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CodeDocsSearchTool

Experimental

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

Description

The CodeDocsSearchTool is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within code documentation. It enables users to efficiently find specific information or topics within code documentation. By providing a docs_url during initialization, the tool narrows down the search to that particular documentation site. Alternatively, without a specific docs_url, it searches across a wide array of code documentation known or discovered throughout its execution, making it versatile for various documentation search needs.

Installation

To start using the CodeDocsSearchTool, first, install the crewai_tools package via pip:

pip install 'crewai[tools]'

Example

Utilize the CodeDocsSearchTool as follows to conduct searches within code documentation:

from crewai_tools import CodeDocsSearchTool

# To search any code documentation content if the URL is known or discovered during its execution:
tool = CodeDocsSearchTool()

# OR

# To specifically focus your search on a given documentation site by providing its URL:
tool = CodeDocsSearchTool(docs_url='https://docs.example.com/reference')
Note: Substitute 'https://docs.example.com/reference' with your target documentation URL and 'How to use search tool' with the search query relevant to your needs.

Arguments

  • docs_url: Optional. Specifies the URL of the code documentation to be searched. Providing this during the tool's initialization focuses the search on the specified documentation content.

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 = YoutubeVideoSearchTool(
    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",
            ),
        ),
    )
)