Daita Documentation

#Installation

bash

pip install aiomysql

#Quick Start

python

from daita import Agent
from daita.plugins import mysql
 
# Create plugin
db = mysql(
    host="localhost",
    database="mydb",
    username="user",
    password="password"
)
 
# Agent uses database tools autonomously
agent = Agent(
    name="DB Agent",
    prompt="You are a database analyst. Help users query and analyze data.",
    tools=[db]
)
 
await agent.start()
result = await agent.run("Show me all users")

#Direct Usage

The plugin can be used directly without agents for programmatic access. For comprehensive MySQL documentation, see the official MySQL docs. The main value of this plugin is agent integration - enabling LLMs to autonomously query and analyze database data.

#Connection Parameters

python

mysql(
    host: str = "localhost",
    port: int = 3306,
    database: str = "",
    username: str = "",
    password: str = "",
    connection_string: Optional[str] = None,
    min_size: int = 1,
    max_size: int = 10,
    charset: str = "utf8mb4",
    autocommit: bool = True,
    **kwargs
)

#Parameters

host (str): Database host address (default: "localhost")
port (int): Database port (default: 3306)
database (str): Database name to connect to
username (str): Username for authentication (note: use username not user)
password (str): Password for authentication
connection_string (str): Full MySQL connection string (overrides individual params)
min_size (int): Minimum connections in pool (default: 1)
max_size (int): Maximum connections in pool (default: 10)
charset (str): Character set (default: "utf8mb4")
autocommit (bool): Enable autocommit mode (default: True)
****kwargs**: Additional aiomysql configuration

#Connection Methods

python

# Individual parameters
async with mysql(
    host="localhost",
    database="ecommerce",
    username="app_user",
    password="secure_password"
) as db:
    results = await db.query("SELECT * FROM products")
 
# Connection string
async with mysql(
    connection_string="mysql://user:pass@localhost:3306/ecommerce"
) as db:
    results = await db.query("SELECT * FROM products")
 
# Advanced configuration
async with mysql(
    host="localhost",
    database="mydb",
    username="user",
    password="password",
    charset="utf8mb4",      # Full Unicode support
    autocommit=True,
    min_size=5,
    max_size=20
) as db:
    results = await db.query("SELECT * FROM users")

#Query Operations

SELECT queries - Use query() method, returns list of dictionaries:

python

async with mysql(host="localhost", database="app") as db:
    # Simple query
    users = await db.query("SELECT id, name, email FROM users")
    # Returns: [{"id": 1, "name": "John", "email": "john@example.com"}, ...]
 
    # Parameterized query (use %s, %s, %s, etc.)
    active_users = await db.query(
        "SELECT * FROM users WHERE status = %s AND created_at > %s",
        ["active", "2024-01-01"]
    )

Parameter syntax: MySQL uses %s for all types (not $1 like PostgreSQL)

#Data Modification

INSERT, UPDATE, DELETE - Use execute() method, returns row count:

python

async with mysql(host="localhost", database="app") as db:
    # Insert
    await db.execute(
        "INSERT INTO users (name, email, status) VALUES (%s, %s, %s)",
        ["Jane Doe", "jane@example.com", "active"]
    )
 
    # Update
    await db.execute("UPDATE users SET last_login = NOW() WHERE id = %s", [user_id])
 
    # Delete
    await db.execute("DELETE FROM users WHERE status = %s", ["inactive"])

#Bulk Operations & Schema

Bulk insert:

python

async with mysql(host="localhost", database="app") as db:
    users_data = [
        {"name": "Alice", "email": "alice@example.com"},
        {"name": "Bob", "email": "bob@example.com"}
    ]
    count = await db.insert_many("users", users_data)

List tables:

python

async with mysql(host="localhost", database="app") as db:
    tables = await db.tables()
    # Returns: ["users", "orders", "products"]

Describe table:

python

async with mysql(host="localhost", database="app") as db:
    schema = await db.describe("users")
    # Returns: [{"column_name": "id", "data_type": "int", ...}, ...]

