lancedb/docs/src/integrations/voxel51.md

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
```
pip install lancedb
```

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")

More in depth walkthrough of the integration, visit the LanceDB guide on Voxel51 - LaceDB x Voxel51