Building a Chatbot Using RAG and Llama3 for Custom Table Database

Question

I’m working on a project to create a chatbot using Retrieval-Augmented Generation (RAG) with Llama3 (1B model). The chatbot needs to interact with a custom database that is structured in table format. The database contains details about products, including:

Product ID Product Name Price Number in Stock Features Last Updated Date And more... The goal is to allow users to ask natural language queries like:

“What is the price of Product X?” “Show me all products under $100.” “Which products were updated recently?” I’m trying to figure out the best approach to implement this. Specifically:

How can I integrate Llama3 with a retrieval mechanism for the database? Should I preprocess the table into embeddings for faster retrieval, or rely on real-time SQL queries? Are there any open-source libraries or frameworks to simplify building RAG-based chatbots? Any tips for ensuring the chatbot can handle filtering, sorting, or aggregating data dynamically? Any advice, examples, or resources you could share would be a huge help! Thanks in advance. :)

Answer 1

You can use Langchain for building a RAG + Text to SQL LLM chat bot. Please see this Langchain tutorial.