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Available models

There are various embedding functions available out of the box with LanceDB to manage your embeddings implicitly. We're actively working on adding other popular embedding APIs and models.

Text embedding functions

Contains the text embedding functions registered by default.

  • Embedding functions have an inbuilt rate limit handler wrapper for source and query embedding function calls that retry with exponential backoff.
  • Each EmbeddingFunction implementation automatically takes max_retries as an argument which has the default value of 7.

Sentence transformers

Allows you to set parameters when registering a sentence-transformers object.

Info

Sentence transformer embeddings are normalized by default. It is recommended to use normalized embeddings for similarity search.

Parameter Type Default Value Description
name str all-MiniLM-L6-v2 The name of the model
device str cpu The device to run the model on (can be cpu or gpu)
normalize bool True Whether to normalize the input text before feeding it to the model
Check out available sentence-transformer models here!
- sentence-transformers/all-MiniLM-L12-v2
- sentence-transformers/paraphrase-mpnet-base-v2
- sentence-transformers/gtr-t5-base
- sentence-transformers/LaBSE
- sentence-transformers/all-MiniLM-L6-v2
- sentence-transformers/bert-base-nli-max-tokens
- sentence-transformers/bert-base-nli-mean-tokens
- sentence-transformers/bert-base-nli-stsb-mean-tokens
- sentence-transformers/bert-base-wikipedia-sections-mean-tokens
- sentence-transformers/bert-large-nli-cls-token
- sentence-transformers/bert-large-nli-max-tokens
- sentence-transformers/bert-large-nli-mean-tokens
- sentence-transformers/bert-large-nli-stsb-mean-tokens
- sentence-transformers/distilbert-base-nli-max-tokens
- sentence-transformers/distilbert-base-nli-mean-tokens
- sentence-transformers/distilbert-base-nli-stsb-mean-tokens
- sentence-transformers/distilroberta-base-msmarco-v1
- sentence-transformers/distilroberta-base-msmarco-v2
- sentence-transformers/nli-bert-base-cls-pooling
- sentence-transformers/nli-bert-base-max-pooling
- sentence-transformers/nli-bert-base
- sentence-transformers/nli-bert-large-cls-pooling
- sentence-transformers/nli-bert-large-max-pooling
- sentence-transformers/nli-bert-large
- sentence-transformers/nli-distilbert-base-max-pooling
- sentence-transformers/nli-distilbert-base
- sentence-transformers/nli-roberta-base
- sentence-transformers/nli-roberta-large
- sentence-transformers/roberta-base-nli-mean-tokens
- sentence-transformers/roberta-base-nli-stsb-mean-tokens
- sentence-transformers/roberta-large-nli-mean-tokens
- sentence-transformers/roberta-large-nli-stsb-mean-tokens
- sentence-transformers/stsb-bert-base
- sentence-transformers/stsb-bert-large
- sentence-transformers/stsb-distilbert-base
- sentence-transformers/stsb-roberta-base
- sentence-transformers/stsb-roberta-large
- sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens
- sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens
- sentence-transformers/xlm-r-base-en-ko-nli-ststb
- sentence-transformers/xlm-r-bert-base-nli-mean-tokens
- sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens
- sentence-transformers/xlm-r-large-en-ko-nli-ststb
- sentence-transformers/bert-base-nli-cls-token
- sentence-transformers/all-distilroberta-v1
- sentence-transformers/multi-qa-MiniLM-L6-dot-v1
- sentence-transformers/multi-qa-distilbert-cos-v1
- sentence-transformers/multi-qa-distilbert-dot-v1
- sentence-transformers/multi-qa-mpnet-base-cos-v1
- sentence-transformers/multi-qa-mpnet-base-dot-v1
- sentence-transformers/nli-distilroberta-base-v2
