Creating collections

The vector database stores documents as embeddings for semantic search, powering AI agent knowledge bases and retrieval-augmented generation (RAG).

What is a collection?

A collection is a container for related documents. Each collection uses a specific embedding model to convert text into vectors for similarity search. You might create separate collections for different topics, projects, or data sources.

Navigate to Vector Database

In the dashboard sidebar, click Vector Database to view your existing collections and create new ones.

Creating a collection

Click Create Collection and provide the following details:

• Name – lowercase alphanumeric with hyphens or underscores, 3–50 characters
• Description – a brief description of the collection’s purpose
• Embedding model – the model used to generate vector embeddings

Embedding models

Choose an embedding model based on your content and language requirements. The models available to your organisation are listed in the collection form, or by the AI API:

curl -s "https://dashboard.quantcdn.io/api/v3/organizations/$ORG/ai/models?feature=embeddings" \
  -H "Authorization: Bearer $QUANT_API_TOKEN"

Use the model ID from that list as the collection’s embedding model. Typical choices are a general-purpose English model, or a multilingual model if your documents are in more than one language. On QuantGov (https://dash.quantgov.cloud), only Australian-resident models are listed.

Caution

Choose your embedding model carefully. You cannot change the embedding model after creating a collection. To use a different model, create a new collection and re-upload your documents.

Deleting collections

To delete a collection, open it from the Vector Database page and click Delete Collection. This removes all documents, embeddings, and metadata permanently. This action cannot be undone.

Next steps

• Uploading documents – add content to your collections
• Semantic search – query your collections using natural language