InstructorEmbeddingFunction
InstructorEmbeddingFunction is a class in pymilvus that handles encoding text into embeddings using the Instructor embedding model to support embedding retrieval in Milvus.
pymilvus.model.dense.InstructorEmbeddingFunction
Constructor
Constructs a MistralAIEmbeddingFunction for common use cases.
InstructorEmbeddingFunction(
model_name: str = "hkunlp/instructor-xl",
batch_size: int = 32,
query_instruction: str = "Represent the question for retrieval:",
doc_instruction: str = "Represent the document for retrieval:",
device: str = "cpu",
normalize_embeddings: bool = True,
**kwargs
)
PARAMETERS:
model_name (string)
The name of the Mistral AI embedding model to use for encoding. The value defaults to
hkunlp/instructor-xl. For more information, refer to Model List.batch_size (int)
The batch size used for the computation. It determines the number of sentences processed together in each batch.
query_instruction (string)
Task-specific instruction that guides the model on how to generate an embedding for a query or question.
doc_instruction (string)
Task-specific instruction that guides the model to generate an embedding for a document.
device (string)
Specifies the torch.device to use for the computation. If not specified, the function uses the default device.
normalize_embeddings (bool)
If set to True, the returned vectors will have a length of 1, indicating that they are normalized. In this case, similarity search would use the faster dot-product (util.dot_score), instead of cosine similarity.
kwargs
Allows additional keyword arguments to be passed to the model initialization. For more information, refer to instructor-embedding.
Examples
from pymilvus.model.dense import InstructorEmbeddingFunction
ef = InstructorEmbeddingFunction(
model_name="hkunlp/instructor-xl", # Defaults to `hkunlp/instructor-xl`
query_instruction="Represent the question for retrieval:",
doc_instruction="Represent the document for retrieval:"
)