Files
crewAI/docs/edge/ar/tools/search-research/youtubechannelsearchtool.mdx
Lucas Gomide a237ebabba feat: adopt directory-based docs versioning with Edge channel (#6202)
* 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>
2026-06-17 11:56:59 -04:00

195 lines
8.3 KiB
Plaintext

---
title: البحث في قنوات YouTube باستخدام RAG
description: أداة `YoutubeChannelSearchTool` مصممة لإجراء بحث RAG (التوليد المعزز بالاسترجاع) داخل محتوى قناة YouTube.
icon: youtube
mode: "wide"
---
# `YoutubeChannelSearchTool`
<Note>
لا نزال نعمل على تحسين الأدوات، لذا قد يحدث سلوك غير متوقع أو تغييرات في المستقبل.
</Note>
## الوصف
هذه الأداة مصممة لإجراء عمليات بحث دلالية داخل محتوى قناة YouTube محددة.
من خلال الاستفادة من منهجية RAG (التوليد المعزز بالاسترجاع)، توفر نتائج بحث ذات صلة،
مما يجعلها لا تقدر بثمن لاستخراج المعلومات أو العثور على محتوى محدد دون الحاجة إلى تصفح الفيديوهات يدوياً.
تبسّط عملية البحث داخل قنوات YouTube، مما يخدم الباحثين ومنشئي المحتوى والمشاهدين الذين يبحثون عن معلومات أو مواضيع محددة.
## التثبيت
لاستخدام YoutubeChannelSearchTool، يجب تثبيت حزمة `crewai_tools`. نفّذ الأمر التالي في الطرفية للتثبيت:
```shell
pip install 'crewai[tools]'
```
## مثال
يوضح المثال التالي كيفية استخدام `YoutubeChannelSearchTool` مع وكيل CrewAI:
```python Code
from crewai import Agent, Task, Crew
from crewai_tools import YoutubeChannelSearchTool
# Initialize the tool for general YouTube channel searches
youtube_channel_tool = YoutubeChannelSearchTool()
# Define an agent that uses the tool
channel_researcher = Agent(
role="Channel Researcher",
goal="Extract relevant information from YouTube channels",
backstory="An expert researcher who specializes in analyzing YouTube channel content.",
tools=[youtube_channel_tool],
verbose=True,
)
# Example task to search for information in a specific channel
research_task = Task(
description="Search for information about machine learning tutorials in the YouTube channel {youtube_channel_handle}",
expected_output="A summary of the key machine learning tutorials available on the channel.",
agent=channel_researcher,
)
# Create and run the crew
crew = Crew(agents=[channel_researcher], tasks=[research_task])
result = crew.kickoff(inputs={"youtube_channel_handle": "@exampleChannel"})
```
يمكنك أيضاً تهيئة الأداة بمعرّف قناة YouTube محدد:
```python Code
# Initialize the tool with a specific YouTube channel handle
youtube_channel_tool = YoutubeChannelSearchTool(
youtube_channel_handle='@exampleChannel'
)
# Define an agent that uses the tool
channel_researcher = Agent(
role="Channel Researcher",
goal="Extract relevant information from a specific YouTube channel",
backstory="An expert researcher who specializes in analyzing YouTube channel content.",
tools=[youtube_channel_tool],
verbose=True,
)
```
## المعاملات
تقبل أداة `YoutubeChannelSearchTool` المعاملات التالية:
- **youtube_channel_handle**: اختياري. معرّف قناة YouTube للبحث داخلها. إذا تم تقديمه أثناء التهيئة، لن يحتاج الوكيل إلى تحديده عند استخدام الأداة. إذا لم يبدأ المعرّف بـ '@'، سيتم إضافته تلقائياً.
- **config**: اختياري. تكوين لنظام RAG الأساسي، بما في ذلك إعدادات LLM والتضمينات.
- **summarize**: اختياري. ما إذا كان يجب تلخيص المحتوى المسترجع. الافتراضي هو `False`.
