# Structured Output Extraction with LangChain and Pydantic

This project demonstrates how to extract structured data from raw text using LangChain and Pydantic.  
It supports two schemas:

- **PersonInfo** – name, age, profession, skills
- **MeetingNotes** – date, participants, topics, decisions, next steps

The CLI automatically selects the appropriate schema based on the input text and prints the parsed object and a short summary.

## Prerequisites

- Python 3.10+
- An OpenAI API key (or compatible LLM provider)

## Installation

```bash
# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # On Windows use `.venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt
```

## Configuration

Create a `.env` file in the project root with your OpenAI key:

```
OPENAI_API_KEY=sk-...
```

## Usage

Run the CLI:

```bash
python src/cli.py run
```

You will be prompted to provide text or a file path.  
If no input is given, example texts for both schemas are displayed.

### Example

```bash
python src/cli.py run --file example.txt
```

The output will look like:

```
Detected schema: person

Parsed object:
{
  "name": "Анна",
  "age": 28,
  "profession": "Python-разработчик",
  "skills": [
    "FastAPI",
    "Docker"
  ]
}

Summary:
Person: Анна, age=28, profession=Python-разработчик, skills=FastAPI, Docker
```

## Project Structure

```
src/
├── __init__.py
├── cli.py
├── main.py
└── models.py
requirements.txt
README.md
```

## License

MIT License