Ragbits docs#

🐰 ragbits

Building blocks for rapid development of GenAI applications.

Features#

🔨 Build Reliable & Scalable GenAI Apps#

Swap LLMs anytime – Switch between 100+ LLMs via LiteLLM or run local models).
Type-safe LLM calls – Use Python generics to enforce strict type safety in model interactions.
Bring your own vector store – Connect to Qdrant, PgVector, and more with built-in support.
Developer tools included – Manage vector stores, query pipelines, and test prompts from your terminal.
Modular installation – Install only what you need, reducing dependencies and improving performance.

📚 Fast & Flexible RAG Processing#

Ingest 20+ formats – Process PDFs, HTML, spreadsheets, presentations, and more. Process data using unstructured or create a custom provider.
Handle complex data – Extract tables, images, and structured content with built-in VLMs support.
Connect to any data source – Use prebuilt connectors for S3, GCS, Azure, or implement your own.
Scale ingestion – Process large datasets quickly with Ray-based parallel processing.

🚀 Deploy & Monitor with Confidence#

Real-time observability – Track performance with OpenTelemetry and CLI insights.
Built-in testing – Validate prompts with promptfoo before deployment.
Auto-optimization – Continuously evaluate and refine model performance.
Visual testing UI (Coming Soon) – Test and optimize applications with a visual interface.

Installation#

You can install the latest version of Ragbits using pip:

pip install ragbits

Additionally, you can install one of the extensions to Ragbits:

ragbits[document-search] - provides tools for building document search applications.

Quickstart#

To build the simplest documents search, you can use the following code snippet:

import asyncio

from ragbits.core.embeddings import LiteLLMEmbedder
from ragbits.core.vector_stores import InMemoryVectorStore
from ragbits.document_search import DocumentSearch
from ragbits.document_search.documents.document import DocumentMeta

documents = [
    DocumentMeta.create_text_document_from_literal("RIP boiled water. You will be mist."),
    DocumentMeta.create_text_document_from_literal(
        "Why doesn't James Bond fart in bed? Because it would blow his cover."
    ),
    DocumentMeta.create_text_document_from_literal(
        "Why programmers don't like to swim? Because they're scared of the floating points."
    ),
]


async def main():
    document_search = DocumentSearch(vector_store=InMemoryVectorStore(embedder=LiteLLMEmbedder()))

    await document_search.ingest(documents)

    return await document_search.search("I'm boiling my water and I need a joke")


if __name__ == "__main__":
    print(asyncio.run(main()))

How Ragbits documentation is organized#

Quickstart - Get started with Ragbits in a few minutes
How-to guides - Learn how to use Ragbits in your projects
CLI - Learn how to manage Ragbits from the command line
API reference - Explore the underlying API of Ragbits