citation_sentinel/README.md

Citation Sentinel

A source-grounded research assistant that employs cosine similarity scoring for LLM generated query responses to provide a "groundedness" score. This is a metric in Retrieval-Augmented Generation (RAG) systems that quantifies how well an AI-generated answer is supported by retrieved context. It measures "faithfulness" to source documents, ensuring the answer is not hallucinated or pulled from the model's pre-training data.

Users upload source documents, or provide links to online sources including audio/video (i.e. links to youtube videos). Users may then ask questions and receive answers (with inline citations) verifiably grounded in the provided information sources.

Built with a React/Vite frontend and a Node.js/Express backend, using Anthropic Claude for generation, OpenAI Whisper for video audio track transcription, Voyage AI for embeddings and response cosine similarity scoring (the "groundedness" score).

MIT license, by @sjdev.

Prerequisites

• Node.js (v18+)
• yt-dlp -- required for YouTube video source support (brew install yt-dlp or pip install yt-dlp)

Getting Started

# clone and install
git clone <repo-url> && cd notebooklm_clone
cd server && npm install && cd ..
cd client && npm install && cd ..

# configure
cp server/.env.example server/.env
# edit server/.env and add your ANTHROPIC_API_KEY, VOYAGE_API_KEY,
# and optionally OPENAI_API_KEY (only needed for audio source transcription via Whisper)

# run
make dev

Rationale

This app demonstrates the core source-grounded Q&A pattern with transparent retrieval, generation, and groundedness scoring (LLM response quality cosine scoring) -- all with swappable models and is fully open source.

Design

Two-package monorepo:

• server/ -- single Express backend with layered architecture (routes -> services -> stores). Routes orchestrate; services contain business logic; stores manage in-memory state.

• client/ -- React 19 SPA via Vite. Two-panel layout: sidebar for notebooks/data sources, main area for LLM chat with explorable citations, groundedness badges (cosine similarity scoring of LLM responses), and follow-up question chips.

Query pipeline

Source Ingestion -> Parsing -> Chunking -> Embedding -> Storage (vector store) -> { user query submission } -> evaluation of user query → Retrieval -> Ranking -> Response Generation -> Response Groundedness Scoring

License

MIT. See LICENSE.

Author

@sjdev