Embeddings

curl https://gateway.toolken.ai/v1/embeddings \
  -H "X-Toolken-Key: tk_live_..." \
  -H "Authorization: Bearer sk-..." \
  -H "Content-Type: application/json" \
  -d '{"model": "text-embedding-3-small", "input": "The gateway tracks costs per feature."}'

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.toolken.ai/v1",
    api_key="sk-...",
    default_headers={"X-Toolken-Key": "tk_live_..."},
)
client.embeddings.create(
    model="text-embedding-3-small",
    input="The gateway tracks costs per feature.",
)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://gateway.toolken.ai/v1",
  apiKey: "sk-...",
  defaultHeaders: { "X-Toolken-Key": "tk_live_..." },
});
await client.embeddings.create({
  model: "text-embedding-3-small",
  input: "The gateway tracks costs per feature.",
});

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0098, 0.0156]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

POST

embeddings

curl https://gateway.toolken.ai/v1/embeddings \
  -H "X-Toolken-Key: tk_live_..." \
  -H "Authorization: Bearer sk-..." \
  -H "Content-Type: application/json" \
  -d '{"model": "text-embedding-3-small", "input": "The gateway tracks costs per feature."}'

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.toolken.ai/v1",
    api_key="sk-...",
    default_headers={"X-Toolken-Key": "tk_live_..."},
)
client.embeddings.create(
    model="text-embedding-3-small",
    input="The gateway tracks costs per feature.",
)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://gateway.toolken.ai/v1",
  apiKey: "sk-...",
  defaultHeaders: { "X-Toolken-Key": "tk_live_..." },
});
await client.embeddings.create({
  model: "text-embedding-3-small",
  input: "The gateway tracks costs per feature.",
});

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0098, 0.0156]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Pass-through to the provider’s embeddings endpoint. Request and response bodies follow the OpenAI Embeddings schema. The provider is inferred from the model name (e.g. text-embedding-* routes to OpenAI).

Headers

string

required

Your Toolken API key. Validated by the gateway and stripped before forwarding.

string

required

Your provider key in Bearer format (BYOK), e.g. Bearer sk-.... Forwarded untouched, never stored.

string

Primary attribution channel. JSON object of key-value pairs (e.g. {"agent":"research-agent","customer_id":"cust_abc"}). Each key becomes a groupable dimension in the dashboard.

string

Flat-header shorthand. X-Toolken-Metadata-Agent: research-agent writes metadata key agent. Equivalent to sending the same key in X-Toolken-Metadata.

string

Sets the feature metadata key. Use to group costs by agent name, workflow, UI surface, or job type.

string

Sets the customer_id metadata key. Use to attribute costs to an end-customer, org slug, or account ID.

Body

string

required

Embedding model identifier (e.g. text-embedding-3-small).

string or array

required

Text or array of texts to embed. Forwarded verbatim to the provider.

string

Output format for the embedding vectors. Either float or base64.

Response

string

Always list.

array

Array of embedding objects. Each has object (embedding), index, and embedding (array of floats).

string

The embedding model used.

object

Token counts: prompt_tokens and total_tokens.

curl https://gateway.toolken.ai/v1/embeddings \
  -H "X-Toolken-Key: tk_live_..." \
  -H "Authorization: Bearer sk-..." \
  -H "Content-Type: application/json" \
  -d '{"model": "text-embedding-3-small", "input": "The gateway tracks costs per feature."}'

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.toolken.ai/v1",
    api_key="sk-...",
    default_headers={"X-Toolken-Key": "tk_live_..."},
)
client.embeddings.create(
    model="text-embedding-3-small",
    input="The gateway tracks costs per feature.",
)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://gateway.toolken.ai/v1",
  apiKey: "sk-...",
  defaultHeaders: { "X-Toolken-Key": "tk_live_..." },
});
await client.embeddings.create({
  model: "text-embedding-3-small",
  input: "The gateway tracks costs per feature.",
});

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0098, 0.0156]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Gateway API

AI Gateway

Models

Toolken API

Headers

Body

Response

​Headers

​Body

​Response

Headers

Body

Response