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Embeddings

Base URL https://spideriq.ai/api/gate/v1
POST /api/gate/v1/embeddings

Create Embeddings

Generate vector embeddings for one or more texts using an OpenAI embedding model or a SpiderGate alias (``agent/embed-small|large|embed``). OpenAI-compatible — drop-in for the ``openai`` SDK's ``embeddings.create()`` with ``base_url='https://spideriq.ai/api/gate/v1'``. Metered + per-key-capped like the chat gate.

Responses
  • 200Successful embedding
  • 400Invalid request
  • 401Authentication error
  • 429Rate limit exceeded
  • 500Internal error
  • 503No embeddings-capable OpenAI key available
  • 422Validation Error
POST /api/gate/v1/embeddings
curl -X POST 'https://spideriq.ai/api/gate/v1/embeddings' \
  -H 'Authorization: Bearer ' \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "agent/embed-small",
  "input": [
    "first text",
    "second text"
  ]
}'
import httpx

resp = httpx.post(
    "https://spideriq.ai/api/gate/v1/embeddings",
    headers={"Authorization": "Bearer ", "Content-Type": "application/json"},
    json={"model": "agent/embed-small", "input": ["first text", "second text"]},
)
resp.raise_for_status()
print(resp.json())
const resp = await fetch("https://spideriq.ai/api/gate/v1/embeddings", {
  method: "POST",
  headers: { "Authorization": "Bearer ", "Content-Type": "application/json" },
  body: JSON.stringify({"model": "agent/embed-small", "input": ["first text", "second text"]})
});
const data = await resp.json();
console.log(data);
package main

import (
	"net/http"
	"strings"
)

func main() {
	body := strings.NewReader(`{"model": "agent/embed-small", "input": ["first text", "second text"]}`)
	req, _ := http.NewRequest("POST", "https://spideriq.ai/api/gate/v1/embeddings", body)
	req.Header.Set("Authorization", "Bearer ")
	req.Header.Set("Content-Type", "application/json")
	resp, _ := http.DefaultClient.Do(req)
	defer resp.Body.Close()
}
Request body
{"input":["first text","second text"],"model":"agent/embed-small"}