Files
crewAI/docs/edge/ko/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

146 lines
5.2 KiB
Plaintext

---
title: S3 리더 도구
description: S3ReaderTool은 CrewAI 에이전트가 Amazon S3 버킷에서 파일을 읽을 수 있도록 합니다.
icon: aws
mode: "wide"
---
# `S3ReaderTool`
## 설명
`S3ReaderTool`은 Amazon S3 버킷에서 파일을 읽기 위해 설계되었습니다. 이 도구를 사용하면 CrewAI 에이전트가 S3에 저장된 콘텐츠에 접근하고 가져올 수 있어, 데이터를 읽거나 설정 파일 또는 AWS S3 스토리지에 저장된 기타 콘텐츠를 필요로 하는 워크플로우에 이상적입니다.
## 설치
이 도구를 사용하려면 필요한 종속성을 설치해야 합니다:
```shell
uv add boto3
```
## 시작 단계
`S3ReaderTool`을 효과적으로 사용하려면 다음 단계를 따르세요:
1. **의존성 설치**: 위 명령어를 사용하여 필요한 패키지를 설치합니다.
2. **AWS 자격 증명 구성**: 환경 변수로 AWS 자격 증명을 설정합니다.
3. **도구 초기화**: 도구의 인스턴스를 생성합니다.
4. **S3 경로 지정**: 읽고자 하는 파일의 S3 경로를 제공합니다.
## 예시
다음 예시는 `S3ReaderTool`을 사용하여 S3 버킷에서 파일을 읽는 방법을 보여줍니다:
```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"})
```
## 매개변수
`S3ReaderTool`은 에이전트에 의해 사용될 때 다음과 같은 매개변수를 허용합니다:
- **file_path**: 필수입니다. `s3://bucket-name/file-name` 형식의 S3 파일 경로입니다.
## AWS 자격 증명
이 도구는 S3 버킷에 접근하기 위해 AWS 자격 증명이 필요합니다. 환경 변수를 사용하여 이러한 자격 증명을 구성할 수 있습니다:
- **CREW_AWS_REGION**: S3 버킷이 위치한 AWS 리전입니다. 기본값은 `us-east-1`입니다.
- **CREW_AWS_ACCESS_KEY_ID**: AWS 액세스 키 ID입니다.
- **CREW_AWS_SEC_ACCESS_KEY**: AWS 시크릿 액세스 키입니다.
## 사용법
`S3ReaderTool`을 agent와 함께 사용할 때, agent는 S3 파일 경로를 제공해야 합니다:
```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"})
```
## 오류 처리
`S3ReaderTool`은 일반적인 S3 문제에 대한 오류 처리를 포함하고 있습니다:
- 잘못된 S3 경로 형식
- 누락되었거나 접근할 수 없는 파일
- 권한 문제
- AWS 자격 증명 문제
오류가 발생하면, 도구는 문제에 대한 세부 정보가 포함된 오류 메시지를 반환합니다.
## 구현 세부사항
`S3ReaderTool`은 AWS SDK for Python(boto3)을 사용하여 S3와 상호작용합니다:
```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)}"
```
## 결론
`S3ReaderTool`은 Amazon S3 버킷에서 파일을 읽을 수 있는 간단한 방법을 제공합니다. 에이전트가 S3에 저장된 콘텐츠에 액세스할 수 있도록 하여, 클라우드 기반 파일 액세스가 필요한 워크플로우를 지원합니다. 이 도구는 데이터 처리, 구성 관리, 그리고 AWS S3 스토리지에서 정보를 검색하는 모든 작업에 특히 유용합니다.