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/samples/02-agents/embeddings/azure_ai_inference_embeddings.py

@@ -0,0 +1,87 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "agent-framework-azure-ai",
+# ]
+# ///
+# Run with: uv run samples/02-agents/embeddings/azure_ai_inference_embeddings.py
+
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import pathlib
+
+from agent_framework import Content
+from agent_framework_azure_ai import AzureAIInferenceEmbeddingClient
+from dotenv import load_dotenv
+
+load_dotenv()
+
+"""Azure AI Inference Image Embedding Example
+
+This sample demonstrates how to generate image embeddings using the
+Azure AI Inference embedding client with the Cohere-embed-v3-english model.
+Images are passed as ``Content`` objects created with ``Content.from_data()``.
+
+Prerequisites:
+    Set the following environment variables or add them to a .env file:
+    - AZURE_AI_INFERENCE_ENDPOINT: Your Azure AI model inference endpoint URL
+    - AZURE_AI_INFERENCE_API_KEY: Your API key
+    - AZURE_AI_INFERENCE_EMBEDDING_MODEL_ID: The text embedding model name
+      (e.g. "text-embedding-3-small")
+    - AZURE_AI_INFERENCE_IMAGE_EMBEDDING_MODEL_ID: The image embedding model name
+      (e.g. "Cohere-embed-v3-english")
+"""
+
+SAMPLE_IMAGE_PATH = pathlib.Path(__file__).parent.parent.parent / "shared" / "sample_assets" / "sample_image.jpg"
+
+
+async def main() -> None:
+    """Generate image embeddings with Azure AI Inference."""
+    async with AzureAIInferenceEmbeddingClient() as client:
+        # 1. Generate an image embedding.
+        image_bytes = SAMPLE_IMAGE_PATH.read_bytes()
+        image_content = Content.from_data(data=image_bytes, media_type="image/jpeg")
+        result = await client.get_embeddings([image_content])
+        print(f"Image embedding dimensions: {result[0].dimensions}")
+        print(f"First 5 values: {result[0].vector[:5]}")
+        print(f"Model: {result[0].model_id}")
+        print(f"Usage: {result.usage}")
+        print()
+
+        # 2. Generate image and text embeddings separately in one call.
+        # The client dispatches text to the text endpoint and images to the image
+        # endpoint, then reassembles results in the original input order.
+        result = await client.get_embeddings(["A half-timbered house in a forested valley", image_content])
+        print(f"Text embedding dimensions: {result[0].dimensions}")
+        print(f"First 5 values: {result[0].vector[:5]}")
+        print(f"Image embedding dimensions: {result[1].dimensions}")
+        print(f"First 5 values: {result[1].vector[:5]}")
+        print()
+
+        # 3. Generate image embeddings with input_type option.
+        result = await client.get_embeddings(
+            [image_content],
+            options={"input_type": "document"},
+        )
+        print(f"Document embedding dimensions: {result[0].dimensions}")
+        print(f"First 5 values: {result[0].vector[:5]}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+
+"""
+Sample output (using Cohere-embed-v3-english):
+Image embedding dimensions: 1024
+First 5 values: [0.023, -0.045, 0.067, -0.089, 0.011]
+Model: Cohere-embed-v3-english
+Usage: {'prompt_tokens': 1, 'total_tokens': 1}
+
+Image+text (separate) results:
+Text embedding dimensions: 1536
+Image embedding dimensions: 1024
+
+Document embedding dimensions: 1024
+"""