add README.md

README.md

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-# task-6a1865008a94f887e50d471c
+# 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.