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POST /v1/embeddings

Converts input into a high-dimensional vector representation (embedding). Embeddings are used for semantic search, clustering, classification, and retrieval-augmented generation (RAG). The gateway supports text, image, and video embedding inputs depending on the provider. The response always includes a data array with embedding vectors indexed to match the input.

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Request headers

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x-portkey-provider
string
The provider to route the request to (e.g. openai, cohere, google-vertex-ai). Required when not using a config.
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x-portkey-api-key
string
Your provider API key.
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x-portkey-config
string
A JSON config object or config ID that defines routing, fallbacks, retries, and more.
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x-portkey-virtual-key
string
A virtual key ID from Portkey Cloud.

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Request body

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model
string
required
The embedding model to use (e.g. text-embedding-3-small, embed-english-v3.0).
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input
string | string[] | object[]
required
The text or texts to embed. Accepts:
  • A single string
  • An array of strings for batch embedding
  • An array of EmbedInput objects with text, image, or video fields for multimodal providers
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encoding_format
string
default:"float"
The format of the returned embeddings. One of float or base64.
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dimensions
integer
The number of dimensions in the output embedding. Only supported by models that accept a dimensions parameter (e.g. text-embedding-3-small).
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user
string
A unique identifier for the end user, used for monitoring.

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Response

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object
string
Always list.
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data
object[]
An array of embedding objects, one per input item, in the same order as the input.
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model
string
The model used to generate the embeddings.
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usage
object
Token usage for this request.

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Code examples

curl http://localhost:8787/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "x-portkey-provider: openai" \
  -H "x-portkey-api-key: $OPENAI_API_KEY" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "The Portkey AI Gateway routes requests to 250+ LLMs.",
    "encoding_format": "float"
  }'

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