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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_azure_ai_search

Python: Azure AI Search provider improvements - EmbeddingGenerator, async context manager, KB message handling (#4212 )

2026-02-25 06:47:26 +00:00

tests

Python: Azure AI Search provider improvements - EmbeddingGenerator, async context manager, KB message handling (#4212 )

2026-02-25 06:47:26 +00:00

AGENTS.md

Python: [BREAKING] Simplify API: ChatAgent -> Agent, ChatMessage -> Message (#3747 )

2026-02-10 23:04:32 +00:00

LICENSE

Python: renamed ai search and cleanup of samples and unified import logic (#2369 )

2025-11-24 17:06:22 +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: restructure: Python samples into progressive 01-05 layout (#3862 )

2026-02-12 17:36:36 +00:00

README.md

Get Started with Microsoft Agent Framework Azure AI Search

Please install this package via pip:

pip install agent-framework-azure-ai-search --pre

Azure AI Search Integration

The Azure AI Search integration provides context providers for RAG (Retrieval Augmented Generation) capabilities with two modes:

Semantic Mode: Fast hybrid search (vector + keyword) with semantic ranking
Agentic Mode: Multi-hop reasoning using Knowledge Bases for complex queries

Basic Usage Example

See the Azure AI Search context provider examples which demonstrate:

Semantic search with hybrid (vector + keyword) queries
Agentic mode with Knowledge Bases for complex multi-hop reasoning
Environment variable configuration with Settings class
API key and managed identity authentication