How I Built a Personal AI Knowledge Base with Amazon Aurora pgvector and Next.js — AWS H0 Hackathon

A developer built ChatScroll for the AWS H0 Hackathon, an app that uses Amazon Aurora PostgreSQL with pgvector to create a personal AI knowledge base. The app saves AI answers as searchable 'Scrolls' with semantic search, combining pgvector embeddings, ltree for folder hierarchies, and tsvector for full-text search. The architecture separates structured data in Aurora from chat messages in DynamoDB.

I built ChatScroll for the AWS H0 Hackathon — an app that lets you save AI answers as searchable "Scrolls" using Amazon Aurora PostgreSQL with pgvector for semantic search. Every day people ask AI assistants valuable questions and get great answers — then lose them forever. Chat history is linear, unsearchable, and ephemeral. I kept re-Googling the same questions knowing I had already found the answer somewhere but couldn't find it again. ChatScroll transforms AI conversations into a personal knowledge library. Save any AI answer as a "Scroll", organize it automatically, and find it later with semantic search. Making search understand MEANING not just keywords. When you search "blood thinner medication" it should find your warfarin scroll even though "blood thinner" doesn't appear in the title. Amazon Aurora PostgreSQL with the pgvector extension stores 3072-dimensional vector embeddings for every saved Scroll. When a user saves a Scroll: When a user searches: js -- Semantic search with threshold WHERE 1 - embedding <= $queryVec 0.5 ORDER BY embedding <= $queryVec LIMIT 5 What makes Aurora special for this use case is three extensions working together: pgvector — stores 3072-dim embeddings, enables cosine similarity search between vectors ltree — stores folder paths as dot-separated label trees programming.containers , enables subtree queries without recursive CTEs tsvector — powers full-text search with ranking via ts rank, combined with pgvector for hybrid search I made a deliberate choice to use TWO AWS databases: Amazon Aurora PostgreSQL for structured data: Amazon DynamoDB for chat messages: This separation keeps Aurora lean for complex queries while DynamoDB handles the high-volume chat stream. Searching "containerization technology" correctly surfaces the Docker scroll. Searching "blood thinner medication" finds warfarin — no programming results contaminating it. Semantic search scoped to the same folder category ensures results are always relevant. Live app: https://chatscroll.vercel.app https://chatscroll.vercel.app AWS Architecture: https://chatscroll.vercel.app/aws-showcase https://chatscroll.vercel.app/aws-showcase I created this content for the purposes of entering the AWS H0 Hackathon.