chore: Remove deprecated models

.changeset/dark-flies-try.md

@@ -0,0 +1,6 @@
+---
+'@sap-ai-sdk/core': minor
+---
+
+[Compatibility Note] Removed deprecated model `text-embedding-ada-002`.
+Use `text-embedding-3-small` or `text-embedding-3-large` instead.

README.md

@@ -20,6 +20,7 @@ Setup your SAP AI Core instance with SAP Cloud SDK for AI.
   - [@sap-ai-sdk/document-grounding](#sap-ai-sdkdocument-grounding)
   - [@sap-ai-sdk/prompt-registry](#sap-ai-sdkprompt-registry)
 - [SAP Cloud SDK for AI Sample Project](#sap-cloud-sdk-for-ai-sample-project)
+- [Deprecated Models](#deprecated-models)
 - [Error Handling](#error-handling)
   - [Accessing Error Information](#accessing-error-information)
 - [Local Testing](#local-testing)
@@ -124,6 +125,17 @@ For details on prompt registry client, refer to this [document](https://github.c
 We have created a sample project demonstrating the different clients' usage of the SAP Cloud SDK for AI for TypeScript/JavaScript.
 The [project README](https://github.com/SAP/ai-sdk-js/blob/main/sample-code/README.md) outlines the set-up needed to build and run it locally.
 
+## Deprecated Models
+
+The following models are deprecated and should not be used.
+Update your code to use one of the recommended replacement models.
+
+| Model Name               | Replacement                                        |
+|--------------------------|----------------------------------------------------|
+| `text-embedding-ada-002` | `text-embedding-3-small`, `text-embedding-3-large` |
+
+For more information, refer to the SAP note on [Availability of Generative AI Models](https://me.sap.com/notes/3437766).
+
 ## Error Handling
 
 A common error scenario is `Request failed with status code STATUS_CODE` coming from `AxiosError`. 

packages/core/src/model-types.ts

@@ -18,7 +18,7 @@ export type AzureOpenAiChatModel = LiteralUnion<
  * Azure OpenAI models for embedding.
  */
 export type AzureOpenAiEmbeddingModel = LiteralUnion<
-  'text-embedding-ada-002' | 'text-embedding-3-small' | 'text-embedding-3-large'
+  'text-embedding-3-small' | 'text-embedding-3-large'
 >;
 
 /**

packages/document-grounding/README.md

@@ -55,7 +55,7 @@ const response = await VectorApi.createCollection(
   {
     title: 'ai-sdk-js-e2e',
     embeddingConfig: {
-      modelName: 'text-embedding-ada-002'
+      modelName: 'text-embedding-3-small'
     },
     metadata: []
   },

packages/document-grounding/src/tests/vector-api.test.ts

@@ -23,7 +23,7 @@ describe('vector api', () => {
         {
           title: 'ai-sdk-js-demo',
           embeddingConfig: {
-            modelName: 'text-embedding-ada-002'
+            modelName: 'text-embedding-3-small'
           },
           metadata: [],
           id: '7171c2b2-835b-4745-8824-ff5c7e98a416'

packages/foundation-models/README.md

@@ -233,7 +233,7 @@ Use the `AzureOpenAiEmbeddingClient` to send embedding requests to an OpenAI mod
 import { AzureOpenAiEmbeddingClient } from '@sap-ai-sdk/foundation-models';
 
 const embeddingClient = new AzureOpenAiEmbeddingClient(
-  'text-embedding-ada-002'
+  'text-embedding-3-small'
 );
 const response = await embeddingClient.run({
   input: 'AI is fascinating'

packages/langchain/src/openai/README.md

@@ -165,7 +165,7 @@ const splits = await textSplitter.splitDocuments(docs);
 
 // Initialize the embedding client
 const embeddingClient = new AzureOpenAiEmbeddingClient({
-  modelName: 'text-embedding-ada-002'
+  modelName: 'text-embedding-3-small'
 });
 
 // Create a vector store from the document

sample-code/src/document-grounding.ts

@@ -13,7 +13,7 @@ export async function createCollection(): Promise<string> {
     {
       title: 'ai-sdk-js-e2e',
       embeddingConfig: {
-        modelName: 'text-embedding-ada-002'
+        modelName: 'text-embedding-3-small'
       },
       metadata: []
     },

sample-code/src/foundation-models/azure-openai.ts

@@ -60,7 +60,7 @@ export async function chatCompletionStream(
  */
 export async function computeEmbedding(): Promise<AzureOpenAiEmbeddingResponse> {
   const response = await new AzureOpenAiEmbeddingClient(
-    'text-embedding-ada-002'
+    'text-embedding-3-small'
   ).run({
     input: 'Hello, world!'
   });

sample-code/src/langchain-azure-openai.ts

@@ -87,7 +87,7 @@ export async function invokeRagChain(): Promise<string> {
 
   // Initialize the embedding client with 0 retries for fast testing
   const embeddingClient = new AzureOpenAiEmbeddingClient({
-    modelName: 'text-embedding-ada-002',
+    modelName: 'text-embedding-3-small',
     maxRetries: 0
   });
 

tests/type-tests/test/azure-openai.test-d.ts

@@ -123,7 +123,7 @@ expectType<Promise<AzureOpenAiChatCompletionResponse>>(
  */
 
 expectType<Promise<AzureOpenAiEmbeddingResponse>>(
-  new AzureOpenAiEmbeddingClient('text-embedding-ada-002').run({
+  new AzureOpenAiEmbeddingClient('text-embedding-3-small').run({
     input: 'test input'
   })
 );
@@ -136,7 +136,7 @@ expectType<AzureOpenAiEmbeddingClient>(
  * Embeddings with optional parameters.
  */
 expectType<Promise<AzureOpenAiEmbeddingResponse>>(
-  new AzureOpenAiEmbeddingClient('text-embedding-ada-002').run(
+  new AzureOpenAiEmbeddingClient('text-embedding-3-small').run(
     {
       input: ['test input 1', 'test input 2'],
       user: 'some-guid'
@@ -150,7 +150,7 @@ expectType<Promise<AzureOpenAiEmbeddingResponse>>(
 );
 
 expectType<Promise<AzureOpenAiEmbeddingResponse>>(
-  new AzureOpenAiEmbeddingClient('text-embedding-ada-002', {
+  new AzureOpenAiEmbeddingClient('text-embedding-3-small', {
     destinationName: 'destinationName',
     useCache: false
   }).run({