LanceDB Integration

LanceDB is a serverless vector database with deep integrations with the Python ecosystem. It requires no setup and is free to use.

FiftyOne provides an API to create LanceDB tables and run similarity queries, both programmatically in Python and via point-and-click in the App.

Basic recipe

The basic workflow to use LanceDB to create a similarity index on your FiftyOne datasets and use this to query your data is as follows:

1. Load a dataset into FiftyOne

2. Compute embedding vectors for samples or patches in your dataset, or select a model to use to generate embeddings

3. Use the compute_similarity() method to generate a LanceDB table for the samples or object patches embeddings in a dataset by setting the parameter backend="lancedb" and specifying a brain_key of your choice

4. Use this LanceDB table to query your data with sort_by_similarity()

5. If desired, delete the table

The example below demonstrates this workflow.

Note

You must install the LanceDB Python client to run this example:

pip install lancedb

Note that, if you are using a custom LanceDB URI, you can store your credentials as described in this section to avoid entering them manually each time you interact with your LanceDB index.

import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz

# Step 1: Load your data into FiftyOne
dataset = foz.load_zoo_dataset("quickstart")

# Steps 2 and 3: Compute embeddings and create a similarity index
lancedb_index = fob.compute_similarity(
    dataset,
    model="clip-vit-base32-torch",
    brain_key="lancedb_index",
    backend="lancedb",
)

Once the similarity index has been generated, we can query our data in FiftyOne by specifying the brain_key:

# Step 4: Query your data
query = dataset.first().id  # query by sample ID
view = dataset.sort_by_similarity(
    query,
    brain_key="lancedb_index",
    k=10,  # limit to 10 most similar samples
)

# Step 5 (optional): Cleanup

# Delete the LanceDB table
lancedb_index.cleanup()

# Delete run record from FiftyOne
dataset.delete_brain_run("lancedb_index")

Setup

You can get started using LanceDB by simply installing the LanceDB Python client:

pip install lancedb

Using the LanceDB backend

By default, calling compute_similarity() or sort_by_similarity() will use an sklearn backend.

To use the LanceDB backend, simply set the optional backend parameter of compute_similarity() to "lancedb":

import fiftyone.brain as fob

fob.compute_similarity(..., backend="lancedb", ...)

Alternatively, you can permanently configure FiftyOne to use the LanceDB backend by setting the following environment variable:

export FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND=lancedb

or by setting the default_similarity_backend parameter of your brain config located at ~/.fiftyone/brain_config.json:

{
    "default_similarity_backend": "lancedb"
}

LanceDB config parameters

The LanceDB backend supports query parameters that can be used to customize your similarity queries. These parameters include:

• table_name (None): the name of the LanceDB table to use. If none is provided, a new table will be created

• metric (“cosine”): the embedding distance metric to use when creating a new table. The supported values are ("cosine", "euclidean")

• uri (“/tmp/lancedb”): the database URI to use

You can specify these parameters via any of the strategies described in the previous section. Here’s an example of a brain config that includes all of the available parameters:

{
    "similarity_backends": {
        "lancedb": {
            "table_name": "your-table",
            "metric": "euclidean",
            "uri": "/tmp/lancedb"
        }
    }
}

However, typically these parameters are directly passed to compute_similarity() to configure a specific new index:

lancedb_index = fob.compute_similarity(
    ...
    backend="lancedb",
    brain_key="lancedb_index",
    table_name="your-table",
    metric="euclidean",
    uri="/tmp/lancedb",
)

Managing brain runs

FiftyOne provides a variety of methods that you can use to manage brain runs.

For example, you can call list_brain_runs() to see the available brain keys on a dataset:

import fiftyone.brain as fob

# List all brain runs
dataset.list_brain_runs()

# Only list similarity runs
dataset.list_brain_runs(type=fob.Similarity)

# Only list specific similarity runs
dataset.list_brain_runs(
    type=fob.Similarity,
    patches_field="ground_truth",
    supports_prompts=True,
)

Or, you can use get_brain_info() to retrieve information about the configuration of a brain run:

info = dataset.get_brain_info(brain_key)
print(info)

Use load_brain_results() to load the SimilarityIndex instance for a brain run.

You can use rename_brain_run() to rename the brain key associated with an existing similarity results run:

dataset.rename_brain_run(brain_key, new_brain_key)

Finally, you can use delete_brain_run() to delete the record of a similarity index computation from your FiftyOne dataset:

dataset.delete_brain_run(brain_key)

Note

Calling delete_brain_run() only deletes the record of the brain run from your FiftyOne dataset; it will not delete any associated LanceDB table, which you can do as follows:

# Delete the LanceDB table
lancedb_index = dataset.load_brain_results(brain_key)
lancedb_index.cleanup()

Examples

This section demonstrates how to perform some common vector search workflows on a FiftyOne dataset using the LanceDB backend.

