Adds Azure OpenAI Embedding Model (#2545)

docs/changelog.mdx

@@ -127,15 +127,19 @@ mode: "wide"
 
 <Tab title="TypeScript">
 
-<Update label="2025-04-14" description="v2.1.17">
+
+<Update label="2025-04-15" description="v2.1.17">
 **New Features:**
 - **OSS SDK:** Added support for Langchain LLM
 - **OSS SDK:** Added support for Langchain Embedder
 - **OSS SDK:** Added support for Langchain Vector Store
+- **OSS SDK:** Added support for Azure OpenAI Embedder
+
 
 **Improvements:**
 - **OSS SDK:** Changed `model` in LLM and Embedder to use type any from `string` to use langchain llm models
 - **OSS SDK:** Added client to vector store config for langchain vector store
+- **OSS SDK:** - Updated Azure OpenAI to use new OpenAI SDK
 </Update>
 
 <Update label="2025-04-11" description="v2.1.16-patch.1">

docs/components/embedders/models/azure_openai.mdx

@@ -6,7 +6,8 @@ To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`,
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -46,6 +47,36 @@ messages = [
 m.add(messages, user_id="john")
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+    embedder: {
+        provider: "azure_openai",
+        config: {
+            model: "text-embedding-3-large",
+            modelProperties: {
+                endpoint: "your-api-base-url",
+                deployment: "your-deployment-name",
+                apiVersion: "version-to-use",
+            }
+        }
+    }
+}
+
+const memory = new Memory(config);
+
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+
+await memory.add(messages, { userId: "john" });
+```
+</CodeGroup>
+
 ### Config
 
 Here are the parameters available for configuring Azure OpenAI embedder:

mem0-ts/src/oss/src/config/manager.ts

@@ -27,6 +27,10 @@ export class ConfigManager {
                 : defaultConf.apiKey,
             model: finalModel,
             url: userConf?.url,
+            modelProperties:
+              userConf?.modelProperties !== undefined
+                ? userConf.modelProperties
+                : defaultConf.modelProperties,
           };
         })(),
       },

mem0-ts/src/oss/src/embeddings/azure.ts

@@ -0,0 +1,39 @@
+import { AzureOpenAI } from "openai";
+import { Embedder } from "./base";
+import { EmbeddingConfig } from "../types";
+
+export class AzureOpenAIEmbedder implements Embedder {
+  private client: AzureOpenAI;
+  private model: string;
+
+  constructor(config: EmbeddingConfig) {
+    if (!config.apiKey || !config.modelProperties?.endpoint) {
+      throw new Error("Azure OpenAI requires both API key and endpoint");
+    }
+
+    const { endpoint, ...rest } = config.modelProperties;
+
+    this.client = new AzureOpenAI({
+      apiKey: config.apiKey,
+      endpoint: endpoint as string,
+      ...rest,
+    });
+    this.model = config.model || "text-embedding-3-small";
+  }
+
+  async embed(text: string): Promise<number[]> {
+    const response = await this.client.embeddings.create({
+      model: this.model,
+      input: text,
+    });
+    return response.data[0].embedding;
+  }
+
+  async embedBatch(texts: string[]): Promise<number[][]> {
+    const response = await this.client.embeddings.create({
+      model: this.model,
+      input: texts,
+    });
+    return response.data.map((item) => item.embedding);
+  }
+}

mem0-ts/src/oss/src/index.ts

@@ -5,6 +5,7 @@ export * from "./embeddings/base";
 export * from "./embeddings/openai";
 export * from "./embeddings/ollama";
 export * from "./embeddings/google";
+export * from "./embeddings/azure";
 export * from "./embeddings/langchain";
 export * from "./llms/base";
 export * from "./llms/openai";

mem0-ts/src/oss/src/types/index.ts

@@ -16,6 +16,7 @@ export interface EmbeddingConfig {
   apiKey?: string;
   model?: string | any;
   url?: string;
+  modelProperties?: Record<string, any>;
 }
 
 export interface VectorStoreConfig {
@@ -112,6 +113,7 @@ export const MemoryConfigSchema = z.object({
   embedder: z.object({
     provider: z.string(),
     config: z.object({
+      modelProperties: z.record(z.string(), z.any()).optional(),
       apiKey: z.string().optional(),
       model: z.union([z.string(), z.any()]).optional(),
     }),

mem0-ts/src/oss/src/utils/factory.ts

@@ -26,6 +26,7 @@ import { HistoryManager } from "../storage/base";
 import { GoogleEmbedder } from "../embeddings/google";
 import { GoogleLLM } from "../llms/google";
 import { AzureOpenAILLM } from "../llms/azure";
+import { AzureOpenAIEmbedder } from "../embeddings/azure";
 import { LangchainLLM } from "../llms/langchain";
 import { LangchainEmbedder } from "../embeddings/langchain";
 import { LangchainVectorStore } from "../vector_stores/langchain";
@@ -39,6 +40,8 @@ export class EmbedderFactory {
         return new OllamaEmbedder(config);
       case "google":
         return new GoogleEmbedder(config);
+      case "azure_openai":
+        return new AzureOpenAIEmbedder(config);
       case "langchain":
         return new LangchainEmbedder(config);
       default:

mem0-ts/src/oss/src/vector_stores/supabase.ts

@@ -103,12 +103,16 @@ export class SupabaseDB implements VectorStore {
     try {
       // Verify table exists and vector operations work by attempting a test insert
       const testVector = Array(1536).fill(0);
+
+      // First try to delete any existing test vector
       try {
         await this.client.from(this.tableName).delete().eq("id", "test_vector");
-      } catch (error) {
+      } catch {
-        console.warn("No test vector to delete, safe to ignore.");
+        // Ignore delete errors - table might not exist yet
       }
-      const { error: testError } = await this.client
+
+      // Try to insert the test vector
+      const { error: insertError } = await this.client
         .from(this.tableName)
         .insert({
           id: "test_vector",
@@ -117,8 +121,9 @@ export class SupabaseDB implements VectorStore {
         })
         .select();
 
-      if (testError) {
+      // If we get a duplicate key error, that's actually fine - it means the table exists
-        console.error("Test insert error:", testError);
+      if (insertError && insertError.code !== "23505") {
+        console.error("Test insert error:", insertError);
         throw new Error(
           `Vector operations failed. Please ensure:
 1. The vector extension is enabled
@@ -178,8 +183,12 @@ See the SQL migration instructions in the code comments.`,
         );
       }
 
-      // Clean up test vector
+      // Clean up test vector - ignore errors here too
-      await this.client.from(this.tableName).delete().eq("id", "test_vector");
+      try {
+        await this.client.from(this.tableName).delete().eq("id", "test_vector");
+      } catch {
+        // Ignore delete errors
+      }
 
       console.log("Connected to Supabase successfully");
     } catch (error) {