Pre-release
This release is a pre-release and may not be stable for production use.
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
2 maintainers
Metadata
2 maintainers
Release files for ragbits-document-search 1.7.0.dev202605130309
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Source distribution (sdist)
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| ragbits_document_search-1.7.0.dev202605130309.tar.gz | 722.6 kB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ragbits_document_search-1.7.0.dev202605130309-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 771.9 kB
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Release files / ragbits_document_search-1.7.0.dev202605130309-py3-none-any.whl
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|---|---|
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| Tags | Python 3 |
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