عند استخدام الأداة مع وكيل، سيحتاج الوكيل إلى تقديم:
- **search_query**: مطلوب. استعلام البحث للعثور على معلومات ذات صلة في محتوى القناة.
- **youtube_channel_handle**: مطلوب فقط إذا لم يتم تقديمه أثناء التهيئة. معرّف قناة YouTube للبحث داخلها.
## النموذج المخصص والتضمينات
بشكل افتراضي، تستخدم الأداة OpenAI لكل من التضمينات والتلخيص. لتخصيص النموذج، يمكنك استخدام قاموس تكوين كما يلي:
```python Code
youtube_channel_tool = YoutubeChannelSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google-generativeai", # or openai, ollama, ...
config=dict(
model_name="gemini-embedding-001",
task_type="RETRIEVAL_DOCUMENT",
# title="Embeddings",
),
),
)
)
```
## مثال على التكامل مع الوكيل
إليك مثالاً أكثر تفصيلاً لكيفية دمج `YoutubeChannelSearchTool` مع وكيل CrewAI:
```python Code
from crewai import Agent, Task, Crew
from crewai_tools import YoutubeChannelSearchTool
# Initialize the tool
youtube_channel_tool = YoutubeChannelSearchTool()
# Define an agent that uses the tool
channel_researcher = Agent(
role="Channel Researcher",
goal="Extract and analyze information from YouTube channels",
backstory="""You are an expert channel researcher who specializes in extracting
and analyzing information from YouTube channels. You have a keen eye for detail
and can quickly identify key points and insights from video content across an entire channel.""",
tools=[youtube_channel_tool],
verbose=True,
)
# Create a task for the agent
research_task = Task(
description="""
Search for information about data science projects and tutorials
in the YouTube channel {youtube_channel_handle}.
Focus on:
1. Key data science techniques covered
2. Popular tutorial series
3. Most viewed or recommended videos
Provide a comprehensive summary of these points.
""",
expected_output="A detailed summary of data science content available on the channel.",
agent=channel_researcher,
)
# Run the task
crew = Crew(agents=[channel_researcher], tasks=[research_task])
result = crew.kickoff(inputs={"youtube_channel_handle": "@exampleDataScienceChannel"})
```
## تفاصيل التنفيذ
أداة `YoutubeChannelSearchTool` مُنفّذة كفئة فرعية من `RagTool`، التي توفر الوظائف الأساسية للتوليد المعزز بالاسترجاع:
```python Code
class YoutubeChannelSearchTool(RagTool):
name: str = "Search a Youtube Channels content"
description: str = "A tool that can be used to semantic search a query from a Youtube Channels content."
args_schema: Type[BaseModel] = YoutubeChannelSearchToolSchema
def __init__(self, youtube_channel_handle: Optional[str] = None, **kwargs):
super().__init__(**kwargs)
if youtube_channel_handle is not None:
kwargs["data_type"] = DataType.YOUTUBE_CHANNEL
self.add(youtube_channel_handle)
self.description = f"A tool that can be used to semantic search a query the {youtube_channel_handle} Youtube Channels content."
self.args_schema = FixedYoutubeChannelSearchToolSchema
self._generate_description()
def add(
self,
youtube_channel_handle: str,
**kwargs: Any,
) -> None:
if not youtube_channel_handle.startswith("@"):
youtube_channel_handle = f"@{youtube_channel_handle}"
super().add(youtube_channel_handle, **kwargs)
```
## الخلاصة
توفر أداة `YoutubeChannelSearchTool` طريقة قوية للبحث واستخراج المعلومات من محتوى قنوات YouTube باستخدام تقنيات RAG. من خلال تمكين الوكلاء من البحث عبر فيديوهات قناة كاملة، تسهّل مهام استخراج المعلومات والتحليل التي قد يكون من الصعب تنفيذها بطريقة أخرى. هذه الأداة مفيدة بشكل خاص للبحث وتحليل المحتوى واستخراج المعرفة من قنوات YouTube.