encode_queries()

This operation takes in a list of query strings and encodes each query into a vector embedding.

Request syntax

encode_queries(
    queries: List[str], 
) -> List[np.array]

PARAMETERS:

• queries (List[str])

  A list of string values, where each string represents a query 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:

List[np.array]

RETURNS:

A list where each element is a NumPy array.

Exceptions:

• ValueError

  This exception will be raised when api_key is not provided and the MISTRALAI_API_KEY environment variable is also not set.

Examples

from pymilvus.model.dense import MistralAIEmbeddingFunction

ef = MistralAIEmbeddingFunction(
    model_name="mistral-embed", # Defaults to `mistral-embed`
    api_key="MISTRAL_API_KEY" # Provide your Mistral AI API key
)

queries = ["When was artificial intelligence founded", 
           "Where was Alan Turing born?"]

query_embeddings = ef.encode_queries(queries)

print("Embeddings:", query_embeddings)
print("Dim", ef.dim, query_embeddings[0].shape)

# Embeddings: [array([-0.04916382,  0.04568481,  0.03594971, ..., -0.02653503,
#         0.02804565,  0.00600815]), array([-0.05938721,  0.07098389,  0.01773071, ..., -0.01708984,
#         0.03582764,  0.00366592])]
# Dim 1024 (1024,)