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https://github.com/crewAIInc/crewAI.git
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docs: Tool docs improvements (#2259)
* docs: add Qdrant vector search tool documentation * Update installation docs to use uv and improve quickstart guide * docs: improve installation instructions and add structured outputs video * Update tool documentation with agent integration examples and consistent formatting
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Tony Kipkemboi
@@ -29,35 +29,73 @@ pip install 'crewai[tools]'
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## Example
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To integrate the YoutubeVideoSearchTool into your Python projects, follow the example below.
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This demonstrates how to use the tool both for general Youtube content searches and for targeted searches within a specific video's content.
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The following example demonstrates how to use the `YoutubeVideoSearchTool` with a CrewAI agent:
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```python Code
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from crewai import Agent, Task, Crew
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from crewai_tools import YoutubeVideoSearchTool
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# General search across Youtube content without specifying a video URL,
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# so the agent can search within any Youtube video content
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# it learns about its url during its operation
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tool = YoutubeVideoSearchTool()
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# Initialize the tool for general YouTube video searches
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youtube_search_tool = YoutubeVideoSearchTool()
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# Targeted search within a specific Youtube video's content
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tool = YoutubeVideoSearchTool(
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# Define an agent that uses the tool
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video_researcher = Agent(
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role="Video Researcher",
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goal="Extract relevant information from YouTube videos",
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backstory="An expert researcher who specializes in analyzing video content.",
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tools=[youtube_search_tool],
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verbose=True,
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)
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# Example task to search for information in a specific video
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research_task = Task(
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description="Search for information about machine learning frameworks in the YouTube video at {youtube_video_url}",
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expected_output="A summary of the key machine learning frameworks mentioned in the video.",
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agent=video_researcher,
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)
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# Create and run the crew
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crew = Crew(agents=[video_researcher], tasks=[research_task])
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result = crew.kickoff(inputs={"youtube_video_url": "https://youtube.com/watch?v=example"})
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```
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You can also initialize the tool with a specific YouTube video URL:
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```python Code
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# Initialize the tool with a specific YouTube video URL
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youtube_search_tool = YoutubeVideoSearchTool(
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youtube_video_url='https://youtube.com/watch?v=example'
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)
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# Define an agent that uses the tool
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video_researcher = Agent(
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role="Video Researcher",
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goal="Extract relevant information from a specific YouTube video",
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backstory="An expert researcher who specializes in analyzing video content.",
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tools=[youtube_search_tool],
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verbose=True,
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)
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```
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## Arguments
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## Parameters
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The YoutubeVideoSearchTool accepts the following initialization arguments:
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The `YoutubeVideoSearchTool` accepts the following parameters:
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- `youtube_video_url`: An optional argument at initialization but required if targeting a specific Youtube video. It specifies the Youtube video URL path you want to search within.
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- **youtube_video_url**: Optional. The URL of the YouTube video to search within. If provided during initialization, the agent won't need to specify it when using the tool.
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- **config**: Optional. Configuration for the underlying RAG system, including LLM and embedder settings.
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- **summarize**: Optional. Whether to summarize the retrieved content. Default is `False`.
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## Custom model and embeddings
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When using the tool with an agent, the agent will need to provide:
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- **search_query**: Required. The search query to find relevant information in the video content.
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- **youtube_video_url**: Required only if not provided during initialization. The URL of the YouTube video to search within.
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## Custom Model and Embeddings
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By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
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```python Code
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tool = YoutubeVideoSearchTool(
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youtube_search_tool = YoutubeVideoSearchTool(
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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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@@ -78,4 +116,72 @@ tool = YoutubeVideoSearchTool(
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),
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)
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)
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```
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```
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## Agent Integration Example
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Here's a more detailed example of how to integrate the `YoutubeVideoSearchTool` with a CrewAI agent:
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```python Code
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from crewai import Agent, Task, Crew
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from crewai_tools import YoutubeVideoSearchTool
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# Initialize the tool
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youtube_search_tool = YoutubeVideoSearchTool()
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# Define an agent that uses the tool
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video_researcher = Agent(
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role="Video Researcher",
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goal="Extract and analyze information from YouTube videos",
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backstory="""You are an expert video researcher who specializes in extracting
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and analyzing information from YouTube videos. You have a keen eye for detail
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and can quickly identify key points and insights from video content.""",
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tools=[youtube_search_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 recent advancements in artificial intelligence
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in the YouTube video at {youtube_video_url}.
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Focus on:
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1. Key AI technologies mentioned
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2. Real-world applications discussed
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3. Future predictions made by the speaker
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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 AI advancements, applications, and future predictions from the video.",
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agent=video_researcher,
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)
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# Run the task
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crew = Crew(agents=[video_researcher], tasks=[research_task])
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result = crew.kickoff(inputs={"youtube_video_url": "https://youtube.com/watch?v=example"})
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```
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## Implementation Details
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The `YoutubeVideoSearchTool` is implemented as a subclass of `RagTool`, which provides the base functionality for Retrieval-Augmented Generation:
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```python Code
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class YoutubeVideoSearchTool(RagTool):
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name: str = "Search a Youtube Video content"
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description: str = "A tool that can be used to semantic search a query from a Youtube Video content."
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args_schema: Type[BaseModel] = YoutubeVideoSearchToolSchema
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def __init__(self, youtube_video_url: Optional[str] = None, **kwargs):
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super().__init__(**kwargs)
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if youtube_video_url is not None:
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kwargs["data_type"] = DataType.YOUTUBE_VIDEO
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self.add(youtube_video_url)
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self.description = f"A tool that can be used to semantic search a query the {youtube_video_url} Youtube Video content."
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self.args_schema = FixedYoutubeVideoSearchToolSchema
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self._generate_description()
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```
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## Conclusion
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The `YoutubeVideoSearchTool` provides a powerful way to search and extract information from YouTube video content using RAG techniques. By enabling agents to search within video content, it facilitates information extraction and analysis tasks that would otherwise be difficult to perform. This tool is particularly useful for research, content analysis, and knowledge extraction from video sources.
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