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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
3.0 KiB
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55 lines
3.0 KiB
Plaintext
---
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title: "Azure OpenAI 설정"
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description: "엔터프라이즈 LLM 연결을 위해 Crew Studio와 함께 Azure OpenAI를 구성합니다"
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icon: "microsoft"
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mode: "wide"
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---
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이 가이드는 Azure OpenAI와 Crew Studio를 연동하여 원활한 엔터프라이즈 AI 운영을 수행하는 방법을 안내합니다.
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## 설정 프로세스
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<Steps>
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<Step title="Azure OpenAI Studio 접속">
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1. Azure에서 `Azure AI Services > 배포 선택 > Azure OpenAI Studio 열기`로 이동합니다.
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2. 왼쪽 메뉴에서 `Deployments`를 클릭합니다. 배포가 없다면 원하는 모델로 새 배포를 생성하세요.
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3. 생성이 완료되면 해당 배포를 선택하고, 페이지 오른쪽에서 `Target URI`와 `Key`를 찾습니다. 이 정보가 필요하니 페이지를 열어둔 상태로 두세요.
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<Frame>
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<img src="/images/enterprise/azure-openai-studio.png" alt="Azure OpenAI Studio" />
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</Frame>
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</Step>
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<Step title="CrewAI AMP 연결 구성">
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4. 다른 탭에서 `CrewAI AMP > LLM Connections`를 엽니다. LLM Connection에 이름을 지정하고, 공급자로 Azure를 선택한 다음, Azure에서 선택한 것과 동일한 모델을 선택하세요.
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5. 같은 페이지에서 3단계에서 가져온 환경 변수를 추가하세요:
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- 하나는 `AZURE_DEPLOYMENT_TARGET_URL` (Target URI 사용)로 명명합니다. URL은 다음과 같이 표시됩니다: https://your-deployment.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview
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- 다른 하나는 `AZURE_API_KEY` (Key 사용)로 명명합니다.
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6. `Add Connection`을 클릭하여 LLM Connection을 저장합니다.
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</Step>
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<Step title="기본 구성 설정">
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7. `CrewAI AMP > Settings > Defaults > Crew Studio LLM Settings`에서 새 LLM Connection과 모델을 기본값으로 설정합니다.
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</Step>
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<Step title="네트워크 액세스 구성">
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8. 네트워크 액세스 설정을 확인하세요:
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- Azure에서 `Azure OpenAI > 배포 선택`으로 이동합니다.
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- `Resource Management > Networking`으로 이동합니다.
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- `Allow access from all networks`가 활성화되어 있는지 확인하세요. 이 설정이 제한되어 있으면 CrewAI가 Azure OpenAI 엔드포인트에 접근하지 못할 수 있습니다.
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</Step>
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</Steps>
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## 확인
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모두 준비되었습니다! 이제 Crew Studio는 Azure OpenAI 연결을 사용합니다. 모든 기능이 제대로 작동하는지 확인하려면 간단한 crew 또는 task를 만들어 연결을 테스트해 보세요.
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## 문제 해결
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문제가 발생한 경우:
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- Target URI 형식이 예상 패턴과 일치하는지 확인하세요
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- API 키가 올바르고 적절한 권한을 가지고 있는지 확인하세요
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- 네트워크 액세스가 CrewAI 연결을 허용하도록 구성되어 있는지 확인하세요
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- 배포 모델이 CrewAI에서 구성한 것과 일치하는지 확인하세요
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