- sentence-transformers/all-MiniLM-L6-v1
- sentence-transformers/all-mpnet-base-v1
- sentence-transformers/all-mpnet-base-v2
- sentence-transformers/all-roberta-large-v1
- sentence-transformers/allenai-specter
- sentence-transformers/average_word_embeddings_glove.6B.300d
- sentence-transformers/average_word_embeddings_glove.840B.300d
- sentence-transformers/average_word_embeddings_komninos
- sentence-transformers/average_word_embeddings_levy_dependency
- sentence-transformers/clip-ViT-B-32-multilingual-v1
- sentence-transformers/clip-ViT-B-32
- sentence-transformers/distilbert-base-nli-stsb-quora-ranking
- sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking
- sentence-transformers/distilroberta-base-paraphrase-v1
- sentence-transformers/distiluse-base-multilingual-cased-v1
- sentence-transformers/distiluse-base-multilingual-cased-v2
- sentence-transformers/distiluse-base-multilingual-cased
- sentence-transformers/facebook-dpr-ctx_encoder-multiset-base
- sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base
- sentence-transformers/facebook-dpr-question_encoder-multiset-base
- sentence-transformers/facebook-dpr-question_encoder-single-nq-base
- sentence-transformers/gtr-t5-large
- sentence-transformers/gtr-t5-xl
- sentence-transformers/gtr-t5-xxl
- sentence-transformers/msmarco-MiniLM-L-12-v3
- sentence-transformers/msmarco-MiniLM-L-6-v3
- sentence-transformers/msmarco-MiniLM-L12-cos-v5
- sentence-transformers/msmarco-MiniLM-L6-cos-v5
- sentence-transformers/msmarco-bert-base-dot-v5
- sentence-transformers/msmarco-bert-co-condensor
- sentence-transformers/msmarco-distilbert-base-dot-prod-v3
- sentence-transformers/msmarco-distilbert-base-tas-b
- sentence-transformers/msmarco-distilbert-base-v2
- sentence-transformers/msmarco-distilbert-base-v3
- sentence-transformers/msmarco-distilbert-base-v4
- sentence-transformers/msmarco-distilbert-cos-v5
- sentence-transformers/msmarco-distilbert-dot-v5
- sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned
- sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch
- sentence-transformers/msmarco-distilroberta-base-v2
- sentence-transformers/msmarco-roberta-base-ance-firstp
- sentence-transformers/msmarco-roberta-base-v2
- sentence-transformers/msmarco-roberta-base-v3
- sentence-transformers/multi-qa-MiniLM-L6-cos-v1
- sentence-transformers/nli-mpnet-base-v2
- sentence-transformers/nli-roberta-base-v2
- sentence-transformers/nq-distilbert-base-v1
- sentence-transformers/paraphrase-MiniLM-L12-v2
- sentence-transformers/paraphrase-MiniLM-L3-v2
- sentence-transformers/paraphrase-MiniLM-L6-v2
- sentence-transformers/paraphrase-TinyBERT-L6-v2
- sentence-transformers/paraphrase-albert-base-v2
- sentence-transformers/paraphrase-albert-small-v2
- sentence-transformers/paraphrase-distilroberta-base-v1
- sentence-transformers/paraphrase-distilroberta-base-v2
- sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- sentence-transformers/paraphrase-multilingual-mpnet-base-v2
- sentence-transformers/paraphrase-xlm-r-multilingual-v1
- sentence-transformers/quora-distilbert-base
- sentence-transformers/quora-distilbert-multilingual
- sentence-transformers/sentence-t5-base
- sentence-transformers/sentence-t5-large
- sentence-transformers/sentence-t5-xxl
- sentence-transformers/sentence-t5-xl
- sentence-transformers/stsb-distilroberta-base-v2
- sentence-transformers/stsb-mpnet-base-v2
- sentence-transformers/stsb-roberta-base-v2
- sentence-transformers/stsb-xlm-r-multilingual
- sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
- sentence-transformers/clip-ViT-L-14
- sentence-transformers/clip-ViT-B-16
- sentence-transformers/use-cmlm-multilingual
- sentence-transformers/all-MiniLM-L12-v1

