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

* 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>

python/.env.example

@@ -3,6 +3,14 @@ AZURE_AI_PROJECT_ENDPOINT=""
 AZURE_AI_MODEL_DEPLOYMENT_NAME=""
 # Bing connection for web search (optional, used by samples with web search)
 BING_CONNECTION_ID=""
+# Azure AI Search (optional, used by AzureAISearchContextProvider samples)
+AZURE_SEARCH_ENDPOINT=""
+AZURE_SEARCH_API_KEY=""
+AZURE_SEARCH_INDEX_NAME=""
+AZURE_SEARCH_SEMANTIC_CONFIG=""
+AZURE_SEARCH_KNOWLEDGE_BASE_NAME=""
+# Note: For agentic mode Knowledge Bases, also set AZURE_OPENAI_ENDPOINT below
+# (different from AZURE_AI_PROJECT_ENDPOINT - Knowledge Base needs OpenAI endpoint for model calls)
 # OpenAI
 OPENAI_API_KEY=""
 OPENAI_CHAT_MODEL_ID=""