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47 lines
1.3 KiB
47 lines
1.3 KiB
1 year ago
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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.
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Let's build a support agent that uses GPT-4:
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```python
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import boto3
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from swarms.drivers import AmazonRedshiftSqlDriver, OpenAiPromptDriver
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from swarms.loaders import SqlLoader
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from swarms.rules import Ruleset, Rule
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from swarms.structures import Agent
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from swarms.tools import SqlClient, FileManager
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from swarms.utils import Chat
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session = boto3.Session(region_name="REGION_NAME")
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sql_loader = SqlLoader(
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sql_driver=AmazonRedshiftSqlDriver(
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database="DATABASE",
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session=session,
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workgroup_name="WORKGROUP_NAME"
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)
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)
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sql_tool = SqlClient(
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sql_loader=sql_loader,
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table_name="people",
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table_description="contains information about tech industry professionals",
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engine_name="redshift"
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)
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agent = Agent(
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tools=[sql_tool, FileManager())],
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rulesets=[
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Ruleset(
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name="HumansOrg Agent",
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rules=[
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Rule("Act and introduce yourself as a HumansOrg, Inc. support agent"),
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Rule("Your main objective is to help with finding information about people"),
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Rule("Only use information about people from the sources available to you")
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]
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)
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]
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)
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Chat(agent).start()
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```
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