Python: Embeddings batch size (#2331)

### Motivation and Context

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Adding optional batch size parameter for when Azure OpenAI limits number
of inputs for embeddings requests. Fixes
#2099
### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [ ] The code builds clean without any errors or warnings
- [ ] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [ ] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄

---------

Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>

python/semantic_kernel/connectors/ai/open_ai/services/open_ai_text_embedding.py

@@ -52,26 +52,31 @@ def __init__(
         self._org_id = org_id
         self._log = log if log is not None else NullLogger()
 
-    async def generate_embeddings_async(self, texts: List[str]) -> ndarray:
+    async def generate_embeddings_async(
+        self, texts: List[str], batch_size: Optional[int] = None
+    ) -> ndarray:
         model_args = {}
         if self._api_type in ["azure", "azure_ad"]:
             model_args["engine"] = self._model_id
         else:
             model_args["model"] = self._model_id
 
         try:
-            response: Any = await openai.Embedding.acreate(
-                **model_args,
-                api_key=self._api_key,
-                api_type=self._api_type,
-                api_base=self._endpoint,
-                api_version=self._api_version,
-                organization=self._org_id,
-                input=texts,
-            )
-
-            # make numpy arrays from the response
-            raw_embeddings = [array(x["embedding"]) for x in response["data"]]
+            raw_embeddings = []
+            batch_size = batch_size or len(texts)
+            for i in range(0, len(texts), batch_size):
+                batch = texts[i : i + batch_size]
+                response: Any = await openai.Embedding.acreate(
+                    **model_args,
+                    api_key=self._api_key,
+                    api_type=self._api_type,
+                    api_base=self._endpoint,
+                    api_version=self._api_version,
+                    organization=self._org_id,
+                    input=batch,
+                )
+                # make numpy arrays from the response
+                raw_embeddings.extend([array(x["embedding"]) for x in response["data"]])
             return array(raw_embeddings)
         except Exception as ex:
             raise AIException(

python/tests/integration/embeddings/test_azure_oai_embedding_service.py

@@ -33,3 +33,23 @@ async def test_azure_text_embedding_service(create_kernel, get_aoai_config):
         text="this is a test",
         external_source_name="external source",
     )
+
+
+@pytest.mark.asyncio
+async def test_batch_azure_embeddings(get_aoai_config):
+    # Configure LLM service
+    _, api_key, endpoint = get_aoai_config
+
+    if "Python_Integration_Tests" in os.environ:
+        deployment_name = os.environ["AzureOpenAIEmbeddings__DeploymentName"]
+
+    else:
+        deployment_name = "ada-002"
+
+    embeddings_service = sk_oai.AzureTextEmbedding(deployment_name, endpoint, api_key)
+    texts = ["hello world", "goodbye world"]
+    results = await embeddings_service.generate_embeddings_async(texts)
+    batch_results = await embeddings_service.generate_embeddings_async(
+        texts, batch_size=1
+    )
+    assert len(results) == len(batch_results)

python/tests/unit/ai/open_ai/services/test_azure_text_embedding.py

@@ -1,7 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 from logging import Logger
-from unittest.mock import AsyncMock, patch
+from unittest.mock import AsyncMock, call, patch
 
 import pytest
 
@@ -122,7 +122,7 @@ async def test_azure_text_embedding_calls_with_parameters() -> None:
         api_type = "azure"
         api_version = "2023-03-15-preview"
         logger = Logger("test_logger")
-        texts = ["hello world"]
+        texts = ["hello world", "goodbye world"]
 
         azure_text_embedding = AzureTextEmbedding(
             deployment_name=deployment_name,
@@ -143,3 +143,55 @@ async def test_azure_text_embedding_calls_with_parameters() -> None:
             organization=None,
             input=texts,
         )
+
+
+@pytest.mark.asyncio
+async def test_azure_text_embedding_calls_with_batches() -> None:
+    mock_openai = AsyncMock()
+    with patch(
+        "semantic_kernel.connectors.ai.open_ai.services.open_ai_text_embedding.openai",
+        new=mock_openai,
+    ):
+        deployment_name = "test_deployment"
+        endpoint = "https://test-endpoint.com"
+        api_key = "test_api_key"
+        api_type = "azure"
+        api_version = "2023-03-15-preview"
+        logger = Logger("test_logger")
+        texts = [i for i in range(0, 5)]
+
+        azure_text_embedding = AzureTextEmbedding(
+            deployment_name=deployment_name,
+            endpoint=endpoint,
+            api_key=api_key,
+            api_version=api_version,
+            logger=logger,
+        )
+
+        await azure_text_embedding.generate_embeddings_async(texts, batch_size=3)
+
+        mock_openai.assert_has_calls(
+            [
+                call.Embedding.acreate(
+                    engine=deployment_name,
+                    api_key=api_key,
+                    api_type=api_type,
+                    api_base=endpoint,
+                    api_version=api_version,
+                    organization=None,
+                    input=texts[0:3],
+                ),
+                call.Embedding.acreate().__getitem__("data"),
+                call.Embedding.acreate().__getitem__().__iter__(),
+                call.Embedding.acreate(
+                    engine=deployment_name,
+                    api_key=api_key,
+                    api_type=api_type,
+                    api_base=endpoint,
+                    api_version=api_version,
+                    organization=None,
+                    input=texts[3:5],
+                ),
+            ],
+            any_order=False,
+        )