This example shows how to use the`mixedbread-ai/mxbai-embed-large-v1` transformer, which has an embedding size of512 and requires specific parameters for recall.

This example shows how to use the`mixedbread-ai/mxbai-embed-large-v1` transformer, which has an embedding size of1024 and requires specific parameters for recall.

from django.dbimport models

content= models.TextField()

content_embedding= VectorField(

field_to_embed="content",

dimensions=512,

dimensions=1024,

transformer="mixedbread-ai/mxbai-embed-large-v1",

transformer_recall_parameters={

"query":"Represent this sentence for searching relevant passages:"

"prompt":"Represent this sentence for searching relevant passages:"

}

)

results= Article.vector_search("content_embedding","search query")

results= Article.vector_search("content_embedding","some query to search against")

Note the differences between the two examples:

1. The`dimensions` parameter is set to 384 for`intfloat/e5-small-v2` and512 for`mixedbread-ai/mxbai-embed-large-v1`.

1. The`dimensions` parameter is set to 384 for`intfloat/e5-small-v2` and1024 for`mixedbread-ai/mxbai-embed-large-v1`.

2. The`mixedbread-ai/mxbai-embed-large-v1` transformer requires additional parameters for recall, which are specified in the`transformer_recall_parameters` argument.

Both examples will automatically generate embeddings when instances are saved and allow for vector similarity searches using the`vector_search` method.

query_embedding=GenerateEmbedding(

Value(query_text),

"intfloat/e5-small-v2",

field_instance.transformer,

field_instance.transformer_recall_parameters,

)