fix: Fix Langchain performance issue

.changeset/grumpy-apes-beam.md

@@ -0,0 +1,5 @@
+---
+'@sap-ai-sdk/foundation-models': minor
+---
+
+[New Functionality] Add convenience method to access all embeddings in an Azure OpenAI response (`AzureOpenAiEmbeddingResponse`).

.changeset/many-zebras-repair.md

@@ -0,0 +1,5 @@
+---
+'@sap-ai-sdk/foundation-models': minor
+---
+
+[Compatibility Note] Adjust `AzureOpenAiEmbeddingOutput` type to include multiple embedding responses as opposed to one.

.changeset/smooth-cameras-care.md

@@ -0,0 +1,5 @@
+---
+'@sap-ai-sdk/langchain': patch
+---
+
+[Fixed Issue] Fix performance issues when creating embeddings for split documents by sending all documents in one request instead of splitting it up in separate requests.

packages/foundation-models/src/azure-openai/azure-openai-chat-client.test.ts

@@ -36,7 +36,7 @@ describe('Azure OpenAI chat client', () => {
     };
 
     const mockResponse =
-      parseMockResponse<AzureOpenAiCreateChatCompletionResponse>(
+      await parseMockResponse<AzureOpenAiCreateChatCompletionResponse>(
         'foundation-models',
         'azure-openai-chat-completion-success-response.json'
       );
@@ -58,7 +58,7 @@ describe('Azure OpenAI chat client', () => {
 
   it('throws on bad request', async () => {
     const prompt = { messages: [] };
-    const mockResponse = parseMockResponse(
+    const mockResponse = await parseMockResponse(
       'foundation-models',
       'azure-openai-error-response.json'
     );
@@ -85,7 +85,7 @@ describe('Azure OpenAI chat client', () => {
     };
 
     const mockResponse =
-      parseMockResponse<AzureOpenAiCreateChatCompletionResponse>(
+      await parseMockResponse<AzureOpenAiCreateChatCompletionResponse>(
         'foundation-models',
         'azure-openai-chat-completion-success-response.json'
       );

packages/foundation-models/src/azure-openai/azure-openai-chat-completion-response.test.ts

@@ -2,22 +2,30 @@ import { createLogger } from '@sap-cloud-sdk/util';
 import { jest } from '@jest/globals';
 import { parseMockResponse } from '../../../../test-util/mock-http.js';
 import { AzureOpenAiChatCompletionResponse } from './azure-openai-chat-completion-response.js';
-import type { AzureOpenAiCreateChatCompletionResponse } from './client/inference/schema';
+import type { HttpResponse } from '@sap-cloud-sdk/http-client';
+import type { AzureOpenAiCreateChatCompletionResponse } from './client/inference/schema/index.js';
+
 describe('OpenAI chat completion response', () => {
-  const mockResponse =
-    parseMockResponse<AzureOpenAiCreateChatCompletionResponse>(
-      'foundation-models',
-      'azure-openai-chat-completion-success-response.json'
+  let mockResponse: AzureOpenAiCreateChatCompletionResponse;
+  let rawResponse: HttpResponse;
+  let azureOpenAiChatResponse: AzureOpenAiChatCompletionResponse;
+
+  beforeAll(async () => {
+    mockResponse =
+      await parseMockResponse<AzureOpenAiCreateChatCompletionResponse>(
+        'foundation-models',
+        'azure-openai-chat-completion-success-response.json'
+      );
+    rawResponse = {
+      data: mockResponse,
+      status: 200,
+      headers: {},
+      request: {}
+    };
+    azureOpenAiChatResponse = new AzureOpenAiChatCompletionResponse(
+      rawResponse
     );
-  const rawResponse = {
-    data: mockResponse,
-    status: 200,
-    headers: {},
-    request: {}
-  };
-  const azureOpenAiChatResponse = new AzureOpenAiChatCompletionResponse(
-    rawResponse
-  );
+  });
 
   it('should return the chat completion response', () => {
     expect(azureOpenAiChatResponse.data).toStrictEqual(mockResponse);

packages/foundation-models/src/azure-openai/azure-openai-embedding-client.test.ts

@@ -31,7 +31,7 @@ describe('Azure OpenAI embedding client', () => {
     const prompt = {
       input: ['AI is fascinating']
     } as AzureOpenAiEmbeddingParameters;
-    const mockResponse = parseMockResponse<AzureOpenAiEmbeddingOutput>(
+    const mockResponse = await parseMockResponse<AzureOpenAiEmbeddingOutput>(
       'foundation-models',
       'azure-openai-embeddings-success-response.json'
     );
@@ -52,7 +52,7 @@ describe('Azure OpenAI embedding client', () => {
 
   it('throws on bad request', async () => {
     const prompt = { input: [] };
-    const mockResponse = parseMockResponse(
+    const mockResponse = await parseMockResponse(
       'foundation-models',
       'azure-openai-error-response.json'
     );

packages/foundation-models/src/azure-openai/azure-openai-embedding-response.test.ts

