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Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850)
* PR2: Wire context provider pipeline and update all internal consumers - Replace AgentThread with AgentSession across all packages - Replace ContextProvider with BaseContextProvider across all packages - Replace context_provider param with context_providers (Sequence) - Replace thread= with session= in run() signatures - Replace get_new_thread() with create_session() - Add get_session(service_session_id) to agent interface - DurableAgentThread -> DurableAgentSession - Remove _notify_thread_of_new_messages from WorkflowAgent - Wire before_run/after_run context provider pipeline in RawAgent - Auto-inject InMemoryHistoryProvider when no providers configured * fix: update all tests for context provider pipeline, fix lazy-loaders, remove old test files * refactor: update all sample files for context provider pipeline (AgentThread→AgentSession, ContextProvider→BaseContextProvider) * fix: update remaining ag-ui references (client docstring, getting_started sample) * fix: make get_session service_session_id keyword-only to avoid confusion with session_id * refactor: rename _RunContext.thread_messages to session_messages * refactor: remove _threads.py, _memory.py, and old provider files; migrate devui to use plain message lists * rename: remove _new_ prefix from test files * refactor: rewrite SlidingWindowChatMessageStore as SlidingWindowHistoryProvider(InMemoryHistoryProvider) * fix: read full history from session state directly instead of reaching into provider internals * fix: update stale .pyi stubs, sample imports, and README references for new provider types * fix: remove stale message_store, _notify_thread_of_new_messages, and session_id.key references in samples * refactor: merge context_providers and sessions sample folders into sessions, remove aggregate_context_provider * refactor: UserInfoMemory stores state in session.state instead of instance attributes * feat: add Pydantic BaseModel support to session state serialization Pydantic models stored in session.state are now automatically serialized via model_dump() and restored via model_validate() during to_dict()/from_dict() round-trips. Models are auto-registered on first serialization; use register_state_type() for cold-start deserialization. Also export register_state_type as a public API. * fix mem0 * Update sample README links and descriptions for session terminology - Replace 'thread' with 'session' in sample descriptions across all READMEs - Update file links for renamed samples (mem0_sessions, redis_sessions, etc.) - Fix Threads section → Sessions section in main samples/README.md - Update tools, middleware, workflows, durabletask, azure_functions READMEs - Update architecture diagrams in concepts/tools/README.md - Update migration guides (autogen, semantic-kernel) * Fix broken Redis README link to renamed sample * Fix Mem0 OSS client search: pass scoping params as direct kwargs AsyncMemory (OSS) expects user_id/agent_id/run_id as direct kwargs, while AsyncMemoryClient (Platform) expects them in a filters dict. Adds tests for both client types. Port of fix from #3844 to new Mem0ContextProvider. * Fix rebase issues: restore missing _conversation_state.py and checkpoint decode logic - Add back _conversation_state.py (encode/decode_chat_messages) lost in rebase - Fix on_checkpoint_restore to decode cache/conversation with decode_chat_messages - Fix on_checkpoint_restore to use decode_checkpoint_value for pending requests - Add tests/workflow/__init__.py for relative import support - Fix test_agent_executor checkpoint selection (checkpoints[1] not superstep) * Add STORES_BY_DEFAULT ClassVar to skip redundant InMemoryHistoryProvider injection Chat clients that store history server-side by default (OpenAI Responses API, Azure AI Agent) now declare STORES_BY_DEFAULT = True. The agent checks this during auto-injection and skips InMemoryHistoryProvider unless the user explicitly sets store=False. * Fix broken markdown links in azure_ai and redis READMEs * Fix getting-started samples to use session API instead of removed thread/ContextProvider API * updates to workflow as agent * fix group chat import * Rename Thread→Session throughout, fix service_session_id propagation, remove stale AGUIThread - Fix: Propagate conversation_id from ChatResponse back to session.service_session_id in both streaming and non-streaming paths in _agents.py - Rename AgentThreadException → AgentSessionException - Remove stale AGUIThread from ag_ui lazy-loader - Rename use_service_thread → use_service_session in ag-ui package - Rename test functions from *_thread_* to *_session_* - Rename sample files from *_thread* to *_session* - Update docstrings and comments: thread → session - Update _mcp.py kwargs filter: add 'session' alongside 'thread' - Fix ContinuationToken docstring example: thread=thread → session=session - Fix _clients.py docstring: 'Agent threads' → 'Agent sessions' * Fix broken markdown links after thread→session file renames * fix azure ai test
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# Azure AI Search Context Provider Examples
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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.
