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 do not have it:

git clone https://github.com/open-edge-platform/edge-ai-libraries.git -b release-2026.2.0
cd edge-ai-libraries/microservices

Run the command to build images:

docker build -t dataprep-visualdata-milvus:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy --build-arg no_proxy=$no_proxy -f visual-data-preparation-for-retrieval/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="2025.2.0"

Note: If you are using a release version package, you will have a pre-defined docker compose file where 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: Prepare host directories for data#

mkdir -p $HOME/data

Make sure to put all your data (images and video) in the created data directory ($HOME/data in the example commands) BEFORE deploying the service.

Additionally, make sure the created path matches with the HOST_DATA_PATH variable in deployment/docker-compose/env.sh.

Note: The supported media types are: jpg, png, mp4.

Step 3: Deploy#

Deploy the application together with the Milvus Server#

  1. Go to the deployment files

    cd visual-data-preparation-for-retrieval/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 Supported Models for Multimodal Embedding Serving for available options.

    Note: env.sh sets HF_ENDPOINT to a Hugging Face mirror, which is necessary for users in the PRC to download models. Users in other regions may remove or unset this variable to use the default Hugging Face endpoint:

    unset HF_ENDPOINT
    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 some time to start the services for the first time, as the service prepares the models.

Check if all microservices are up and running:

docker compose -f compose_milvus.yaml ps

Example expected output:

NAME                         COMMAND                  SERVICE                                 STATUS              PORTS
dataprep-visualdata-milvus   "uvicorn dataprep_vi…"   dataprep-visualdata-milvus              running (healthy)   0.0.0.0:9990->9990/tcp, :::9990->9990/tcp
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

Sample curl commands#

Info#

curl -X GET http://localhost:$DATAPREP_SERVICE_PORT/v1/dataprep/info

Ingest Files#

Note: the file directory or single file sent in the request should be under the specific host directory created in Step 2.

  • For Directory:

    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
}'

  • For Single File:

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

Get File Info#

curl -X GET http://localhost:$DATAPREP_SERVICE_PORT/v1/dataprep/get?file_path=/path/to/file

Delete File in Database#

curl -X DELETE http://localhost:$DATAPREP_SERVICE_PORT/v1/dataprep/delete?file_path=/path/to/file

Clear Database#

curl -X DELETE http://localhost:$DATAPREP_SERVICE_PORT/v1/dataprep/delete_all

Troubleshooting#

Network failure when downloading models#

If service startup fails with errors that look like a network failure while downloading models from Hugging Face, the configured HF_ENDPOINT mirror may be unreachable from your network. Try unsetting it before redeploying:

unset HF_ENDPOINT
docker compose -f compose_milvus.yaml down
docker compose -f compose_milvus.yaml up -d

This falls back to the default Hugging Face endpoint, which is typically the right choice for users outside the PRC.

Learn More#