mirror of https://github.com/microsoft/agent-framework.git synced 2026-06-16 21:04:09 +08:00

Files

T

Evan Mattson acec9caa2f Python: Bump Python package versions for a release. (#5432 )

* Bump Python version for a release.

* Revert lockstep bumps on unchanged connectors

Per PR review: only connectors that changed (or whose published metadata
changed) should get new versions. Keeps released tier at 1.1.1, a2a/ag-ui
at 1.0.0b260422, foundry-hosting at 1.0.0a260422; reverts the 19 unchanged
betas and 2 unchanged alphas to 1.0.0b260421/1.0.0a260421. Reverts all 26
non-core agent-framework-core floors to >=1.1.0,<2 since no connector
actually depends on a 1.1.1 API or bug fix.

* Restore lockstep prerelease bumps and raise core floors to >=1.1.1

Reverses the lean-revert: all beta packages stamped 1.0.0b260423 and alpha
packages stamped 1.0.0a260423 (Asia date, matching release cut time). All
26 non-core packages raise agent-framework-core lower bound from >=1.1.0,<2
to >=1.1.1,<2 to signal the validated cohort for this release. CHANGELOG
date updated to 2026-04-23.

acec9caa2f · 2026-04-23 16:40:14 +09:00

History

agent_framework_azure_ai_search

Python: [BREAKING] update to v1.0.0 (#5062 )

2026-04-02 15:26:30 +00:00

tests

Python: [BREAKING] update to v1.0.0 (#5062 )

2026-04-02 15:26:30 +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: Bump Python package versions for a release. (#5432 )

2026-04-23 16:40:14 +09:00

README.md

Python: [BREAKING] Python: Provider-leading client design & OpenAI package extraction (#4818 )

2026-03-25 09:56:29 +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