Yashotantra Academy logo
← all simulations

slide 17 · AI & Data

Vector Embeddings & Cosine Search Space

Understand how high-dimensional embedding vectors enable semantic RAG search and top-K context retrieval.

  1. 1Vector Geometry
  2. 2Cosine Similarity
  3. 3RAG Pipeline
live · 0 players in this room

send coins to another player

10.0000 BTC10.0000 ETH
recipient

waiting for another player to join this room… open this page in a second tab or share the link with the class.

fee 0.0005 · total 0.5005 BTC
live transfers in this room
room ledger · tap a row to replay its steps
  • no transfers yet — be the first to pay a classmate.
Semantic Vector Search Space (RAG)
Top-K:
PostgreSQL B-Tre...
Redis In-Memory ...
Solidity EVM Sto...
Ethereum JSON-RP...
Transformer Self...
Dense Vector Sea...
Query Vector
Top-3 Cosine Similarity Neighbors (RAG Context Payload):
#1Redis In-Memory Key-ValueScore: 96.4%

Sub-millisecond latency caching and Pub/Sub event brokers.

#2PostgreSQL B-Tree IndexingScore: 91.4%

Optimizing range queries and write-ahead log flush cycles.

#3Dense Vector Search & HNSW IndexingScore: 73.9%

Approximate nearest neighbors over high-dimensional embeddings.