mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-07-01 13:18:10 +00:00
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>
152 lines
5.5 KiB
Plaintext
152 lines
5.5 KiB
Plaintext
---
|
|
title: S3 Writer Tool
|
|
description: S3WriterTool은 CrewAI 에이전트가 Amazon S3 버킷의 파일에 콘텐츠를 쓸 수 있도록 해줍니다.
|
|
icon: aws
|
|
mode: "wide"
|
|
---
|
|
|
|
# `S3WriterTool`
|
|
|
|
## 설명
|
|
|
|
`S3WriterTool`은 Amazon S3 버킷의 파일에 콘텐츠를 기록하도록 설계되었습니다. 이 도구를 사용하면 CrewAI 에이전트가 S3에서 파일을 생성하거나 업데이트할 수 있어, 데이터를 저장하거나 구성 파일을 저장하거나 기타 콘텐츠를 AWS S3 스토리지에 영구적으로 보관해야 하는 워크플로우에 이상적입니다.
|
|
|
|
## 설치
|
|
|
|
이 도구를 사용하려면 필요한 종속성을 설치해야 합니다:
|
|
|
|
```shell
|
|
uv add boto3
|
|
```
|
|
|
|
## 시작 단계
|
|
|
|
`S3WriterTool`을 효과적으로 사용하려면 다음 단계를 따르세요:
|
|
|
|
1. **필수 패키지 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 및 내용 지정**: 파일을 작성할 S3 경로와 작성할 내용을 제공합니다.
|
|
|
|
## 예시
|
|
|
|
다음 예시는 `S3WriterTool`을 사용하여 S3 버킷의 파일에 콘텐츠를 쓰는 방법을 보여줍니다:
|
|
|
|
```python Code
|
|
from crewai import Agent, Task, Crew
|
|
from crewai_tools.aws.s3 import S3WriterTool
|
|
|
|
# Initialize the tool
|
|
s3_writer_tool = S3WriterTool()
|
|
|
|
# Define an agent that uses the tool
|
|
file_writer_agent = Agent(
|
|
role="File Writer",
|
|
goal="Write content to files in S3 buckets",
|
|
backstory="An expert in storing and managing files in cloud storage.",
|
|
tools=[s3_writer_tool],
|
|
verbose=True,
|
|
)
|
|
|
|
# Example task to write a report
|
|
write_task = Task(
|
|
description="Generate a summary report of the quarterly sales data and save it to {my_bucket}.",
|
|
expected_output="Confirmation that the report was successfully saved to S3.",
|
|
agent=file_writer_agent,
|
|
)
|
|
|
|
# Create and run the crew
|
|
crew = Crew(agents=[file_writer_agent], tasks=[write_task])
|
|
result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/reports/quarterly-summary.txt"})
|
|
```
|
|
|
|
## 파라미터
|
|
|
|
`S3WriterTool`은 에이전트가 사용할 때 다음 파라미터를 허용합니다:
|
|
|
|
- **file_path**: 필수. `s3://bucket-name/file-name` 형식의 S3 파일 경로입니다.
|
|
- **content**: 필수. 파일에 쓸 내용입니다.
|
|
|
|
## AWS 자격 증명
|
|
|
|
이 도구는 S3 버킷에 접근하기 위해 AWS 자격 증명이 필요합니다. 다음과 같이 환경 변수로 자격 증명을 설정할 수 있습니다:
|
|
|
|
- **CREW_AWS_REGION**: S3 버킷이 위치한 AWS 리전. 기본값은 `us-east-1`입니다.
|
|
- **CREW_AWS_ACCESS_KEY_ID**: AWS 액세스 키 ID.
|
|
- **CREW_AWS_SEC_ACCESS_KEY**: AWS 시크릿 액세스 키.
|
|
|
|
## 사용법
|
|
|
|
`S3WriterTool`을 agent와 함께 사용할 때, agent는 S3 파일 경로와 작성할 내용을 모두 제공해야 합니다:
|
|
|
|
```python Code
|
|
# Example of using the tool with an agent
|
|
file_writer_agent = Agent(
|
|
role="File Writer",
|
|
goal="Write content to files in S3 buckets",
|
|
backstory="An expert in storing and managing files in cloud storage.",
|
|
tools=[s3_writer_tool],
|
|
verbose=True,
|
|
)
|
|
|
|
# Create a task for the agent to write a specific file
|
|
write_config_task = Task(
|
|
description="""
|
|
Create a configuration file with the following database settings:
|
|
- host: db.example.com
|
|
- port: 5432
|
|
- username: app_user
|
|
- password: secure_password
|
|
|
|
Save this configuration as JSON to {my_bucket}.
|
|
""",
|
|
expected_output="Confirmation that the configuration file was successfully saved to S3.",
|
|
agent=file_writer_agent,
|
|
)
|
|
|
|
# Run the task
|
|
crew = Crew(agents=[file_writer_agent], tasks=[write_config_task])
|
|
result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/db-config.json"})
|
|
```
|
|
|
|
## 오류 처리
|
|
|
|
`S3WriterTool`은 일반적인 S3 문제에 대한 오류 처리를 포함합니다:
|
|
|
|
- 잘못된 S3 경로 형식
|
|
- 권한 문제(예: 버킷에 대한 쓰기 권한 없음)
|
|
- AWS 자격 증명 문제
|
|
- 버킷이 존재하지 않음
|
|
|
|
오류가 발생하면 도구는 문제에 대한 세부 정보가 포함된 오류 메시지를 반환합니다.
|
|
|
|
## 구현 세부 정보
|
|
|
|
`S3WriterTool`은 S3와 상호 작용하기 위해 AWS SDK for Python(boto3)를 사용합니다:
|
|
|
|
```python Code
|
|
class S3WriterTool(BaseTool):
|
|
name: str = "S3 Writer Tool"
|
|
description: str = "Writes content to a file in Amazon S3 given an S3 file path"
|
|
|
|
def _run(self, file_path: str, content: 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')
|
|
)
|
|
|
|
s3.put_object(Bucket=bucket_name, Key=object_key, Body=content.encode('utf-8'))
|
|
return f"Successfully wrote content to {file_path}"
|
|
except ClientError as e:
|
|
return f"Error writing file to S3: {str(e)}"
|
|
```
|
|
|
|
## 결론
|
|
|
|
`S3WriterTool`은 Amazon S3 버킷의 파일에 콘텐츠를 간편하게 작성할 수 있는 방법을 제공합니다. 이 도구를 통해 에이전트가 S3에서 파일을 생성하고 업데이트할 수 있어 클라우드 기반 파일 저장소가 필요한 워크플로우를 지원합니다. 이 도구는 데이터 영속성, 구성 관리, 보고서 생성 및 AWS S3 저장소에 정보를 저장해야 하는 작업에 특히 유용합니다.
|