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This folder contains examples demonstrating how to use the Azure AI Search context provider with the Agent Framework.
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## Examples
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| File | Description |
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|------|-------------|
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| [`azure_ai_with_search_context_agentic.py`](azure_ai_with_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://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/foundry-iq-boost-response-relevance-by-36-with-agentic-retrieval/4470720) |
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| [`azure_ai_with_search_context_semantic.py`](azure_ai_with_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. |
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## Installation
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```bash
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pip install agent-framework-azure-ai-search agent-framework-azure-ai
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```
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## Prerequisites
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### Required Resources
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1. **Azure AI Search service** with a search index containing your documents
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- [Create Azure AI Search service](https://learn.microsoft.com/azure/search/search-create-service-portal)
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- [Create and populate a search index](https://learn.microsoft.com/azure/search/search-what-is-an-index)
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2. **Azure AI Foundry project** with a model deployment
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- [Create Azure AI Foundry project](https://learn.microsoft.com/azure/ai-studio/how-to/create-projects)
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- Deploy a model (e.g., GPT-4o)
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3. **For Agentic mode only**: Azure OpenAI resource for Knowledge Base model calls
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- [Create Azure OpenAI resource](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource)
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- Note: This is separate from your Azure AI Foundry project endpoint
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### Authentication
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Both examples support two authentication methods:
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- **API Key**: Set `AZURE_SEARCH_API_KEY` environment variable
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- **Entra ID (Managed Identity)**: Uses `DefaultAzureCredential` when API key is not provided
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Run `az login` if using Entra ID authentication.
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## Configuration
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### Environment Variables
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**Common (both modes):**
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- `AZURE_SEARCH_ENDPOINT`: Your Azure AI Search endpoint (e.g., `https://myservice.search.windows.net`)
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- `AZURE_SEARCH_INDEX_NAME`: Name of your search index
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- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
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- `AZURE_AI_MODEL_DEPLOYMENT_NAME`: Model deployment name (e.g., `gpt-4o`, defaults to `gpt-4o`)
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- `AZURE_SEARCH_API_KEY`: _(Optional)_ Your search API key - if not provided, uses DefaultAzureCredential
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**Agentic mode only:**
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- `AZURE_SEARCH_KNOWLEDGE_BASE_NAME`: Name of your Knowledge Base in Azure AI Search
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- `AZURE_OPENAI_RESOURCE_URL`: Your Azure OpenAI resource URL (e.g., `https://myresource.openai.azure.com`)
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- **Important**: This is different from `AZURE_AI_PROJECT_ENDPOINT` - Knowledge Base needs the OpenAI endpoint for model calls
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### Example .env file
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**For Semantic Mode:**
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```env
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AZURE_SEARCH_ENDPOINT=https://myservice.search.windows.net
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AZURE_SEARCH_INDEX_NAME=my-index
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AZURE_AI_PROJECT_ENDPOINT=https://<resource-name>.services.ai.azure.com/api/projects/<project-name>
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AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
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# Optional - omit to use Entra ID
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AZURE_SEARCH_API_KEY=your-search-key
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```
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**For Agentic Mode (add these to semantic mode variables):**
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```env
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AZURE_SEARCH_KNOWLEDGE_BASE_NAME=my-knowledge-base
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AZURE_OPENAI_RESOURCE_URL=https://myresource.openai.azure.com
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```
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## Search Modes Comparison
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| Feature | Semantic Mode | Agentic Mode |
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|---------|--------------|--------------|
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| **Speed** | Fast | Slower (query planning overhead) |
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| **Token Usage** | Lower | Higher (query reformulation) |
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| **Retrieval Strategy** | Hybrid search + semantic ranking | Multi-hop reasoning with Knowledge Base |
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| **Query Handling** | Direct search | Automatic query reformulation |
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| **Best For** | Simple queries, speed-critical apps | Complex queries, multi-document reasoning |
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| **Additional Setup** | None | Requires Knowledge Base + OpenAI resource |
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### When to Use Semantic Mode
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- **Simple queries** where direct keyword/vector search is sufficient
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- **Speed is critical** and you need low latency
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- **Straightforward retrieval** from single documents
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- **Lower token costs** are important
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### When to Use Agentic Mode
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- **Complex queries** requiring multi-hop reasoning
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- **Cross-document analysis** where information spans multiple sources
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- **Ambiguous queries** that benefit from automatic reformulation
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- **Higher accuracy** is more important than speed
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- You need **intelligent query planning** and document synthesis
