# Assignment Card Extraction

This project demonstrates how to convert a natural language assignment description into a structured data object using **LangChain** and **Pydantic**.

## Features

- Parses assignment details such as title, subject, deadline, deliverable type, and grading hints.
- Uses OpenAI's GPT model to interpret free‑form text.
- Validates the output with a Pydantic model to ensure type safety.

## Prerequisites

- Python 3.10 or newer
- An OpenAI API key

## Setup

1. **Clone the repository** (or copy the files into a directory):

   ```bash
   git clone https://github.com/your-username/assignment-card-extractor.git
   cd assignment-card-extractor
   ```

2. **Create a virtual environment** (recommended):

   ```bash
   python -m venv .venv
   source .venv/bin/activate   # On Windows: .venv\Scripts\activate
   ```

3. **Install dependencies**:

   ```bash
   pip install -r requirements.txt
   ```

4. **Configure the OpenAI API key**:

   - Open the `.env` file.
   - Replace `your_openai_api_key_here` with your actual key.

   ```dotenv
   OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX
   ```

## Running the Example

```bash
python src/main.py
```

You should see output similar to:

```
=== Parsed Assignment Card ===
{
  "title": "Мини-отчёт по LangChain",
  "subject": "LangChain",
  "deadline_hint": "к пятнице",
  "deliverable_type": "отчёт",
  "grading_hints": [
    "полнота",
    "пример кода"
  ]
}

=== Human-readable Summary ===
Title: Мини-отчёт по LangChain
Subject: LangChain
Deadline: к пятнице
Deliverable: отчёт
Grading Hints: полнота, пример кода
```

## Customization

- **Change the LLM**: Edit `src/main.py` to use a different model or adjust temperature.
- **Add more fields**: Update the `AssignmentCard` model and the prompt template accordingly.
- **Use in a pipeline**: Import the `chain` object from `src/main.py` into your own application.

## License

MIT License