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Giles Odigwe
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* Python: Fix broken samples and add missing READMEs - simple_context_provider: move instructions kwarg into options dict - suspend_resume_session: use OpenAIChatCompletionClient for in-memory demo - foundry_chat_client_with_hosted_mcp: move store kwarg into options dict - Add README.md for context_providers and conversations sample folders Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Fix additional sample issues in context_providers - mem0_basic: send preferences query before sleep so Mem0 can learn them, print result from new session recall - mem0_sessions: add session for multi-turn conversation in agent-scoped example, remove user_id from agent-scoped provider (Mem0 API stores memories without user_id when agent_id is provided), use single message for storing preferences - redis_basics: print retrieved context messages instead of raw object - redis_sessions: add missing load_dotenv() call - redis_basics/redis_sessions: fix docstrings referencing wrong client type - azure_redis_conversation: replace duplicate copyright with load_dotenv() Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Fix broken link in declarative README openai_responses_agent.py was renamed to openai_agent.py Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Context Provider Samples
These samples demonstrate how to use context providers to enrich agent conversations with external knowledge — from custom logic to Azure AI Search (RAG) and memory services.
Samples
| File / Folder | Description |
|---|---|
simple_context_provider.py |
Implement a custom context provider by extending BaseContextProvider to extract and inject structured user information across turns. |
azure_ai_foundry_memory.py |
Use FoundryMemoryProvider to add semantic memory — automatically retrieves, searches, and stores memories via Azure AI Foundry. |
azure_ai_search/ |
Retrieval Augmented Generation (RAG) with Azure AI Search in semantic and agentic modes. See its own README. |
mem0/ |
Memory-powered context using the Mem0 integration (open-source and managed). See its own README. |
redis/ |
Redis-backed context providers for conversation memory and sessions. See its own README. |
Prerequisites
For simple_context_provider.py:
FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpointFOUNDRY_MODEL: Model deployment name- Azure CLI authentication (
az login)
For azure_ai_foundry_memory.py:
FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpointFOUNDRY_MODEL: Chat/responses model deployment nameAZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: Embedding model deployment name (e.g.,text-embedding-ada-002)- Azure CLI authentication (
az login)
See each subfolder's README for provider-specific prerequisites.