How to Convert Text Into Dense Vector Representations
Validated on 27 Apr 2026 • Last edited on 27 Apr 2026
Inference provides a single control plane for managing inference workflows. It includes a Model Catalog where you can view available foundation models, including both DigitalOcean-hosted and third-party commercial models, compare model capabilities and pricing, use routing to match inference requests to the best-fit model, and run inference using serverless or dedicated deployments.
Use the Embeddings API to convert text into dense vector representations for use in semantic search, retrieval-augmented generation (RAG), clustering, classification, and similarity matching. Embedding models include Qwen3 Embedding 0.6B, BGE-M3, and E5-Large. They offer a range of options across multilingual support, token window sizes, and dimensionality to match your use case.
For embedding requests, send a POST request to the https://inference.do-ai.run base URL using your existing Model Access Key. Embeddings are returned synchronously as float arrays that you can store in a vector database or use directly in your application pipeline.
The following cURL example generates a vector embedding using the Qwen3 Embedding 0.6B model:
curl -X POST "https://inference.do-ai.run/v1/embeddings" \
-H "Authorization: Bearer $MODEL_ACCESS_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-embedding-0.6b",
"input": "DigitalOcean makes it simple to launch in the cloud."
}'