chore: import upstream snapshot with attribution

python/samples/02-agents/context_providers/azure_ai_search/README.md

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+# Azure AI Search Context Provider Examples
+
+Azure AI Search context provider enables Retrieval Augmented Generation (RAG) with your agents by retrieving relevant documents from Azure AI Search indexes. It supports two search modes optimized for different use cases.
+
+This folder contains examples demonstrating how to use the Azure AI Search context provider with the Agent Framework.
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| [`search_context_agentic.py`](search_context_agentic.py) | **Agentic mode** (recommended for most scenarios): Uses Knowledge Bases in Azure AI Search for query planning and multi-hop reasoning. Provides more accurate results through intelligent retrieval with automatic query reformulation. Slightly slower with more token consumption for query planning. [Learn more](https://learn.microsoft.com/azure/search/agentic-retrieval-overview) |
+| [`search_context_semantic.py`](search_context_semantic.py) | **Semantic mode** (fast queries): Fast hybrid search combining vector and keyword search with semantic ranking. Returns raw search results as context. Best for scenarios where speed is critical and simple retrieval is sufficient. |
+
+## Installation
+
+```bash
+pip install agent-framework-azure-ai-search agent-framework-foundry
+```
+
+## Prerequisites
+
+### Required Resources
+
+1. **Azure AI Search service** with a search index containing your documents
+   - [Create Azure AI Search service](https://learn.microsoft.com/azure/search/search-create-service-portal)
+   - [Create and populate a search index](https://learn.microsoft.com/azure/search/search-what-is-an-index)
+
+2. **Azure AI Foundry project** with a model deployment
+   - [Create Azure AI Foundry project](https://learn.microsoft.com/azure/ai-studio/how-to/create-projects)
+   - Deploy a model (e.g., GPT-4o)
+
+3. **For Agentic mode only**: Azure OpenAI resource for Knowledge Base model calls
+   - [Create Azure OpenAI resource](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource)
+   - Note: This is separate from your Azure AI Foundry project endpoint
+
+### Authentication
+
+Both examples support two authentication methods:
+
+- **API Key**: Set `AZURE_SEARCH_API_KEY` environment variable
+- **Entra ID (Managed Identity)**: Uses `DefaultAzureCredential` when API key is not provided
+
+Run `az login` if using Entra ID authentication.
+
+### API versions (stable vs preview)
+
+The provider auto-detects which build of `azure-search-documents` is installed — nothing to
+configure in code:
+
+- **Stable / GA** — `pip install azure-search-documents` (`>=12.0.0`) → api-version `2026-04-01`.
+- **Preview** — `pip install --pre azure-search-documents` (e.g. `12.1.0b1`) → api-version `2026-05-01-preview`.
+
+The installed build picks its own api-version, so newer releases work without code changes.
+
+Agentic `knowledge_base_output_mode="answer_synthesis"` and `retrieval_reasoning_effort` of
+`"low"`/`"medium"` ship **only** in the preview build. On a stable build the provider uses
+extractive output with minimal reasoning effort and raises an actionable error if a preview-only
+option is requested. To enable them, just install the preview build (`pip install --pre
+azure-search-documents`) — no code change.
+
+## Configuration
+
+### Environment Variables
+
+**Common (both modes):**
+- `AZURE_SEARCH_ENDPOINT`: Your Azure AI Search endpoint (e.g., `https://myservice.search.windows.net`)
+- `AZURE_SEARCH_INDEX_NAME`: Name of your search index
+- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
+- `FOUNDRY_MODEL`: Model deployment name (e.g., `gpt-4o`, defaults to `gpt-4o`)
+- `AZURE_SEARCH_API_KEY`: _(Optional)_ Your search API key - if not provided, uses DefaultAzureCredential
+
+**Agentic mode only:**
+- `AZURE_SEARCH_KNOWLEDGE_BASE_NAME`: Name of your Knowledge Base in Azure AI Search
+- `AZURE_OPENAI_RESOURCE_URL`: Your Azure OpenAI resource URL (e.g., `https://myresource.openai.azure.com`)
+  - **Important**: This is different from `FOUNDRY_PROJECT_ENDPOINT` - Knowledge Base needs the OpenAI endpoint for model calls
+
+### Example .env file
+
+**For Semantic Mode:**
+```env
+AZURE_SEARCH_ENDPOINT=https://myservice.search.windows.net
