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* feat: adopt directory-based docs versioning with Edge channel Switch docs.crewai.com from navigation-only versioning (every version selector entry rendered the same docs/<lang>/* source files) to Mintlify's directory-based versioning so each version selector entry renders its own snapshot. Add an "Edge" channel under docs/edge/<lang>/* that always reflects main HEAD for unreleased work, eliminating pre-release leakage onto frozen release labels. External links to canonical /<lang>/* URLs are preserved via wildcard redirects that always land on the current default version. Layout: - docs/edge/<lang>/* rolling source (you edit here) - docs/edge/enterprise-api.*.yaml - docs/v<X.Y.Z>/<lang>/* frozen, immutable snapshots - docs/v<X.Y.Z>/enterprise-api.*.yaml - docs/images/ shared, append-only - docs/docs.json nav + redirects URLs follow the Mintlify-idiomatic shape: /edge/<lang>/<page> for Edge, /v<X.Y.Z>/<lang>/<page> for every frozen snapshot. The wildcard redirects /<lang>/:slug* -> /<default>/<lang>/:slug* keep stale links working, and every freeze rewrites them (plus all per-section/per-page redirects) so destinations always resolve to the current default without depending on a second redirect hop. Release flow integration (devtools release): - New module crewai_devtools.docs_versioning.freeze() materialises docs/v<X.Y.Z>/ from docs/edge/, rewrites openapi: refs inside the snapshot, inserts the version into every language block in docs.json, and refreshes all redirect destinations. - _update_docs_and_create_pr() in cli.py now calls that freeze during Phase 2 of devtools release. Edge changelogs are updated first (so the snapshot freeze picks them up), then the snapshot is staged alongside docs.json, branched as docs/freeze-v<X.Y.Z>, and the PR is titled [docs-freeze] docs: snapshot and changelog for v<X.Y.Z> — the title prefix the new CI guard reads. - The PR still gates tag, GitHub release, PyPI publish, and the enterprise release as before; no new PRs are added. - Pre-releases (1.X.YaN, 1.X.YbN, ...) skip the snapshot — they ride Edge — and the docs PR title omits the [docs-freeze] prefix. - docs_check (AI-generated docs scaffolding) writes to docs/edge/<lang>/* so newly-generated unreleased docs land in Edge and never accidentally touch a frozen snapshot. Migration scripts (one-shot): - scripts/docs/freeze_historical_versions.py reconstructs all 16 historical snapshots (v1.10.0 .. v1.14.7) from git tags via git archive | tar, rewriting openapi: MDX refs so each snapshot reads its own enterprise-api YAML rather than the live one. - scripts/docs/prefix_version_paths.py one-shot-migrates docs.json: rewrites every page path in 16 versioned blocks to point under docs/v<X.Y.Z>/, inserts a new Edge entry per language, tags v1.14.7 as Latest (default), prunes pages whose target file doesn't exist in the snapshot (e.g. docs/ar/ didn't exist before v1.12.0), and writes the wildcard + per-section redirects. - scripts/docs/freeze_current_edge.py is now a thin CLI wrapper around docs_versioning.freeze for manual one-off freezes (e.g. retroactively snapshotting a forgotten release). CI guards (.github/workflows/docs-snapshots.yml): - Frozen snapshots under docs/v[0-9]*/ are immutable; only PRs whose title contains [docs-freeze] (i.e. release-cut PRs generated by devtools release or the manual wrapper) may modify them. - Images under docs/images/ are append-only since snapshots share a single image directory. Deleting or renaming an image breaks every historical snapshot that still references it. Restored docs/images/crewai-otel-export.png from PR #3673; it was deleted in PR #4908 but v1.10.0 / v1.10.1 snapshots still reference it. Restoring instead of editing the snapshots preserves historical rendering fidelity and validates the new append-only rule retroactively. Tests: - lib/devtools/tests/test_docs_versioning.py covers the freeze: file copy, openapi rewrite, version insertion, default demotion, redirect upserts, per-section redirect rewriting, idempotency, and invalid inputs. Verified locally with mintlify broken-links: 0 broken links across the full site (Edge + 16 frozen versions, 4 locales). AGENTS.md (repo root) is the contributor guide for the new model; RELEASING.md is the release-cut runbook; README's Contribution section links to both. Co-authored-by: Cursor <cursoragent@cursor.com> * style: resolve linter issues --------- Co-authored-by: Cursor <cursoragent@cursor.com>
55 lines
2.6 KiB
Plaintext
55 lines
2.6 KiB
Plaintext
---
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title: "Azure OpenAI Setup"
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description: "Configure Azure OpenAI with Crew Studio for enterprise LLM connections"
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icon: "microsoft"
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mode: "wide"
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---
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This guide walks you through connecting Azure OpenAI with Crew Studio for seamless enterprise AI operations.
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## Setup Process
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<Steps>
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<Step title="Access Azure AI Foundry">
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1. In Azure, go to [Azure AI Foundry](https://ai.azure.com/) > select your Azure OpenAI deployment.
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2. On the left menu, click `Deployments`. If you don't have one, create a deployment with your desired model.
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3. Once created, select your deployment and locate the `Target URI` and `Key` on the right side of the page. Keep this page open, as you'll need this information.
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<Frame>
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<img src="/images/enterprise/azure-openai-studio.png" alt="Azure AI Foundry" />
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</Frame>
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</Step>
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<Step title="Configure CrewAI AMP Connection">
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4. In another tab, open `CrewAI AMP > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure.
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5. On the same page, add environment variables from step 3:
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- One named `AZURE_DEPLOYMENT_TARGET_URL` (using the Target URI). The URL should look like this: https://your-deployment.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview
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- Another named `AZURE_API_KEY` (using the Key).
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6. Click `Add Connection` to save your LLM Connection.
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</Step>
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<Step title="Set Default Configuration">
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7. In `CrewAI AMP > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults.
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</Step>
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<Step title="Configure Network Access">
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8. Ensure network access settings:
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- In Azure, go to `Azure OpenAI > select your deployment`.
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- Navigate to `Resource Management > Networking`.
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- Ensure that `Allow access from all networks` is enabled. If this setting is restricted, CrewAI may be blocked from accessing your Azure OpenAI endpoint.
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</Step>
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</Steps>
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## Verification
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You're all set! Crew Studio will now use your Azure OpenAI connection. Test the connection by creating a simple crew or task to ensure everything is working properly.
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## Troubleshooting
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If you encounter issues:
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- Verify the Target URI format matches the expected pattern
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- Check that the API key is correct and has proper permissions
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- Ensure network access is configured to allow CrewAI connections
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- Confirm the deployment model matches what you've configured in CrewAI
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