graphragzen.text_embedding.vector_databases.VectorDatabase

class graphragzen.text_embedding.vector_databases.VectorDatabase[source]

Attributes

`distance_measure`
`vector_size`

Methods

`add_vectors`(vectors)	Add vectors to the database
`search`(query_vectors, k[, score_threshold, ...])	Similarity search for each of the query vectors

abstract __init__()[source]

Initialize the client to communicate to the vector DB backend of your choice

abstract add_vectors(vectors)[source]

Add vectors to the database

Parameters:: vectors (List[dict]) – Each dict containing {“uuid”: …, “vector”: …}. Each dict may also contain the key and values {“metadata”: …}, which will be store with the vector and retrieved upon search.
Return type:: None

abstract search(query_vectors, k, score_threshold=0.0, filters={})[source]

Similarity search for each of the query vectors

Parameters:

query_vectors (np.ndarray) – Vectors for n queries, shaped (n x embedding_size)
k (int) – Max number of results to return per query vector.
score_threshold (float, optional) – Exclude all vector search results with a score worse than his. Defaults to 0.0
filters (dict, optional) – {“key”: “value_it_should_have”, “key2”: “value_it …..

Returns:

Per query List[dict] with each dict containing: {“uuid”: …, “score”: …, “metadata”: …}

Return type:

List[List[dict]]