ragbits-document-search 1.4.0.dev202512021005

Document Search module for Ragbits

Ragbits Document Search

Ragbits Document Search is a Python package that provides tools for building RAG applications. It helps ingest, index, and search documents to retrieve relevant information for your prompts.

Installation

You can install the latest version of Ragbits Document Search using pip:

pip install ragbits-document-search

Quickstart

import asyncio

from ragbits.core.embeddings import LiteLLMEmbedder
from ragbits.core.vector_stores.in_memory import InMemoryVectorStore
from ragbits.document_search import DocumentSearch

async def main() -> None:
    """
    Run the example.
    """
    embedder = LiteLLMEmbedder(
        model_name="text-embedding-3-small",
    )
    vector_store = InMemoryVectorStore(embedder=embedder)
    document_search = DocumentSearch(
        vector_store=vector_store,
    )

    # Ingest all .txt files from the "biographies" directory
    await document_search.ingest("local://biographies/*.txt")

    # Search the documents for the query
    results = await document_search.search("When was Marie Curie-Sklodowska born?")
    print(results)


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

Documentation

• Quickstart 2: Adding RAG Capabilities
• How-To Guides - Document Search
• API Reference - Document Search