docs/examples/talk-to-redshift.md

This example demonstrates how to build an agent that can dynamically query Amazon Redshift Serverless tables and store its contents on the local hard drive.

Let's build a support agent that uses GPT-4:

import boto3
from swarms.drivers import AmazonRedshiftSqlDriver, OpenAiPromptDriver
from swarms.loaders import SqlLoader
from swarms.rules import Ruleset, Rule
from swarms.structures import Agent
from swarms.tools import SqlClient, FileManager
from swarms.utils import Chat

session = boto3.Session(region_name="REGION_NAME")

sql_loader = SqlLoader(
    sql_driver=AmazonRedshiftSqlDriver(
        database="DATABASE",
        session=session,
        workgroup_name="WORKGROUP_NAME"
    )
)

sql_tool = SqlClient(
    sql_loader=sql_loader,
    table_name="people",
    table_description="contains information about tech industry professionals",
    engine_name="redshift"
)

agent = Agent(
    tools=[sql_tool, FileManager())],
    rulesets=[
        Ruleset(
            name="HumansOrg Agent",
            rules=[
                Rule("Act and introduce yourself as a HumansOrg, Inc. support agent"),
                Rule("Your main objective is to help with finding information about people"),
                Rule("Only use information about people from the sources available to you")
            ]
        )
    ]
)

Chat(agent).start()