Generative AI Series

Retrieval Augmented Generation(RAG) — Chatbot for database with LlamaIndex (Text2SQL)

Implement the RAG technique using Langchain, and LlamaIndex for conversational chatbot on a database, using Text-SQL.

A B Vijay Kumar
4 min readFeb 7, 2024

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This blog is an ongoing series on GenerativeAI and is a continuation of the previous blog, which talks about the RAG pattern and how RAG is used to augment prompts and enhance the content and context of an LLM, with specific data

Introduction

I covered the RAG pattern in my previous blog “Prompt Engineering: Retrieval Augmented Generation(RAG)”. I introduced LlamaIndex to implement RAG, and we built a chatbot to ask-the-documents in another blog. In this blog, we will explore how we can build a chatbot that converts chat conversations to SQL and queries the database.

Configuration

I have installed Postgres on my MacBook and created 2 tables.

  • product_master: Where I store the product_id and the name of the product.
  • inventor: where I use the product_id and store the inventory of that product.

The following screenshots show the schema and the data that I inserted

Lets now create a requirements.txt with all the dependent libraries that we need for this project.

python-dotenv
openai
llama-index
streamlit
nltk
sqlalchemy
psycopg2

The key libraries here other than llama-index are sqlalchemy, which is the Python SQL toolkit, and psycopg2, which is the adapter we will be using to connect to Postgres.

Let's set the environment variables in .env file. In the env file, we provide the OPENAI_API_KEY and the database variables, as shown below

OPENAI_API_KEY=<<YourOPENAI key>>
DB_USER=orderadmin
DB_PASSWORD=orderadmin
DB_HOST=localhost
DB_NAME=postgres

Let's now walk through the application code.

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A B Vijay Kumar

IBM Fellow, Master Inventor, Mobile, RPi & Cloud Architect & Full-Stack Programmer