lnclite 0.2.0

Lite usages of lancedb.

lnclite

lnclite is a small async LanceDB document store for OpenAI-compatible embeddings. It gives you a compact API for creating a local vector database, adding documents, filtering by tags, and running semantic search.

Installation

pip install lnclite

For local development from this repository:

poetry install --all-groups

Quick Start

import asyncio

from openai import AsyncOpenAI
from openai_embeddings_model import ModelSettings

from lnclite import DocumentCreate, Lnclite, get_openai_embeddings_model


async def main():
    embeddings = get_openai_embeddings_model(
        openai_client=AsyncOpenAI(),
    )

    client = await Lnclite.new(
        lancedb_path="outputs/demo.lance",
        openai_embeddings_model=embeddings,
        model_settings=ModelSettings(dimensions=1536),
        name="Demo",
        description="Local demo documents",
    )

    await client.documents.batch_create(
        [
            DocumentCreate(
                content="A note about async Python clients.",
                tags=["type:note", "topic:python"],
            ),
            DocumentCreate(
                content="A note about vector search and indexing.",
                tags=["type:note", "topic:search"],
            ),
        ]
    )

    await client.create_index()

    results = await client.search(
        "How should I design vector search?",
        tags_any=["topic:search"],
    )

    for result in results.results:
        print(result.document.content)
        print(result.document.tags)
        print(result.distance)

    # Vectors are hidden in returned documents by default.
    results_with_vectors = await client.search(
        "How should I design vector search?",
        include_vector=True,
    )
    print(len(results_with_vectors.results[0].document.vector))


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

Documentation

Full documentation is published at allen2c.github.io/lnclite.

Useful pages:

• Installation
• Basic usage
• Directory ingest
• Managing multiple stores
• API reference

Examples

Runnable end-to-end examples live in examples/. They cover basic search, tag filtering, directory ingest, managing multiple stores, and an optional downloaded open-dataset demo.

License

MIT