GPT Action Library: AWS RedShift

GPT Action Library: AWS RedShift

Introduction

This page provides an instruction & guide for developers building a GPT Action for a specific application. Before you proceed, make sure to first familiarize yourself with the following information:

• Introduction to GPT Actions
• Introduction to GPT Actions Library
• Example of Building a GPT Action from Scratch

This solution enables a GPT action to retrieve data from Redshift and perform data analysis.It uses AWS Functions, performing every action from AWS ecosystem and network. The middleware (AWS function) will perform the SQL query, wait for its completion and return the data as a file. The code is provided for information purpose only and should be modified to your needs.

This solution uses the ability to retrieve files in Actions and use them as if you had uploaded them directly to a conversation.

This solution highlight a connection to Redshift serverless, the integration with a provisioned Redshift might differ slighltly to retrieve networks and set-up connection, the overall code and (minimal) integration should be similar.

Value & Example Business Use Cases

Value: Leverage ChatGPT's natural language capabilities to connect to Redshift's DWH.

Example Use Cases:

• Data scientists can connect to tables and run data analyses using ChatGPT's Data Analysis
• Citizen data users can ask basic questions of their transactional data
• Users gain more visibility into their data & potential anomalies

Application Information

Application Prerequisites

Before you get started, make sure that:

• You have access to a Redshift environment
• You have the rights to deploy AWS function in the same VPC (Virtual Private Network)
• Your AWS CLI is authenticated

Middleware Information

Install required libraries

• Install AWS CLI, required for AWS SAM (docs)
• Install AWS SAM CLI (docs)
• Install Python
• Install yq docs

Middleware function

To create a function, follow the steps in the AWS Middleware Action cookbook.

To deploy specifically an application that connects to Redshift, use the following code instead of the "hello-world" GitHub repository referenced in the Middleware AWS Function cookbook. You can either clone the repository or take the code pasted below and modify it to your needs.

This code is meant to be directional - while it should work out of the box, it is designed to be customized to your needs (see examples towards the end of this document).

To get the code, you can clone openai-cookbook repository and navigate to the redshift-middleware directory

git clone https://github.com/pap-openai/redshift-middleware
cd redshift-middleware

Retrieve VPC information

We will need to connnect our function to Redshift, therefore we need to find the network used by Redshift. You can find this on your Redshift interface the AWS console, under Amazon Redshift Serverless > Workgroup configuration > your_workgroup > Data access, or through the CLI:

Set up AWS function

Copy env.sample.yaml to env.yaml and replace with the values obtained above. You will need a Redshift user with access to your DB/schema.

cp env.sample.yaml env.yaml

Fill in env.yaml with the values retrieved by the previous command as well as your credentials to Redshift.
Alternatively, you can create a file named env.yaml manually and fill the following variables:

RedshiftHost: default-workgroup.xxxxx.{region}.redshift-serverless.amazonaws.com
RedshiftPort: 5439
RedshiftUser: username
RedshiftPassword: password
RedshiftDb: my-db
SecurityGroupId: sg-xx
SubnetId1: subnet-xx
SubnetId2: subnet-xx
SubnetId3: subnet-xx
SubnetId4: subnet-xx
SubnetId5: subnet-xx
SubnetId6: subnet-xx

This file will be used to deploy your function with parameters, as shown below:

PARAM_FILE="env.yaml"
PARAMS=$(yq eval -o=json $PARAM_FILE | jq -r 'to_entries | map("\(.key)=\(.value|tostring)") | join(" ")')
sam deploy --template-file template.yaml --stack-name redshift-middleware --capabilities CAPABILITY_IAM --parameter-overrides $PARAMS

The template.yaml has the following content:

Retrieve the URL information from the previous command output, you can then run a cURL request, which should return data in a file format:

ChatGPT Steps

Custom GPT Instructions

Once you've created a Custom GPT, copy the text below in the Instructions panel.

OpenAPI Schema

Once you've created a Custom GPT, copy the text below in the Actions panel.

This expects a response that matches the file retrieval structure in our doc here and passes in a query as a parameter to execute.

Make sure to follow the steps in the AWS Middleware cookbook to set up authentication.

Make sure to switch the function app name based on your function deployment.

Conclusion

You now have deployed a GPT that uses a middleware in AWS, in an authenticated manner, that's able to connect to Redsfhit. Users with access (that are in Cognito) can now query your databases to perform data analysis task: