test

update README.md

README.md

@@ -1,6 +1,6 @@
 # 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. 
 
@@ -8,18 +8,18 @@ Built with a React/Vite frontend and a Typescript/Node/Express backend, using An
 
 ## Query pipeline
 
-Source Ingestion -> Parsing -> Chunking -> Embedding (using Voayge 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` or `pip install yt-dlp`)
+- **yt-dlp** -- required for YouTube video source support (`brew install yt-dlp`)
 
 ## Getting Started
 
 ```bash
 # clone and install
-git clone <repo-url> && cd notebooklm_clone
+git clone <repo-url> && cd citation_sentinel
 cd server && npm install && cd ..
 cd client && npm install && cd ..
 
@@ -42,15 +42,24 @@ This application is a RAG (Retrieval-Augmented Generation) system that allows us
 
 When a user submits a query, the system enforces groundedness through a multi-layered strategy:
 
-1. Retrieval constraint — The query is embedded (also via voyage-3) and compared against stored chunk vectors using cosine similarity, returning the top 20 candidates. Only user-supplied source material is searched; the system has no web search capability.
-   
-3. 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.
-   
-5. Prompt-level constraint — The top 5 chunks are passed to the Primary LLM (Claude claude-opus-4-6) with an explicit system instruction: "Answer the user's question using ONLY the source documents provided below." The LLM must cite sources using bracketed indices (e.g., [1], [2]) and admit when sources are insufficient.
-   
-7. Schema enforcement — The LLM's response is constrained to a JSON schema requiring structured fields (answer, citedSourceIndices, followUpQuestions), and any cited source indices that don't correspond to real source groups are programmatically stripped out.
-   
-9. Post-generation groundedness scoring — After the answer is generated, it is split into individual sentences, each sentence is embedded via voyage-3, and each sentence embedding is compared (cosine similarity) against the vectors of the cited chunks. The raw similarities are calibrated to a 0–1 scale and averaged, producing a single groundedness score that is surfaced to the user as a visual indicator (green/gold/red).
+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).
+   The 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.
+
+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).
 
 ## Design
 
@@ -58,7 +67,7 @@ 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.
+- `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
 

server/src/stores/vectorStore.ts

@@ -19,3 +19,4 @@ export function deleteVectorsForChunks(chunkIds: string[]): void {
     vectors.delete(id);
   }
 }
+// test