All API tools
AI & ML
AI Text Embeddings API
GET
/v1/text-embeddings Paid4 credits / callAuthentication
Send your API key as a bearer token. Create one free in the dashboard.
Authorization: Bearer $TOOLSXPO_KEYParameters
Passed as query-string parameters. Example values shown.
| Name | Example |
|---|---|
| text | hello |
Example request
curl "https://api.toolsxpo.com/v1/text-embeddings?text=hello" \
-H "Authorization: Bearer $TOOLSXPO_KEY"Response
A standard JSON envelope: { ok, data, meta }. The result is in data (json); meta.credits reports what the call cost.
How to use the Text Embeddings API
Turn text into a numeric embedding vector for semantic search, clustering, deduplication or RAG. Send text; get the vector in data.embedding and its size in data.dimensions.
Bashcurl "https://api.toolsxpo.com/v1/text-embeddings?text=How%20do%20I%20reset%20my%20password" \ -H "Authorization: Bearer $TOOLSXPO_KEY"
Using the vector
- Store each vector alongside its source text in a vector store (or a plain array for small sets).
- Compare two vectors with cosine similarity — closer to
1means more semantically similar. - Always embed your query with the same model you used for the documents, or the distances are meaningless.
Tips & gotchas
- Keep each input focused (a sentence, a paragraph, a chunk) rather than a whole document — chunk long text first.
data.dimensionstells you the vector size to provision in your store.- Paid tool. One vector per call.