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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>
149 lines
5.9 KiB
Plaintext
149 lines
5.9 KiB
Plaintext
---
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title: الاستدلال
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description: "تعرّف على كيفية تفعيل واستخدام استدلال الوكيل لتحسين تنفيذ المهام."
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icon: brain
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mode: "wide"
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---
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## نظرة عامة
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استدلال الوكيل هو ميزة تتيح للوكلاء التأمل في المهمة وإنشاء خطة قبل التنفيذ. يساعد هذا الوكلاء على التعامل مع المهام بشكل أكثر منهجية ويضمن استعدادهم لأداء العمل المطلوب.
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## الاستخدام
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لتفعيل الاستدلال لوكيل، ما عليك سوى تعيين `reasoning=True` عند إنشاء الوكيل:
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```python
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from crewai import Agent
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agent = Agent(
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role="Data Analyst",
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goal="Analyze complex datasets and provide insights",
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backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
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reasoning=True, # تفعيل الاستدلال
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max_reasoning_attempts=3 # اختياري: تعيين حد أقصى لمحاولات الاستدلال
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)
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```
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## كيف يعمل
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عند تفعيل الاستدلال، قبل تنفيذ المهمة، سيقوم الوكيل بما يلي:
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1. التأمل في المهمة وإنشاء خطة مفصلة
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2. تقييم ما إذا كان مستعدًا لتنفيذ المهمة
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3. تحسين الخطة حسب الحاجة حتى يصبح مستعدًا أو يصل إلى max_reasoning_attempts
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4. حقن خطة الاستدلال في وصف المهمة قبل التنفيذ
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تساعد هذه العملية الوكيل على تقسيم المهام المعقدة إلى خطوات يمكن إدارتها وتحديد التحديات المحتملة قبل البدء.
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## خيارات التهيئة
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<ParamField body="reasoning" type="bool" default="False">
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تفعيل أو تعطيل الاستدلال
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</ParamField>
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<ParamField body="max_reasoning_attempts" type="int" default="None">
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الحد الأقصى لعدد المحاولات لتحسين الخطة قبل المتابعة بالتنفيذ. إذا كانت القيمة None (الافتراضي)، سيستمر الوكيل في التحسين حتى يصبح مستعدًا.
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</ParamField>
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## مثال
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إليك مثالًا كاملًا:
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```python
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from crewai import Agent, Task, Crew
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# إنشاء وكيل مع تفعيل الاستدلال
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analyst = Agent(
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role="Data Analyst",
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goal="Analyze data and provide insights",
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backstory="You are an expert data analyst.",
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reasoning=True,
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max_reasoning_attempts=3 # اختياري: تعيين حد لمحاولات الاستدلال
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)
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# إنشاء مهمة
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analysis_task = Task(
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description="Analyze the provided sales data and identify key trends.",
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expected_output="A report highlighting the top 3 sales trends.",
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agent=analyst
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)
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# إنشاء طاقم وتشغيل المهمة
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crew = Crew(agents=[analyst], tasks=[analysis_task])
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result = crew.kickoff()
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print(result)
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```
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## معالجة الأخطاء
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صُممت عملية الاستدلال لتكون متينة، مع معالجة أخطاء مدمجة. إذا حدث خطأ أثناء الاستدلال، سيتابع الوكيل تنفيذ المهمة بدون خطة الاستدلال. يضمن هذا إمكانية تنفيذ المهام حتى في حالة فشل عملية الاستدلال.
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إليك كيفية التعامل مع الأخطاء المحتملة في الكود الخاص بك:
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```python
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from crewai import Agent, Task
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import logging
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# إعداد التسجيل لالتقاط أي أخطاء في الاستدلال
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logging.basicConfig(level=logging.INFO)
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# إنشاء وكيل مع تفعيل الاستدلال
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agent = Agent(
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role="Data Analyst",
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goal="Analyze data and provide insights",
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reasoning=True,
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max_reasoning_attempts=3
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)
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# إنشاء مهمة
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task = Task(
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description="Analyze the provided sales data and identify key trends.",
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expected_output="A report highlighting the top 3 sales trends.",
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agent=agent
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)
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# تنفيذ المهمة
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# إذا حدث خطأ أثناء الاستدلال، سيتم تسجيله وسيستمر التنفيذ
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result = agent.execute_task(task)
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```
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## مثال على مخرجات الاستدلال
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إليك مثالًا على شكل خطة الاستدلال لمهمة تحليل البيانات:
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```
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Task: Analyze the provided sales data and identify key trends.
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Reasoning Plan:
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I'll analyze the sales data to identify the top 3 trends.
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1. Understanding of the task:
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I need to analyze sales data to identify key trends that would be valuable for business decision-making.
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2. Key steps I'll take:
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- First, I'll examine the data structure to understand what fields are available
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- Then I'll perform exploratory data analysis to identify patterns
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- Next, I'll analyze sales by time periods to identify temporal trends
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- I'll also analyze sales by product categories and customer segments
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- Finally, I'll identify the top 3 most significant trends
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3. Approach to challenges:
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- If the data has missing values, I'll decide whether to fill or filter them
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- If the data has outliers, I'll investigate whether they're valid data points or errors
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- If trends aren't immediately obvious, I'll apply statistical methods to uncover patterns
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4. Use of available tools:
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- I'll use data analysis tools to explore and visualize the data
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- I'll use statistical tools to identify significant patterns
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- I'll use knowledge retrieval to access relevant information about sales analysis
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5. Expected outcome:
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A concise report highlighting the top 3 sales trends with supporting evidence from the data.
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READY: I am ready to execute the task.
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```
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تساعد خطة الاستدلال هذه الوكيل على تنظيم نهجه تجاه المهمة، والنظر في التحديات المحتملة، وضمان تقديم المخرجات المتوقعة.
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