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YoutubeChannelSearchTool

Experimental

We are still working on improving tools, so there might be unexpected behavior or changes in the future.

Description

This tool is designed to perform semantic searches within a specific Youtube channel's content. Leveraging the RAG (Retrieval-Augmented Generation) methodology, it provides relevant search results, making it invaluable for extracting information or finding specific content without the need to manually sift through videos. It streamlines the search process within Youtube channels, catering to researchers, content creators, and viewers seeking specific information or topics.

Installation

To utilize the YoutubeChannelSearchTool, the crewai_tools package must be installed. Execute the following command in your shell to install:

pip install 'crewai[tools]'

Example

To begin using the YoutubeChannelSearchTool, follow the example below. This demonstrates initializing the tool with a specific Youtube channel handle and conducting a search within that channel's content.

from crewai_tools import YoutubeChannelSearchTool

# Initialize the tool to search within any Youtube channel's content the agent learns about during its execution
tool = YoutubeChannelSearchTool()

# OR

# Initialize the tool with a specific Youtube channel handle to target your search
tool = YoutubeChannelSearchTool(youtube_channel_handle='@exampleChannel')

Arguments

  • youtube_channel_handle : A mandatory string representing the Youtube channel handle. This parameter is crucial for initializing the tool to specify the channel you want to search within. The tool is designed to only search within the content of the provided channel handle.

Custom model and embeddings

By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:

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",
            config=dict(
                model="models/embedding-001",
                task_type="retrieval_document",
                # title="Embeddings",
            ),
        ),
    )
)