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<a id="redis-integration"></a>

# Redis Vector Search Integration

[Redis](https://redis.com) is the leading open source in-memory data store,
and we’ve made it easy to use Redis’
[vector search capabilities](https://redis.com/solutions/use-cases/vector-database)
on your computer vision data directly from FiftyOne!

Follow these [simple instructions](#redis-setup) to configure a Redis
server and get started using Redis + FiftyOne.

FiftyOne provides an API to create Redis vector search indexes, upload vectors,
and run similarity queries, both [programmatically](#redis-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 Redis
similarity indexes!

![image-similarity](images/brain/brain-image-similarity.gif)

<a id="redis-basic-recipe"></a>

## Basic recipe

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

1. Start a Redis service locally
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 Redis similarity index for the samples or object
   patches in a dataset by setting the parameter `backend="redis"` and
   specifying a `brain_key` of your choice
5. Use this Redis 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

<br />
The example below demonstrates this workflow.

#### NOTE
You must [launch a Redis server](https://redis.io/docs/install/install-stack)
and install the [Redis Python client](https://github.com/redis/redis-py)
to run this example:

```shell
brew tap redis-stack/redis-stack
brew install redis-stack
redis-stack-server

pip install redis
```

Note that, if you are using a custom Redis server, you can store your
credentials as described in [this section](#redis-setup) to avoid
entering them manually each time you interact with your Redis 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
redis_index = fob.compute_similarity(
    dataset,
    brain_key="redis_index",
    backend="redis",
)
```

Once the similarity index has been generated, we can query our 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="redis_index",
    k=10,  # limit to 10 most similar samples
)

# Step 5 (optional): Cleanup

# Delete the Redis vector search index
redis_index.cleanup()

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

#### NOTE
Skip to [this section](#redis-examples) for a variety of common
Redis query patterns.

<a id="redis-setup"></a>

## Setup

The easiest way to get started with Redis is to
[install Redis Stack](https://redis.io/docs/install/install-stack):

```shell
brew tap redis-stack/redis-stack
brew install redis-stack
redis-stack-server
```

### Installing the Redis client

In order to use the Redis backend, you must also install the
[Redis Python client](https://github.com/redis/redis-py):

```shell
pip install redis
```

### Using the Redis 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 Redis backend, simply set the optional `backend` parameter of
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity) to `"redis"`:

```python
import fiftyone.brain as fob

fob.compute_similarity(..., backend="redis", ...)
```

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

```shell
export FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND=redis
```

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": "redis"
}
```

### Authentication

If you are using a custom Redis server, you can provide your credentials in a
variety of ways.

**Environment variables (recommended)**

The recommended way to configure your Redis credentials is to store them in
the environment variables shown below, which are automatically accessed by
FiftyOne whenever a connection to Redis is made.

```shell
export FIFTYONE_BRAIN_SIMILARITY_REDIS_HOST=localhost
export FIFTYONE_BRAIN_SIMILARITY_REDIS_PORT=6379
export FIFTYONE_BRAIN_SIMILARITY_REDIS_DB=0
export FIFTYONE_BRAIN_SIMILARITY_REDIS_USERNAME=username
export FIFTYONE_BRAIN_SIMILARITY_REDIS_PASSWORD=password
```

**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": {
        "redis": {
            "host": "localhost",
            "port": 6379,
            "db": 0,
            "username": "username",
            "password": "password"
        }
    }
}
```

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 Redis:

```python
import fiftyone.brain as fob

redis_index = fob.compute_similarity(
    ...
    backend="redis",
    brain_key="redis_index",
    host="localhost",
    port=6379,
    db=0,
    username="username",
    password="password",
)
```

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
redis_index = dataset.load_brain_results(
    "redis_index",
    host="localhost",
    port=6379,
    db=0,
    username="username",
    password="password",
)
```

<a id="redis-config-parameters"></a>

### Redis config parameters

The Redis backend supports a variety of query parameters that can be used to
customize your similarity queries. These parameters include:

- **index_name** (*None*): the name of the Redis vector search index to use
  or create. If not specified, a new unique name is generated automatically
- **metric** ( *“cosine”*): the distance/similarity metric to use when
  creating a new index. The supported values are
  `("cosine", "dotproduct", "euclidean")`
- **algorithm** ( *“FLAT”*): the search algorithm to use. The supported values
  are `("FLAT", "HNSW")`

For detailed information on these parameters, see the
[Redis documentation](https://redis.io/docs/get-started/vector-database).

