tomoyukiorita/llm-langchain-sql-demo

Using LangChain's SQL Database Chain and Agent with various LLMs to perform Natural Language Queries (NLQ) of an Amazon RDS for PostgreSQL database.

NLQ on Amazon RDS using Amazon SageMaker, LangChain, and LLMs

Source code for the upcoming blog post, Generative AI for Analytics: Performing Natural Language Queries on Amazon RDS using SageMaker, LangChain, and LLMs. Learn to use LangChain's SQL Database Chain and Agent with large language models to perform natural language queries (NLQ) of Amazon RDS for PostgreSQL.

Using dotenv

Your .env files should look as follows:

# API Keys
OPENAI_API_KEY=<your_value_here>

# Demo 1: Amazon RDS Connection 
RDS_ENDPOINT=<your_value_here>
RDS_PORT=<your_value_here>
RDS_USERNAME=<your_value_here>
RDS_PASSWORD=<your_value_here>
RDS_DB_NAME=<your_value_here>

# Demo 2: Amazon Redshift Connection 
REDSHIFT_HOST=<your_value_here>
REDSHIFT_PORT=<your_value_here>
REDSHIFT_USERNAME=<your_value_here>
REDSHIFT_PASSWORD=<your_value_here>
REDSHIFT_DATABASE=<your_value_here>

Checking RDS Connection from SageMaker Notebook

# Get your SageMaker Notebook environment IP
dig +short txt ch whoami.cloudflare @1.0.0.1

# Curl RDS database instance to check connectivity
curl -v ******.******.us-east-1.rds.amazonaws.com:5432

Notebook Formatting

jupyter-black was used to format notebooks.

pip install "black[jupyter]"

black *.ipynb

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The contents of this repository represent my viewpoints and not of my past or current employers, including Amazon Web Services (AWS). All third-party libraries, modules, plugins, and SDKs are the property of their respective owners.