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