# Structured Output Extraction with Pydantic

## What this project does
This repository demonstrates how to extract structured data from free‑form text using **LangChain** and **Pydantic**. Two schemas are supported:

1. **PersonInfo** – name, optional age, profession, skills.
2. **MeetingNotes** – date, participants, topics, decisions, next steps.

The extraction is performed in a single LLM call with `PydanticOutputParser` and the schema’s format instructions.

## File structure
- `models.py` – Pydantic models with field descriptions.
- `agent.py` – Extraction logic: schema detection, prompt construction, LLM invocation.
- `main.py` – CLI demonstrating three example inputs.
- `requirements.txt` – Runtime dependencies.
- `README.md` – Project description (this file).

## Installation
```bash
pip install -r requirements.txt
```
Make sure the environment variable **JOURNAL_MCP_PAT** is set to your BroJS API key.

## Usage examples
Run the script and follow the prompts:
```bash
python main.py
```
You will see three pre‑defined inputs (person, meeting, custom) and the parsed JSON output.

## How it works
1. **Schema detection** – a simple regex checks for words like *meeting* or *date* to choose between `PersonInfo` and `MeetingNotes`.
2. **Prompt** – the same template is used; format instructions are injected from the chosen parser.
3. **LLM call** – one request to BroJS GPT‑OSS‑20B.
4. **Parsing** – `PydanticOutputParser.parse()` validates and returns a Pydantic instance.

## Extending
Add more schemas by creating new Pydantic models in `models.py` and extending `_detect_schema`. The rest of the pipeline remains unchanged.