fix(embeddings): guard embed_batch count mismatch in OpenAI and Azure OpenAI (#5966)

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

@@ -52,6 +52,11 @@ export class AzureOpenAIEmbedder implements Embedder {
           .map((item) => item.embedding),
       );
     }
+    if (allEmbeddings.length !== texts.length) {
+      throw new Error(
+        `Azure OpenAI embedBatch() returned ${allEmbeddings.length} embeddings for ${texts.length} texts using model '${this.model}'`,
+      );
+    }
     return allEmbeddings;
   }
 }

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

@@ -47,6 +47,11 @@ export class OpenAIEmbedder implements Embedder {
           .map((item) => item.embedding),
       );
     }
+    if (allEmbeddings.length !== texts.length) {
+      throw new Error(
+        `OpenAI embedBatch() returned ${allEmbeddings.length} embeddings for ${texts.length} texts using model '${this.model}'`,
+      );
+    }
     return allEmbeddings;
   }
 }

mem0-ts/src/oss/tests/azure-embedder.test.ts

@@ -134,6 +134,19 @@ describe("AzureOpenAIEmbedder (unit)", () => {
       expect(result).toEqual(batch);
     });
 
+    it("embedBatch() throws when provider returns fewer embeddings than texts", async () => {
+      // Provider returns only 1 embedding for 2 texts (short-batch response).
+      mockEmbeddingsCreate.mockResolvedValue({
+        data: [{ index: 0, embedding: mockEmbedding }],
+      });
+
+      const embedder = new AzureOpenAIEmbedder(baseConfig);
+
+      await expect(embedder.embedBatch(["text1", "text2"])).rejects.toThrow(
+        /returned 1 embeddings for 2 texts/,
+      );
+    });
+
     it("uses custom model when provided", async () => {
       const embedder = new AzureOpenAIEmbedder({
         ...baseConfig,

mem0-ts/src/oss/tests/openai-embedder.test.ts

@@ -142,6 +142,21 @@ describe("OpenAIEmbedder (unit)", () => {
       expect(result).toEqual(batch);
     });
 
+    it("embedBatch() throws when provider returns fewer embeddings than texts", async () => {
+      // Provider returns only 1 embedding for 2 texts (short-batch response).
+      mockEmbeddingsCreate.mockResolvedValue({
+        data: [{ index: 0, embedding: mockEmbedding }],
+      });
+
+      const embedder = new OpenAIEmbedder({
+        apiKey: "test-key",
+      });
+
+      await expect(embedder.embedBatch(["text1", "text2"])).rejects.toThrow(
+        /returned 1 embeddings for 2 texts/,
+      );
+    });
+
     it("uses custom model when provided", async () => {
       const embedder = new OpenAIEmbedder({
         apiKey: "test-key",