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 -b main
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 the environment variables; note that first you need to set an embedding model 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 a while to start the services for the first time, while models are being prepared.

    Check if all microservices are up and running.

    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
}'

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#