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## How the Examples Work
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### Semantic Mode Flow
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1. User query is sent to Azure AI Search
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2. Hybrid search (vector + keyword) retrieves relevant documents
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3. Semantic ranking reorders results for relevance
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4. Top-k documents are returned as context
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5. Agent generates response using retrieved context
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### Agentic Mode Flow
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1. User query is sent to the Knowledge Base
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2. Knowledge Base plans the retrieval strategy
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3. Multiple search queries may be executed (multi-hop)
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4. Retrieved information is synthesized
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5. Enhanced context is provided to the agent
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6. Agent generates response with comprehensive context
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## Code Example
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### Semantic Mode
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```python
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from agent_framework import Agent
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from agent_framework.azure import AzureAIAgentClient, AzureAISearchContextProvider
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from azure.identity.aio import DefaultAzureCredential
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# Create search provider with semantic mode (default)
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search_provider = AzureAISearchContextProvider(
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endpoint=search_endpoint,
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index_name=index_name,
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api_key=search_key, # Or use credential for Entra ID
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mode="semantic", # Default mode
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top_k=3, # Number of documents to retrieve
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)
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# Create agent with search context
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async with AzureAIAgentClient(credential=DefaultAzureCredential()) as client:
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async with Agent(
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client=client,
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model=model_deployment,
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context_providers=[search_provider],
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) as agent:
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response = await agent.run("What information is in the knowledge base?")
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```
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### Agentic Mode
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```python
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from agent_framework.azure import AzureAISearchContextProvider
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# Create search provider with agentic mode
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search_provider = AzureAISearchContextProvider(
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endpoint=search_endpoint,
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index_name=index_name,
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api_key=search_key,
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mode="agentic", # Enable agentic retrieval
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knowledge_base_name=knowledge_base_name,
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azure_openai_resource_url=azure_openai_resource_url,
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top_k=5,
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)
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# Use with agent (same as semantic mode)
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async with Agent(
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client=client,
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model=model_deployment,
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context_providers=[search_provider],
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) as agent:
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response = await agent.run("Analyze and compare topics across documents")
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```
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## Running the Examples
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1. **Set up environment variables** (see Configuration section above)
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2. **Ensure you have an Azure AI Search index** with documents:
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```bash
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# Verify your index exists
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curl -X GET "https://myservice.search.windows.net/indexes/my-index?api-version=2024-07-01" \
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-H "api-key: YOUR_API_KEY"
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```
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3. **For agentic mode**: Create a Knowledge Base in Azure AI Search
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- [Knowledge Base documentation](https://learn.microsoft.com/azure/search/knowledge-store-create-portal)
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4. **Run the examples**:
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```bash
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# Semantic mode (fast, simple)
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python azure_ai_with_search_context_semantic.py
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# Agentic mode (intelligent, complex)
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python azure_ai_with_search_context_agentic.py
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```
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## Key Parameters
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### Common Parameters
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- `endpoint`: Azure AI Search service endpoint
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- `index_name`: Name of the search index
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- `api_key`: API key for authentication (optional, can use credential instead)
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- `credential`: Azure credential for Entra ID auth (e.g., `DefaultAzureCredential()`)
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- `mode`: Search mode - `"semantic"` (default) or `"agentic"`
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- `top_k`: Number of documents to retrieve (default: 3 for semantic, 5 for agentic)
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### Semantic Mode Parameters
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- `semantic_configuration`: Name of semantic configuration in your index (optional)
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- `query_type`: Query type - `"semantic"` for semantic search (default)
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### Agentic Mode Parameters
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- `knowledge_base_name`: Name of your Knowledge Base (required)
