A vector store is a structured repository for storing and retrieving high-dimensional vector embeddings. The collections within a vector store enable fast similarity searches, making them useful for Retrieval-Augmented Generation (RAG) and AI applications.

Follow this guide to create a vector store collection on Vultr Serverless Inference using the Vultr API.

1. Send a GET request to the List Serverless Inference endpoint and note the target inference subscription’s ID.
   console

   $ curl "https://api.vultr.com/v2/inference" \
       -X GET \
       -H "Authorization: Bearer ${VULTR_API_KEY}"

2. Send a GET request to the Serverless Inference endpoint and note the target inference subscription’s API key.
   console

   $ curl "https://api.vultr.com/v2/inference/{inference-id}" \
       -X GET \
       -H "Authorization: Bearer ${VULTR_API_KEY}"

3. Send a POST request to the Create Collection endpoint to create a vector store collection.
   console

   $ curl "https://api.vultrinference.com/v1/vector_store" \
       -X POST \
       -H "Authorization: Bearer ${INFERENCE_API_KEY}" \
       -H "Content-Type: application/json" \
       --data '{
           "name": "{collection-name}"
       }'

4. Send a GET request to the List Collections endpoint to list all the available vector store collections.
   console

   $ curl "https://api.vultrinference.com/v1/vector_store" \
       -X GET \
       -H "Authorization: Bearer ${INFERENCE_API_KEY}"