provision-foundry-iq

name	provision-foundry-iq
description	Provision or create an Azure AI Foundry IQ knowledge base from local files, especially VoiceAgent/samples/sample_foundry_iq_doc. Use when asked to upload documents to Foundry IQ, create a knowledge source or knowledge base, create its Foundry project MCP connection, or produce AZURE_VOICE_AGENTS_FOUNDRY_IQ_URL and AZURE_VOICE_AGENTS_FOUNDRY_IQ_CONNECTION_ID.
argument-hint	[document folder] [optional knowledge-base name]

Provision Foundry IQ

Create a new Foundry IQ knowledge source, knowledge base, and project connection from a local document folder. Always derive the Foundry account and project from AZURE_VOICE_AGENTS_ENDPOINT; do not copy account identifiers from another environment.

Use the bundled provisioner. It discovers the endpoint account across all subscriptions visible to Azure CLI, selects the account's Azure AI Search connection, uploads the documents, grants the project managed identity Search Index Data Reader, and creates the MCP connection.

Prerequisites

• Run from the VoiceAgent directory with its Python environment active.
• Install samples/requirements.txt.
• Sign in with az login using an identity that can read the endpoint Foundry, provision Search data-plane resources, read account keys, create project connections, and assign the Search Index Data Reader role.
• Set AZURE_VOICE_AGENTS_ENDPOINT in samples/.env.
• The endpoint Foundry account must have an Azure AI Search project connection.

Workflow

1. Resolve the requested document folder. Default to samples/sample_foundry_iq_doc.

2. Discover all supported files recursively. The provisioner accepts Markdown, text, JSON, PDF, HTML, Word, and PowerPoint files and rejects other types.

3. Choose one resource name and use it for both preflight and creation. Derive a lowercase hyphenated name from the folder plus a short unique suffix when the user does not provide one.

4. Run a read-only preflight:

   python skills/provision-foundry-iq/scripts/provision_foundry_iq.py --name <name> --dry-run
   

5. If preflight reports that text-embedding-3-small or gpt-4.1-mini is missing, explain that adding model capacity can incur cost and obtain the user's explicit approval. After approval, add --create-missing-model-deployments. Never add this flag implicitly.

6. Create the knowledge base with the same name used during preflight:

   python skills/provision-foundry-iq/scripts/provision_foundry_iq.py --name <name>
   

   With approved model deployment creation:

   python skills/provision-foundry-iq/scripts/provision_foundry_iq.py --name <name> --create-missing-model-deployments
   

7. For a different folder, pass --docs-dir <path>. If the Foundry has multiple Search connections, pass --search-connection-name <name>.

8. The final two output lines are the values to return to the user:

   AZURE_VOICE_AGENTS_FOUNDRY_IQ_URL=https://<search>.search.windows.net/knowledgebases/<name>/mcp?api-version=2026-05-01-preview
   AZURE_VOICE_AGENTS_FOUNDRY_IQ_CONNECTION_ID=/subscriptions/<subscription>/resourceGroups/<resource-group>/providers/Microsoft.CognitiveServices/accounts/<account>/connections/<connection>
   

9. Add --update-env only when the user asks to replace those two values in samples/.env.

Safety and validation

• A normal invocation creates Azure resources. Run --dry-run first.
• Never print or persist account keys. They are sent only to the Search service in the knowledge source and knowledge base definitions.
• Do not delete knowledge bases, knowledge sources, model deployments, project connections, or indexes. The script only removes stale files when rerunning the same explicitly named knowledge source.
• Do not use an existing AZURE_VOICE_AGENTS_FOUNDRY_IQ_URL to choose the Foundry or Search service. The endpoint and its Search connection control the destination.
• On failure, report the failed Azure operation and leave the existing .env values unchanged.