Embeddings - NeuralTrust Docs | TrustGate, TrustGuard, TrustTest

Clients send the OpenAI embeddings body to /v1/embeddings on the application URL, with the same key they use for chat.

from openai import OpenAI

client = OpenAI(base_url="https://<llm-host>/<application-slug>/v1", api_key="<application key>")

client.embeddings.create(model="text-embedding-3-small", input=["Hello from TrustGate"])

import OpenAI from "openai"

const client = new OpenAI({ baseURL: "https://<llm-host>/<application-slug>/v1", apiKey: "<application key>" })

await client.embeddings.create({ model: "text-embedding-3-small", input: ["Hello from TrustGate"] })

curl -X POST "https://<llm-host>/<application-slug>/v1/embeddings" \
  -H "X-AG-API-Key: <application key>" \
  -H "Content-Type: application/json" \
  -d '{"model": "text-embedding-3-small", "input": ["Hello from TrustGate"]}'

Which registries can serve it

Provider	Notes
OpenAI, Azure OpenAI, Mistral, OpenAI-compatible	Native embeddings endpoints.
Cohere	Same body, adapted to Cohere’s embed API.
Vertex AI	Gemini embed — one input or a batch.
Amazon Bedrock	Titan embed models only; a non-Titan model is a 400 from Bedrock.

Anthropic, Gemini AI Studio, and Bedrock’s chat models do not embed and are left out of the candidate pool automatically.

How it routes

The application’s registries are narrowed to those that advertise embeddings, then routing runs on that pool as usual. Two edges:

Pinning a registry that cannot embed — with @provider/model — is a 400, not a silent failover. You asked for something impossible; the gateway says so.
No capable registry at all is a 503. Attach one.

A pool that mixes chat and embeddings providers is the normal case. Chat uses every chat-capable member; embeddings skip the rest. Use the Capability filter in Registry to see which providers qualify.