Code Docs RAG Search

The CodeDocsSearchTool is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within code documentation.

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:

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.

Arguments

The following parameters can be used to customize the CodeDocsSearchTool’s behavior:

Argument	Type	Description
docs_url	string	Optional. Specifies the URL of the code documentation to be searched.

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",
            ),
        ),
    )
)