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CodeDocsSearchTool

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

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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.

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Installation

To start using the CodeDocsSearchTool, first, install the crewai_tools package via pip: 
pip install 'crewai[tools]'

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Example

Utilize the CodeDocsSearchTool as follows to conduct searches within code documentation:
Code
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')
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.

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Arguments

The following parameters can be used to customize the CodeDocsSearchTool’s behavior:
ArgumentTypeDescription
docs_urlstringOptional. Specifies the URL of the code documentation to be searched.

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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:
Code
tool = CodeDocsSearchTool(
    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", # or openai, ollama, ...
            config=dict(
                model="models/embedding-001",
                task_type="retrieval_document",
                # title="Embeddings",
            ),
        ),
    )
)
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