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- `azure_openai_resource_url`: Azure OpenAI resource URL (required)
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- `max_search_queries`: Maximum number of search queries to generate (default: 3)
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## Troubleshooting
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### Common Issues
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1. **Authentication errors**
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- Ensure `AZURE_SEARCH_API_KEY` is set, or run `az login` for Entra ID auth
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- Verify your credentials have search permissions
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2. **Index not found**
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- Verify `AZURE_SEARCH_INDEX_NAME` matches your index name exactly
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- Check that the index exists and contains documents
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3. **Agentic mode errors**
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- Ensure `AZURE_SEARCH_KNOWLEDGE_BASE_NAME` is correctly configured
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- Verify `AZURE_OPENAI_RESOURCE_URL` points to your Azure OpenAI resource (not AI Foundry endpoint)
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- Check that your OpenAI resource has the necessary model deployments
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4. **No results returned**
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- Verify your index has documents with vector embeddings (for semantic/hybrid search)
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- Check that your queries match the content in your index
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- Try increasing `top_k` parameter
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5. **Slow responses in agentic mode**
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- This is expected - agentic mode trades speed for accuracy
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- Reduce `max_search_queries` if needed
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- Consider semantic mode for speed-critical applications
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## Performance Tips
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- **Use semantic mode** as the default for most scenarios - it's fast and effective
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- **Switch to agentic mode** when you need multi-hop reasoning or complex queries
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- **Adjust `top_k`** based on your needs - higher values provide more context but increase token usage
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- **Enable semantic configuration** in your index for better semantic ranking
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- **Use Entra ID authentication** in production for better security
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## Additional Resources
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- [Azure AI Search Documentation](https://learn.microsoft.com/azure/search/)
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- [Azure AI Foundry Documentation](https://learn.microsoft.com/azure/ai-studio/)
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- [RAG with Azure AI Search](https://learn.microsoft.com/azure/search/retrieval-augmented-generation-overview)
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- [Semantic Search in Azure AI Search](https://learn.microsoft.com/azure/search/semantic-search-overview)
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- [Knowledge Bases in Azure AI Search](https://learn.microsoft.com/azure/search/knowledge-store-concept-intro)
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- [Agentic Retrieval Blog Post](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/foundry-iq-boost-response-relevance-by-36-with-agentic-retrieval/4470720)
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+141
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from agent_framework import Agent
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from agent_framework.azure import AzureAIAgentClient, AzureAISearchContextProvider
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from azure.identity.aio import AzureCliCredential
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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"""
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This sample demonstrates how to use Azure AI Search with agentic mode for RAG
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(Retrieval Augmented Generation) with Azure AI agents.
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**Agentic mode** is recommended for most scenarios:
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- Uses Knowledge Bases in Azure AI Search for query planning
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- Performs multi-hop reasoning across documents
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- Provides more accurate results through intelligent retrieval
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- Slightly slower with more token consumption for query planning
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- See: https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/foundry-iq-boost-response-relevance-by-36-with-agentic-retrieval/4470720
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For simple queries where speed is critical, use semantic mode instead (see azure_ai_with_search_context_semantic.py).
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Prerequisites:
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1. An Azure AI Search service
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2. An Azure AI Foundry project with a model deployment
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3. Either an existing Knowledge Base OR a search index (to auto-create a KB)
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Environment variables:
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- AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint
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- AZURE_SEARCH_API_KEY: (Optional) API key - if not provided, uses DefaultAzureCredential
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- AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
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- AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o")
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For using an existing Knowledge Base (recommended):
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- AZURE_SEARCH_KNOWLEDGE_BASE_NAME: Your Knowledge Base name
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For auto-creating a Knowledge Base from an index:
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- AZURE_SEARCH_INDEX_NAME: Your search index name
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- AZURE_OPENAI_RESOURCE_URL: Azure OpenAI resource URL (e.g., "https://myresource.openai.azure.com")
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"""
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# Sample queries to demonstrate agentic RAG
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USER_INPUTS = [
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"What information is available in the knowledge base?",
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"Analyze and compare the main topics from different documents",
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"What connections can you find across different sections?",
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]
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async def main() -> None:
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"""Main function demonstrating Azure AI Search agentic mode."""
