ragbits docs

Building blocks for rapid development of GenAI applications.

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ragbits is a Python package that offers essential "bits" for building powerful Retrieval-Augmented Generation (RAG) applications.

ragbits prioritizes an exceptional developer experience by providing a simple and intuitive API. It also includes a comprehensive set of tools for seamlessly building, testing, and deploying your RAG applications efficiently.

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 LiteLLMEmbeddings
from ragbits.core.vector_store 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(embedder=LiteLLMEmbeddings(), vector_store=InMemoryVectorStore())

    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()))