encode_documents()

This operation takes in documents and encodes them into vector embeddings.

Request syntax

encode_documents(
    documents: List[str], 
) -> Dict

PARAMETERS:

• documents (List[str])

  A list of string values, where each string represents a document that will be passed to the embedding model for encoding. The model will generate an embedding vector for each string in the list.

RETURN TYPE:

Dict

RETURNS:

A dictionary that contains the encoded embeddings, both dense and sparse.

Exceptions:

None

Examples

from pymilvus.model.hybrid import MGTEEmbeddingFunction

ef = MGTEEmbeddingFunction()

docs = [
    "Artificial intelligence was founded as an academic discipline in 1956.",
    "Alan Turing was the first person to conduct substantial research in AI.",
    "Born in Maida Vale, London, Turing was raised in southern England.",
]

docs_embeddings = ef.encode_documents(docs)

# Print embeddings
print("Embeddings:", docs_embeddings)
# Print dimension of embeddings
print(ef.dim)

# Embeddings: {'dense': [tensor([-4.9149e-03,  1.6553e-02, -9.5524e-03, -2.1800e-02,  1.2075e-02,
#          1.8500e-02, -3.0632e-02,  5.5909e-02,  8.7365e-02,  1.8763e-02,
#          2.1708e-03, -2.7530e-02, -1.1523e-01,  6.5810e-03, -6.4674e-02,
#          6.7966e-02,  1.3005e-01,  1.1942e-01, -1.2174e-02, -4.0426e-02,
#         ...
#          2.0129e-02, -2.3657e-02,  2.2626e-02,  2.1858e-02, -1.9181e-02,
#          6.0706e-02, -2.0558e-02, -4.2050e-02], device='mps:0')], 'sparse': <Compressed Sparse Row sparse array of dtype 'float64'
#         with 41 stored elements and shape (3, 250002)>}

# {'dense': 768, 'sparse': 250002}