Smart FAQ automation with RAG and embeddings

Traditional FAQ pages are static and rely on exact keyword matches, leaving users frustrated when they use different terminology. Consequently, human support teams are constantly overwhelmed with repetitive, simple questions.

The Solution

I developed an AI-powered chatbot utilizing Retrieval-Augmented Generation (RAG) and semantic search to accurately resolve queries directly from an organization's FAQ database, maintaining conversation memory for nuanced follow-up questions.

Technical Stack

• Language & Framework: Python, FastAPI
• AI & ML: OpenAI APIs, LangChain
• Database: PostgreSQL (with pgvector for embedding storage)

How It Works

1. Ingestion: The system chunks existing documentation and FAQs, converting them into vector embeddings via OpenAI's embedding models.
2. Retrieval: When a user asks a question, the system embeds the query and performs a semantic search (cosine similarity) to find the most relevant context.
3. Generation: An LLM is prompted with the retrieved context and the user's question to generate a highly accurate, context-aware response.

Results

Hosted via AWS API Gateway and Lambda functions, this serverless RAG implementation currently handles over 10,000+ queries monthly, significantly reducing Level 1 support tickets.