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Create a local Docker instance

Can I use a cloud instance?

Generating multimodal vectors is currently only possible with local models, and as a result this course uses a local, Docker instance of Weaviate. If you are generating vectors outside of Weaviate, you can use a cloud instance. See the Work with: your own vectors course for more information.

Here, you will create a Weaviate instance and a multi-modal vectorizer container using Docker.

Download and run the docker-compose file​

Install Docker on your machine. We recommend following the official Docker installation guide.

Create a new directory and navigate to it in your terminal. Then, create a new file called docker-compose.yml and add the following content:

---
version: '3.4'
services:
  weaviate:
    command:
    - --host
    - 0.0.0.0
    - --port
    - '8080'
    - --scheme
    - http
    image: cr.weaviate.io/semitechnologies/weaviate:1.25.9
    ports:
    - 8080:8080
    - 50051:50051
    volumes:
    - weaviate_data:/var/lib/weaviate
    restart: on-failure:0
    environment:
      CLIP_INFERENCE_API: 'http://multi2vec-clip:8080'
      OPENAI_APIKEY: $OPENAI_APIKEY
      QUERY_DEFAULTS_LIMIT: 25
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
      PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
      DEFAULT_VECTORIZER_MODULE: 'multi2vec-clip'
      ENABLE_MODULES: 'multi2vec-clip,generative-openai,generative-cohere'
      CLUSTER_HOSTNAME: 'node1'
  multi2vec-clip:
    image: cr.weaviate.io/semitechnologies/multi2vec-clip:sentence-transformers-clip-ViT-B-32-multilingual-v1
    environment:
      ENABLE_CUDA: '0'
volumes:
  weaviate_data:
...

Create a Weaviate instance​

Run the following command to start Weaviate:

docker compose up

Your Weaviate instance details​

Once the instance is created, you can access it at http://localhost:8080.

Connect to your Weaviate instance​

To connect to the Weaviate instance, use the connect_to_local function.

import weaviate

client = weaviate.connect_to_local()

Provide inference API keys​

Some Weaviate modules can use inference APIs for vectorizing data or large language model integration. You can provide the API keys for these services to Weaviate at instantiation.

This course uses OpenAI (for retrieval augmented generation), so you can provide the OpenAI API key to Weaviate through headers={"X-OpenAI-Api-Key": <YOUR_KEY>} as shown below:

import weaviate
import os

headers = {
    "X-OpenAI-Api-Key": os.getenv("OPENAI_APIKEY")
}  # Replace with your OpenAI API key

client = weaviate.connect_to_local(headers=headers)

Questions and feedback​

If you have any questions or feedback, let us know in the user forum.