# Text Extractor CLI

## Overview

This project provides a command‑line tool that extracts structured data from free‑form text using **LangChain** and **Pydantic**. It supports two schemas:

1. **PersonInfo** – name, age, profession, skills.
2. **MeetingNotes** – title, date, participants, agenda.

The tool automatically detects which schema to use based on the input text.

## Installation

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

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

## Environment variables

The tool uses OpenAI’s API. Create a `.env` file in the project root with the following variables:

```
OPENAI_API_KEY=your_api_key_here
OPENAI_MODEL=gpt-3.5-turbo   # optional, defaults to gpt-3.5-turbo
OPENAI_BASE_URL=               # optional, for local LLMs (e.g. http://localhost:11434/v1)
```

If you are using a local model (Ollama/LM Studio) provide the `OPENAI_BASE_URL` and set `OPENAI_API_KEY` to any non‑empty string (e.g. `ollama`).

## Usage

```bash
# Pass text as a command‑line argument
python agent.py "Anna, 28 years old, Python developer. Skills: FastAPI, Docker."

# Or pipe text via stdin
cat <<EOF | python agent.py
Meeting: Sprint Planning
Date: 2024-06-01
Participants: Alice, Bob, Charlie
Agenda: Review backlog, assign tasks, estimate effort
EOF
```

### Output

The tool prints two sections:

1. **JSON** – a pretty‑printed JSON representation of the Pydantic model.
2. **Summary** – a human‑readable summary of the key fields.

Example output for a person:

```json
{
  "name": "Anna",
  "age": 28,
  "profession": "Python developer",
  "skills": [
    "FastAPI",
    "Docker"
  ]
}

Summary:
Anna, 28 years old, works as Python developer.
Skills: FastAPI, Docker.
```

## Extending

To add a new schema:
1. Define a new `Pydantic` model.
2. Create a prompt and parser similar to the existing ones.
3. Update the `detect_schema` logic or add a new classifier.

## License
MIT