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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
2.8 KiB
Plaintext
55 lines
2.8 KiB
Plaintext
---
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title: "Configuração do Azure OpenAI"
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description: "Configure o Azure OpenAI com o Crew Studio para conexões empresariais de LLM"
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icon: "microsoft"
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mode: "wide"
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---
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Este guia orienta você na conexão do Azure OpenAI com o Crew Studio para operações de IA empresarial sem interrupções.
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## Processo de Configuração
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<Steps>
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<Step title="Acesse o Azure OpenAI Studio">
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1. No Azure, vá para `Serviços de IA do Azure > selecione sua implantação > abra o Azure OpenAI Studio`.
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2. No menu à esquerda, clique em `Implantações`. Se não houver nenhuma, crie uma implantação com o modelo desejado.
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3. Uma vez criada, selecione sua implantação e localize o `Target URI` e a `Key` no lado direito da página. Mantenha esta página aberta, pois você precisará dessas informações.
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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="Configure a Conexão Enterprise do CrewAI">
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4. Em outra aba, abra `CrewAI AMP > LLM Connections`. Dê um nome à sua LLM Connection, selecione Azure como provedor e escolha o mesmo modelo que você selecionou no Azure.
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5. Na mesma página, adicione as variáveis de ambiente do passo 3:
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- Uma chamada `AZURE_DEPLOYMENT_TARGET_URL` (usando o Target URI). A URL deve ser parecida com: https://your-deployment.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview
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- Outra chamada `AZURE_API_KEY` (usando a Key).
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6. Clique em `Add Connection` para salvar sua LLM Connection.
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</Step>
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<Step title="Defina Configurações Padrão">
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7. Em `CrewAI AMP > Settings > Defaults > Crew Studio LLM Settings`, defina a nova LLM Connection e o modelo como padrão.
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</Step>
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<Step title="Configure o Acesso à Rede">
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8. Certifique-se das configurações de acesso à rede:
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- No Azure, vá para `Azure OpenAI > selecione sua implantação`.
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- Navegue até `Resource Management > Networking`.
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- Certifique-se de que a opção `Allow access from all networks` está habilitada. Se essa configuração estiver restrita, o CrewAI pode ser impedido de acessar seu endpoint do Azure OpenAI.
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</Step>
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</Steps>
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## Verificação
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Tudo pronto! O Crew Studio agora utilizará sua conexão Azure OpenAI. Teste a conexão criando um crew ou task simples para garantir que tudo está funcionando corretamente.
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## Solução de Problemas
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Se você encontrar problemas:
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- Verifique se o formato do Target URI corresponde ao padrão esperado
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- Confira se a API key está correta e com as permissões adequadas
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- Certifique-se de que o acesso à rede está configurado para permitir conexões do CrewAI
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- Confirme se o modelo da implantação corresponde ao que você configurou no CrewAI
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