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70 lines
2.4 KiB
Plaintext
70 lines
2.4 KiB
Plaintext
---
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title: MySQL RAG Search
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description: The `MySQLSearchTool` is designed to search MySQL databases and return the most relevant results.
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icon: database
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mode: "wide"
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---
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## Overview
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This tool is designed to facilitate semantic searches within MySQL database tables. Leveraging the RAG (Retrieve and Generate) technology,
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the MySQLSearchTool provides users with an efficient means of querying database table content, specifically tailored for MySQL databases.
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It simplifies the process of finding relevant data through semantic search queries, making it an invaluable resource for users needing
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to perform advanced queries on extensive datasets within a MySQL database.
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## Installation
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To install the `crewai_tools` package and utilize the MySQLSearchTool, execute the following command in your terminal:
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```shell
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pip install 'crewai[tools]'
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```
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## Example
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Below is an example showcasing how to use the MySQLSearchTool to conduct a semantic search on a table within a MySQL database:
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```python Code
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from crewai_tools import MySQLSearchTool
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# Initialize the tool with the database URI and the target table name
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tool = MySQLSearchTool(
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db_uri='mysql://user:password@localhost:3306/mydatabase',
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table_name='employees'
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)
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```
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## Arguments
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The MySQLSearchTool requires the following arguments for its operation:
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- `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.
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- `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.
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## Custom model and embeddings
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By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
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```python Code
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tool = MySQLSearchTool(
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config=dict(
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llm=dict(
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provider="ollama", # or google, openai, anthropic, llama2, ...
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config=dict(
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model="llama2",
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# temperature=0.5,
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# top_p=1,
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# stream=true,
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),
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),
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embedder=dict(
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provider="google-generativeai",
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config=dict(
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model_name="gemini-embedding-001",
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task_type="RETRIEVAL_DOCUMENT",
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# title="Embeddings",
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),
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),
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)
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)
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``` |