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Python: Feature/azure ai search agentic rag (search as separate package) (#2328)

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* Python: Fix pyright errors and move search provider to core (#1546)

* address pablo coments

* update azure ai search pypi version to latest prev

* init update

* Fix MyPy type annotation errors in search provider

- Add type annotation to DEFAULT_CONTEXT_PROMPT
- Add type annotation to vectorizable_fields
- Add union type annotation to vector_queries

* Fix DEFAULT_CONTEXT_PROMPT MyPy error and update test

- Rename DEFAULT_CONTEXT_PROMPT to _DEFAULT_SEARCH_CONTEXT_PROMPT to avoid conflict with base class Final variable
- Update test to use new constant name
- All core package tests passing (1123 passed)

* Python: Move Azure AI Search to separate package per PR feedback

Addresses reviewer feedback from PR #1546 by isolating the beta dependency
(azure-search-documents==11.7.0b2) into a new agent-framework-aisearch package.

Changes:
- Created new agent-framework-aisearch package with complete structure
- Moved AzureAISearchContextProvider from core to aisearch package
- Added AzureAISearchSettings class for environment variable auto-loading
- Added support for direct API key string (auto-converts to AzureKeyCredential)
- Added azure_openai_api_key parameter for Knowledge Base authentication
- Updated embedding_function type to Callable[[str], Awaitable[list[float]]]
- Moved Role import to top-level imports
- Maintained lazy loading through agent_framework.azure module
- Removed beta dependency from core package
- Updated all tests to use new package location
- All quality checks pass: ruff format/lint, pyright, mypy (0 errors)
- All 21 unit tests pass with 59% coverage

Semantic search mode verified working with both API key and managed identity authentication.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* Python: Clarify top_k parameter only applies to semantic mode

Updated documentation to clarify that the top_k parameter only affects
semantic search mode. In agentic mode, the server-side Knowledge Base
determines retrieval based on query complexity and reasoning effort.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* Python: Add Knowledge Base output mode and retrieval reasoning effort parameters

Added support for configurable Knowledge Base behavior in agentic mode:

- knowledge_base_output_mode: "extractive_data" (default) or "answer_synthesis"
  Some knowledge sources require answer_synthesis mode for proper functionality.

- retrieval_reasoning_effort: "minimal" (default), "medium", or "low"
  Controls query planning complexity and multi-hop reasoning depth.

These parameters give users fine-grained control over Knowledge Base behavior
and enable support for knowledge sources that require answer synthesis.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* effort and outputmode query params

* Address PR review feedback for Azure AI Search context provider

* comments eduward

* ed latest comments

---------

Co-authored-by: Farzad Sunavala <farzad.sunavala.enovate.ai>
Co-authored-by: farzad528 <farzad528@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Farzad Sunavala
2025-11-20 22:34:46 +00:00
committed by GitHub
co-authored by Claude Farzad Sunavala <farzad.sunavala.enovate.ai> farzad528
parent ab3d898979
commit 04e711cd55
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python/samples/getting_started/agents/azure_ai/README.md
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@@ -20,6 +20,8 @@ This folder contains examples demonstrating different ways to create and use age
| [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Shows how to use the `HostedFileSearchTool` with Azure AI agents to upload files, create vector stores, and enable agents to search through uploaded documents to answer user questions. |
| [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate hosted Model Context Protocol (MCP) tools with Azure AI Agent. |
| [`azure_ai_with_response_format.py`](azure_ai_with_response_format.py) | Shows how to use structured outputs (response format) with Azure AI agents using Pydantic models to enforce specific response schemas. |
| [`azure_ai_with_search_context_agentic.py`](azure_ai_with_search_context_agentic.py) | Shows how to use AzureAISearchContextProvider with agentic mode. Uses Knowledge Bases for multi-hop reasoning across documents with query planning. Recommended for most scenarios - slightly slower with more token consumption for query planning, but more accurate results. |
| [`azure_ai_with_search_context_semantic.py`](azure_ai_with_search_context_semantic.py) | Shows how to use AzureAISearchContextProvider with semantic mode. Fast hybrid search with vector + keyword search and semantic ranking for RAG. Best for simple queries where speed is critical. |
| [`azure_ai_with_sharepoint.py`](azure_ai_with_sharepoint.py) | Shows how to use SharePoint grounding with Azure AI agents to search through SharePoint content and answer user questions with proper citations. Requires a SharePoint connection configured in your Azure AI project. |
| [`azure_ai_with_thread.py`](azure_ai_with_thread.py) | Demonstrates thread management with Azure AI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
| [`azure_ai_with_image_generation.py`](azure_ai_with_image_generation.py) | Shows how to use the `ImageGenTool` with Azure AI agents to generate images based on text prompts. |