daily-news: RAG inside a production mobile app

TL;DR

1. An "Ask the News" RAG assistant with cosine top-k retrieval and tappable source citations.
2. Local MiniLM embeddings + Groq (Llama 3.3): grounded answers with no vector database.
3. Clean Architecture + BLoC, with CI-tested Firebase security rules.

Problem

A news assistant you can trust.

A chat assistant over the news is only useful if every claim is traceable to a real article. Ungrounded models confidently invent quotes and dates. The bar here was zero hallucinated facts on a phone: limited compute, intermittent network, and a small backend budget.

Architecture

article ingest → MiniLM local embed → Firestore store → cosine top-k retrieve → Groq grounded answer → cited sources

Key decisions

Local embeddings + brute-force cosine over a vector DB

Chose MiniLM on-device plus a plain cosine scan in Firestore over standing up a vector database. Trade-off: it won't scale past a few thousand articles, but at this corpus size a vector DB is cost and ops I don't need yet.

Grounding + refusal over free generation

Chose to force every answer to cite retrieved sources and refuse when nothing matches. Trade-off: more "I don't have that" replies, but every claim has to trace back to a cited article instead of the model's memory, the right call for news.

Clean Architecture + BLoC over quick widgets

Chose layered architecture and BLoC over wiring logic straight into widgets. Trade-off: more boilerplate up front, but the LLM and retrieval layers stay swappable and the security rules stay testable.

For a news assistant, "I don't know" is a feature. A refusal is recoverable; a confidently wrong fact is not. Grounding and refusal did more for trust than any model upgrade.

the design principle

Harder than expected

Making on-device embeddings fast enough on low-end phones. Running MiniLM per query without freezing the UI meant moving inference off the main isolate and caching aggressively, more work than the retrieval and prompting combined.

Results

• Top-k cosine retrieval with tappable citations
• 0 vector DBs (runs inside Firestore)
• CI-tested Firebase security rules

Demo

The Ask-the-News flow: question → grounded answer → tap a citation.

Repo