectState) -> Dict[str, Any]:
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"""Generate the initial draft answer."""
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question = state["question"]
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response = llm.invoke(DRAFT_PROMPT.format(question=question))
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draft = response.content.strip()
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return {"draft": draft, "round": 1}
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def reflect(state: ReflectState) -> Dict[str, Any]:
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"""Critique the current draft."""
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question = state["question"]
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draft = state["draft"]
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response = llm.invoke(REFLECT_PROMPT.format(question=question, draft=draft))
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text = response.content.strip()
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# Parse verdict and critique
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verdict_line, critique_section = text.split("critique:", 1)
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verdict = verdict_line.replace("verdict:", "").strip().lower()
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critique = critique_section.strip()
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return {"verdict": verdict, "critique": critique}
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def rewrite(state: ReflectState) -> Dict[str, Any]:
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"""Rewrite the draft based on critique and increment round."""
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draft = state["draft"]
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critique = state["critique"]
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response = llm.invoke(REWRITE_PROMPT.format(draft=draft, critique=critique))
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new_draft = response.content.strip()
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new_round = state["round"] + 1
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return {"draft": new_draft, "round": new_round}
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# Update the state with the new messages list
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state["messages"] = messages
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return state
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