Python: Phase 2: Embedding clients for Ollama, Bedrock, and Azure AI Inference (#4207)

* Phase 2: Embedding clients for Ollama, Bedrock, and Azure AI Inference

Add embedding client implementations to existing provider packages:

- OllamaEmbeddingClient: Text embeddings via Ollama's embed API
- BedrockEmbeddingClient: Text embeddings via Amazon Titan on Bedrock
- AzureAIInferenceEmbeddingClient: Text and image embeddings via Azure AI
  Inference, supporting Content | str input with separate model IDs for
  text (AZURE_AI_INFERENCE_EMBEDDING_MODEL_ID) and image
  (AZURE_AI_INFERENCE_IMAGE_EMBEDDING_MODEL_ID) endpoints

Additional changes:
- Rename EmbeddingCoT -> EmbeddingT, EmbeddingOptionsCoT -> EmbeddingOptionsT
- Add otel_provider_name passthrough to all embedding clients
- Register integration pytest marker in all packages
- Add lazy-loading namespace exports for Ollama and Bedrock embeddings
- Add image embedding sample using Cohere-embed-v3-english
- Add azure-ai-inference dependency to azure-ai package

Part of #1188

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix mypy duplicate name and ruff lint issues

- Rename second 'vector' variable to 'img_vector' in image embedding loop
- Combine nested with statements in tests
- Remove unused result assignments in tests

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* updates from feedback

* Fix CI failures in embedding usage handling

- Fix Azure AI embedding mypy issues by normalizing vectors to list[float],
  safely accumulating optional usage token fields, and filtering None entries
  before constructing GeneratedEmbeddings
- Avoid Bandit false positive by initializing usage details as an empty dict
- Update OpenAI embedding tests to assert canonical usage keys
  (input_token_count/total_token_count)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

