Building RAG-Enabled Chatbots for Local Businesses

During the fall of 2024, I developed a project to bring the power of AI to small and medium-sized businesses (SMBs) by creating custom, retrieval-augmented generation (RAG) chatbots for their websites. The goal was to provide customers with instant, context-aware answers to their questions based on the business’s own data.

The Challenge

Many local businesses struggle to manage customer inquiries effectively. Information is often scattered across FAQs, policy pages, and product catalogs. This project aimed to centralize that knowledge and make it accessible through a simple chat interface.

The Solution

I designed a data pipeline using n8n.io to automate the process of scraping, chunking, and embedding business data. Here’s how it worked:

1. Data Scraping: The pipeline would scrape relevant pages from a business’s website.
2. Document Chunking: The scraped text was broken down into smaller, manageable chunks.
3. Vector Embedding: I used OpenAI’s embedding models to convert these chunks into vector representations.
4. Indexing: The vectors were then stored and indexed in a Pinecone vector database for efficient similarity search.

The chatbot itself was a RAG workflow that combined vector search with GPT-4 completions. When a user asked a question, the system would first search the Pinecone database for the most relevant document chunks and then feed that context to GPT-4 to generate a grounded, accurate answer.

Tech Stack

• Orchestration: n8n
• AI & Embeddings: OpenAI API
• Vector Database: Pinecone
• Backend: Node.js, TypeScript
• Communication: Webhooks

This project was a fantastic learning experience in applied AI and building practical solutions for real-world problems.