Supabot: Personalized AI Chat Bot for Supabase

Supabot transforms raw Supabase documentation into intelligent, contextual AI responses using a hybrid search approach.

Pipeline Overview

Find the pipeline source code on GitHub.

1. HTML Scraping
   Extracts raw content from Supabase documentation.

2. Text Cleaning
   Processes and structures the scraped text for consistency.

3. Vector Embeddings
   Generates 1536-dimensional embeddings locally (no LLM required).

4. Hybrid Search
   Combines semantic vector search with traditional keyword matching for optimal results.

5. AI Response Generation
   Delivers contextual answers based on relevant documentation.

Technology Stack

• Supabase + pgvector
  Uses PostgreSQL with the pgvector extension to store and search 1536-dimensional embeddings.

• Hybrid Search Engine
  Integrates semantic search and keyword matching for improved accuracy.

checkout my latest blog for more details : https://medium.com/@krishvsoni/building-an-advanced-documentation-search-system-supabase-web-crawling-with-local-embeddings-and-678cc6cdcef4