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# 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.
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.
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 Typescript/Node/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).
## 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)
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)
## Prerequisites
- **Node.js** (v18+)
- **yt-dlp** -- required for YouTube video source support (`brew install yt-dlp`)
- **yt-dlp** -- Required for YouTube video source support (`brew install yt-dlp`)
## Getting Started
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## Methodology
This application is a RAG (Retrieval-Augmented Generation) system that allows users to upload source documents — PDFs, DOCX files, plain text, audio files, web URLs, and YouTube videos — which are then parsed, split into ~2000-character overlapping chunks, and converted into vector embeddings using Voyage AI's voyage-3 model. Those embeddings are stored in an in-memory vector store.
This is a RAG (Retrieval-Augmented Generation) and query-response source-groundedness assurance system allowing users to create a research knowledge corpus, including:
When a user submits a query, the system enforces groundedness through a multi-layered strategy:
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.
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.
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).
The LLM must cite sources using bracketed indices (e.g., [1], [2]) and admit when sources are
insufficient.
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 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 stripped out.
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 single
groundedness score that is surfaced to the user with a visual indicator (green/yellow/red).
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 single
groundedness score that is surfaced to the user with a visual indicator (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.
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:
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## Author
[@sjdev](https://sjdev.co)
@sjdev