# Databricks Mosaic AI Integration
Available in:
Open SourceEnterprise
[Mosaic AI Search](https://docs.databricks.com/en/generative-ai/vector-search.html) is
a vector database that is built into the Databricks Data Intelligence Platform and integrated
with its governance and productivity tools, and we’ve made it easy to
use Mosaic’s vector search capabilities on your computer vision data
directly from FiftyOne!
Follow these [simple instructions](#mosaic-setup) to get started
using Mosaic + FiftyOne.
FiftyOne provides an API to create Mosaic indexes, upload vectors, and
run similarity queries, both [programmatically](#mosaic-query) in
Python and via point-and-click in the App.
#### NOTE
Did you know? You can
[search by natural language](../user_guide/similarity.md#brain-similarity-text) using
Mosaic similarity indexes!
## Basic recipe
The basic workflow to use Mosaic to create a similarity index on your
FiftyOne datasets and query your data is as follows:
1. Connect to your databricks workspace and [create an AI search endpoint](https://docs.databricks.com/aws/en/ai-search/create-ai-search#create-a-vector-search-endpoint).
2. [Load a dataset](../user_guide/import_datasets.md#importing-datasets) into FiftyOne
3. Compute embedding vectors for samples or patches in your dataset, or select
a model to use to generate embeddings
4. Use the [`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity)
method to generate a Mosaic similarity index for the samples or
object patches in a dataset by setting the parameter
`backend="mosaic"` and specifying a `brain_key` of your choice
5. Use this Mosaic similarity index to query your data with
[`sort_by_similarity()`](../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.sort_by_similarity)
6. If desired, delete the index
The example below demonstrates this workflow.
#### NOTE
You must have access to a databricks account with
[AI search enabled](https://docs.databricks.com/aws/en/ai-search/ai-search#requirements)
and install the
[Databricks AI Search Python package](https://api-docs.databricks.com/python/ai-search/databricks.ai_search.html)
to run this example:
```shell
pip install databricks-ai-search
```
Note that you need to provide credentials as described in [this section](#mosaic-setup)
to avoid entering them manually each time you interact with your
Mosaic index.
First, let’s load a dataset into FiftyOne and compute embeddings for the samples:
```python
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
mosaic_index = fob.compute_similarity(
dataset,
brain_key="mosaic_index",
backend="mosaic",
)
```
Once the similarity index has been generated, you can query your data in FiftyOne
by specifying the `brain_key`:
```python
# Step 4: Query your data
query = dataset.first().id # query by sample ID
view = dataset.sort_by_similarity(
query,
brain_key="mosaic_index",
k=10, # limit to 10 most similar samples
)
# Step 5 (optional): Cleanup
# Delete the Mosaic index
mosaic_index.cleanup()
# Delete run record from FiftyOne
dataset.delete_brain_run("mosaic_index")
```
#### NOTE
Skip to [this section](#mosaic-examples) for a variety of
common Mosaic query patterns.
## Setup
To get started with Mosaic AI Search, you need to have access to a Databricks workspace which satisfies the
[requirements for AI Search](https://docs.databricks.com/aws/en/ai-search/ai-search#requirements)
and [create an AI search endpoint](https://docs.databricks.com/aws/en/ai-search/create-ai-search#create-a-vector-search-endpoint).
You also need to have a catalog and schema in Databricks where you want to create the AI search index.
### Installing the Mosaic AI Vector Search client
In order to use the Mosaic backend, you must also install the
[Databricks Vector Search Python package](https://api-docs.databricks.com/python/vector-search/databricks.vector_search.html)
```shell
pip install databricks-ai-search
```
### Using the Mosaic backend
By default, calling
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity) or
[`sort_by_similarity()`](../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.sort_by_similarity)
will use an sklearn backend.
To use the Mosaic backend, simply set the optional `backend` parameter of
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity) to
`"mosaic"`:
```python
import fiftyone.brain as fob
fob.compute_similarity(..., backend="mosaic", ...)
```
Alternatively, you can permanently configure FiftyOne to use the Mosaic
backend by setting the following environment variable:
```shell
export FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND=mosaic
```
or by setting the `default_similarity_backend` parameter of your
[brain config](../brain/index.md#brain-config) located at `~/.fiftyone/brain_config.json`:
```json
{
"default_similarity_backend": "mosaic"
}
```
### Authentication
You can provide your credentials in a
[variety of ways](https://docs.databricks.com/aws/en/ai-search/ai-search#data-protection-and-authentication).
**Environment variables (recommended)**
The recommended way to configure your Databricks credentials is to store
them in the environment variables shown below, which are automatically accessed
by FiftyOne whenever a connection to Databricks is made.