python/packages/ollama/tests/ollama/test_ollama_embedding_client.py

@@ -0,0 +1,150 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import os
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from agent_framework import Embedding, GeneratedEmbeddings
+
+from agent_framework_ollama import OllamaEmbeddingClient, OllamaEmbeddingOptions
+
+# region: Unit Tests
+
+
+def test_ollama_embedding_construction(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Test construction with explicit parameters."""
+    monkeypatch.setenv("OLLAMA_EMBEDDING_MODEL_ID", "nomic-embed-text")
+    with patch("agent_framework_ollama._embedding_client.AsyncClient") as mock_client_cls:
+        mock_client_cls.return_value = MagicMock()
+        client = OllamaEmbeddingClient()
+        assert client.model_id == "nomic-embed-text"
+
+
+def test_ollama_embedding_construction_with_params() -> None:
+    """Test construction with explicit parameters."""
+    with patch("agent_framework_ollama._embedding_client.AsyncClient") as mock_client_cls:
+        mock_client_cls.return_value = MagicMock()
+        client = OllamaEmbeddingClient(
+            model_id="nomic-embed-text",
+            host="http://localhost:11434",
+        )
+        assert client.model_id == "nomic-embed-text"
+
+
+def test_ollama_embedding_construction_missing_model_raises(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Test that missing model_id raises an error."""
+    monkeypatch.delenv("OLLAMA_EMBEDDING_MODEL_ID", raising=False)
+    monkeypatch.delenv("OLLAMA_MODEL_ID", raising=False)
+    from agent_framework.exceptions import SettingNotFoundError
+
+    with pytest.raises(SettingNotFoundError):
+        OllamaEmbeddingClient()
+
+
+async def test_ollama_embedding_get_embeddings() -> None:
+    """Test generating embeddings via the Ollama API."""
+    mock_response = {
+        "model": "nomic-embed-text",
+        "embeddings": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
+        "prompt_eval_count": 10,
+    }
+
+    with patch("agent_framework_ollama._embedding_client.AsyncClient") as mock_client_cls:
+        mock_client = MagicMock()
+        mock_client.embed = AsyncMock(return_value=mock_response)
+        mock_client_cls.return_value = mock_client
+
+        client = OllamaEmbeddingClient(model_id="nomic-embed-text")
+        result = await client.get_embeddings(["hello", "world"])
+
+        assert isinstance(result, GeneratedEmbeddings)
+        assert len(result) == 2
+        assert result[0].vector == [0.1, 0.2, 0.3]
+        assert result[1].vector == [0.4, 0.5, 0.6]
+        assert result[0].model_id == "nomic-embed-text"
+        assert result.usage == {"input_token_count": 10}
+
+        mock_client.embed.assert_called_once_with(
+            model="nomic-embed-text",
+            input=["hello", "world"],
+        )
+
+
+async def test_ollama_embedding_get_embeddings_empty_input() -> None:
+    """Test generating embeddings with empty input."""
+    with patch("agent_framework_ollama._embedding_client.AsyncClient") as mock_client_cls:
+        mock_client = MagicMock()
+        mock_client_cls.return_value = mock_client
+
+        client = OllamaEmbeddingClient(model_id="nomic-embed-text")
+        result = await client.get_embeddings([])
+
+        assert isinstance(result, GeneratedEmbeddings)
+        assert len(result) == 0
+        mock_client.embed.assert_not_called()
+
+
+async def test_ollama_embedding_get_embeddings_with_options() -> None:
+    """Test generating embeddings with custom options."""
+    mock_response = {
+        "model": "nomic-embed-text",
+        "embeddings": [[0.1, 0.2, 0.3]],
+    }
+
+    with patch("agent_framework_ollama._embedding_client.AsyncClient") as mock_client_cls:
+        mock_client = MagicMock()
+        mock_client.embed = AsyncMock(return_value=mock_response)
+        mock_client_cls.return_value = mock_client
+
+        client = OllamaEmbeddingClient(model_id="nomic-embed-text")
+        options: OllamaEmbeddingOptions = {
+            "truncate": True,
+            "dimensions": 512,
+        }
+        result = await client.get_embeddings(["hello"], options=options)
+
+        assert len(result) == 1
+        mock_client.embed.assert_called_once_with(
+            model="nomic-embed-text",
+            input=["hello"],
+            truncate=True,
+            dimensions=512,
+        )
+
+
+async def test_ollama_embedding_get_embeddings_no_model_raises() -> None:
+    """Test that missing model_id at call time raises ValueError."""
+    with patch("agent_framework_ollama._embedding_client.AsyncClient") as mock_client_cls:
+        mock_client = MagicMock()
+        mock_client_cls.return_value = mock_client
+
+        client = OllamaEmbeddingClient(model_id="nomic-embed-text")
+        client.model_id = None  # type: ignore[assignment]
+
+        with pytest.raises(ValueError, match="model_id is required"):
+            await client.get_embeddings(["hello"])
+
+
+# region: Integration Tests
+
+skip_if_ollama_embedding_integration_tests_disabled = pytest.mark.skipif(
+    os.getenv("OLLAMA_EMBEDDING_MODEL_ID", "") in ("", "test-model"),
+    reason="No real Ollama embedding model provided; skipping integration tests.",
+)
+
+
+@pytest.mark.flaky
+@pytest.mark.integration
+@skip_if_ollama_embedding_integration_tests_disabled
+async def test_ollama_embedding_integration() -> None:
+    """Integration test for Ollama embedding client."""
+    client = OllamaEmbeddingClient()
+    result = await client.get_embeddings(["Hello, world!", "How are you?"])
+
+    assert isinstance(result, GeneratedEmbeddings)
+    assert len(result) == 2
+    for embedding in result:
+        assert isinstance(embedding, Embedding)
+        assert isinstance(embedding.vector, list)
+        assert len(embedding.vector) > 0
+        assert all(isinstance(v, float) for v in embedding.vector)