# Source Sentinel - 2023 @sjDev - LICENSE: MIT


![SOURCE Sentinel UI showing the source sidebar, chat answer with citations to the source, and groundedness badge](demo.jpg)


Image one:  source-grounded research assistant that empowers users to transform an otherwise-unmanageably-large corpus of data on any topic of interest into refined, easily-digested subtopics and pose focused inquiries, to instantly receive concise, quantifiably-evaluated, accurate responses.  The system also provides suggested follow-up questions further refining in user inquiries by detecting the query goals.


Image Two:  demo co clickable inline citations that take user to source-grounded research basis/knowledge corpus supporting query response, with rated “groundedness” score.


# Reliability and Understandability


Source Sentinel's Retrieval-Augmented Generation (RAG) pipeline uses two-stage pass-through to Voyage AI embedding and ranking models, which uses cosine similarity methodology to scoring to evaluate query-source embeddings in multi-dimensional vector space.  This yields  a "groundedness" score.


Groundedness metrics quantify how well an AI-generated answer is supported by retrieved context. It measures "faithfulness" to source documents, ensuring the answer is not hallucinated, inaccurate or stale (pulled from the model's training data).


# Architecture - Basic Overview


Built with a React/Vote frontend and a Typescript/Node/Express backend, using Anthropic Claude for generation, OpenAI Whisper for video audio track transcription, Voyage AI voyage-3 for embeddings and Voyage AI rerank-2 for response cosine similarity scoring, to generate the "groundedness" score, presented as an easily-understandable red/yellow/green "badge" style tooltip in the response (see above.)


## Query pipeline


Source Ingestion -> Parsing -> Chunking -> Embedding (using Voyage AI voyage-3 model) -> Storage (vector store) -> { user query submission } -> Evaluation of User Query -> Retrieval -> Ranking -> Response Generation (Using Anthopic's claude-opus-4-6 model) -> Response Groundedness Scoring (using Voyage AI rerank-r model)

## Why Database Math Can't Replace Groundedness Scoring

Many recent additions to the Vector database/store product space use distance metrics (Cosine, L2, Inner Product) for Bi-Encoder Similarity. They compare the vector of the user query against the vectors of chunks independently.

The rerank-r model at the end of the Source Sentinel pipeline performs Cross-Encoder Evaluation, taking two entirely separate text inputs:

1) The generated response from claude-opus-4-6 and 

2) The raw source chunks

It processes them simultaneously through deep attention layers to check for hallucinations, missing context, and factual alignment.

A vector database cosine metric only calculates how close two embeddings are in coordinate space. It **does not read the generated text of a response to verify if it accurately reflects the source chunks**.

## Prerequisites


- **Node.js** (v18+)
- **yt-dlp** -- Required for YouTube video source support (`brew install yt-dlp`)


## Getting Started


```bash
# clone and install
git clone <repo-url> && cd citation_sentinel
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.


## Methodology


This is a RAG (Retrieval-Augmented Generation) and query-response source-groundedness assurance system allowing users to create a research knowledge corpus, including:


1. Documents — PDFs, DOCX files, plain text audio files.
2. Internet sources: via  web URLs.
2. Audio sources: i.e. YouTube videos, audio from which is transcribed to text.


These are then parsed, split into ~2000-character overlapping chunks, and converted into vector embeddings using Voyage AI's voyage-3 model.


The embeddings are stored in an in-memory vector store.


When a user submits a query, the system enforces groundedness through a multi-layered strategy:


1. **Retrieval constraint** — The query is embedded (via Voyage AI voyage-3) and compared against stored chunk vectors using cosine similarity, returning the top 20 candidates.


2. **Reranking for precision** — Those 20 candidates are sent to Voyage AI's rerank-2 cross-encoder, which re-scores each query-chunk pair with deeper semantic analysis. Only the top 5 survive.


3. **Prompt-level constraint** — The top 5 chunks are passed to the "Primary LLM" (Claude opus-4-6) for natural language query response. The Primary LLM must cite sources using bracketed indices (e.g., [1], [2]) and admit when sources are insufficient.


4. **Schema enforcement** — The Primary LLM's response is constrained to a JSON schema requiring structured fields (answer, citedSourceIndices, followUpQuestions). Any cited source indices that do not correspond to real source groups are programmatically removed.


5. **Post-generation groundedness scoring** — The answer is split into individual sentences. Voyage AI voyage-3 embeds each sentence, and compares it (using cosine similarity) against the vectors of the cited chunks. The similarity is calibrated to a 0–1 scale and averaged, producing a groundedness score surfaced to the user with a visual indicator (“badge” in green/yellow/red).


## Similarity Metrics for Semantic Understanding


Cosine similarity measures how closely two vectors (representing data like words, images, or preferences) are aligned in a multi-dimensional space by calculating the cosine of the angle between them.


Archetypical cosine scores range from -1 to 1.


1: Vectors point in the exact same direction (highly similar).
0: Vectors are at a 90-degree angle (orthogonal/unrelated).
-1: Vectors point in opposite directions.


Cosine distance between high-dimensional text embeddings is compressed: unrelated notes sit close to orthogonal, so both the within-cluster and nearest-cluster distances land near 0.8. In this application, scores are normalized to a bounded range wherein unrelated query-response pair scores are nearly orthogonal.


## 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 data sources, main area for LLM chat with explorable citations, groundedness badges (cosine similarity scoring of LLM responses), and follow-up question chips.


## License


MIT. See [LICENSE](./LICENSE).


## Author


@sjdev