fix: deleting useless embedding module and adding tests for openai em…

…bedding

backend/src/embedding/__tests__/loadAllEmbModels.spec.ts

@@ -1,6 +1,6 @@
 import { localEmbProvider } from '../local-embedding-provider';
 import { EmbeddingModel } from 'fastembed';
-import { openAIEmbProvider } from '../../../../llm-server/src/embedding/openai-embedding-provider';
+import { OpenAIEmbProvider } from '../openai-embbeding-provider';
 const originalIsArray = Array.isArray;
 
 Array.isArray = jest.fn((type: any): type is any[] => {
@@ -16,7 +16,8 @@ Array.isArray = jest.fn((type: any): type is any[] => {
 }) as unknown as (arg: any) => arg is any[];
 
 describe('testing embedding provider', () => {
-  it('should load real models specified in config', async () => {
+  let openAiProvider: OpenAIEmbProvider;
+  it('should load real local models specified in config', async () => {
     const documents = [
       'passage: Hello, World!',
       'query: Hello, World!',
@@ -30,6 +31,16 @@ describe('testing embedding provider', () => {
       documents,
     );
   }, 6000000);
+
+  it('should load real openai embedding models', async () => {
+    openAiProvider = OpenAIEmbProvider.getInstance();
+    let res = await openAiProvider.generateEmbResponse(
+     'text-embedding-3-small','test document'
+    );
+    console.log(res);
+  }, 6000000);
+
+
 });
 
 afterAll(() => {

backend/src/embedding/openai-embbeding-provider.ts

@@ -3,29 +3,39 @@ import { EmbeddingModel } from 'fastembed';
 import openai, { OpenAI } from 'openai';
 import { EmbeddingDownloader } from 'src/downloader/embedding-downloader';
 
-export class openAIEmbProvider {
-  private static logger = new Logger(openAIEmbProvider.name);
-
-  private static openai = () => {
-    return new OpenAI({
-      apiKey: process.env.OPEN_API_KEY,
+export class OpenAIEmbProvider {
+  private logger = new Logger(OpenAIEmbProvider.name);
+  private static instance: OpenAIEmbProvider;
+  private openAi: OpenAI;
+  static openAi: any;
+  static getInstance(){
+    if(this.instance){
+      return this.instance;
+    }
+    return new OpenAIEmbProvider();
+  }
+  constructor(){
+    this.openAi = new OpenAI({
+      apiKey: process.env.OPENAI_API_KEY,
     });
-  };
+  }
 
-  static async generateEmbResponse(model: string, message: string) {
-    const embedding = await this.openai().embeddings.create({
+  async generateEmbResponse(model: string, message: string): Promise<any> {
+    const embedding = await this.openAi.embeddings.create({
       model: model,
       input: message,
       encoding_format: 'float',
     });
     console.log(embedding.data[0].embedding);
+    return embedding.data[0].embedding;
   }
 
-  static async getEmbList() {
+  async getEmbList() : Promise<any>{
     try {
-      const models = await this.openai().models.list();
-      Object.values(models).filter((model) => model.object === 'embedding');
+      const models = await this.openAi.models.list();
+      const modelList = Object.values(models).filter((model) => model.object === 'embedding');
       this.logger.log(`Models fetched: ${models.data.length}`);
+      return modelList;
     } catch (error) {
       this.logger.error('Error fetching models:', error);
     }