test

README.md

@@ -61,24 +61,6 @@ When a user submits a query, the system enforces groundedness through a multi-l
    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
-
-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.
-
-## 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.
-
 ## Design
 
 Two-package monorepo:

server/src/stores/vectorStore.ts

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