+AZURE_SEARCH_INDEX_NAME=my-index
+FOUNDRY_PROJECT_ENDPOINT=https://<resource-name>.services.ai.azure.com/api/projects/<project-name>
+FOUNDRY_MODEL=gpt-4o
+# Optional - omit to use Entra ID
+AZURE_SEARCH_API_KEY=your-search-key
+```
+
+**For Agentic Mode (add these to semantic mode variables):**
+```env
+AZURE_SEARCH_KNOWLEDGE_BASE_NAME=my-knowledge-base
+AZURE_OPENAI_RESOURCE_URL=https://myresource.openai.azure.com
+```
+
+## Search Modes Comparison
+
+| Feature | Semantic Mode | Agentic Mode |
+|---------|--------------|--------------|
+| **Speed** | Fast | Slower (query planning overhead) |
+| **Token Usage** | Lower | Higher (query reformulation) |
+| **Retrieval Strategy** | Hybrid search + semantic ranking | Multi-hop reasoning with Knowledge Base |
+| **Query Handling** | Direct search | Automatic query reformulation |
+| **Best For** | Simple queries, speed-critical apps | Complex queries, multi-document reasoning |
+| **Additional Setup** | None | Requires Knowledge Base + OpenAI resource |
+
+### When to Use Semantic Mode
+
+- **Simple queries** where direct keyword/vector search is sufficient
+- **Speed is critical** and you need low latency
+- **Straightforward retrieval** from single documents
+- **Lower token costs** are important
+
+### When to Use Agentic Mode
+
+- **Complex queries** requiring multi-hop reasoning
+- **Cross-document analysis** where information spans multiple sources
+- **Ambiguous queries** that benefit from automatic reformulation
+- **Higher accuracy** is more important than speed
+- You need **intelligent query planning** and document synthesis
+
+## How the Examples Work
+
+### Semantic Mode Flow
+
+1. User query is sent to Azure AI Search
+2. Hybrid search (vector + keyword) retrieves relevant documents
+3. Semantic ranking reorders results for relevance
+4. Top-k documents are returned as context
+5. Agent generates response using retrieved context
+
+### Agentic Mode Flow
+
+1. User query is sent to the Knowledge Base
+2. Knowledge Base plans the retrieval strategy
+3. Multiple search queries may be executed (multi-hop)
+4. Retrieved information is synthesized
+5. Enhanced context is provided to the agent
+6. Agent generates response with comprehensive context
+
+## Code Example
+
+### Semantic Mode
+
+```python
+from agent_framework import Agent
+from agent_framework.azure import AzureAISearchContextProvider
+from agent_framework.foundry import FoundryChatClient
+from azure.identity.aio import DefaultAzureCredential
+
+# Create search provider with semantic mode (default)
+search_provider = AzureAISearchContextProvider(
+    endpoint=search_endpoint,
+    index_name=index_name,
+    api_key=search_key,  # Or use credential for Entra ID
+    mode="semantic",  # Default mode
+    top_k=3,  # Number of documents to retrieve
+)
+
+# Create agent with search context
+async with FoundryChatClient(
+    project_endpoint=project_endpoint,
+    model=model_deployment,
+    credential=DefaultAzureCredential(),
+) as client:
+    async with Agent(
+        client=client,
+        context_providers=[search_provider],
+    ) as agent:
+        response = await agent.run("What information is in the knowledge base?")
+```
+
+### Agentic Mode
+
+```python
+from agent_framework.azure import AzureAISearchContextProvider
+
+# Create search provider with agentic mode
+search_provider = AzureAISearchContextProvider(
+    endpoint=search_endpoint,
+    index_name=index_name,
+    api_key=search_key,
+    mode="agentic",  # Enable agentic retrieval
+    knowledge_base_name=knowledge_base_name,
+    azure_openai_resource_url=azure_openai_resource_url,
+    top_k=5,
+)
+
+# Use with agent (same as semantic mode)
+async with Agent(
+    client=client,
+    model=model_deployment,
+    context_providers=[search_provider],
+) as agent:
+    response = await agent.run("Analyze and compare topics across documents")
+```
+
+## Running the Examples
+
+1. **Set up environment variables** (see Configuration section above)
+
+2. **Ensure you have an Azure AI Search index** with documents:
+   ```bash
+   # Verify your index exists
+   curl -X GET "https://myservice.search.windows.net/indexes/my-index?api-version=2024-07-01" \
+        -H "api-key: YOUR_API_KEY"
+   ```
+
+3. **For agentic mode**: Create a Knowledge Base in Azure AI Search
+   - [Knowledge Base documentation](https://learn.microsoft.com/azure/search/knowledge-store-create-portal)
