Embeddings - NinjaChat Developers

Use your API key to convert text into vectors. Store them in your own vector database and compare them with an embedded query.

Embed documents

cURL

curl https://www.ninjachat.ai/api/v1/embeddings \
  -H "Authorization: Bearer $NINJACHAT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"text-embedding-3-small","input":["Returns are accepted within 30 days.","Delivery takes 3 to 5 business days."]}'

Each vector is returned in data[i].embedding, with its input position in data[i].index. The response also includes token usage and cost.

Model	Default dimensions
`text-embedding-3-small`	1,536
`voyage-4-large`	1,024

Use the same model and dimensions for documents and queries. For Voyage, set input_type to document when indexing and query when searching.

Build retrieval into your app

Split your documents into passages and embed them.
Store the vectors with the original text.
Embed a user’s question and retrieve similar passages.
Rerank the matches, then pass the best passages to text generation.