Create a similarity index

In order to create a new LanceDB similarity index, you need to specify either the embeddings or model argument to compute_similarity(). Here’s a few possibilities:

import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")
model_name = "clip-vit-base32-torch"
model = foz.load_zoo_model(model_name)
brain_key = "lancedb_index"

# Option 1: Compute embeddings on the fly from model name
fob.compute_similarity(
    dataset,
    model=model_name,
    backend="lancedb",
    brain_key=brain_key,
)

# Option 2: Compute embeddings on the fly from model instance
fob.compute_similarity(
    dataset,
    model=model,
    backend="lancedb",
    brain_key=brain_key,
)

# Option 3: Pass precomputed embeddings as a numpy array
embeddings = dataset.compute_embeddings(model)
fob.compute_similarity(
    dataset,
    embeddings=embeddings,
    backend="lancedb",
    brain_key=brain_key,
)

# Option 4: Pass precomputed embeddings by field name
dataset.compute_embeddings(model, embeddings_field="embeddings")
fob.compute_similarity(
    dataset,
    embeddings="embeddings",
    backend="lancedb",
    brain_key=brain_key,
)

Note

You can customize the LanceDB index by passing any supported parameters as extra kwargs.

Create a patch similarity index

You can also create a similarity index for object patches within your dataset by specifying a patches_field argument to compute_similarity():

import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")

fob.compute_similarity(
    dataset,
    patches_field="ground_truth",
    model="clip-vit-base32-torch",
    backend="lancedb",
    brain_key="lancedb_index",
)

Note

You can customize the LanceDB index by passing any supported parameters as extra kwargs.

Connect to an existing index

If you have already created a LanceDB table storing the embedding vectors for the samples or patches in your dataset, you can connect to it by passing the table_name to compute_similarity():

import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")

fob.compute_similarity(
    dataset,
    model="clip-vit-base32-torch",      # zoo model used (if applicable)
    embeddings=False,                   # don't compute embeddings
    table_name="your-table",            # the existing LanceDB table
    brain_key="lancedb_index",
    backend="lancedb",
)

Add/remove embeddings from an index

You can use add_to_index() and remove_from_index() to add and remove embeddings from an existing Lancedb index.

These methods can come in handy if you modify your FiftyOne dataset and need to update the LanceDB index to reflect these changes:

import numpy as np

import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")

lancedb_index = fob.compute_similarity(
    dataset,
    model="clip-vit-base32-torch",
    brain_key="lancedb_index",
    backend="lancedb",
)
print(lancedb_index.total_index_size)  # 200

view = dataset.take(10)
ids = view.values("id")

# Delete 10 samples from a dataset
dataset.delete_samples(view)

# Delete the corresponding vectors from the index
lancedb_index.remove_from_index(sample_ids=ids)

# Add 20 samples to a dataset
samples = [fo.Sample(filepath="tmp%d.jpg" % i) for i in range(20)]
sample_ids = dataset.add_samples(samples)

# Add corresponding embeddings to the index
embeddings = np.random.rand(20, 512)
lancedb_index.add_to_index(embeddings, sample_ids)

print(lancedb_index.total_index_size)  # 210

Retrieve embeddings from an index

You can use get_embeddings() to retrieve embeddings from a LanceDB index by ID:

import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")

lancedb_index = fob.compute_similarity(
    dataset,
    model="clip-vit-base32-torch",
    brain_key="lancedb_index",
    backend="lancedb",
)

# Retrieve embeddings for the entire dataset
ids = dataset.values("id")
embeddings, sample_ids, _ = lancedb_index.get_embeddings(sample_ids=ids)
print(embeddings.shape)  # (200, 512)
print(sample_ids.shape)  # (200,)

# Retrieve embeddings for a view
ids = dataset.take(10).values("id")
embeddings, sample_ids, _ = lancedb_index.get_embeddings(sample_ids=ids)
print(embeddings.shape)  # (10, 512)
print(sample_ids.shape)  # (10,)

Querying a LanceDB index

You can query a LanceDB index by appending a sort_by_similarity() stage to any dataset or view. The query can be any of the following:

• An ID (sample or patch)

• A query vector of same dimension as the index

• A list of IDs (samples or patches)

• A text prompt (if supported by the model)

import numpy as np

import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")

fob.compute_similarity(
    dataset,
    model="clip-vit-base32-torch",
    brain_key="lancedb_index",
    backend="lancedb",
)

# Query by vector
query = np.random.rand(512)  # matches the dimension of CLIP embeddings
view = dataset.sort_by_similarity(query, k=10, brain_key="lancedb_index")

# Query by sample ID
query = dataset.first().id
view = dataset.sort_by_similarity(query, k=10, brain_key="lancedb_index")

# Query by a list of IDs
query = [dataset.first().id, dataset.last().id]
view = dataset.sort_by_similarity(query, k=10, brain_key="lancedb_index")

# Query by text prompt
query = "a photo of a dog"
view = dataset.sort_by_similarity(query, k=10, brain_key="lancedb_index")

Note

Performing a similarity search on a DatasetView will only return results from the view; if the view contains samples that were not included in the index, they will never be included in the result.

This means that you can index an entire Dataset once and then perform searches on subsets of the dataset by constructing views that contain the images of interest.

Advanced usage

LanceDB is compatible with the Python ecosystem and can be used with pandas, numpy, and arrow:

lancedb_index = fob.compute_similarity(..., backend="lancedb", ...)

# Retrieve the raw LanceDB table
table = lancedb_index.table

df = table.to_pandas()  # get the table as a pandas dataframe
pa = table.to_arrow()   # get the table as an arrow table