Get Started Guide#

  • Time to Complete: 10 mins

  • Programming Language: Python

Get Started#

Prerequisites#

Step 1: Get the docker images#

Option 1: Build from source#

Clone the source code repository, if you have not done so already.

git clone https://github.com/open-edge-platform/edge-ai-libraries.git
cd edge-ai-libraries/microservices

Run the command to build images:

docker build -t retriever-milvus:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy --build-arg no_proxy=$no_proxy -f vector-retriever/milvus/src/Dockerfile .

# build the dependency image
cd multimodal-embedding-serving
docker build -t multimodal-embedding-serving:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy --build-arg no_proxy=$no_proxy -f docker/Dockerfile .

Option 2: use remote prebuilt images#

Set a remote registry by exporting environment variables:

export REGISTRY="intel/"
export TAG="latest"

Note: If you are using a release version package, you will have a pre-defined docker compose file where the image registry and tag are already set to the release version. In such case, you do not need to set the environment variables above, simply move forward to the next step. You may refer to the release notes for details on the version number or check the docker compose file that is used in the steps below.

Step 2: Deploy#

Deploy the application together with the Milvus Server#

  1. Go to the deployment files.

    cd vector-retriever/milvus/deployment/docker-compose/

  2. Set up environment variables, note that you need to set an embedding model first for multimodal-embedding-serving.

    export EMBEDDING_MODEL_NAME="CLIP/clip-vit-h-14" # Replace with your preferred model
source env.sh

    Important: You must set EMBEDDING_MODEL_NAME before running env.sh. See multimodal-embedding-serving’s Supported Models for available options.

    For EMT-S platform If you are on an EMT-S platform, please set up the variables correspondingly by running 
    cd emt-s   # go to emt-s specific files
export EMBEDDING_MODEL_NAME="CLIP/clip-vit-h-14" # Replace with your preferred model
source env.sh

  3. Deploy with docker compose.

    docker compose -f compose_milvus.yaml up -d

    It might take a while to start the services for the first time, as there are some models to be prepared.

    Check if all microservices are up and runnning.

    docker compose -f compose_milvus.yaml ps

    Output:

    NAME                         COMMAND                  SERVICE                                 STATUS              PORTS
milvus-etcd                  "etcd -advertise-cli…"   milvus-etcd                             running (healthy)   2379-2380/tcp
milvus-minio                 "/usr/bin/docker-ent…"   milvus-minio                            running (healthy)   0.0.0.0:9000-9001->9000-9001/tcp, :::9000-9001->9000-9001/tcp
milvus-standalone            "/tini -- milvus run…"   milvus-standalone                       running (healthy)   0.0.0.0:9091->9091/tcp, 0.0.0.0:19530->19530/tcp, :::9091->9091/tcp,    :::19530->19530/tcp
multimodal-embedding   gunicorn -b 0.0.0.0:8000 - ...   Up (health: starting)   0.0.0.0:9777->8000/tcp,:::9777->8000/tcp
retriever-milvus             "uvicorn retriever_s…"   retriever-milvus                        running (healthy)   0.0.0.0:7770->7770/tcp, :::7770->7770/tcp

Sample curl commands#

Note: This microservice retrieves data from a Milvus database. If there is no data added into the database, the curl commands below will return collection not found. To test data retrieval, please insert some data with the Visual Data Preparation for Retrieval service first. After setting up the data preparation service, you can insert, for example a directory, with the curl command:

curl -X POST http://localhost:$DATAPREP_SERVICE_PORT/v1/dataprep/ingest \
-H "Content-Type: application/json" \
-d '{
    "file_dir": "/path/to/directory",
    "frame_extract_interval": 15,
    "do_detect_and_crop": true
}'

Basic Query#

curl -X POST http://localhost:$RETRIEVER_SERVICE_PORT/v1/retrieval \
-H "Content-Type: application/json" \
-d '{
    "query": "example query",
    "max_num_results": 5
}'

Query with Filter#

curl -X POST http://localhost:$RETRIEVER_SERVICE_PORT/v1/retrieval \
-H "Content-Type: application/json" \
-d '{
    "query": "example query",
    "filter": {
        "type": "example"
    },
    "max_num_results": 10
}'

Learn More#