Chat with Your Business Data: LLM, RAG, SQL and NoSQL

About this course

Many AI courses stop at a simple chatbot or a basic “chat with PDF” demo.

This course goes further.

In this hands-on course, you will build a practical AI data assistant for real business data. The final project can answer questions from structured databases, NoSQL documents, business files, mock REST APIs, and a vector knowledge base. You will combine LLMs, Text-to-SQL, Retrieval-Augmented Generation (RAG), PostgreSQL pgvector, MongoDB query generation, tool-calling, routing, guardrails, testing, FastAPI, Streamlit, and Docker Compose into one end-to-end application.

The project uses a realistic enterprise scenario called BizData AI Assistant.

SQL Server stores CRM data such as customers, accounts, contacts, opportunities, activities, segments, and loyalty profiles. PostgreSQL stores commerce data such as users, products, inventory, orders, order items, payments, shipments, branches, and sales targets. MongoDB stores support tickets, product reviews, customer chats, and agent notes. PostgreSQL pgvector stores document embeddings for RAG over policies, SOPs, PDFs, Markdown files, and business documents. Local JSON files power a mock business API for shipment status, payment status, customer risk score, and inventory data.

You will learn how to build an AI assistant that does not rely on one generic prompt for everything. Instead, the assistant uses the right method for the right data source:

• Text-to-SQL for SQL Server CRM and PostgreSQL commerce data

Natural language to MongoDB filters and aggregation pipelines for support and review dataRAG with PostgreSQL pgvector for business documents and policiespandas and staging tables for CSV, Excel, and JSON datatool calling for mock REST API accessrouting and answer synthesis for multi-source business questions</

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