#Using with Agents

#Tool-Based Integration (Recommended)

MySQL plugin exposes database operations as tools that agents can use autonomously:

python

from daita import Agent
from daita.plugins import mysql
import os
 
# Create database plugin
db = mysql(
    host="localhost",
    database="ecommerce",
    username=os.getenv("DB_USER"),
    password=os.getenv("DB_PASSWORD")
)
 
# Pass plugin to agent - agent can now use database tools autonomously
agent = Agent(
    name="Product Analyst",
    prompt="You are a product analyst. Help users analyze inventory and sales data.",
    llm_provider="openai",
    model="gpt-4",
    tools=[db]
)
 
await agent.start()
 
# Agent autonomously uses database tools to answer questions
result = await agent.run("Show me products that need restocking (inventory < 10)")
 
# The agent will autonomously:
# 1. Use list_tables to explore database
# 2. Use get_table_schema to understand structure
# 3. Use query_database to fetch the data
# 4. Analyze and present results in natural language
 
await agent.stop()

#Direct Database Operations (Scripts)

For scripts that don't need agent capabilities:

python

from daita.plugins import mysql
 
async with mysql(
    host="localhost",
    database="analytics",
    username="analyst",
    password="password"
) as db:
    # Direct queries
    user = await db.query("SELECT * FROM users WHERE id = %s", [user_id])
    activity = await db.query(
        "SELECT DATE(created_at) as date, COUNT(*) as count "
        "FROM user_events WHERE user_id = %s GROUP BY DATE(created_at)",
        [user_id]
    )
 
    print(f"User: {user[0] if user else None}")
    print(f"Activity: {activity}")

#Available Tools

The MySQL plugin exposes these tools to LLM agents:

Tool	Description	Parameters
query_database	Execute SELECT queries	`sql` (required), `params` (optional array)
execute_sql	Run INSERT/UPDATE/DELETE	`sql` (required), `params` (optional array)
list_tables	List all tables	None
get_table_schema	Get table column info	`table_name` (required)

Tool Categories: database Tool Source: plugin Parameter Syntax: Uses %s for all parameter placeholders

#Tool Usage Example

python

from daita import Agent
from daita.plugins import mysql
 
# Setup database with tool integration
db = mysql(host="localhost", database="inventory")
 
agent = Agent(
    name="Inventory Manager",
    prompt="You are an inventory manager. Help users track and manage inventory.",
    llm_provider="openai",
    model="gpt-4",
    tools=[db]
)
 
await agent.start()
 
# Natural language query - agent uses tools autonomously
result = await agent.run("""
Check inventory status and identify:
1. Products below reorder point
2. Top 5 selling products this month
3. Products with zero stock
""")
 
# Agent orchestrates tool calls autonomously to answer the query
print(result)
await agent.stop()

#Error Handling

python

try:
    async with mysql(host="localhost", database="mydb") as db:
        results = await db.query("SELECT * FROM users WHERE id = %s", [user_id])
except RuntimeError as e:
    if "aiomysql not installed" in str(e):
        print("Install aiomysql: pip install aiomysql")
    elif "connection" in str(e).lower():
        print(f"Connection failed: {e}")

#MySQL-Specific Features

Character sets - Use utf8mb4 for full Unicode support (including emojis):

python

async with mysql(
    host="localhost",
    database="mydb",
    charset="utf8mb4"
) as db:
    await db.execute("INSERT INTO posts (content) VALUES (%s)", ["Hello 👋"])

Autocommit - Default is True (each statement commits immediately)

#Best Practices

Connection Management:

Always use context managers (async with) for automatic cleanup
Configure pool size based on workload (min_size, max_size)
Use utf8mb4 charset for full Unicode support

Security:

Use parameterized queries to prevent SQL injection (%s placeholders)
Store credentials in environment variables, never hardcode
Use read-only accounts when possible
Grant minimal privileges to application users

Performance:

Use insert_many() for bulk inserts instead of loops
Add indexes for frequently queried columns
Limit result sets to avoid memory issues

#PostgreSQL vs MySQL Differences

Feature	PostgreSQL	MySQL
Parameter syntax	`$1`, `$2`, `$3`	`%s`, `%s`, `%s`
Auto-increment	`SERIAL`	`AUTO_INCREMENT`
Boolean type	`BOOLEAN`	`TINYINT(1)`
String concat	`\|\|` operator	`CONCAT()` function

#Troubleshooting

Issue	Solution
`aiomysql not installed`	`pip install aiomysql`
Connection refused	Check MySQL is running, verify host/port/credentials
Access denied	Verify username/password, check user privileges
Character encoding errors	Use `charset="utf8mb4"` in connection
Too many connections	Reduce `max_size` in pool, check for connection leaks

#Next Steps

PostgreSQL Plugin - PostgreSQL database operations
MongoDB Plugin - Document database operations
REST Plugin - API integrations
Plugin Overview - All available plugins

MySQL Plugin

#Installation

#Quick Start

#Direct Usage

#Connection Parameters

#Parameters

#Connection Methods

#Query Operations

#Data Modification

#Bulk Operations & Schema

#Using with Agents

#Tool-Based Integration (Recommended)

#Direct Database Operations (Scripts)

#Available Tools

#Tool Usage Example

#Error Handling

#MySQL-Specific Features

#Best Practices

#PostgreSQL vs MySQL Differences

#Troubleshooting

#Next Steps