+
+4. **Run the examples**:
+   ```bash
+   # Semantic mode (fast, simple)
+   python azure_ai_with_search_context_semantic.py
+
+   # Agentic mode (intelligent, complex)
+   python azure_ai_with_search_context_agentic.py
+   ```
+
+## Key Parameters
+
+### Common Parameters
+
+- `endpoint`: Azure AI Search service endpoint
+- `index_name`: Name of the search index
+- `api_key`: API key for authentication (optional, can use credential instead)
+- `credential`: Azure credential for Entra ID auth (e.g., `DefaultAzureCredential()`)
+- `mode`: Search mode - `"semantic"` (default) or `"agentic"`
+- `top_k`: Number of documents to retrieve (default: 3 for semantic, 5 for agentic)
+
+### Semantic Mode Parameters
+
+- `semantic_configuration`: Name of semantic configuration in your index (optional)
+- `query_type`: Query type - `"semantic"` for semantic search (default)
+
+### Agentic Mode Parameters
+
+- `knowledge_base_name`: Name of your Knowledge Base (required)
+- `azure_openai_resource_url`: Azure OpenAI resource URL (required)
+- `max_search_queries`: Maximum number of search queries to generate (default: 3)
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Authentication errors**
+   - Ensure `AZURE_SEARCH_API_KEY` is set, or run `az login` for Entra ID auth
+   - Verify your credentials have search permissions
+
+2. **Index not found**
+   - Verify `AZURE_SEARCH_INDEX_NAME` matches your index name exactly
+   - Check that the index exists and contains documents
+
+3. **Agentic mode errors**
+   - Ensure `AZURE_SEARCH_KNOWLEDGE_BASE_NAME` is correctly configured
+   - Verify `AZURE_OPENAI_RESOURCE_URL` points to your Azure OpenAI resource (not AI Foundry endpoint)
+   - Check that your OpenAI resource has the necessary model deployments
+
+4. **No results returned**
+   - Verify your index has documents with vector embeddings (for semantic/hybrid search)
+   - Check that your queries match the content in your index
+   - Try increasing `top_k` parameter
+
+5. **Slow responses in agentic mode**
+   - This is expected - agentic mode trades speed for accuracy
+   - Reduce `max_search_queries` if needed
+   - Consider semantic mode for speed-critical applications
+
+## Performance Tips
+
+- **Use semantic mode** as the default for most scenarios - it's fast and effective
+- **Switch to agentic mode** when you need multi-hop reasoning or complex queries
+- **Adjust `top_k`** based on your needs - higher values provide more context but increase token usage
+- **Enable semantic configuration** in your index for better semantic ranking
+- **Use Entra ID authentication** in production for better security
+
+## Additional Resources
+
+- [Azure AI Search Documentation](https://learn.microsoft.com/azure/search/)
+- [Azure AI Foundry Documentation](https://learn.microsoft.com/azure/ai-studio/)
+- [RAG with Azure AI Search](https://learn.microsoft.com/azure/search/retrieval-augmented-generation-overview)
+- [Semantic Search in Azure AI Search](https://learn.microsoft.com/azure/search/semantic-search-overview)
+- [Knowledge Bases in Azure AI Search](https://learn.microsoft.com/azure/search/knowledge-store-concept-intro)
+- [Agentic Retrieval in Azure AI Search](https://learn.microsoft.com/azure/search/agentic-retrieval-overview)

python/samples/02-agents/context_providers/azure_ai_search/search_context_agentic.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+
+from agent_framework import Agent
+from agent_framework.azure import AzureAISearchContextProvider
+from agent_framework.foundry import FoundryChatClient
+from azure.identity.aio import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+"""
+This sample demonstrates how to use Azure AI Search with agentic mode for RAG
+(Retrieval Augmented Generation) with Azure AI agents.
+
+**Agentic mode** is recommended for most scenarios:
+- Uses Knowledge Bases in Azure AI Search for query planning
+- Performs multi-hop reasoning across documents
+- Provides more accurate results through intelligent retrieval
+- Slightly slower with more token consumption for query planning
+- See: https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/foundry-iq-boost-response-relevance-by-36-with-agentic-retrieval/4470720
+
+For simple queries where speed is critical, use semantic mode instead (see azure_ai_with_search_context_semantic.py).
