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Prism image search

This demo application provides semantic search for a set of images by indexing them using the CLIP model created by OpenAI. This model generates vectors with semantic meaning from each image and stores it as a vector embedding in Aerospike. When a user performs a query a vector embedding for the provided text is generated and Aerospike Vector Search (AVS) performs Approximate Nearest Neighbor(ANN) search to find relevant results .

Prerequisites

You don't have to know Aerospike to get started, but you do need the following:

  1. A Python 3.10 - 3.11 environment and familiarity with the Python programming language (see Setup Python Virtual Environment).
  2. An Aerospike Vector Search host (preview environment or local).

Configure AVS host

If you are connecting to a preview environment, you'll need to set the following:

export AVS_HOST=<PREVIEW_ENV_IP>

Install Dependencies

Change directories into the prism folder.

cd prism

Install dependencies using requirements.text

python3 -m pip install -r requirements.txt

Link your photos

To index your local photos, create a symlink to a location with photos directory.

ln -s ~/Pictures static/images/data

Start the application

Important

If you did not use an virtualenv when installing dependencies waitress-serve will likely not be in your path.

 waitress-serve --host 127.0.0.1 --port 8080 --threads 32 prism:app

Performing an image search

Navigate to http://127.0.0.1:8080 and perform a search for images based on a description.

Install using docker compose

If you have a license key, you can easily setup Aerospike, AVS, and the prism-image-search app using docker-compose. When using docker-compose, you'll need to place your images in container-volumes

ln -s ~/Pictures container-volumes/prism/images/static/data

1. Build the prism image

cd prism-image-search && \\
docker build -t prism . -f Dockerfile-prism

2. Add features.conf

AVS needs an Aerospike features.conf file with the vector-search feature enabled. Add your features.conf file to container-volumes/avs/etc/avs.

3. Start the environment

docker compose up

Developing

This demo is built using Python Flask and Vue.js. To start developing, follow the steps to set up your Python environment.

Set up Python Virtual Environment

# Virtual environment to isolate dependencies.
# Use your Operating system specific installation method
sudo apt-get install python3-venv
python3 -m venv .venv
source .venv/bin/activate

Install dependencies

cd prism
python3 -m pip install -r requirements.txt

Configuration

The application can be configured by setting the following environment variable. If not set defaults are used.

[!NOTE] It is best practice to store AVS index and record data in separate namespaces. By default this application stores its AVS index in the "avs-index" namespace, and AVS records in "avs-data". If your Aerospike database configuration does not define these namespaces you will see an error. You may change the AVS_NAMESPACE and AVS_INDEX_NAMESPACE to other values, like the default Aerospike "test" namespace, to use other namespaces.

[!NOTE] Using a load balancer with AVS is best practice. Therefore AVS_IS_LOADBAlANCER defaults to True. This works fine for AVS clusters with a load balancer or clusters with only 1 node. If you are using the examples with an AVS cluster larger than 1 node without load balancing you should set AVS_IS_LOADBAlANCER to False.

Environment Variable Default Description
APP_USERNAME If set, the username for basic authentication
APP_PASSWORD If set, the password for basic authentication
APP_INDEXER_PARALLELISM 1 To speed up indexing of quotes set this equal to or less than the number of CPU cores
AVS_HOST localhost AVS server seed host
AVS_PORT 5000 AVS server seed host port
AVS_ADVERTISED_LISTENER An optional advertised listener to use if configured on the AVS server
AVS_NAMESPACE avs-data The Aerospike namespace for storing the image records
AVS_SET image-data The Aerospike set for storing the image records
AVS_INDEX_NAMESPACE avs-index The Aerospike namespace for storing the HNSW index
AVS_INDEX_SET image-index The Aerospike set for storing the HNSW index
AVS_INDEX_NAME prism-image-search The name of the index
AVS_MAX_RESULTS 20 Maximum number of vector search results to return
AVS_IS_LOADBALANCER True If true, the first seed address will be treated as a load balancer node.```

Setup networking (optional)

Run a proxy server like Nginx

Setup nginx to handle TLS as shown here.

Run for development

This mode is not recommended for demo on hosting for use. The server is known to hang after being idle for some time. This mode will reflect changes to the code without server restart and hence is ideal for development.

FLASK_ENV=development FLASK_DEBUG=1 python3 -m flask --app prism  run --port 8080