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# Get configuration from environment
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search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
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search_key = os.environ.get("AZURE_SEARCH_API_KEY")
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project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
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model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o")
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# Agentic mode requires exactly ONE of: knowledge_base_name OR index_name
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# Option 1: Use existing Knowledge Base (recommended)
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knowledge_base_name = os.environ.get("AZURE_SEARCH_KNOWLEDGE_BASE_NAME")
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# Option 2: Auto-create KB from index (requires azure_openai_resource_url)
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index_name = os.environ.get("AZURE_SEARCH_INDEX_NAME")
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azure_openai_resource_url = os.environ.get("AZURE_OPENAI_RESOURCE_URL")
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# Create Azure AI Search context provider with agentic mode (recommended for accuracy)
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print("Using AGENTIC mode (Knowledge Bases with query planning, recommended)\n")
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print("This mode is slightly slower but provides more accurate results.\n")
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# Configure based on whether using existing KB or auto-creating from index
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if knowledge_base_name:
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# Use existing Knowledge Base - simplest approach
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search_provider = AzureAISearchContextProvider(
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endpoint=search_endpoint,
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api_key=search_key,
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credential=AzureCliCredential() if not search_key else None,
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mode="agentic",
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knowledge_base_name=knowledge_base_name,
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# Optional: Configure retrieval behavior
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knowledge_base_output_mode="extractive_data", # or "answer_synthesis"
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retrieval_reasoning_effort="minimal", # or "medium", "low"
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)
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else:
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# Auto-create Knowledge Base from index
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if not index_name:
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raise ValueError("Set AZURE_SEARCH_KNOWLEDGE_BASE_NAME or AZURE_SEARCH_INDEX_NAME")
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if not azure_openai_resource_url:
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raise ValueError("AZURE_OPENAI_RESOURCE_URL required when using index_name")
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search_provider = AzureAISearchContextProvider(
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endpoint=search_endpoint,
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index_name=index_name,
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api_key=search_key,
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credential=AzureCliCredential() if not search_key else None,
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||||
mode="agentic",
|
||||
azure_openai_resource_url=azure_openai_resource_url,
|
||||
model_deployment_name=model_deployment,
|
||||
# Optional: Configure retrieval behavior
|
||||
knowledge_base_output_mode="extractive_data", # or "answer_synthesis"
|
||||
retrieval_reasoning_effort="minimal", # or "medium", "low"
|
||||
top_k=3,
|
||||
)
|
||||
|
||||
# Create agent with search context provider
|
||||
async with (
|
||||
search_provider,
|
||||
AzureAIAgentClient(
|
||||
project_endpoint=project_endpoint,
|
||||
model_deployment_name=model_deployment,
|
||||
credential=AzureCliCredential(),
|
||||
) as client,
|
||||
Agent(
|
||||
client=client,
|
||||
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)
|
||||
|
||||
print("\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
+97
@@ -0,0 +1,97 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from agent_framework import Agent
|
||||
from agent_framework.azure import AzureAIAgentClient, AzureAISearchContextProvider
|
||||
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 DefaultAzureCredential for Entra ID
|
||||
- AZURE_SEARCH_INDEX_NAME: Your search index name
|
||||
- AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
|
||||
- AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o")
|
||||
"""
|
||||
|
||||
# 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."""
|
||||
|
||||
# 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["AZURE_AI_PROJECT_ENDPOINT"]
|
||||
model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o")
|
||||
|
||||
# Create Azure AI Search context provider with semantic mode (recommended, fast)
|
||||
print("Using SEMANTIC mode (hybrid search + semantic ranking, fast)\n")
|
||||
search_provider = AzureAISearchContextProvider(
|
||||
endpoint=search_endpoint,
|
||||
index_name=index_name,
|
||||
api_key=search_key, # Use api_key for API key auth, or credential for managed identity
|
||||
credential=AzureCliCredential() if not search_key else None,
|
||||
mode="semantic", # Default mode
|
||||
top_k=3, # Retrieve top 3 most relevant documents
|
||||
)
|
||||
|
||||
# Create agent with search context provider
|
||||
async with (
|
||||
search_provider,
|
||||
AzureAIAgentClient(
|
||||
project_endpoint=project_endpoint,
|
||||
model_deployment_name=model_deployment,
|
||||
credential=AzureCliCredential(),
|
||||
) as client,
|
||||
Agent(
|
||||
client=client,
|
||||
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())
|
||||
Reference in New Issue
Block a user