Embeddings

#Use it when

• You are building semantic search
• You want retrieval-augmented generation
• You need deduplication or text similarity scoring

#Code examples

1curl -X POST https://api.navy/v1/embeddings \
2  -H "Authorization: Bearer sk-navy-YOUR_KEY" \
3  -H "Content-Type: application/json" \
4  -d '{
5    "model": "text-embedding-3-large",
6    "input": ["NavyAI API docs", "Embedding search example"]
7  }'

#Parameters

• model (string, required) — Embedding model ID such as text-embedding-3-large or text-embedding-ada-002
• input (string or array, required) — Text or array of texts to embed
• encoding_format (string, optional) — Return format: "float" or "base64"

#Notes

• The response follows the familiar OpenAI embeddings shape
• Batch inputs are supported through an array in input