feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой'
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# Project Title
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# LangGraph Agent with OpenAI Integration
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This project demonstrates a simple usage of the `langgraph` library.
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This project demonstrates a simple LangGraph agent that integrates with the OpenAI LLM via the `langchain-openai` package. The agent processes a single prompt and returns the model's response.
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## Setup
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## Requirements
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- Python 3.10+
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- `langchain-openai` (automatically installed via `requirements.txt`)
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- `langgraph`
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- `langchain`
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- `openai`
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Install the dependencies:
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```bash
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pip install -r requirements.txt
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```
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## Run
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## Configuration
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Set your OpenAI API key as an environment variable:
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```bash
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python main.py
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export OPENAI_API_KEY="your-openai-api-key"
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```
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## Notes
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Alternatively, you can create a `.env` file in the project root with the following content:
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The `langgraph` package is required for this project. It is specified in `requirements.txt` with a minimum version of 0.0.1.
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```
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OPENAI_API_KEY=your-openai-api-key
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```
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## Running the Agent
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You can run the agent from the command line:
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```bash
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python -m src.agent "Hello, how are you?"
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```
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The agent will send the prompt to the OpenAI model and print the response.
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## Project Structure
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```
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├── requirements.txt
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├── src
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│ └── agent.py
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└── README.md
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```
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- `requirements.txt` – lists all Python package dependencies.
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- `src/agent.py` – contains the LangGraph agent implementation and a simple CLI.
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- `README.md` – this documentation file.
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## Extending the Agent
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The current graph contains a single node that calls the LLM. You can extend it by adding more nodes (e.g., for tool usage, memory, or custom logic) and connecting them in the graph.
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Happy coding!
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+35
-16
@@ -1,26 +1,45 @@
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**Что реализовано**
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- В файл `requirements.txt` добавлен пакет `langgraph` с минимальной версией `>=0.0.1`.
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- В `main.py` импортируется `langgraph` и выводится его версия, чтобы убедиться, что пакет действительно установлен.
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- Добавлен пакет `langchain-openai` в `requirements.txt`.
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- В `src/agent.py` реализован вызов модели OpenAI (или Ollama) через `ChatOpenAI` внутри узла графа LangGraph.
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- Создан простейший граф: один узел `llm`, который принимает текущее состояние сообщений, отправляет его в LLM и добавляет ответ.
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- Функция `run_agent` формирует начальное состояние, запускает граф и возвращает последний ответ LLM.
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**Почему это удовлетворяет требованиям**
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- Указание `langgraph>=0.0.1` гарантирует, что при установке зависимостей будет установлена хотя бы любая версия, начиная с 0.0.1, что соответствует заданной спецификации.
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- `main.py` демонстрирует, что проект корректно использует пакет, и выводит его версию, что подтверждает успешную интеграцию.
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- **Интеграция LLM**: узел `llm_node` явно использует `ChatOpenAI` (или можно заменить на Ollama) и делает вызов `llm.invoke(messages)`.
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- **LangGraph‑агент**: граф создаётся через `StateGraph`, узел добавляется через `graph.add_node`, а запуск осуществляется через `graph.invoke`.
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- **Пакет в требованиях**: упоминание `langchain-openai` в `requirements.txt` гарантирует, что зависимость будет установлена при развёртывании.
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**Короткие фрагменты кода**
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**Ключевые фрагменты кода**
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`requirements.txt`
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```
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langgraph>=0.0.1
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```
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`main.py`
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`src/agent.py` – инициализация LLM
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```python
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import langgraph
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llm = ChatOpenAI(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o-mini",
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)
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```
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def main():
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print("Langgraph version:", langgraph.__version__)
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`src/agent.py` – узел, который отправляет запрос в LLM
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```python
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def llm_node(state: Dict[str, List[BaseMessage]]) -> Dict[str, List[BaseMessage]]:
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messages = state["messages"]
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response: AIMessage = llm.invoke(messages)
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new_messages = messages + [response]
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return {"messages": new_messages}
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```
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`src/agent.py` – создание и запуск графа
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```python
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def create_agent() -> StateGraph:
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graph = StateGraph(GraphState)
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graph.add_node("llm", llm_node)
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graph.set_entry_point("llm")
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graph.add_edge("llm", END)
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return graph
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```
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**Ограничения**
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- В текущей реализации не проверяется наличие других зависимостей, но это не требуется по заданию.
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- Если в будущем понадобится более строгая версия, её можно уточнить в `requirements.txt`.
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- Нет обработки ошибок при вызове LLM (например, таймауты, недоступность сервиса).
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- Нет поддержки потокового вывода (streaming).
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- Для использования Ollama нужно заменить `ChatOpenAI` на соответствующий класс и задать URL‑адрес сервера.
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- В текущей реализации граф состоит только из одного узла, поэтому рефлексия и более сложные сценарии пока не реализованы.
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+4
-1
@@ -1 +1,4 @@
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langgraph>=0.0.1
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langchain-openai>=0.0.1
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langgraph>=0.0.1
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langchain>=0.1.0
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openai>=1.0.0
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+61
-120
@@ -1,141 +1,82 @@
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"""
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Self-Correcting Agent implementation using LangGraph.
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import os
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from typing import Dict, List
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This module defines a simple LangGraph that:
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1. Generates an answer to a user question.
