hnsqlite 0.0.5

A minimalist integration of sqlite and hnswlib focused on providing simple embedding persistence and search for text applications.

hnsqlite

hnsqlite is a text-centric integration of SQLite and HNSWLIB to provide a persistent collection of embeddings (strings, vectors, and metadata) and search time filtering based on the metadata.

Classes

Collection

The Collection class represents a combination of a SQLite database and an HNSWLIB index. The purpose of this class is to provide a persistent collection of embeddings (strings, vectors, and metadata) and search time filtering based on the metadata.

Methods

• create: Initializes a new Collection as a SQLite database file and associated HNSWLIB index.
• from_db: Creates a Collection object from a SQLite collection database file.
• save_index: Saves the current index after updates.
• make_index: Creates an HNSW index that includes all embeddings in the collection database and uses this new index for the collection going forward.
• load_index: Loads the latest HNSW index from disk and uses it for the collection.
• add_items: Adds new items to the collection.
• add_embedding: Adds a single Embedding object to the collection.
• add_embeddings: Adds a list of Embedding objects to the collection.
• search: Queries the HNSW index for the nearest neighbors of the given vector.

HnswIndex

The HnswIndex class represents the configuration associated with an HNSWLIB index as stored in the database.

CollectionConfig

The CollectionConfig class represents the configuration associated with a collection of strings and embeddings as persisted in the database.

Embedding

The Embedding class represents an embedding as stored in the database.

SearchResponse

The SearchResponse class represents the response of a search operation, containing the item (embedding) and its distance from the query vector.

Usage

To use hnsqlite, you can create a new collection, add items to it, and perform search operations. Here's an example:

from hnsqlite import Collection
import numpy as np

# Create a new collection
collection = Collection.create(name="example", dim=128)

# Add items to the collection
vectors = [np.random.rand(128) for _ in range(10)]
texts = [f"Text {i}" for i in range(10)]
collection.add_items(vectors, texts)

# Search for the nearest neighbors of a query vector
query_vector = np.random.rand(128)
results = collection.search(query_vector, k=5)

# Print the search results
for result in results:
    print(f"Item: {result.item}, Distance: {result.distance}")

This will create a new collection with 10 random embeddings, and then search for the 5 nearest neighbors of a random query vector.