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Eduard van Valkenburg 534e5f5bf7 Python: improve .env handling and observability samples (#4032 )

* Python: improve .env precedence and observability samples

- Switch load_settings to explicit precedence: overrides -> explicit .env -> environment -> defaults\n- Raise when env_file_path is provided but missing\n- Update settings docs and tests for new behavior\n- Refresh observability samples and README guidance for env loading options\n\nCloses #3864\n\nCo-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fixed some imports

* Fix load_settings CI regressions

Allow explicit env_file_path values that exist but are not regular files (for example /dev/null) by checking path existence before dotenv parsing, and restore a dict accumulator with typed return cast to satisfy mypy.

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

* Avoid implicit dotenv in observability

Only load dotenv in observability helpers when env_file_path is explicitly provided, and remove test os.devnull workarounds that are no longer necessary.

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

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

534e5f5bf7 · 2026-02-18 11:18:52 +00:00

History

agent_framework_azure_ai_search

Python: improve .env handling and observability samples (#4032 )

2026-02-18 11:18:52 +00:00

tests

Python: Improve Azure AI Search package test coverage (#4019 )

2026-02-17 23:19:58 +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

update package versions (#3902 )

2026-02-13 00:00:57 +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