README.md

@@ -1,19 +1,19 @@
 # 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
 
@@ -38,46 +38,49 @@ This app demonstrates the core source-grounded Q&A pattern with transparent retr
 
 ## 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 - basics
+## 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.
 
-## The role of rerank models
-
-Rerankers are high-precision, fine second (or third) stage filters.  Rerankers run "from scratch" on query-source/answer-source pairs and are computationally expensive. 
-
-A reranker assigns relevance scoring by evaluating query-source/answer-source pair similarity within a multi-dimensional vector space. They should be RAG-pipeline-downstream of embedding models, which are "coarse" - and occasionally yield false positives on semantically irrelevant chunks. 
-
-Reranker are justifiable and high-value where 1. accuracy is at an absolute premium because of the magnitude of downside risk (ex. uses cases: litigation, health care and medical research science) 2. token-economization heuristics exist at earlier pipeline stages, and 3. system and network latency reduction is optimized.
+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
 
@@ -93,4 +96,4 @@ MIT. See [LICENSE](./LICENSE).
 
 ## Author
 
-[@sjdev](https://sjdev.co)
+@sjdev