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

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

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

Create a virtual environment (recommended):

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

Install dependencies:
```
pip install -r requirements.txt
```
Configure the OpenAI API key:
- Open the .env file.
- Replace your_openai_api_key_here with your actual key.
```
OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX
```

Running the Example

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

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