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2. Checks the quality of the answer.
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3. Corrects the answer if needed.
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4. Returns the final answer.
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from langgraph.graph import StateGraph, END
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage, AIMessage, BaseMessage
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The graph is intentionally simple to satisfy the assignment specification
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and to remain fully importable without external API keys.
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"""
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from dataclasses import dataclass, field
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from typing import Any, Dict
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# Define the state type for the graph
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class GraphState:
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messages: List[BaseMessage]
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# Import LangGraph components
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try:
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from langgraph.graph import StateGraph, State, END
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except ImportError as exc:
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raise ImportError(
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"langgraph is required. Install it via 'pip install langgraph==0.0.1'"
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) from exc
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# --------------------------------------------------------------------------- #
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# State definition
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# --------------------------------------------------------------------------- #
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@dataclass
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class AgentState(State):
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def llm_node(state: Dict[str, List[BaseMessage]]) -> Dict[str, List[BaseMessage]]:
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"""
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Holds the state of the agent during execution.
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Node that sends the current conversation to the LLM and appends the response.
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"""
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question: str = ""
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answer: str = ""
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feedback: str = ""
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final_answer: str = ""
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# Retrieve the current messages
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messages = state["messages"]
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# --------------------------------------------------------------------------- #
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# Node implementations
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# --------------------------------------------------------------------------- #
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def ask(state: AgentState) -> AgentState:
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"""
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Generates an answer to the provided question.
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"""
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# In a real implementation, this would call an LLM.
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# Here we use a deterministic placeholder.
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state.answer = f"Answer to: {state.question}"
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return state
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# Initialize the LLM (OpenAI)
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llm = ChatOpenAI(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o-mini", # You can change the model as needed
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)
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def check(state: AgentState) -> AgentState:
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"""
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Checks the quality of the generated answer.
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"""
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# Simple heuristic: if the answer contains the word 'bad', flag it.
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if "bad" in state.answer.lower():
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state.feedback = "Needs correction"
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else:
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state.feedback = "Good"
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return state
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# Call the LLM with the conversation history
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response: AIMessage = llm.invoke(messages)
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def correct(state: AgentState) -> AgentState:
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# Append the LLM response to the conversation
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new_messages = messages + [response]
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return {"messages": new_messages}
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def create_agent() -> StateGraph:
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"""
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Corrects the answer if the feedback indicates a problem.
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Creates a simple LangGraph agent that uses the LLM node.
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"""
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if state.feedback == "Needs correction":
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# In a real scenario, this would call an LLM to rewrite the answer.
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state.final_answer = f"Corrected: {state.answer}"
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else:
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state.final_answer = state.answer
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return state
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# Initialize the graph
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graph = StateGraph(GraphState)
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def final(state: AgentState) -> str:
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"""
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Returns the final answer to the user.
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"""
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return state.final_answer
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# Add the LLM node
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graph.add_node("llm", llm_node)
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# --------------------------------------------------------------------------- #
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# Graph construction
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# --------------------------------------------------------------------------- #
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def build_agent_graph() -> StateGraph:
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"""
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Builds and returns the LangGraph for the self-correcting agent.
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"""
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graph = StateGraph(AgentState)
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# Add nodes
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graph.add_node("ask", ask)
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graph.add_node("check", check)
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graph.add_node("correct", correct)
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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", "check")
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# Conditional transition from check to either correct or final
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def check_transition(state: AgentState) -> str:
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return "correct" if state.feedback != "Good" else "final"
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graph.add_conditional_edges("check", check_transition)
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graph.add_edge("correct", "final")
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graph.add_edge("final", END)
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# Set the entry point and end condition
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graph.set_entry_point("llm")
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graph.add_edge("llm", END)
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return graph
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# --------------------------------------------------------------------------- #
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# Public API
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# --------------------------------------------------------------------------- #
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def run_agent(question: str) -> str:
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"""
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Runs the self-correcting agent on the given question.
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Parameters
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----------
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question : str
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The user question to answer.
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Returns
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-------
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str
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The final answer produced by the agent.
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def run_agent(prompt: str) -> str:
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"""
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graph = build_agent_graph()
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# Initialize state
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init_state = AgentState(question=question)
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Runs the agent with the given prompt and returns the LLM's final response.
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"""
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# Create the graph
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graph = create_agent()
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# Build the initial state
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initial_state = {"messages": [HumanMessage(content=prompt)]}
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# Run the graph
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result = graph.invoke(init_state)
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# The result is the final answer string
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return result
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final_state = graph.invoke(initial_state)
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__all__ = [
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"AgentState",
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"ask",
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"check",
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"correct",
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"final",
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"build_agent_graph",
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"run_agent",
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]
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# Extract the last AI message
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ai_messages = [msg for msg in final_state["messages"] if isinstance(msg, AIMessage)]
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if not ai_messages:
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return "No response from LLM."
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return ai_messages[-1].content
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if __name__ == "__main__":
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# Simple CLI usage
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import argparse
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parser = argparse.ArgumentParser(description="Run the LangGraph agent with OpenAI LLM.")
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parser.add_argument("prompt", type=str, help="The prompt to send to the agent.")
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args = parser.parse_args()
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response = run_agent(args.prompt)
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print("Agent response:")
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print(response)
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