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

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:

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.