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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>
140 lines
6.3 KiB
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140 lines
6.3 KiB
Plaintext
---
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title: أداة استخراج عنصر من موقع
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description: أداة `ScrapeElementFromWebsiteTool` تتيح لوكلاء CrewAI استخراج عناصر محددة من المواقع باستخدام محددات CSS.
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icon: code
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mode: "wide"
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---
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# `ScrapeElementFromWebsiteTool`
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## الوصف
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أداة `ScrapeElementFromWebsiteTool` مصممة لاستخراج عناصر محددة من المواقع باستخدام محددات CSS. تسمح هذه الأداة لوكلاء CrewAI باستخراج محتوى مستهدف من صفحات الويب، مما يجعلها مفيدة لمهام استخراج البيانات حيث تكون أجزاء محددة فقط من صفحة الويب مطلوبة.
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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 requests beautifulsoup4
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```
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## خطوات البدء
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لاستخدام `ScrapeElementFromWebsiteTool` بفعالية، اتبع هذه الخطوات:
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1. **تثبيت التبعيات**: ثبّت الحزم المطلوبة باستخدام الأمر أعلاه.
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2. **تحديد محددات CSS**: حدد محددات CSS للعناصر التي تريد استخراجها من الموقع.
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3. **تهيئة الأداة**: أنشئ نسخة من الأداة بالمعاملات اللازمة.
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## مثال
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يوضح المثال التالي كيفية استخدام `ScrapeElementFromWebsiteTool` لاستخراج عناصر محددة من موقع:
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```python Code
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from crewai import Agent, Task, Crew
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from crewai_tools import ScrapeElementFromWebsiteTool
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# Initialize the tool
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scrape_tool = ScrapeElementFromWebsiteTool()
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# Define an agent that uses the tool
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web_scraper_agent = Agent(
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role="Web Scraper",
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goal="Extract specific information from websites",
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backstory="An expert in web scraping who can extract targeted content from web pages.",
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tools=[scrape_tool],
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verbose=True,
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)
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# Example task to extract headlines from a news website
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scrape_task = Task(
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description="Extract the main headlines from the CNN homepage. Use the CSS selector '.headline' to target the headline elements.",
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expected_output="A list of the main headlines from CNN.",
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agent=web_scraper_agent,
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)
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# Create and run the crew
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crew = Crew(agents=[web_scraper_agent], tasks=[scrape_task])
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result = crew.kickoff()
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```
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يمكنك أيضاً تهيئة الأداة بمعاملات محددة مسبقاً:
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```python Code
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# Initialize the tool with predefined parameters
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scrape_tool = ScrapeElementFromWebsiteTool(
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website_url="https://www.example.com",
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css_element=".main-content"
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)
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```
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## المعاملات
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تقبل أداة `ScrapeElementFromWebsiteTool` المعاملات التالية أثناء التهيئة:
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- **website_url**: اختياري. عنوان URL للموقع المراد استخراجه. إذا تم تقديمه أثناء التهيئة، لن يحتاج الوكيل إلى تحديده عند استخدام الأداة.
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- **css_element**: اختياري. محدد CSS للعناصر المراد استخراجها. إذا تم تقديمه أثناء التهيئة، لن يحتاج الوكيل إلى تحديده عند استخدام الأداة.
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- **cookies**: اختياري. قاموس يحتوي على ملفات تعريف الارتباط لإرسالها مع الطلب. يمكن أن يكون مفيداً للمواقع التي تتطلب مصادقة.
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## الاستخدام
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عند استخدام `ScrapeElementFromWebsiteTool` مع وكيل، سيحتاج الوكيل إلى تقديم المعاملات التالية (ما لم يتم تحديدها أثناء التهيئة):
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- **website_url**: عنوان URL للموقع المراد استخراجه.
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- **css_element**: محدد CSS للعناصر المراد استخراجها.
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ستُرجع الأداة المحتوى النصي لجميع العناصر المطابقة لمحدد CSS، مفصولة بأسطر جديدة.
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```python Code
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# Example of using the tool with an agent
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web_scraper_agent = Agent(
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role="Web Scraper",
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goal="Extract specific elements from websites",
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backstory="An expert in web scraping who can extract targeted content using CSS selectors.",
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tools=[scrape_tool],
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verbose=True,
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)
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# Create a task for the agent to extract specific elements
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extract_task = Task(
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description="""
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Extract all product titles from the featured products section on example.com.
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Use the CSS selector '.product-title' to target the title elements.
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""",
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expected_output="A list of product titles from the website",
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agent=web_scraper_agent,
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)
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# Run the task through a crew
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crew = Crew(agents=[web_scraper_agent], tasks=[extract_task])
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result = crew.kickoff()
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```
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## تفاصيل التنفيذ
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تستخدم أداة `ScrapeElementFromWebsiteTool` مكتبة `requests` لجلب صفحة الويب و `BeautifulSoup` لتحليل HTML واستخراج العناصر المحددة:
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```python Code
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class ScrapeElementFromWebsiteTool(BaseTool):
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name: str = "Read a website content"
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description: str = "A tool that can be used to read a website content."
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# Implementation details...
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def _run(self, **kwargs: Any) -> Any:
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website_url = kwargs.get("website_url", self.website_url)
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css_element = kwargs.get("css_element", self.css_element)
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page = requests.get(
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website_url,
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headers=self.headers,
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cookies=self.cookies if self.cookies else {},
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)
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parsed = BeautifulSoup(page.content, "html.parser")
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elements = parsed.select(css_element)
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return "\n".join([element.get_text() for element in elements])
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```
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## الخلاصة
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توفر أداة `ScrapeElementFromWebsiteTool` طريقة قوية لاستخراج عناصر محددة من المواقع باستخدام محددات CSS. من خلال تمكين الوكلاء من استهداف المحتوى الذي يحتاجونه فقط، تجعل مهام استخراج البيانات من الويب أكثر كفاءة وتركيزاً. هذه الأداة مفيدة بشكل خاص لاستخراج البيانات ومراقبة المحتوى ومهام البحث حيث تحتاج معلومات محددة إلى استخراجها من صفحات الويب. |