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:
-- 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
AWS Architecture: https://chatscroll.vercel.app/aws-showcase
I created this content for the purposes of entering
the AWS H0 Hackathon.