@@ -2,35 +2,57 @@ import { createLogger } from '@sap-cloud-sdk/util';
 import { jest } from '@jest/globals';
 import { parseMockResponse } from '../../../../test-util/mock-http.js';
 import { AzureOpenAiEmbeddingResponse } from './azure-openai-embedding-response.js';
+import type { HttpResponse } from '@sap-cloud-sdk/http-client';
+import type { AzureOpenAiEmbeddingOutput } from './azure-openai-embedding-types.js';
 
 describe('Azure OpenAI embedding response', () => {
-  const mockResponse = parseMockResponse<AzureOpenAiEmbeddingResponse>(
-    'foundation-models',
-    'azure-openai-embeddings-success-response.json'
-  );
-  const rawResponse = {
-    data: mockResponse,
-    status: 200,
-    headers: {},
-    request: {}
-  };
-  const embeddingResponse = new AzureOpenAiEmbeddingResponse(rawResponse);
+  let embeddingResponse: AzureOpenAiEmbeddingResponse;
+  let rawResponse: HttpResponse;
+  let mockedData: AzureOpenAiEmbeddingOutput;
+  beforeAll(async () => {
+    mockedData = await parseMockResponse<AzureOpenAiEmbeddingOutput>(
+      'foundation-models',
+      'azure-openai-embeddings-success-response.json'
+    );
+
+    rawResponse = {
+      data: mockedData,
+      status: 200,
+      headers: {},
+      request: {}
+    };
+    embeddingResponse = new AzureOpenAiEmbeddingResponse(rawResponse);
+  });
 
   it('should return the embedding response', () => {
-    expect(embeddingResponse.data).toStrictEqual(mockResponse);
+    expect(embeddingResponse.data).toStrictEqual(mockedData);
   });
 
   it('should return raw response', () => {
     expect(embeddingResponse.rawResponse).toBe(rawResponse);
   });
+
+  it('should return the first embedding', () => {
+    expect(embeddingResponse.getEmbedding()).toEqual(
+      mockedData.data[0].embedding
+    );
+  });
+
   it('should return undefined when convenience function is called with incorrect index', () => {
     const logger = createLogger({
       package: 'foundation-models',
       messageContext: 'azure-openai-embedding-response'
     });
     const errorSpy = jest.spyOn(logger, 'error');
-    expect(embeddingResponse.getEmbedding(1)).toBeUndefined();
-    expect(errorSpy).toHaveBeenCalledWith('Data index 1 is out of bounds.');
+    expect(embeddingResponse.getEmbedding(2)).toBeUndefined();
+    expect(errorSpy).toHaveBeenCalledWith('Data index 2 is out of bounds.');
     expect(errorSpy).toHaveBeenCalledTimes(1);
   });
+
+  it('should return all embeddings', () => {
+    expect(embeddingResponse.getEmbeddings()).toEqual([
+      mockedData.data[0].embedding,
+      mockedData.data[1]?.embedding
+    ]);
+  });
 });

packages/foundation-models/src/azure-openai/azure-openai-embedding-response.ts

@@ -30,6 +30,14 @@ export class AzureOpenAiEmbeddingResponse {
     return this.data.data[dataIndex]?.embedding;
   }
 
+  /**
+   * Parses the Azure OpenAI response and returns all embeddings.
+   * @returns The embedding vectors.
+   */
+  getEmbeddings(): number[][] {
+    return this.data.data.map(({ embedding }) => embedding);
+  }
+
   private logInvalidDataIndex(dataIndex: number): void {
     if (dataIndex < 0 || dataIndex >= this.data.data.length) {
       logger.error(`Data index ${dataIndex} is out of bounds.`);

packages/foundation-models/src/azure-openai/azure-openai-embedding-types.ts

@@ -27,22 +27,20 @@ export interface AzureOpenAiEmbeddingOutput {
   /**
    * Array of result candidates.
    */
-  data: [
-    {
-      /**
-       * Embedding object.
-       */
-      object: 'embedding';
-      /**
-       * Array of size `1536` (Azure OpenAI's embedding size) containing embedding vector.
-       */
-      embedding: number[];
-      /**
-       * Index of choice.
-       */
-      index: number;
-    }
-  ];
+  data: {
+    /**
+     * Embedding object.
+     */
+    object: 'embedding';
+    /**
+     * Array of size `1536` (Azure OpenAI's embedding size) containing embedding vector.
+     */
+    embedding: number[];
+    /**
+     * Index of choice.
+     */
+    index: number;
+  }[];
   /**
    * Token Usage.
    */

packages/langchain/README.md

@@ -4,17 +4,22 @@ This package provides LangChain model clients built on top of the foundation mod
 