Info

You can also load many other model architectures from the library. For example models from sources such as BAAI, nomic, salesforce research, etc. See this HF hub page for all supported models.

BAAI Embeddings example

Here is an example that uses BAAI embedding model from the HuggingFace Hub supported models

import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry

db = lancedb.connect("/tmp/db")
model = get_registry.get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")

class Words(LanceModel):
    text: str = model.SourceField()
    vector: Vector(model.ndims()) = model.VectorField()

table = db.create_table("words", schema=Words)
table.add(
    [
        {"text": "hello world"},
        {"text": "goodbye world"}
    ]
)

query = "greetings"
actual = table.search(query).limit(1).to_pydantic(Words)[0]
print(actual.text)

Visit sentence-transformers HuggingFace HUB page for more information on the available models.

Huggingface embedding models

We offer support for all huggingface models (which can be loaded via transformers library). The default model is colbert-ir/colbertv2.0 which also has its own special callout - registry.get("colbert")

Example usage -

import lancedb
import pandas as pd

from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector

model = get_registry().get("huggingface").create(name='facebook/bart-base')

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

df = pd.DataFrame({"text": ["hi hello sayonara", "goodbye world"]})
table = db.create_table("greets", schema=Words)
table.add()
query = "old greeting"
actual = table.search(query).limit(1).to_pydantic(Words)[0]
print(actual.text)

OpenAI embeddings

LanceDB registers the OpenAI embeddings function in the registry by default, as openai. Below are the parameters that you can customize when creating the instances:

Parameter Type Default Value Description
name str "text-embedding-ada-002" The name of the model.
dim int Model default For OpenAI's newer text-embedding-3 model, we can specify a dimensionality that is smaller than the 1536 size. This feature supports it
import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry

db = lancedb.connect("/tmp/db")
func = get_registry().get("openai").create(name="text-embedding-ada-002")

class Words(LanceModel):
    text: str = func.SourceField()
    vector: Vector(func.ndims()) = func.VectorField()

table = db.create_table("words", schema=Words, mode="overwrite")
table.add(
    [
        {"text": "hello world"},
        {"text": "goodbye world"}
    ]
    )

query = "greetings"
actual = table.search(query).limit(1).to_pydantic(Words)[0]
print(actual.text)

Instructor Embeddings

Instructor is an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g. classification, retrieval, clustering, text evaluation, etc.) and domains (e.g. science, finance, etc.) by simply providing the task instruction, without any finetuning.

If you want to calculate customized embeddings for specific sentences, you can follow the unified template to write instructions.

Info

Represent the domain text_type for task_objective:

  • domain is optional, and it specifies the domain of the text, e.g. science, finance, medicine, etc.
  • text_type is required, and it specifies the encoding unit, e.g. sentence, document, paragraph, etc.
  • task_objective is optional, and it specifies the objective of embedding, e.g. retrieve a document, classify the sentence, etc.

More information about the model can be found at the source URL.

Argument Type Default Description
name str "hkunlp/instructor-base" The name of the model to use
batch_size int 32 The batch size to use when generating embeddings
device str "cpu" The device to use when generating embeddings
show_progress_bar bool True Whether to show a progress bar when generating embeddings
normalize_embeddings bool True Whether to normalize the embeddings
quantize bool False Whether to quantize the model
source_instruction str "represent the docuement for retreival" The instruction for the source column
query_instruction str "represent the document for retreiving the most similar documents" The instruction for the query
import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry, InstuctorEmbeddingFunction

instructor = get_registry().get("instructor").create(
                            source_instruction="represent the docuement for retreival",
                            query_instruction="represent the document for retreiving the most similar documents"
                            )

class Schema(LanceModel):
    vector: Vector(instructor.ndims()) = instructor.VectorField()
    text: str = instructor.SourceField()

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

texts = [{"text": "Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that..."},
        {"text": "The disparate impact theory is especially controversial under the Fair Housing Act because the Act..."},
        {"text": "Disparate impact in United States labor law refers to practices in employment, housing, and other areas that.."}]

tbl.add(texts)

Gemini Embeddings

With Google's Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity. For more on how and why you should use embeddings, refer to the Embeddings guide. The Gemini Embedding Model API supports various task types:

Task Type Description
"retrieval_query" Specifies the given text is a query in a search/retrieval setting.
"retrieval_document" Specifies the given text is a document in a search/retrieval setting. Using this task type requires a title but is automatically proided by Embeddings API
"semantic_similarity" Specifies the given text will be used for Semantic Textual Similarity (STS).
"classification" Specifies that the embeddings will be used for classification.
"clusering" Specifies that the embeddings will be used for clustering.

Usage Example:

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


model = get_registry().get("gemini-text").create()

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

df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
db = lancedb.connect("~/.lancedb")
tbl = db.create_table("test", schema=TextModel, mode="overwrite")

tbl.add(df)
rs = tbl.search("hello").limit(1).to_pandas()

AWS Bedrock Text Embedding Functions

AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function. You can do so by using awscli and also add your session_token:

aws configure
aws configure set aws_session_token "<your_session_token>"
to ensure that the credentials are set up correctly, you can run the following command:
aws sts get-caller-identity

Supported Embedding modelIDs are: * amazon.titan-embed-text-v1 * cohere.embed-english-v3 * cohere.embed-multilingual-v3

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

Parameter Type Default Value Description
name str "amazon.titan-embed-text-v1" The model ID of the bedrock model to use. Supported base models for Text Embeddings: amazon.titan-embed-text-v1, cohere.embed-english-v3, cohere.embed-multilingual-v3
region str "us-east-1" Optional name of the AWS Region in which the service should be called (e.g., "us-east-1").
profile_name str None Optional name of the AWS profile to use for calling the Bedrock service. If not specified, the default profile will be used.
assumed_role str None Optional ARN of an AWS IAM role to assume for calling the Bedrock service. If not specified, the current active credentials will be used.
role_session_name str "lancedb-embeddings" Optional name of the AWS IAM role session to use for calling the Bedrock service. If not specified, a "lancedb-embeddings" name will be used.
runtime bool True Optional choice of getting different client to perform operations with the Amazon Bedrock service.
max_retries int 7 Optional number of retries to perform when a request fails.

