Files
westey a2018b40f9 Python: [BREAKING] Require approval for file-access tools with read-only auto-approval (#6599)
* Require approvals for file-access and expose auto approval funcs for it

* Scope file-access auto-approval rules to local tools; fix base-Agent sample

Address PR #6599 review feedback:
- read_only/all_tools auto-approval rules now reject any call carrying a
  server_label so they stay scoped to FileAccessProvider's local tools and
  never auto-approve a same-named hosted tool.
- Expand the FileAccessProvider docstring to explain the runtime effect of
  approval_mode="always_require" and point to ToolApprovalMiddleware /
  create_harness_agent.
- Fix the base-Agent file_access_data_processing sample, which would otherwise
  stop executing file tools under the new always_require defaults, by adding
  ToolApprovalMiddleware with all_tools_auto_approval_rule.
- Add tests covering hosted (server_label) calls and update docs.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Clean up comments

* Update sample after merge

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-06-23 09:51:12 +00:00
..

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.