ragpackai

ragpackai Documentation

Welcome to ragpackai - the Portable Retrieval-Augmented Generation Library!

ragpackai allows you to create, save, load, and query portable RAG packs containing documents, embeddings, vectorstores, and configuration metadata in a single .rag file.

Quick Start

from ragpackai import ragpackai

# Create a pack from documents
pack = ragpackai.from_files([
    "docs/manual.pdf", 
    "notes.txt",
    "knowledge_base/"
])

# Save the pack
pack.save("my_knowledge.rag")

# Load and query
pack = ragpackai.load("my_knowledge.rag")
answer = pack.ask("What are the main features?")
print(answer)

Table of Contents

:maxdepth: 2
:caption: Contents:

installation
quickstart
api_reference
examples
providers
cli
security
contributing
changelog

Features

• 🚀 Portable RAG Packs: Bundle everything into a single .rag file
• 🔄 Provider Flexibility: Support for OpenAI, Google, Groq, Cerebras, and HuggingFace
• 🔒 Encryption Support: Optional AES-GCM encryption for sensitive data
• 🎯 Runtime Overrides: Change embedding/LLM providers without rebuilding
• 📚 Multiple Formats: Support for PDF, TXT, MD, and more
• 🛠️ CLI Tools: Command-line interface for easy pack management
• 🔧 Lazy Loading: Efficient dependency management with lazy imports

Indices and tables

• {ref}genindex
• {ref}modindex
• {ref}search

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