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Farzad Sunavala
04e711cd55
* 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. ๐ค 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. ๐ค 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. ๐ค 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>
04e711cd55
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2025-11-20 22:34:46 +00:00
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Agent Examples
This folder contains examples demonstrating how to create and use agents with different chat clients from the Agent Framework. Each sub-folder focuses on a specific provider and client type, showing various capabilities like function tools, code interpreter, thread management, structured outputs, image processing, web search, Model Context Protocol (MCP) integration, and more.
Examples by Provider
Azure AI Foundry Examples
| Folder | Description |
|---|---|
azure_ai_agent/ |
Create agents using Azure AI Agent Service (based on azure-ai-agents V1 package) including function tools, code interpreter, MCP integration, thread management, and more. |
azure_ai/ |
Create agents using Azure AI Agent Service (based on azure-ai-projects V2 package) including function tools, code interpreter, MCP integration, thread management, and more. |
Microsoft Copilot Studio Examples
| Folder | Description |
|---|---|
copilotstudio/ |
Create agents using Microsoft Copilot Studio with streaming and non-streaming responses, authentication handling, and explicit configuration options |
Azure OpenAI Examples
| Folder | Description |
|---|---|
azure_openai/ |
Create agents using Azure OpenAI APIs with multiple client types (Assistants, Chat, and Responses clients) supporting function tools, code interpreter, thread management, and more |
OpenAI Examples
| Folder | Description |
|---|---|
openai/ |
Create agents using OpenAI APIs with comprehensive examples including Assistants, Chat, and Responses clients featuring function tools, code interpreter, file search, web search, MCP integration, image analysis/generation, structured outputs, reasoning, and thread management |
Anthropic Examples
| Folder | Description |
|---|---|
anthropic/ |
Create agents using Anthropic models through OpenAI Chat Client configuration, demonstrating tool calling capabilities |
Custom Implementation Examples
| Folder | Description |
|---|---|
custom/ |
Create custom agents and chat clients by extending the base framework classes, showing complete control over agent behavior and backend integration |