Get Started Guide#
Time to Complete: 10 mins
Programming Language: Python
Get Started#
Prerequisites#
Install Docker: Installation Guide.
Install Docker Compose: Installation Guide.
Install Intel Client GPU driver: Installation Guide.
Step 1: Get the docker images#
Option 1: build from source#
Clone the source code repository if you don’t have it
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 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#
Go to the deployment files
cd deployment/docker-compose/
Set up environment variables, note that you need to set an embedding model first
export EMBEDDING_MODEL_NAME="CLIP/clip-vit-h-14" # Replace with your preferred model source env.sh
Important: You must set
EMBEDDING_MODEL_NAMEbefore runningenv.sh. See multimodal-embedding-serving’s Supported Models for available options.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
bash 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#
Check the API reference
This microservice depends on the multimodal embedding service for embedding extraction.