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.
The Problem
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.
The Solution
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.
The Core Technical Challenge
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.
How pgvector on Aurora Solves This
Amazon Aurora PostgreSQL with the pgvector extension stores
3072-dimensional vector embeddings for every saved Scroll.
When a user saves a Scroll:
- The answer text is sent to Google's gemini-embedding-001
- The model returns a 3072-dimensional vector
- The vector is stored in Aurora alongside the content
When a user searches:
- The search query is converted to a vector
- Aurora finds the most similar vectors using cosine distance
- Results are ranked by semantic similarity
-- Semantic search with threshold
WHERE 1 - (embedding <=> $queryVec) > 0.5
ORDER BY embedding <=> $queryVec
LIMIT 5
Three PostgreSQL Extensions Working Together
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
The Dual Database Architecture
I made a deliberate choice to use TWO AWS databases:
Amazon Aurora PostgreSQL for structured data:
- Scrolls with embeddings
- Folder hierarchy (ltree)
- User accounts (Cognito sub)
- Conversation metadata
Amazon DynamoDB for chat messages:
- PK: conversationId
- SK: timestamp#messageId
- TTL: 90-day auto-expiry
- PAY_PER_REQUEST billing
This separation keeps Aurora lean for complex queries
while DynamoDB handles the high-volume chat stream.
The Result
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.
Try It
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.