first commit of v.02 application

README.md

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+# Citation Sentinel
+
+A source-grounded research assistant inspired by Google's NotebookLM. MIT
+license, by [@sjdev](https://sjdev.co). Users upload documents, links, youtube videos, 
+ask questions and receive answers (with inline citations) grounded in their 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 ("groundedness" score).
+
+## Prerequisites
+
+- **Node.js** (v18+)
+- **yt-dlp** -- required for YouTube video source support (`brew install yt-dlp` or `pip install yt-dlp`)
+
+## Getting Started
+
+```bash
+# 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
+
+NotebookLM is a powerful research tool, but it is proprietary and closed.
+This clone demonstrates the core source-grounded Q&A pattern with transparent
+retrieval, generation, and groundedness scoring (LLM response quality cosine scoring) -- all with swappable models/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
+
+Embed query (Voyage) -> k-NN search -> rerank (Voyage) ->
+generate answer with citations (Claude) -> compute groundedness score
+(cosine similarity of answer vs cited chunks).
+
+## License
+
+MIT. See [LICENSE](./LICENSE).
+
+## Author
+
+[@sjdev](https://sjdev.co)