feat: Add /v1/embeddings endpoint to batches API

This PR extends the Llama Stack Batches API to support the /v1/embeddings endpoint, enabling efficient batch processing of embedding requests alongside the existing /v1/chat/completions and /v1/completions support.

Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>

tests/integration/batches/test_batches.py

@@ -323,3 +323,92 @@ class TestBatchesIntegration:
         if final_batch.error_file_id is not None:
             deleted_error_file = openai_client.files.delete(final_batch.error_file_id)
             assert deleted_error_file.deleted
+
+    def test_batch_e2e_embeddings(self, openai_client, batch_helper, embedding_model_id):
+        """Run an end-to-end batch with embeddings requests including both string and list inputs."""
+        batch_requests = [
+            {
+                "custom_id": "success-1",
+                "method": "POST",
+                "url": "/v1/embeddings",
+                "body": {"model": embedding_model_id, "input": "Hello world", "encoding_format": "float"},
+            },
+            {
+                "custom_id": "success-2",
+                "method": "POST",
+                "url": "/v1/embeddings",
+                "body": {
+                    "model": embedding_model_id,
+                    "input": ["How are you?", "Good morning", "Have a great day"],
+                    "encoding_format": "float",
+                },
+            },
+        ]
+
+        with batch_helper.create_file(batch_requests) as uploaded_file:
+            batch = openai_client.batches.create(
+                input_file_id=uploaded_file.id,
+                endpoint="/v1/embeddings",
+                completion_window="24h",
+                metadata={"test": "e2e_embeddings_success"},
+            )
+
+            final_batch = batch_helper.wait_for(
+                batch.id,
+                max_wait_time=3 * 60,
+                expected_statuses={"completed"},
+                timeout_action="skip",
+            )
+
+        assert final_batch.status == "completed"
+        assert final_batch.request_counts is not None
+        assert final_batch.request_counts.total == 2
+        assert final_batch.request_counts.completed == 2
+        assert final_batch.output_file_id is not None
+
+        output_content = openai_client.files.content(final_batch.output_file_id)
+        if isinstance(output_content, str):
+            output_text = output_content
+        else:
+            output_text = output_content.content.decode("utf-8")
+
+        output_lines = output_text.strip().split("\n")
+        assert len(output_lines) == 2
+
+        # Check first result (string input)
+        result1 = json.loads(output_lines[0])
+        assert result1["custom_id"] in ["success-1", "success-2"]
+        assert "response" in result1
+        assert result1["response"]["status_code"] == 200
+
+        # Verify the response body contains embeddings data
+        response_body1 = json.loads(result1["response"]["body"])
+        assert response_body1["object"] == "list"
+        assert "data" in response_body1
+        assert len(response_body1["data"]) == 1
+        assert "embedding" in response_body1["data"][0]
+        assert "index" in response_body1["data"][0]
+        assert response_body1["data"][0]["index"] == 0
+
+        # Check second result (list input)
+        result2 = json.loads(output_lines[1])
+        assert result2["custom_id"] in ["success-1", "success-2"]
+        assert "response" in result2
+        assert result2["response"]["status_code"] == 200
+
+        # Verify the response body contains embeddings data for list input
+        response_body2 = json.loads(result2["response"]["body"])
+        assert response_body2["object"] == "list"
+        assert "data" in response_body2
+        assert len(response_body2["data"]) == 3  # Three strings in the list
+        for i, embedding_data in enumerate(response_body2["data"]):
+            assert "embedding" in embedding_data
+            assert "index" in embedding_data
+            assert embedding_data["index"] == i
+
+        deleted_output_file = openai_client.files.delete(final_batch.output_file_id)
+        assert deleted_output_file.deleted
+
+        if final_batch.error_file_id is not None:
+            deleted_error_file = openai_client.files.delete(final_batch.error_file_id)
+            assert deleted_error_file.deleted