Getting Started with Ragatanga¶

This guide will help you get started with Ragatanga, from installation to your first query.

Installation¶

Prerequisites¶

Before installing Ragatanga, ensure you have:

Python 3.8 or higher
pip (Python package installer)
(Optional) A virtual environment tool like venv or conda

Installing from PyPI¶

The easiest way to install Ragatanga is from PyPI:

pip install ragatanga

Installing from Source¶

To install the latest development version:

git clone https://github.com/jquant/ragatanga.git
cd ragatanga
pip install -e .

Basic Setup¶

Setting Up Your Ontology¶

Ragatanga requires an ontology file in Turtle (.ttl) format. If you don't have one, you can use a sample ontology:

import os
from ragatanga.utils.samples import download_sample_ontology

# Download a sample ontology
ontology_path = download_sample_ontology()
print(f"Sample ontology downloaded to: {ontology_path}")

Creating a Knowledge Base¶

You can create a knowledge base from Markdown files or other text sources:

import asyncio
from ragatanga.core.ontology import OntologyManager
from ragatanga.core.knowledge import KnowledgeBaseBuilder

async def build_kb():
    # Initialize ontology
    ontology_path = "path/to/ontology.ttl"
    ontology_manager = OntologyManager(ontology_path)
    await ontology_manager.load_and_materialize()

    # Build knowledge base from Markdown
    kb_builder = KnowledgeBaseBuilder(ontology_manager)
    await kb_builder.add_markdown_file("path/to/knowledge.md")

    # Save knowledge base
    await kb_builder.save("knowledge_base.md")

if __name__ == "__main__":
    asyncio.run(build_kb())

Your First Query¶

Here's a simple example to get you started with querying:

import asyncio
from ragatanga.core.ontology import OntologyManager
from ragatanga.core.retrieval import AdaptiveRetriever

async def first_query():
    # Initialize with ontology
    ontology_path = "path/to/ontology.ttl"
    ontology_manager = OntologyManager(ontology_path)
    await ontology_manager.load_and_materialize()

    # Create retriever
    retriever = AdaptiveRetriever(ontology_manager)

    # Make a simple query
    query = "What is Ragatanga?"
    results = await retriever.retrieve(query)

    # Print results
    print(f"Query: {query}")
    for i, result in enumerate(results):
        print(f"\nResult {i+1} (Confidence: {result.confidence:.2f}):")
        print(f"Content: {result.content[:150]}...")

if __name__ == "__main__":
    asyncio.run(first_query())

Configuration¶

Basic Configuration¶

You can configure Ragatanga using environment variables or a configuration file:

# Set API keys
export OPENAI_API_KEY="your-openai-api-key"

# Configure embedding model
export RAGATANGA_EMBEDDING_PROVIDER="openai"
export RAGATANGA_EMBEDDING_MODEL="text-embedding-ada-002"

Using a Configuration File¶

Create a .env file in your project root:

OPENAI_API_KEY=your-openai-api-key
RAGATANGA_EMBEDDING_PROVIDER=openai
RAGATANGA_EMBEDDING_MODEL=text-embedding-ada-002
RAGATANGA_LLM_PROVIDER=openai
RAGATANGA_LLM_MODEL=gpt-3.5-turbo

Next Steps¶

Now that you have Ragatanga set up, you can:

Explore the Usage Guide for more detailed examples
Learn about the Architecture to understand how Ragatanga works
Check out the API Reference for detailed documentation of all classes and methods