hnsqlite 0.1.5

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.
• get_embeddings: return a list of embeddings from an offset
• get_embeddings_doc_ids: return a list of embeddings associated with specified doc_ids
• search: Queries the HNSW index for the nearest neighbors of the given vector. Supply a k parameter (defaults to 12) and an optional filter dictionary.
• delete: Deletes items from the collection based on a filter, a specific list of document_ids, or everything.

dbHnswIndexConfig

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

dbCollectionConfig

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

dbEmbedding

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

Embedding

The Embedding class represents an Embedding as sent to/from the Collection API.

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)

# Get the number of items in the collection
item_count = collection.count()
print(f"Number of items in the collection: {item_count}")

# 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}, Distance: {result.distance}")

Filtering

The filtering function is designed to support metadata filtering similar to MongoDB. It utilizes the hnswlib filtering function to accept or reject nearest neighbot candidates based on the embedding metadata matching a search time filtering criteria.

Supported Metadata

The embedding metadata is a dictionary that stores metadata associated with items in the collection. The keys represent the field names of the metadata, and the supported values are strings, numbers, booleans or lists of strings.

Example of a metadata dictionary:

{
    "author": "John Doe",
    "rating": 4.5,
    "tags": ["python", "database", "search"]
}

Filtering Operations

The search function supports a filter similar to MongoDB.

The following operations are supported:

• $eq: Checks if a metadata value is equal to the specified value.
• $ne: Checks if a metadata value is not equal to the specified value.
• $gt: Checks if a metadata value is greater than the specified value.
• $gte: Checks if a metadata value is greater than or equal to the specified value.
• $lt: Checks if a metadata value is less than the specified value.
• $lte: Checks if a metadata value is less than or equal to the specified value.
• $in: Checks if a metadata value is in the specified list of values.
• $nin: Checks if a metadata value is not in the specified list of values.
• $and: Combines multiple filter conditions using an AND logical operator.
• $or: Combines multiple filter conditions using an OR logical operator.

Usage

filter_dict = {
    "rating": {"$gte": 4},
    "tags": {"$in": ["python", "search"]},
    "$or": [
        {"author": {"$eq": "John Doe"}},
        {"author": {"$eq": "Jane Smith"}}
    ]
}

metadata_dict = {
    "author": "John Doe",
    "rating": 4.5,
    "tags": ["python", "database", "search"]
}

result = filter_item(filter_dict, metadata_dict)

The result will be True if the metadata_dict satisfies the conditions defined in the filter_dict. In this example, the metadata has a rating greater than or equal to 4 and at least one tag from the specified list, the author is either "John Doe" or "Jane Smith", so the result will be True.`

This will create a new collection with 10 random embeddings, get the number of items in the collection, search for the 5 nearest neighbors of a random query vector.