Embeddings - Zylon

Upcoming release — Zylon 1.58These docs are published ahead of release so you can prepare your API integrations. The release image is not yet available. For installations running 1.57, use the 1.57 documentation.

Embeddings turn text into high‑dimensional vectors. Use them for semantic search, clustering, or similarity scoring across your own data.

Prerequisites

An API token. See Backoffice Developer Console.
Your Zylon hostname (replace {BASE_URL} in the examples).

Before you start

Order matters: data[index] aligns with your input order.
Store the vectors in your database or vector index to power search and similarity.
Use consistent preprocessing (same casing and formatting) for better similarity results.

Create embeddings

Generate embeddings with POST /embeddings. The input can be a single string or an array.

`input` shape	When to use
Single string	One text at a time.
Array of strings	Batch inputs and keep output order.

Single input
Batch input

curl -X POST "https://{BASE_URL}/api/gpt/v1/embeddings" \
  -H "Authorization: Bearer {API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Summarize Q1 support trends in one vector."
  }'

Example response

{
  "object": "list",
  "model": "private-gpt",
  "data": [
    {
      "index": 0,
      "object": "embedding",
      "embedding": [
        0.697265625,
        0.5078125,
        0.01129150390625,
        0.244873046875,
        -0.285888671875,
        0.058135986328125,
        0.01922607421875,
        -0.11431884765625
      ]
    }
  ]
}

curl -X POST "https://{BASE_URL}/api/gpt/v1/embeddings" \
  -H "Authorization: Bearer {API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "input": [
      "Billing issues drove 38% of cases.",
      "Priority bugs were resolved within 48 hours."
    ]
  }'

Example response

{
  "object": "list",
  "model": "private-gpt",
  "data": [
    {
      "index": 0,
      "object": "embedding",
      "embedding": [
        -0.435546875,
        -0.49169921875,
        -0.48876953125,
        1.41015625,
        0.11114501953125,
        -1.2626953125
      ]
    },
    {
      "index": 1,
      "object": "embedding",
      "embedding": [
        -0.6748046875,
        0.242431640625,
        -0.31396484375,
        0.53955078125,
        -0.84716796875,
        -0.006961822509765625
      ]
    }
  ]
}

Errors and edge cases

401/403: token missing or invalid.
413: input too large.
400: invalid JSON or input type.