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Evan Mattson
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* Add support for the Foundry Toolbox in MAF Introduces a Foundry Toolbox integration: FoundryChatClient gains a get_toolbox() helper plus select_toolbox_tools(), normalize_tools in the core package flattens tool-collection wrappers (ToolboxVersionObject and generic iterables, while leaving Pydantic BaseModel instances alone), and the new agent_framework.foundry namespace re-exports the toolbox helpers. Ships with unit tests, a sample, and a design doc. azure-ai-projects is pinned to the public >=2.0.0,<3.0 range and the lockfile resolves from public PyPI. The toolbox test module skips when Toolbox* types are unavailable so CI stays green until the public 2.1.0 SDK lands. OMC tooling directories (.omc/, .omx/) are gitignored. * Update to latest azure ai projects package * Improve sample * Rename ADR to 0025 * Update ADR * Apply suggestion from @alliscode Co-authored-by: Ben Thomas <ben.thomas@microsoft.com> * Improve samples * Update test --------- Co-authored-by: Ben Thomas <ben.thomas@microsoft.com>
36 lines
2.3 KiB
Markdown
36 lines
2.3 KiB
Markdown
# Context Provider Samples
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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.
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## Samples
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| File / Folder | Description |
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| [`simple_context_provider.py`](simple_context_provider.py) | Implement a custom context provider by extending `ContextProvider` to extract and inject structured user information across turns. |
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| [`foundry_toolbox_context_provider.py`](foundry_toolbox_context_provider.py) | Compose a Microsoft Foundry toolbox with a `ContextProvider` that caches the toolbox once and picks a subset of its tools per-turn via `select_toolbox_tools`, driven by keywords in the latest user message. |
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| [`azure_ai_foundry_memory.py`](azure_ai_foundry_memory.py) | Use `FoundryMemoryProvider` to add semantic memory — automatically retrieves, searches, and stores memories via Azure AI Foundry. |
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| [`azure_ai_search/`](azure_ai_search/) | Retrieval Augmented Generation (RAG) with Azure AI Search in semantic and agentic modes. See its own [README](azure_ai_search/README.md). |
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| [`mem0/`](mem0/) | Memory-powered context using the Mem0 integration (open-source and managed). See its own [README](mem0/README.md). |
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| [`redis/`](redis/) | Redis-backed context providers for conversation memory and sessions. See its own [README](redis/README.md). |
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## Prerequisites
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**For `simple_context_provider.py`:**
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- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
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- `FOUNDRY_MODEL`: Model deployment name
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- Azure CLI authentication (`az login`)
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**For `foundry_toolbox_context_provider.py`:**
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- `FOUNDRY_PROJECT_ENDPOINT`: Your Microsoft Foundry project endpoint
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- `FOUNDRY_MODEL`: Model deployment name
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- A toolbox already configured in that project; set `TOOLBOX_NAME` / `TOOLBOX_VERSION` at the top of the sample
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- Azure CLI authentication (`az login`)
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**For `azure_ai_foundry_memory.py`:**
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- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
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- `FOUNDRY_MODEL`: Chat/responses model deployment name
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- `AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME`: Embedding model deployment name (e.g., `text-embedding-ada-002`)
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- Azure CLI authentication (`az login`)
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See each subfolder's README for provider-specific prerequisites.
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