Embeddings
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.
Request body
curl -X POST 'https://spideriq.ai/api/gate/v1/embeddings' \
-H 'Authorization: Bearer <token>' \
-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 <token>", "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 <token>", "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 <token>")
req.Header.Set("Content-Type", "application/json")
resp, _ := http.DefaultClient.Do(req)
defer resp.Body.Close()
}Responses
200— Successful embedding400— Invalid request401— Authentication error429— Rate limit exceeded500— Internal error503— No embeddings-capable OpenAI key available422— Validation Error