---
title: MySQL RAG Search
description: The `MySQLSearchTool` is designed to search MySQL databases and return the most relevant results.
icon: database
---

# `MySQLSearchTool`

## Description

This tool is designed to facilitate semantic searches within MySQL database tables. Leveraging the RAG (Retrieve and Generate) technology, 
the MySQLSearchTool provides users with an efficient means of querying database table content, specifically tailored for MySQL databases. 
It simplifies the process of finding relevant data through semantic search queries, making it an invaluable resource for users needing 
to perform advanced queries on extensive datasets within a MySQL database.

## Installation

To install the `crewai_tools` package and utilize the MySQLSearchTool, execute the following command in your terminal:

```shell
pip install 'crewai[tools]'
```

## Example

Below is an example showcasing how to use the MySQLSearchTool to conduct a semantic search on a table within a MySQL database:

```python Code
from crewai_tools import MySQLSearchTool

# Initialize the tool with the database URI and the target table name
tool = MySQLSearchTool(
    db_uri='mysql://user:password@localhost:3306/mydatabase',
    table_name='employees'
)
```

## Arguments

The MySQLSearchTool requires the following arguments for its operation:

- `db_uri`: A string representing the URI of the MySQL database to be queried. This argument is mandatory and must include the necessary authentication details and the location of the database.
- `table_name`: A string specifying the name of the table within the database on which the semantic search will be performed. This argument is mandatory.

## 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:

```python Code
tool = MySQLSearchTool(
    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",
            ),
        ),
    )
)
```