Jina Embedding Models

Text Embedding Models

Jina embeddings are used to generate embeddings for text and image data. You also need to set the JINA_API_KEY environment variable to use the Jina API.

You can find a list of supported models under https://jina.ai/embeddings/

Supported parameters (to be passed in create method) are:

Parameter	Type	Default Value	Description
`name`	`str`	`"jina-clip-v1"`	The model ID of the jina model to use

Usage Example:

python

    import os
    import lancedb
    from lancedb.pydantic import LanceModel, Vector
    from lancedb.embeddings import EmbeddingFunctionRegistry

    os.environ['JINA_API_KEY'] = 'jina_*'

    jina_embed = EmbeddingFunctionRegistry.get_instance().get("jina").create(name="jina-embeddings-v2-base-en")


    class TextModel(LanceModel):
        text: str = jina_embed.SourceField()
        vector: Vector(jina_embed.ndims()) = jina_embed.VectorField()


    data = [{"text": "hello world"},
            {"text": "goodbye world"}]

    db = lancedb.connect("~/.lancedb-2")
    tbl = db.create_table("test", schema=TextModel, mode="overwrite")

    tbl.add(data)

Multimodal Embedding Models

Jina embeddings can also be used to embed both text and image data, only some of the models support image data and you can check the list under https://jina.ai/embeddings/

Supported parameters (to be passed in create method) are:

Parameter	Type	Default Value	Description
`name`	`str`	`"jina-clip-v1"`	The model ID of the jina model to use

Usage Example:

python

    import os
    import requests
    import lancedb
    from lancedb.pydantic import LanceModel, Vector
    from lancedb.embeddings import get_registry
    import pandas as pd

    os.environ['JINA_API_KEY'] = 'jina_*'

    db = lancedb.connect("~/.lancedb")
    func = get_registry().get("jina").create()


    class Images(LanceModel):
        label: str
        image_uri: str = func.SourceField()  # image uri as the source
        image_bytes: bytes = func.SourceField()  # image bytes as the source
        vector: Vector(func.ndims()) = func.VectorField()  # vector column
        vec_from_bytes: Vector(func.ndims()) = func.VectorField()  # Another vector column


    table = db.create_table("images", schema=Images)
    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
    uris = [
        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
    ]
    # get each uri as bytes
    image_bytes = [requests.get(uri).content for uri in uris]
    table.add(
      pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
    )