feat(api): Add embeddings endpoint

.stats.yml

@@ -1,2 +1,2 @@
-configured_endpoints: 6
-openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/groqcloud%2Fgroqcloud-c28de228634e737a173375583a09eef5e0d7fa81fcdf7090d14d194e6ef4fdc5.yml
+configured_endpoints: 7
+openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/groqcloud%2Fgroqcloud-a3e82ffbc462174007a5f736784cde91069ac2fd1468c89b0b9faea2fa82580a.yml

api.md

@@ -1,3 +1,14 @@
+# Embeddings
+
+Types:
+
+- <code><a href="./src/resources/embeddings.ts">CreateEmbeddingResponse</a></code>
+- <code><a href="./src/resources/embeddings.ts">Embedding</a></code>
+
+Methods:
+
+- <code title="post /openai/v1/embeddings">client.embeddings.<a href="./src/resources/embeddings.ts">create</a>({ ...params }) -> CreateEmbeddingResponse</code>
+
 # Chat
 
 ## Completions

src/index.ts

@@ -116,6 +116,7 @@ export class Groq extends Core.APIClient {
     this.apiKey = apiKey;
   }
 
+  embeddings: API.Embeddings = new API.Embeddings(this);
   chat: API.Chat = new API.Chat(this);
   audio: API.Audio = new API.Audio(this);
   models: API.Models = new API.Models(this);
@@ -177,6 +178,11 @@ export import fileFromPath = Uploads.fileFromPath;
 export namespace Groq {
   export import RequestOptions = Core.RequestOptions;
 
+  export import Embeddings = API.Embeddings;
+  export import CreateEmbeddingResponse = API.CreateEmbeddingResponse;
+  export import Embedding = API.Embedding;
+  export import EmbeddingCreateParams = API.EmbeddingCreateParams;
+
   export import Chat = API.Chat;
 
   export import Audio = API.Audio;

src/resources/embeddings.ts

@@ -0,0 +1,114 @@
+// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+import * as Core from '../core';
+import { APIResource } from '../resource';
+import * as EmbeddingsAPI from './embeddings';
+
+export class Embeddings extends APIResource {
+  /**
+   * Creates an embedding vector representing the input text.
+   */
+  create(
+    body: EmbeddingCreateParams,
+    options?: Core.RequestOptions,
+  ): Core.APIPromise<CreateEmbeddingResponse> {
+    return this._client.post('/openai/v1/embeddings', { body, ...options });
+  }
+}
+
+export interface CreateEmbeddingResponse {
+  /**
+   * The list of embeddings generated by the model.
+   */
+  data: Array<Embedding>;
+
+  /**
+   * The name of the model used to generate the embedding.
+   */
+  model: string;
+
+  /**
+   * The object type, which is always "list".
+   */
+  object: 'list';
+
+  /**
+   * The usage information for the request.
+   */
+  usage: CreateEmbeddingResponse.Usage;
+}
+
+export namespace CreateEmbeddingResponse {
+  /**
+   * The usage information for the request.
+   */
+  export interface Usage {
+    /**
+     * The number of tokens used by the prompt.
+     */
+    prompt_tokens: number;
+
+    /**
+     * The total number of tokens used by the request.
+     */
+    total_tokens: number;
+  }
+}
+
+/**
+ * Represents an embedding vector returned by embedding endpoint.
+ */
+export interface Embedding {
+  /**
+   * The embedding vector, which is a list of floats. The length of vector depends on
+   * the model as listed in the [embedding guide](/docs/guides/embeddings).
+   */
+  embedding: Array<number> | string;
+
+  /**
+   * The index of the embedding in the list of embeddings.
+   */
+  index: number;
+
+  /**
+   * The object type, which is always "embedding".
+   */
+  object: 'embedding';
+}
+
+export interface EmbeddingCreateParams {
+  /**
+   * Input text to embed, encoded as a string or array of tokens. To embed multiple
+   * inputs in a single request, pass an array of strings or array of token arrays.
+   * The input must not exceed the max input tokens for the model, cannot be an empty
+   * string, and any array must be 2048 dimensions or less.
+   */
+  input: string | Array<string>;
+
+  /**
+   * ID of the model to use.
+   */
+  model: string;
+
+  /**
+   * The number of dimensions to return the embeddings in.
+   */
+  dimensions?: number;
+
+  /**
+   * The format to return the embeddings in.
+   */
+  encoding_format?: 'float' | 'base64';
+
+  /**
+   * A unique identifier representing your end-user, which can help us monitor and
+   * detect abuse.
+   */
+  user?: string | null;
+}
+
+export namespace Embeddings {
+  export import CreateEmbeddingResponse = EmbeddingsAPI.CreateEmbeddingResponse;
+  export import Embedding = EmbeddingsAPI.Embedding;
+  export import EmbeddingCreateParams = EmbeddingsAPI.EmbeddingCreateParams;
+}

src/resources/index.ts

@@ -1,5 +1,6 @@
 // File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
 
 export { Chat } from './chat/chat';
+export { CreateEmbeddingResponse, Embedding, EmbeddingCreateParams, Embeddings } from './embeddings';
 export { Model, ModelList, Models } from './models';
 export { Translation, Audio } from './audio/audio';

tests/api-resources/embeddings.test.ts

@@ -0,0 +1,35 @@
+// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+import Groq from 'groq-sdk';
+import { Response } from 'node-fetch';
+
+const groq = new Groq({
+  apiKey: 'My API Key',
+  baseURL: process.env['TEST_API_BASE_URL'] ?? 'http://127.0.0.1:4010',
+});
+
+describe('resource embeddings', () => {
+  test('create: only required params', async () => {
+    const responsePromise = groq.embeddings.create({
+      input: 'The quick brown fox jumped over the lazy dog',
+      model: 'nomic-embed-text-v1.5',
+    });
+    const rawResponse = await responsePromise.asResponse();
+    expect(rawResponse).toBeInstanceOf(Response);
+    const response = await responsePromise;
+    expect(response).not.toBeInstanceOf(Response);
+    const dataAndResponse = await responsePromise.withResponse();
+    expect(dataAndResponse.data).toBe(response);
+    expect(dataAndResponse.response).toBe(rawResponse);
+  });
+
+  test('create: required and optional params', async () => {
+    const response = await groq.embeddings.create({
+      input: 'The quick brown fox jumped over the lazy dog',
+      model: 'nomic-embed-text-v1.5',
+      dimensions: 1,
+      encoding_format: 'float',
+      user: 'string',
+    });
+  });
+});