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CSVSearchTool

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

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Description

This tool is used to perform a RAG (Retrieval-Augmented Generation) search within a CSV fileā€™s content. It allows users to semantically search for queries in the content of a specified CSV file. This feature is particularly useful for extracting information from large CSV datasets where traditional search methods might be inefficient. All tools with ā€œSearchā€ in their name, including CSVSearchTool, are RAG tools designed for searching different sources of data.

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Installation

Install the crewai_tools package

pip install 'crewai[tools]'

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Example

Code
from crewai_tools import CSVSearchTool

# Initialize the tool with a specific CSV file. 
# This setup allows the agent to only search the given CSV file.
tool = CSVSearchTool(csv='path/to/your/csvfile.csv')

# OR

# Initialize the tool without a specific CSV file. 
# Agent will need to provide the CSV path at runtime.
tool = CSVSearchTool()

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Arguments

The following parameters can be used to customize the CSVSearchToolā€™s behavior:

ArgumentTypeDescription
csvstringOptional. The path to the CSV file you want to search. This is a mandatory argument if the tool was initialized without a specific CSV file; otherwise, it is optional.

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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 = CSVSearchTool(
    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",
            ),
        ),
    )
)