[docs-freeze] docs: snapshot and changelog for v1.15.4 (#6583)

docs/v1.15.4/en/tools/database-data/mysqltool.mdx

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+---
+title: MySQL RAG Search
+description: The `MySQLSearchTool` is designed to search MySQL databases and return the most relevant results.
+icon: database
+mode: "wide"
+---
+
+## Overview
+
+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-generativeai",
+            config=dict(
+                model_name="gemini-embedding-001",
+                task_type="RETRIEVAL_DOCUMENT",
+                # title="Embeddings",
+            ),
+        ),
+    )
+)
+```