# Get Started Guide - **Time to Complete:** 10 mins - **Programming Language:** Python ## Get Started ### Prerequisites - Verify that your system meets the [minimum requirements](./get-started/system-requirements.md). - Install Docker: [Installation Guide](https://docs.docker.com/get-docker/). - Install Docker Compose: [Installation Guide](https://docs.docker.com/compose/install/). - Install Intel Client GPU driver: [Installation Guide](https://dgpu-docs.intel.com/driver/client/overview.html). ### Step 1: Get the Docker Images #### Option 1: Build from Source Clone the source code repository, if you have not done so already. ```bash 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: ```bash 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: ```bash 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. ```bash 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. ```bash 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](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/multimodal-embedding-serving/supported-models.html) 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: ```bash unset HF_ENDPOINT ```
For EMT-S platform If you are on an EMT-S platform, please set up the variables correspondingly by running ```bash 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. ```bash 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. ```bash docker compose -f compose_milvus.yaml ps ``` **Output:** ```text 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](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/microservices/visual-data-preparation-for-retrieval/milvus/docs/user-guide/get-started.md) first. After setting up the data preparation service, you can insert, for example a directory, with the curl command: > > ```console > curl -X POST http://localhost:$DATAPREP_SERVICE_PORT/v1/dataprep/ingest \ > -H "Content-Type: application/json" \ > -d '{ > "file_dir": "/path/to/directory", > "frame_extract_interval": 15, > "do_detect_and_crop": true > }' > ``` ### Basic Query ```bash curl -X POST http://localhost:$RETRIEVER_SERVICE_PORT/v1/retrieval \ -H "Content-Type: application/json" \ -d '{ "query": "example query", "max_num_results": 5 }' ``` ### Query with Filter ```bash 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: ```bash 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 - Check the [API reference](./api-reference.md). - This microservice depends on the [Multimodal Embedding Serving](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/multimodal-embedding-serving/get-started.html) service for embedding extraction. :::{toctree} :hidden: ./get-started/system-requirements :::