```shell
export FIFTYONE_BRAIN_SIMILARITY_MOSAIC_WORKSPACE_URL=https://.cloud.databricks.com/
export FIFTYONE_BRAIN_SIMILARITY_MOSAIC_PERSONAL_ACCESS_TOKEN=XXXXXXXX
export FIFTYONE_BRAIN_SIMILARITY_MOSAIC_CATALOG_NAME=XXXXXXXX
export FIFTYONE_BRAIN_SIMILARITY_MOSAIC_SCHEMA_NAME=XXXXXXXX
export FIFTYONE_BRAIN_SIMILARITY_MOSAIC_ENDPOINT_NAME=XXXXXXXX
```
This is only one example of variables that can be used to authenticate an
Mosaic client. Find more information
[here](https://docs.databricks.com/aws/en/ai-search/ai-search#data-protection-and-authentication).
**FiftyOne Brain config**
You can also store your credentials in your [brain config](../brain/index.md#brain-config)
located at `~/.fiftyone/brain_config.json`:
```json
{
"similarity_backends": {
"mosaic": {
"workspace_url": "https://.cloud.databricks.com/",
"personal_access_token": "XXXXXXXX",
"catalog_name": "XXXXXXXX",
"schema_name": "XXXXXXXX",
"endpoint_name": "XXXXXXXX"
}
}
}
```
Note that this file will not exist until you create it.
**Keyword arguments**
You can manually provide credentials as keyword arguments each time you call
methods like [`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity)
that require connections to Databricks:
```python
import fiftyone.brain as fob
mosaic_index = fob.compute_similarity(
...
backend="mosaic",
brain_key="mosaic_index",
workspace_url = "https://.cloud.databricks.com/",
personal_access_token = "XXXXXXXX",
catalog_name = "XXXXXXXX",
schema_name = "XXXXXXXX",
endpoint_name = "XXXXXXXX"
)
```
Note that, when using this strategy, you must manually provide the credentials
when loading an index later via
[`load_brain_results()`](../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.load_brain_results):
```python
mosaic_index = dataset.load_brain_results(
"mosaic_index",
workspace_url = "https://.cloud.databricks.com/",
personal_access_token = "XXXXXXXX",
catalog_name = "XXXXXXXX",
schema_name = "XXXXXXXX",
endpoint_name = "XXXXXXXX"
)
```
### Mosaic config parameters
The Mosaic backend supports the following parameter
to customize your similarity queries.
- **index_name** (*None*): the name of the Mosaic AI search index
to use or create. If not specified, a new unique name is generated automatically
You can specify this parameter via any of the strategies described in the
previous section. Here’s an example of a [brain config](../brain/index.md#brain-config)
that includes all of the available parameters:
```json
{
"similarity_backends": {
"mosaic": {
"index_name": "your-index"
}
}
}
```
However, typically this parameter is directly passed to
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity) to configure
a specific new index:
```python
mosaic_index = fob.compute_similarity(
...
backend="mosaic",
brain_key="mosaic_index",
index_name="your-index",
)
```
## 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()`](../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.list_brain_runs)
to see the available brain keys on a dataset:
```python
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()`](../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.get_brain_info)
to retrieve information about the configuration of a brain run:
```python
info = dataset.get_brain_info(brain_key)
print(info)
```
Use [`load_brain_results()`](../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.load_brain_results)
to load the [`SimilarityIndex`](../api/fiftyone.brain.similarity.md#fiftyone.brain.similarity.SimilarityIndex) instance for a brain run.
You can use
[`rename_brain_run()`](../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.rename_brain_run)
to rename the brain key associated with an existing similarity results run:
```python
dataset.rename_brain_run(brain_key, new_brain_key)
```
Finally, you can use
[`delete_brain_run()`](../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.delete_brain_run)
to delete the record of a similarity index computation from your FiftyOne
dataset:
```python
dataset.delete_brain_run(brain_key)
```
#### NOTE
Calling
[`delete_brain_run()`](../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.delete_brain_run)
only deletes the **record** of the brain run from your FiftyOne dataset; it
will not delete any associated Mosaic index, which you can do as
follows:
```python
# Delete the Mosaic index
mosaic_index = dataset.load_brain_results(brain_key)
mosaic_index.cleanup()
```
## Examples
This section demonstrates how to perform some common vector search workflows on
a FiftyOne dataset using the Mosaic backend.
#### NOTE
All of the examples below assume you have configured your Databricks account and
credentials as described in [this section](#mosaic-setup).
