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 ContextProvider 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.
file_access_data_processing/	Use FileAccessProvider with FileSystemAgentFileStore to give an agent read/write/search access to a folder of CSV data files. See its own README.
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 endpoint
• FOUNDRY_MODEL: Model deployment name
• Azure CLI authentication (az login)

For azure_ai_foundry_memory.py:

• FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
• FOUNDRY_MODEL: Chat/responses model deployment name
• AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: Embedding model deployment name (e.g., text-embedding-ada-002)
• Azure CLI authentication (az login)

For file_access_data_processing/:

• FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
• FOUNDRY_MODEL: Chat model deployment name
• Azure CLI authentication (az login)

See each subfolder's README for provider-specific prerequisites.