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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>
146 lines
5.6 KiB
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146 lines
5.6 KiB
Plaintext
---
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title: أداة قراءة S3
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description: تمكّن `S3ReaderTool` وكلاء CrewAI من قراءة الملفات من حاويات 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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## الوصف
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صُممت `S3ReaderTool` لقراءة الملفات من حاويات Amazon S3. تتيح هذه الأداة لوكلاء CrewAI الوصول إلى المحتوى المخزن في S3 واسترجاعه، مما يجعلها مثالية لسير العمل الذي يتطلب قراءة البيانات أو ملفات الإعداد أو أي محتوى آخر مخزن في تخزين AWS S3.
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## التثبيت
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لاستخدام هذه الأداة، تحتاج إلى تثبيت التبعيات المطلوبة:
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```shell
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uv add boto3
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```
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## خطوات البدء
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لاستخدام `S3ReaderTool` بفعالية، اتبع الخطوات التالية:
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1. **تثبيت التبعيات**: ثبّت الحزم المطلوبة باستخدام الأمر أعلاه.
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2. **إعداد بيانات اعتماد AWS**: عيّن بيانات اعتماد AWS كمتغيرات بيئة.
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3. **تهيئة الأداة**: أنشئ مثيلاً من الأداة.
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4. **تحديد مسار S3**: قدّم مسار S3 للملف المراد قراءته.
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## مثال
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يوضح المثال التالي كيفية استخدام `S3ReaderTool` لقراءة ملف من حاوية 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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## المعاملات
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تقبل `S3ReaderTool` المعامل التالي عند استخدامها من قبل وكيل:
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- **file_path**: مطلوب. مسار ملف S3 بتنسيق `s3://bucket-name/file-name`.
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## بيانات اعتماد AWS
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تتطلب الأداة بيانات اعتماد AWS للوصول إلى حاويات S3. يمكنك إعداد هذه البيانات باستخدام متغيرات البيئة:
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- **CREW_AWS_REGION**: منطقة AWS حيث تقع حاوية S3. القيمة الافتراضية `us-east-1`.
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- **CREW_AWS_ACCESS_KEY_ID**: معرّف مفتاح الوصول لـ AWS.
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- **CREW_AWS_SEC_ACCESS_KEY**: مفتاح الوصول السري لـ AWS.
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## الاستخدام
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عند استخدام `S3ReaderTool` مع وكيل، سيحتاج الوكيل لتقديم مسار ملف 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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## معالجة الأخطاء
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تتضمن `S3ReaderTool` معالجة أخطاء لمشكلات S3 الشائعة:
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- تنسيق مسار S3 غير صالح
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- ملفات مفقودة أو غير قابلة للوصول
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- مشكلات الأذونات
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- مشكلات بيانات اعتماد AWS
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عند حدوث خطأ، ستعيد الأداة رسالة خطأ تتضمن تفاصيل حول المشكلة.
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## تفاصيل التنفيذ
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تستخدم `S3ReaderTool` حزمة AWS SDK لـ Python (boto3) للتفاعل مع 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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## الخلاصة
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توفر `S3ReaderTool` طريقة مباشرة لقراءة الملفات من حاويات Amazon S3. من خلال تمكين الوكلاء من الوصول إلى المحتوى المخزن في S3، تسهّل سير العمل الذي يتطلب وصولاً سحابياً للملفات. هذه الأداة مفيدة بشكل خاص لمعالجة البيانات وإدارة الإعدادات وأي مهمة تتضمن استرجاع المعلومات من تخزين AWS S3.
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