Embedded AI Chat - RAG Chatbots
Embedded AI Chat - RAG-Enabled Chatbots for SMB Websites
Timeline: Fall 2024 Type: Personal Project
Overview
Developed retrieval-augmented generation (RAG) chatbots integrated into local business websites to answer customer queries with context-aware responses, helping small and medium businesses provide 24/7 customer support.
Technical Implementation
- Data Pipeline: Designed data pipelines in n8n to scrape business FAQs, policies, and product catalogs
- Vector Storage: Chunked and embedded documents using OpenAI embeddings and stored in Pinecone vector database
- RAG Workflow: Built retrieval-augmented generation workflows that combined vector search with GPT-4 completions
- Context-Aware Responses: Ensured chatbot answers were grounded in business-specific data for accuracy
Tech Stack
- Automation: n8n (workflow automation)
- AI/ML: OpenAI API (GPT-4, text-embedding-ada-002)
- Vector Database: Pinecone
- Backend: Node.js, TypeScript
- Integration: Webhooks for real-time updates
Key Features
- Real-time document ingestion and embedding
- Context-aware response generation
- Business-specific knowledge grounding
- Seamless website integration
- Scalable vector search capabilities
Impact
Successfully demonstrated the ability to create intelligent, context-aware chatbots that could be deployed across multiple business websites, providing accurate customer support based on each business’s specific information and policies.