 ## Table of Contents
 
-- [Table of Contents](#table-of-contents)
-- [Installation](#installation)
-- [Prerequisites](#prerequisites)
-- [Relationship between Models and Deployment ID](#relationship-between-models-and-deployment-id)
-- [Usage](#usage)
-  - [Client Initialization](#client-initialization)
-  - [Chat Client](#chat-client)
-  - [Embedding Client](#embedding-client)
-- [Local Testing](#local-testing)
-- [Support, Feedback, Contribution](#support-feedback-contribution)
-- [License](#license)
+- [@sap-ai-sdk/langchain](#sap-ai-sdklangchain)
+  - [Table of Contents](#table-of-contents)
+  - [Installation](#installation)
+  - [Prerequisites](#prerequisites)
+  - [Relationship between Models and Deployment ID](#relationship-between-models-and-deployment-id)
+  - [Usage](#usage)
+    - [Client Initialization](#client-initialization)
+    - [Chat Client](#chat-client)
+      - [Advanced Example with Templating and Output Parsing](#advanced-example-with-templating-and-output-parsing)
+    - [Embedding Client](#embedding-client)
+      - [Embed Text](#embed-text)
+      - [Embed Document Chunks](#embed-document-chunks)
+      - [Preprocess, embed, and store documents](#preprocess-embed-and-store-documents)
+  - [Local Testing](#local-testing)
+  - [Support, Feedback, Contribution](#support-feedback-contribution)
+  - [License](#license)
 
 ## Installation
 
@@ -128,7 +133,7 @@ return llmChain.invoke({
 
 ### Embedding Client
 
-Embedding clients allow embedding either text or documents (represented as arrays of strings).
+Embedding clients allow embedding either text or document chunks (represented as arrays of strings).
 While you can use them standalone, they are usually used in combination with other LangChain utilities, like a text splitter for preprocessing and a vector store for storage and retrieval of the relevant embeddings.
 For a complete example how to implement RAG with our LangChain client, take a look at our [sample code](https://github.com/SAP/ai-sdk-js/blob/main/sample-code/src/langchain-azure-openai.ts).
 
@@ -140,10 +145,10 @@ const embeddedText = await embeddingClient.embedQuery(
 );
 ```
 
-#### Embed Documents
+#### Embed Document Chunks
 
 ```ts
-const embeddedDocument = await embeddingClient.embedDocuments([
+const embeddedDocuments = await embeddingClient.embedDocuments([
   'Page 1: Paris is the capital of France.',
   'Page 2: It is a beautiful city.'
 ]);

packages/langchain/src/openai/embedding.ts

@@ -2,7 +2,8 @@ import { AzureOpenAiEmbeddingClient as AzureOpenAiEmbeddingClientBase } from '@s
 import { Embeddings } from '@langchain/core/embeddings';
 import type {
   AzureOpenAiEmbeddingModel,
-  AzureOpenAiEmbeddingParameters
+  AzureOpenAiEmbeddingParameters,
+  AzureOpenAiEmbeddingResponse
 } from '@sap-ai-sdk/foundation-models';
 import type { AzureOpenAiEmbeddingModelParams } from './types.js';
 
@@ -24,23 +25,29 @@ export class AzureOpenAiEmbeddingClient extends Embeddings {
     this.resourceGroup = fields.resourceGroup;
   }
 
+  /**
+   * Embed a list of document chunks. All chunks are embedded in one batch.
+   * @param documents - Document chunks to embed.
+   * @returns Embeddings.
+   */
   override async embedDocuments(documents: string[]): Promise<number[][]> {
-    return Promise.all(
-      documents.map(document => this.createEmbedding({ input: document }))
-    );
+    return (await this.createEmbeddings({ input: documents })).getEmbeddings();
   }
 
+  /**
+   * Embed a single string.
+   * @param input - Input string to embed.
+   * @returns Embedding.
+   */
   override async embedQuery(input: string): Promise<number[]> {
-    return this.createEmbedding({ input });
+    return (await this.createEmbeddings({ input })).getEmbedding() ?? [];
   }
 
-  private async createEmbedding(
+  private async createEmbeddings(
     query: AzureOpenAiEmbeddingParameters
-  ): Promise<number[]> {
-    return this.caller.callWithOptions(
-      {},
-      async () =>
-        (await this.openAiEmbeddingClient.run(query)).getEmbedding() ?? []
+  ): Promise<AzureOpenAiEmbeddingResponse> {
+    return this.caller.callWithOptions({}, async () =>
+      this.openAiEmbeddingClient.run(query)
     );
   }
 }

packages/langchain/src/openai/util.test.ts

@@ -15,12 +15,6 @@ import type {
 } from '@sap-ai-sdk/foundation-models';
 import type { AzureOpenAiChatCallOptions } from './types.js';
 
-const openAiMockResponse =
-  parseMockResponse<AzureOpenAiCreateChatCompletionResponse>(
-    'foundation-models',
-    'azure-openai-chat-completion-success-response.json'
-  );
-
 // Signal and Prompt Index are provided by the super class in every call
 const defaultOptions = {
   signal: undefined,
@@ -29,6 +23,11 @@ const defaultOptions = {
 
 describe('Mapping Functions', () => {
   it('should parse an OpenAI response to a (LangChain) chat response', async () => {
+    const openAiMockResponse =
+      await parseMockResponse<AzureOpenAiCreateChatCompletionResponse>(
+        'foundation-models',
+        'azure-openai-chat-completion-success-response.json'
+      );
     const result = mapOutputToChatResult(openAiMockResponse);
     expect(result).toMatchSnapshot();
   });