feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой'

README.md

@@ -1,90 +1,82 @@
-# LangGraph Research Brief Agent
+# LangGraph Reflection Agent
 
-This project demonstrates how to build a LangGraph agent that generates a short research brief for a given topic.  
-The agent:
+This project demonstrates a simple LangGraph agent that:
 
-1. Creates an outline of 4‑5 research steps.  
-2. For each step, performs a web search (via Tavily) and writes a concise note.  
-3. Synthesizes all notes into a coherent brief.
+1. Generates a concise answer to a user‑supplied question.
+2. Critiques the answer using an LLM.
+3. Rewrites the answer if the critic says *needs_revision*, up to a maximum number of rounds.
 
-## Prerequisites
+The agent is implemented in Python 3.10+ and uses the `langgraph` framework together with `langchain-openai`.
 
-- Python 3.10+
-- A **Tavily** API key (free tier available).  
-- An **OpenAI** API key (or any compatible LLM provider).
+## Features
 
-## Setup
+- **Draft generation** – 5–10 sentence answer.
+- **LLM critic** – returns a verdict (`ok` or `needs_revision`) and 2–3 critique points.
+- **Rewrite loop** – rewrites the draft until the verdict is `ok` or the maximum number of rounds is reached.
+- **CLI** – run the agent from the command line.
+
+## Installation
 
 ```bash
-# Clone the repository
-git clone https://github.com/your-username/langgraph-research-brief.git
-cd langgraph-research-brief
-
 # Create a virtual environment (optional but recommended)
-python -m venv venv
-source venv/bin/activate   # On Windows: venv\Scripts\activate
+python -m venv .venv
+source .venv/bin/activate   # On Windows use `.venv\Scripts\activate`
 
 # Install dependencies
 pip install -r requirements.txt
 ```
 
-Create a `.env` file in the project root based on the example:
+### OpenAI API Key
+
+The agent uses OpenAI’s GPT‑3.5‑Turbo by default.  
+Set your API key in the environment:
 
 ```bash
-cp .env.example .env
+export OPENAI_API_KEY="sk-..."
 ```
 
-Edit `.env` and replace the placeholders with your actual keys:
+If you prefer to use a local LLM via Ollama, replace the `langchain-openai` dependency with `langchain-ollama` and adjust the LLM initialization in `src/graph.py`.
 
-```
-OPENAI_API_KEY=sk-...
-TAVILY_API_KEY=your_tavily_key
-```
-
-## Running the Agent
+## Usage
 
 ```bash
-python src/main.py
+python -m src.main "Explain the difference between a tool and a resource in MCP to a student."
 ```
 
-You should see output similar to:
+Optional arguments:
+
+- `--max_rounds N` – maximum number of rewrite rounds (default: 2).
+
+Example:
+
+```bash
+python -m src.main "Explain the difference between a tool and a resource in MCP." --max_rounds 3
+```
+
+The script will print:
 
 ```
-=== Outline ===
-1. Identify the security requirements for MCP integration
-2. Review LangChain's authentication mechanisms
-3. Evaluate secure communication protocols
-4. Test the integration in a sandbox environment
-5. Document best practices and compliance checks
+=== Final Answer ===
+<rewritten answer>
 
-=== Notes ===
-[Step 1] ... (5‑8 sentence note)
-[Step 2] ... (5‑8 sentence note)
-...
+=== Verdict ===
+ok
 
-=== Final Brief ===
-...
 ```
 
+If the final verdict is `needs_revision`, the critique points will also be shown.
+
 ## Project Structure
 
 ```
 src/
-├── main.py          # Entry point
-├── graph.py         # LangGraph definition
-├── nodes.py         # Node implementations
-├── state.py         # TypedDict for state
-├── .env.example     # Environment variable template
+├── main.py      # CLI entry point
+├── graph.py     # LangGraph graph definition
+└── state.py     # TypedDict for the agent state
 requirements.txt
 README.md
 ```
 
-## Customization
-
-- **Topic**: Change the `default_topic` variable in `src/main.py` to generate a brief on a different subject.  
-- **LLM**: Swap `ChatOpenAI` for another provider (e.g., Ollama) by adjusting the imports and initialization in `src/nodes.py`.  
-- **Search**: Replace `TavilySearchResults` with another search tool if desired.
-
 ## License
 
 MIT License