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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: | |
```python | |
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() | |
``` | |