+
+Prerequisites:
+1. An Azure AI Search service
+2. An Azure AI Foundry project with a model deployment
+3. Either an existing Knowledge Base OR a search index (to auto-create a KB)
+
+Environment variables:
+   - AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint
+   - AZURE_SEARCH_API_KEY: (Optional) API key - if not provided, uses AzureCliCredential
+   - FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
+   - FOUNDRY_MODEL: Your model deployment name (e.g., "gpt-4o")
+
+For using an existing Knowledge Base (recommended):
+   - AZURE_SEARCH_KNOWLEDGE_BASE_NAME: Your Knowledge Base name
+
+For auto-creating a Knowledge Base from an index:
+   - AZURE_SEARCH_INDEX_NAME: Your search index name
+   - AZURE_OPENAI_RESOURCE_URL: Azure OpenAI resource URL (e.g., "https://myresource.openai.azure.com")
+"""
+
+# Sample queries to demonstrate agentic RAG
+USER_INPUTS = [
+    "What information is available in the knowledge base?",
+    "Analyze and compare the main topics from different documents",
+    "What connections can you find across different sections?",
+]
+
+
+async def main() -> None:
+    """Main function demonstrating Azure AI Search agentic mode."""
+
+    # Get configuration from environment
+    search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
+    search_key = os.environ.get("AZURE_SEARCH_API_KEY")
+    project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+    model_deployment = os.environ.get("FOUNDRY_MODEL", "gpt-4o")
+
+    # Agentic mode requires exactly ONE of: knowledge_base_name OR index_name
+    # Option 1: Use existing Knowledge Base (recommended)
+    knowledge_base_name = os.environ.get("AZURE_SEARCH_KNOWLEDGE_BASE_NAME")
+    # Option 2: Auto-create KB from index (requires azure_openai_resource_url)
+    index_name = os.environ.get("AZURE_SEARCH_INDEX_NAME")
+    azure_openai_resource_url = os.environ.get("AZURE_OPENAI_RESOURCE_URL")
+
+    # Create Azure AI Search context provider with agentic mode (recommended for accuracy)
+    print("Using AGENTIC mode (Knowledge Bases with query planning, recommended)\n")
+    print("This mode is slightly slower but provides more accurate results.\n")
+
+    # Configure based on whether using existing KB or auto-creating from index
+    if knowledge_base_name:
+        # Use existing Knowledge Base - simplest approach
+        search_provider = AzureAISearchContextProvider(
+            source_id="search_provider",
+            endpoint=search_endpoint,
+            api_key=search_key,
+            credential=AzureCliCredential() if not search_key else None,
+            mode="agentic",
+            knowledge_base_name=knowledge_base_name,
+            # Optional: Configure retrieval behavior. "answer_synthesis" output mode and
+            # "medium"/"low" reasoning effort require the preview build of azure-search-documents
+            # (`pip install --pre azure-search-documents`); the provider auto-detects the build.
+            knowledge_base_output_mode="extractive_data",  # or "answer_synthesis" (preview build only)
+            retrieval_reasoning_effort="minimal",  # or "medium", "low" (preview build only)
+        )
+    else:
+        # Auto-create Knowledge Base from index
+        if not index_name:
+            raise ValueError("Set AZURE_SEARCH_KNOWLEDGE_BASE_NAME or AZURE_SEARCH_INDEX_NAME")
+        if not azure_openai_resource_url:
+            raise ValueError("AZURE_OPENAI_RESOURCE_URL required when using index_name")
+        search_provider = AzureAISearchContextProvider(
+            source_id="search_provider",
+            endpoint=search_endpoint,
+            index_name=index_name,
+            api_key=search_key,
+            credential=AzureCliCredential() if not search_key else None,
+            mode="agentic",
+            azure_openai_resource_url=azure_openai_resource_url,
+            model=model_deployment,
+            # Optional: Configure retrieval behavior. "answer_synthesis" output mode and
+            # "medium"/"low" reasoning effort require the preview build of azure-search-documents
+            # (`pip install --pre azure-search-documents`); the provider auto-detects the build.
+            knowledge_base_output_mode="extractive_data",  # or "answer_synthesis" (preview build only)
+            retrieval_reasoning_effort="minimal",  # or "medium", "low" (preview build only)
+            top_k=3,
+        )
+
+    # Create agent with search context provider
+    async with (
+        search_provider,
+        Agent(
+            client=FoundryChatClient(
+                project_endpoint=project_endpoint,
+                model=model_deployment,
+                credential=AzureCliCredential(),
+            ),
+            name="SearchAgent",
+            instructions=(
+                "You are a helpful assistant with advanced reasoning capabilities. "
+                "Use the provided context from the knowledge base to answer complex "
+                "questions that may require synthesizing information from multiple sources."
