# Task 69dd4221f309a98be0006b2e – Structured Assignment Card

## What this project does
This repository contains a small Python package that turns an informal assignment description into a **structured data card**. The card is represented by a Pydantic model and can be used downstream in pipelines for filtering, storage or further processing.

The core logic lives in `models.py` (the data schema) and `agent.py` (the LangChain chain that calls the LLM).  The entry point `main.py` demonstrates three different example inputs.

## File structure
- **requirements.txt** – Python dependencies required to run the code.
- **README.md** – this documentation file.
- **models.py** – Pydantic model for the assignment card.
- **agent.py** – LangChain chain that parses a raw text into the model.
- **main.py** – example usage and simple CLI.

## Installation
```bash
pip install -r requirements.txt
```
Make sure you have an OpenAI compatible key set in `JOURNAL_MCP_PAT` environment variable (the BroJS endpoint).

## Usage examples
```python
from agent import parse_assignment

text = "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
card = parse_assignment(text)
print(card.model_dump())
```

## Architecture
1. **PromptTemplate** – contains a short instruction and the format instructions from `PydanticOutputParser`.
2. **ChatOpenAI** – calls BroJS LLM.
3. **PydanticOutputParser** – validates that the model output matches the schema.
4. The chain is a simple *prompt → llm → parser* pipeline.

The code is intentionally straightforward to keep the focus on structured output generation.