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.
Send a GET
request to the List Serverless Inference endpoint and note the target inference subscription's ID.
$ curl "https://api.vultr.com/v2/inference" \
-X GET \
-H "Authorization: Bearer ${VULTR_API_KEY}"
Send a GET
request to the Serverless Inference endpoint and note the target inference subscription's API key.
$ curl "https://api.vultr.com/v2/inference/{inference-id}" \
-X GET \
-H "Authorization: Bearer ${VULTR_API_KEY}"
Send a POST
request to the Create Collection endpoint to create a vector store collection.
$ 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}"
}'
Send a GET
request to the List Collections endpoint to list all the available vector store collections.
$ curl "https://api.vultrinference.com/v1/vector_store" \
-X GET \
-H "Authorization: Bearer ${INFERENCE_API_KEY}"
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