Building a RAG Chatbot with WhatsApp Integration
8 min readDec 10, 2024

Building a RAG Chatbot with WhatsApp Integration

WK
Wasi Khalid · Lead Architect

The Challenge


Shiran needed an intelligent chatbot that could ingest custom business data, process XML documents and generate accurate responses using Retrieval Augmented Generation (RAG). They also required WhatsApp integration and a user tier system for paid versus free users.


Our Approach


We built a FastAPI backend with vector embeddings for high precision retrieval, a parsing engine for XML documents and integrated the WhatsApp API. The system includes an advanced user access system that manages usage limits and permissions for premium features.


Architecture Decisions


**FastAPI** provided async support perfect for handling multiple concurrent chat requests. Its automatic OpenAPI documentation made it easy for the team to understand and extend the API.


**Vector embeddings with PostgreSQL** using pgvector allowed us to store and search document embeddings efficiently. When a user asks a question, we find the most relevant document chunks and feed them to the LLM.


**WhatsApp Business API integration** enables seamless chat through a familiar interface. Users can text the bot like they would any contact.


Key Technical Highlights


  • 1. **XML parsing pipeline** - Upload XML documents, extract structured information, create embeddings
  • 2. **RAG implementation** - Retrieve relevant context before generating responses
  • 3. **User tier system** - Free vs paid users with different usage limits
  • 4. **WhatsApp integration** - Two way communication through WhatsApp

    • Results


    Response times dropped from hours to seconds, support workload reduced by 60% and the platform now handles thousands of conversations daily with 99% accuracy.