Usage Example:

import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry

model = get_registry().get("bedrock-text").create()

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

df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
db = lancedb.connect("tmp_path")
tbl = db.create_table("test", schema=TextModel, mode="overwrite")

tbl.add(df)
rs = tbl.search("hello").limit(1).to_pandas()

Multi-modal embedding functions

Multi-modal embedding functions allow you to query your table using both images and text.

OpenClip embeddings

We support CLIP model embeddings using the open source alternative, open-clip which supports various customizations. It is registered as open-clip and supports the following customizations:

Parameter Type Default Value Description
name str "ViT-B-32" The name of the model.
pretrained str "laion2b_s34b_b79k" The name of the pretrained model to load.
device str "cpu" The device to run the model on. Can be "cpu" or "gpu".
batch_size int 64 The number of images to process in a batch.
normalize bool True Whether to normalize the input images before feeding them to the model.

This embedding function supports ingesting images as both bytes and urls. You can query them using both test and other images.

Info

LanceDB supports ingesting images directly from accessible links.

import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry

db = lancedb.connect(tmp_path)
func = get_registry.get("open-clip").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(
    [{"label": labels, "image_uri": uris, "image_bytes": image_bytes}]
)
Now we can search using text from both the default vector column and the custom vector column
# text search
actual = table.search("man's best friend").limit(1).to_pydantic(Images)[0]
print(actual.label) # prints "dog"

frombytes = (
    table.search("man's best friend", vector_column_name="vec_from_bytes")
    .limit(1)
    .to_pydantic(Images)[0]
)
print(frombytes.label)

Because we're using a multi-modal embedding function, we can also search using images

# image search
query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
image_bytes = requests.get(query_image_uri).content
query_image = Image.open(io.BytesIO(image_bytes))
actual = table.search(query_image).limit(1).to_pydantic(Images)[0]
print(actual.label == "dog")

# image search using a custom vector column
other = (
    table.search(query_image, vector_column_name="vec_from_bytes")
    .limit(1)
    .to_pydantic(Images)[0]
)
print(actual.label)

Imagebind embeddings

We have support for imagebind model embeddings. You can download our version of the packaged model via - pip install imagebind-packaged==0.1.2.

This function is registered as imagebind and supports Audio, Video and Text modalities(extending to Thermal,Depth,IMU data):

Parameter Type Default Value Description
name str "imagebind_huge" Name of the model.
device str "cpu" The device to run the model on. Can be "cpu" or "gpu".
normalize bool False set to True to normalize your inputs before model ingestion.

Below is an example demonstrating how the API works:

import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry

db = lancedb.connect(tmp_path)
func = get_registry.get("imagebind").create()

class ImageBindModel(LanceModel):
    text: str
    image_uri: str = func.SourceField()
    audio_path: str
    vector: Vector(func.ndims()) = func.VectorField()

# add locally accessible image paths
text_list=["A dog.", "A car", "A bird"]
image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]

# Load data
inputs = [
    {"text": a, "audio_path": b, "image_uri": c}
    for a, b, c in zip(text_list, audio_paths, image_paths)
]

#create table and add data
table = db.create_table("img_bind", schema=ImageBindModel)
table.add(inputs)

Now, we can search using any modality:

query_image = "./assets/dog_image2.jpg" #download an image and enter that path here
actual = table.search(query_image).limit(1).to_pydantic(ImageBindModel)[0]
print(actual.text == "dog")
query_audio = "./assets/car_audio2.wav" #download an audio clip and enter path here
actual = table.search(query_audio).limit(1).to_pydantic(ImageBindModel)[0]
print(actual.text == "car")

You can add any input query and fetch the result as follows:

query = "an animal which flies and tweets" 
actual = table.search(query).limit(1).to_pydantic(ImageBindModel)[0]
print(actual.text == "bird")

If you have any questions about the embeddings API, supported models, or see a relevant model missing, please raise an issue on GitHub.