The story
Why it exists and what building it taught me. Written for the homepage card, kept here in full.
Why: When I started getting into deeper development with OpenAssistant, the app would start losing context during large document queries. I looked up vector databases, found Pinecone was the standard, and realized they didn’t have an iOS app, so I decided to build one. I wanted to query multiple indexes and namespaces in a single chat thread, which was something the OpenAI Assistants playground was limited in doing.
On-Device File Reader: To feed documents into Pinecone, I built a local parser using Apple’s PDFKit and Vision. It extracts text from PDFs, text and code files, and reads the text in images with OCR, automatically chunking and converting them into embeddings on the fly.
Network Safety: To prevent the app from lagging or getting stuck when a server fails, I built a network circuit breaker. If Pinecone fails twice in a row, the app automatically pauses requests and lets the user know immediately.
What I learned: I took the backend from OpenAssistant and swapped out the storage layer for Pinecone. Honestly, in retrospect, the initial interface looked awful, but I later rebuilt it with the Responses API and clean embeddings. Getting this on the App Store was a personal milestone.
