Files
crewAI/docs/edge/pt-BR/tools/cloud-storage/s3readertool.mdx
Lucas Gomide 93dafe2637 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>
2026-06-17 11:08:45 -03:00

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