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Eduard van Valkenburg 6138487888 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>

6138487888 · 2026-02-25 17:45:08 +00:00

History

agent_framework_durabletask

Python: [BREAKING] Fix #3613 chat/agent message typing alignment (#3920 )

2026-02-16 15:27:25 +00:00

tests

Python: updated integration tests and guidance (#4181 )

2026-02-24 09:35:46 +00:00

AGENTS.md

Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850 )

2026-02-12 21:00:32 +00:00

LICENSE

Python: Merge durabletask changes into main (#3420 )

2026-01-27 21:08:05 +00:00

pyproject.toml

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

2026-02-25 17:45:08 +00:00

README.md

Python: replace pre-commit with prek, add PEP 723 script deps, clean up dev dependencies (#3748 )

2026-02-09 17:51:01 +00:00

README.md

Get Started with Microsoft Agent Framework Durable Task

Please install this package via pip:

pip install agent-framework-durabletask --pre

Durable Task Integration

The durable task integration lets you host Microsoft Agent Framework agents using the Durable Task framework so they can persist state, replay conversation history, and recover from failures automatically.

Basic Usage Example

from durabletask.worker import TaskHubGrpcWorker
from agent_framework.azure import DurableAIAgentWorker

# Create the worker
with TaskHubGrpcWorker(...) as worker:

    # Register the agent worker wrapper
    agent_worker = DurableAIAgentWorker(worker)

    # Register the agent
    agent_worker.add_agent(my_agent)

For more details, review the Python README and the samples directory.