+            ),
+            context_providers=[search_provider],
+        ) as agent,
+    ):
+        print("=== Azure AI Agent with Search Context (Agentic Mode) ===\n")
+
+        for user_input in USER_INPUTS:
+            print(f"User: {user_input}")
+            print("Agent: ", end="", flush=True)
+
+            # Stream response
+            async for chunk in agent.run(user_input, stream=True):
+                if chunk.text:
+                    print(chunk.text, end="", flush=True)
+                for content in chunk.contents:
+                    if content.annotations:
+                        print(f"\n[Sources: {content.annotations}]", end="", flush=True)
+
+            print("\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/02-agents/context_providers/azure_ai_search/search_context_semantic.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+
+from agent_framework import Agent
+from agent_framework.azure import AzureAISearchContextProvider
+from agent_framework.foundry import FoundryChatClient
+from agent_framework.openai import OpenAIEmbeddingClient
+from azure.identity.aio import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+"""
+This sample demonstrates how to use Azure AI Search with semantic mode for RAG
+(Retrieval Augmented Generation) with Azure AI agents.
+
+**Semantic mode** is the recommended default mode:
+- Fast hybrid search combining vector and keyword search
+- Uses semantic ranking for improved relevance
+- Returns raw search results as context
+- Best for most RAG use cases
+
+Prerequisites:
+1. An Azure AI Search service with a search index
+2. An Azure AI Foundry project with a model deployment
+3. Set the following environment variables:
+   - AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint
+   - AZURE_SEARCH_API_KEY: (Optional) Your search API key - if not provided, uses AzureCliCredential for Entra ID
+   - AZURE_SEARCH_INDEX_NAME: Your search index name
+   - FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
+   - FOUNDRY_MODEL: Your model deployment name (e.g., "gpt-4o")
+   - AZURE_OPENAI_EMBEDDING_MODEL: (Optional) Your Azure OpenAI embedding deployment for hybrid search
+   - AZURE_OPENAI_ENDPOINT: (Optional) Your Azure OpenAI resource URL, required if using Azure OpenAI embeddings
+"""
+
+# Sample queries to demonstrate RAG
+USER_INPUTS = [
+    "What information is available in the knowledge base?",
+    "Summarize the main topics from the documents",
+    "Find specific details about the content",
+]
+
+
+async def main() -> None:
+    """Main function demonstrating Azure AI Search semantic mode."""
+
+    credential = AzureCliCredential()
+
+    # Get configuration from environment
+    search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
+    search_key = os.environ.get("AZURE_SEARCH_API_KEY")
+    index_name = os.environ["AZURE_SEARCH_INDEX_NAME"]
+    project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+    model_deployment = os.environ.get("FOUNDRY_MODEL", "gpt-4o")
+    openai_endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
+    embedding_deployment = os.environ.get("AZURE_OPENAI_EMBEDDING_MODEL")
+
+    embedding_client = None
+    if openai_endpoint and embedding_deployment:
+        embedding_client = OpenAIEmbeddingClient(
+            azure_endpoint=openai_endpoint,
+            model=embedding_deployment,
+            credential=credential,
+        )
+
+    # Create Azure AI Search context provider with semantic mode (recommended, fast)
+    print("Using SEMANTIC mode (hybrid search + semantic ranking, fast)\n")
+    search_provider = AzureAISearchContextProvider(
+        source_id="search_provider",
+        endpoint=search_endpoint,
+        index_name=index_name,
+        api_key=search_key,  # Use api_key for API key auth, or credential for managed identity
+        credential=credential if not search_key else None,
+        mode="semantic",  # Default mode
+        top_k=3,  # Retrieve top 3 most relevant documents
+        embedding_function=embedding_client,  # Provide embedding function for hybrid search
+        vector_field_name="DescriptionVector"
+        if embedding_client
+        else None,  # Set vector field for hybrid search if using embeddings
+    )
+
+    # Create agent with search context provider
+    async with (
+        search_provider,
+        Agent(
+            client=FoundryChatClient(
+                project_endpoint=project_endpoint,
+                model=model_deployment,
+                credential=credential,
+            ),
+            name="SearchAgent",
+            instructions=(
+                "You are a helpful assistant. Use the provided context from the "
+                "knowledge base to answer questions accurately."
+            ),
+            context_providers=[search_provider],
+        ) as agent,
+    ):
+        print("=== Azure AI Agent with Search Context (Semantic Mode) ===\n")
+
+        for user_input in USER_INPUTS:
+            print(f"User: {user_input}")
+            print("Agent: ", end="", flush=True)
+
+            # Stream response
+            async for chunk in agent.run(user_input, stream=True):
+                if chunk.text:
+                    print(chunk.text, end="", flush=True)
+
+            print("\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())