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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>
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145 lines
5.0 KiB
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---
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title: S3 Reader Tool
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description: O `S3ReaderTool` permite que agentes CrewAI leiam arquivos de buckets Amazon S3.
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icon: aws
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mode: "wide"
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---
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# `S3ReaderTool`
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## Descrição
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O `S3ReaderTool` foi projetado para ler arquivos de buckets Amazon S3. Esta ferramenta permite que os agentes CrewAI acessem e recuperem conteúdos armazenados no S3, tornando-a ideal para fluxos de trabalho que exigem leitura de dados, arquivos de configuração ou qualquer outro conteúdo armazenado no AWS S3.
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## Instalação
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Para utilizar esta ferramenta, é necessário instalar as dependências requeridas:
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```shell
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uv add boto3
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```
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## Passos para Começar
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Para usar o `S3ReaderTool` efetivamente, siga estes passos:
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1. **Instale as Dependências**: Instale os pacotes necessários usando o comando acima.
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2. **Configure as Credenciais AWS**: Defina suas credenciais AWS como variáveis de ambiente.
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3. **Inicialize a Ferramenta**: Crie uma instância da ferramenta.
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4. **Especifique o Caminho S3**: Forneça o caminho S3 do arquivo que deseja ler.
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## Exemplo
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O exemplo a seguir demonstra como utilizar o `S3ReaderTool` para ler um arquivo de um bucket S3:
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```python Code
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from crewai import Agent, Task, Crew
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from crewai_tools.aws.s3 import S3ReaderTool
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# Initialize the tool
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s3_reader_tool = S3ReaderTool()
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# Define an agent that uses the tool
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file_reader_agent = Agent(
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role="File Reader",
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goal="Read files from S3 buckets",
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backstory="An expert in retrieving and processing files from cloud storage.",
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tools=[s3_reader_tool],
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verbose=True,
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)
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# Example task to read a configuration file
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read_task = Task(
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description="Read the configuration file from {my_bucket} and summarize its contents.",
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expected_output="A summary of the configuration file contents.",
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agent=file_reader_agent,
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)
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# Create and run the crew
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crew = Crew(agents=[file_reader_agent], tasks=[read_task])
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result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/app-config.json"})
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```
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## Parâmetros
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O `S3ReaderTool` aceita o seguinte parâmetro quando utilizado por um agente:
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- **file_path**: Obrigatório. O caminho do arquivo S3 no formato `s3://nome-do-bucket/nome-do-arquivo`.
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## Credenciais AWS
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A ferramenta requer credenciais AWS para acessar buckets S3. Você pode configurar essas credenciais usando variáveis de ambiente:
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- **CREW_AWS_REGION**: Região AWS onde seu bucket S3 está localizado. O padrão é `us-east-1`.
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- **CREW_AWS_ACCESS_KEY_ID**: Sua AWS access key ID.
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- **CREW_AWS_SEC_ACCESS_KEY**: Sua AWS secret access key.
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## Uso
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Ao utilizar o `S3ReaderTool` com um agente, o agente deverá fornecer o caminho do arquivo S3:
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```python Code
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# Example of using the tool with an agent
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file_reader_agent = Agent(
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role="File Reader",
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goal="Read files from S3 buckets",
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backstory="An expert in retrieving and processing files from cloud storage.",
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tools=[s3_reader_tool],
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verbose=True,
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)
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# Create a task for the agent to read a specific file
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read_config_task = Task(
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description="Read the application configuration file from {my_bucket} and extract the database connection settings.",
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expected_output="The database connection settings from the configuration file.",
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agent=file_reader_agent,
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)
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# Run the task
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crew = Crew(agents=[file_reader_agent], tasks=[read_config_task])
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result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/app-config.json"})
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```
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## Tratamento de Erros
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O `S3ReaderTool` inclui tratamento para erros comuns do S3:
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- Formato inválido de caminho S3
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- Arquivos ausentes ou inacessíveis
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- Problemas de permissão
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- Problemas com credenciais AWS
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Quando um erro ocorre, a ferramenta retorna uma mensagem de erro com detalhes sobre o problema.
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## Detalhes da Implementação
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O `S3ReaderTool` utiliza o AWS SDK for Python (boto3) para interagir com o S3:
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```python Code
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class S3ReaderTool(BaseTool):
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name: str = "S3 Reader Tool"
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description: str = "Reads a file from Amazon S3 given an S3 file path"
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def _run(self, file_path: str) -> str:
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try:
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bucket_name, object_key = self._parse_s3_path(file_path)
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s3 = boto3.client(
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's3',
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region_name=os.getenv('CREW_AWS_REGION', 'us-east-1'),
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aws_access_key_id=os.getenv('CREW_AWS_ACCESS_KEY_ID'),
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aws_secret_access_key=os.getenv('CREW_AWS_SEC_ACCESS_KEY')
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)
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# Read file content from S3
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response = s3.get_object(Bucket=bucket_name, Key=object_key)
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file_content = response['Body'].read().decode('utf-8')
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return file_content
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except ClientError as e:
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return f"Error reading file from S3: {str(e)}"
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```
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## Conclusão
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O `S3ReaderTool` oferece uma maneira simples de ler arquivos de buckets Amazon S3. Ao permitir que agentes acessem conteúdos armazenados no S3, facilita fluxos de trabalho que necessitam de acesso a arquivos na nuvem. Esta ferramenta é especialmente útil para processamento de dados, gestão de configurações e qualquer tarefa que envolva a obtenção de informações do armazenamento AWS S3. |