Embeddings

The POST /v1/embeddings endpoint returns OpenAI-compatible embedding vectors. You can provide explicit vectors in fixtures or let llmock generate deterministic embeddings automatically from the input text.

Endpoint

Method	Path	Format
POST	/v1/embeddings	JSON

How It Works

If a fixture matches with an embedding response, that exact vector is returned
If no fixture matches, a deterministic embedding is auto-generated from the input text using a hash-based algorithm
Auto-generated embeddings are deterministic: same input always produces the same output
Default dimension is 1536 (matching text-embedding-3-small), configurable via the dimensions request parameter

Unit Test: Fixture-based Embedding

embedding-fixture.test.ts ts

const mock = new LLMock();
await mock.start();

// Register a fixture with explicit embedding vector
mock.onEmbedding("embed-this", { embedding: [0.1, -0.2, 0.3, 0.4, -0.5] });

const res = await fetch(`${mock.url}/v1/embeddings`, {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    model: "text-embedding-3-small",
    input: "embed-this",
  }),
});

const body = await res.json();
expect(body.object).toBe("list");
expect(body.data[0].embedding).toEqual([0.1, -0.2, 0.3, 0.4, -0.5]);
expect(body.data[0].index).toBe(0);

Unit Test: Auto-generated Embedding

embedding-auto.test.ts ts

import { generateDeterministicEmbedding } from "@copilotkit/llmock/helpers";

// Deterministic: same input always produces the same output
const a = generateDeterministicEmbedding("hello world");
const b = generateDeterministicEmbedding("hello world");
expect(a).toEqual(b);

// Default dimension is 1536
expect(a).toHaveLength(1536);

// Custom dimension
const c = generateDeterministicEmbedding("hello", 768);
expect(c).toHaveLength(768);

// All values are between -1 and 1
for (const val of a) {
  expect(val).toBeGreaterThanOrEqual(-1);
  expect(val).toBeLessThanOrEqual(1);
}

JSON Fixture

fixtures/embeddings.json json

{
  "fixtures": [
    {
      "match": { "inputText": "embed-this" },
      "response": {
        "embedding": [0.1, -0.2, 0.3, 0.4, -0.5]
      }
    }
  ]
}

Response Format

Matches the OpenAI /v1/embeddings response format:

Response shape json

{
  "object": "list",
  "model": "text-embedding-3-small",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.1, -0.2, 0.3, ...]
    }
  ],
  "usage": { "prompt_tokens": 0, "total_tokens": 0 }
}

Embedding fixtures use match.inputText instead of match.userMessage. The inputText matcher checks the embedding input string (or each string in an input array).