Returns documents most similar to an embedding vector. 
similarity_search_by_vector(
    embedding: Union[List[float], np.ndarray],
    k: int = 4,
    filter: Optional[Dict[str, Any]] = None,
    **kwargs
) -> List[Document]

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Parameters

ParameterTypeDescription
embeddingUnion[List[float], np.ndarray]Embedding vector to search with
kintNumber of documents to return (default: 4)
filterOptional[Dict[str, Any]](Optional) Metadata filters to apply
**kwargsAnyAdditional keyword arguments (currently unused)

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Returns

List[Document]: List of most similar Document objects

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Example Usage

# Get embedding for a query
query_embedding = store.get_embeddings("data science concepts")

# Search using the embedding
results = store.similarity_search_by_vector(query_embedding, k=5)

# Search with custom embedding
custom_embedding = np.random.rand(384)  # Example 384-dim embedding
results = store.similarity_search_by_vector(custom_embedding, k=3)