### Create a similarity index
In order to create a new Mosaic similarity index, you need to specify
either the `embeddings` or `model` argument to
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity). Here are a few
possibilities:
```python
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 = "mosaic_index"
# Option 1: Compute embeddings on the fly from model name
fob.compute_similarity(
dataset,
model=model_name,
backend="mosaic",
brain_key=brain_key,
)
# Option 2: Compute embeddings on the fly from model instance
fob.compute_similarity(
dataset,
model=model,
backend="mosaic",
brain_key=brain_key,
)
# Option 3: Pass pre-computed embeddings as a numpy array
embeddings = dataset.compute_embeddings(model)
fob.compute_similarity(
dataset,
embeddings=embeddings,
backend="mosaic",
brain_key=brain_key,
)
# Option 4: Pass pre-computed embeddings by field name
dataset.compute_embeddings(model, embeddings_field="embeddings")
fob.compute_similarity(
dataset,
embeddings="embeddings",
backend="mosaic",
brain_key=brain_key,
)
```
### Create a patch similarity index
You can also create a similarity index for
[object patches](../user_guide/similarity.md#brain-object-similarity) within your dataset by
including the `patches_field` argument to
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity):
```python
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="mosaic",
brain_key="mosaic_patches",
)
```
### Connect to an existing index
If you have already created a Mosaic index storing the embedding vectors
for the samples or patches in your dataset, you can connect to it by passing
the `index_name` to
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity):
```python
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
index_name="your-index", # the existing Mosaic index
brain_key="mosaic_index",
backend="mosaic",
)
```
### Add/remove embeddings from an index
You can use
[`add_to_index()`](../api/fiftyone.brain.similarity.md#fiftyone.brain.similarity.SimilarityIndex.add_to_index)
and
[`remove_from_index()`](../api/fiftyone.brain.similarity.md#fiftyone.brain.similarity.SimilarityIndex.remove_from_index)
to add and remove embeddings from an existing Mosaic index, respectively.
These methods can come in handy if you modify your FiftyOne dataset and need
to update the Mosaic index to reflect these changes:
```python
import numpy as np
import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz
dataset = foz.load_zoo_dataset("quickstart")
mosaic_index = fob.compute_similarity(
dataset,
model="clip-vit-base32-torch",
brain_key="mosaic_index",
backend="mosaic",
)
print(mosaic_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
mosaic_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)
mosaic_index.add_to_index(embeddings, sample_ids)
print(mosaic_index.total_index_size) # 210
```
### Retrieve embeddings from an index
You can use
[`get_embeddings()`](../api/fiftyone.brain.similarity.md#fiftyone.brain.similarity.SimilarityIndex.get_embeddings)
to retrieve embeddings from a Mosaic index by ID:
```python
import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz
dataset = foz.load_zoo_dataset("quickstart")
mosaic_index = fob.compute_similarity(
dataset,
model="clip-vit-base32-torch",
brain_key="mosaic_index",
backend="mosaic",
)
# Retrieve embeddings for the entire dataset
ids = dataset.values("id")
embeddings, sample_ids, _ = mosaic_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, _ = mosaic_index.get_embeddings(sample_ids=ids)
print(embeddings.shape) # (10, 512)
print(sample_ids.shape) # (10,)
```
### Querying a Mosaic index
You can query a Mosaic index by appending a
[`sort_by_similarity()`](../api/fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.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](../user_guide/similarity.md#brain-similarity-text))
```python
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="mosaic_index",
backend="mosaic",
)
# Query by vector
query = np.random.rand(512) # matches the dimension of CLIP embeddings
view = dataset.sort_by_similarity(query, k=10, brain_key="mosaic_index")
# Query by sample ID
query = dataset.first().id
view = dataset.sort_by_similarity(query, k=10, brain_key="mosaic_index")
# Query by a list of IDs
query = [dataset.first().id, dataset.last().id]
view = dataset.sort_by_similarity(query, k=10, brain_key="mosaic_index")
# Query by text prompt
query = "a photo of a dog"
view = dataset.sort_by_similarity(query, k=10, brain_key="mosaic_index")
```
#### NOTE
Performing a similarity search on a [`DatasetView`](../api/fiftyone.core.view.md#fiftyone.core.view.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`](../api/fiftyone.core.dataset.md#fiftyone.core.dataset.Dataset) once and then perform
searches on subsets of the dataset by
[constructing views](../user_guide/using_views.md#using-views) that contain the images of
interest.
### Accessing the Mosaic client
You can use the `client` property of a Mosaic index to directly access
the underlying Mosaic client instance and use its methods as desired:
```python
import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz
dataset = foz.load_zoo_dataset("quickstart")
mosaic_index = fob.compute_similarity(
dataset,
model="clip-vit-base32-torch",
brain_key="mosaic_index",
backend="mosaic",
)
mosaic_client = mosaic_index.client
print(mosaic_client)
```
### Advanced usage
As [previously mentioned](#mosaic-config-parameters), you can
customize your Mosaic indexes by providing optional parameters to
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity).
Here’s an example of creating a similarity index backed by a customized
Mosaic index. Just for fun, we’ll specify a custom index name and populate
the index for only a subset of our dataset:
```python
import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz
dataset = foz.load_zoo_dataset("quickstart")
# Create a custom Mosaic index
mosaic_index = fob.compute_similarity(
dataset,
model="clip-vit-base32-torch",
embeddings=False, # we'll add embeddings below
brain_key="mosaic_index",
backend="mosaic",
index_name="custom-quickstart-index",
)
# Add embeddings for a subset of the dataset
view = dataset.take(10)
embeddings, sample_ids, _ = mosaic_index.compute_embeddings(view)
mosaic_index.add_to_index(embeddings, sample_ids)
```