You can specify these parameters 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": {
        "redis": {
            "index_name": "your-index",
            "metric": "cosine",
            "algorithm": "FLAT"
        }
    }
}
```

However, typically these parameters are directly passed to
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity) to configure
a specific new index:

```python
redis_index = fob.compute_similarity(
    ...
    backend="redis",
    brain_key="redis_index",
    index_name="your-index",
    metric="cosine",
    algorithm="FLAT",
)
```

<a id="redis-managing-brain-runs"></a>

## 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 Redis index, which you can do as
follows:

```python
# Delete the Redis vector search index
redis_index = dataset.load_brain_results(brain_key)
redis_index.cleanup()
```

<a id="redis-examples"></a>

## Examples

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

#### NOTE
All of the examples below assume you have configured your Redis server
as described in [this section](#redis-setup).

<a id="redis-new-similarity-index"></a>

### Create a similarity index

In order to create a new Redis similarity index, you need to specify either
the `embeddings` or `model` argument to
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity). Here’s 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 = "redis_index"

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

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

#### NOTE
You can customize the Redis index by passing any
[supported parameters](#redis-config-parameters) as extra kwargs.

<a id="redis-patch-similarity-index"></a>

### 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="redis",
    brain_key="redis_patches",
)
```

#### NOTE
You can customize the Redis index by passing any
[supported parameters](#redis-config-parameters) as extra kwargs.

<a id="redis-connect-to-existing-index"></a>

### Connect to an existing index

If you have already created a Redis 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 Redis index
    brain_key="redis_index",
    backend="redis",
)
```

<a id="redis-add-remove-embeddings"></a>

### 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 Redis index.

These methods can come in handy if you modify your FiftyOne dataset and need
to update the Redis 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")

redis_index = fob.compute_similarity(
    dataset,
    model="clip-vit-base32-torch",
    brain_key="redis_index",
    backend="redis",
)
print(redis_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
redis_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)
redis_index.add_to_index(embeddings, sample_ids)

print(redis_index.total_index_size)  # 210
```

<a id="redis-get-embeddings"></a>

### 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 Redis index by ID:

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

dataset = foz.load_zoo_dataset("quickstart")

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

# Retrieve embeddings for the entire dataset
ids = dataset.values("id")
embeddings, sample_ids, _ = redis_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, _ = redis_index.get_embeddings(sample_ids=ids)
print(embeddings.shape)  # (10, 512)
print(sample_ids.shape)  # (10,)
```

<a id="redis-query"></a>

### Querying a Redis index

You can query a Redis 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="redis_index",
    backend="redis",
)

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

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

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

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

<a id="redis-access-client"></a>

### Accessing the Redis client

You can use the `client` property of a Redis index to directly access the
underlying Redis 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")

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

redis_client = redis_index.client
index_name = redis_index.config.index_name
print(redis_client)
print(redis_client.ft(index_name).info())
```

<a id="redis-advanced-usage"></a>

### Advanced usage

As [previously mentioned](#redis-config-parameters), you can customize
your Redis index by providing optional parameters to
[`compute_similarity()`](../api/fiftyone.brain.md#fiftyone.brain.compute_similarity).

In particular, the `algorithm` parameter may impact the quality of your query
results, as well as the time and memory required to perform approximate nearest
neighbor searches.

Here’s an example of creating a similarity index backed by a customized Redis
index. Just for fun, we’ll specify a custom index name, use dot product
similarity, 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 Redis index
redis_index = fob.compute_similarity(
    dataset,
    model="clip-vit-base32-torch",
    embeddings=False,  # we'll add embeddings below
    brain_key="redis_index",
    backend="redis",
    index_name="custom-quickstart-index",
    metric="dotproduct",
    algorithm="HNSW",
)

# Add embeddings for a subset of the dataset
view = dataset.take(10)
embeddings, sample_ids, _ = redis_index.compute_embeddings(view)
redis_index.add_to_index(embeddings, sample_ids)

redis_client = redis_index.client
index_name = redis_index.config.index_name
print(redis_client.ft(index_name).info())
```