[ WEBGL KNOWLEDGE GRAPH HOLOGRAM ]
Corpus Health99.9%
Retrieval Latency12ms
Active Nodes14.2M
Vector Dimensions1536
INGESTION & VECTORS
Vector Space Map (2D Projection)
Active Streams
STREAM 01
5.3 MB/s
STREAM 02
6.4 MB/s
STREAM 03
7.5 MB/s
PIPELINE
SEMANTIC SEARCH PLAYGROUND
ACTIVE RETRIEVALONLINE
SELECT relevant_nodes
FROM knowledge_graph
WHERE similarity > 0.92
LIMIT 5;
FROM knowledge_graph
WHERE similarity > 0.92
LIMIT 5;
42QPS
0.99Relevance
TERMINAL_STDOUT
[SYS] Initializing RAG cluster node alpha-01...
[SYS] Connecting to vector database...
[OK] Vector DB connected. Index size: 4.2TB.
[SYS] Loading embedding models into VRAM...
[OK] Models loaded. Tensors ready.
> Waiting for incoming query streams_
Sub-Agent // LVL 1 (XP: 0)
Tone:
Vector Embeddings & pgvector
Step 1 of 1
_
Literature & Citations
An Industrial-Scale Retrieval-Augmented Generation Framework for Requirements Engineering
IEEE
ManuRAG: Multi-modal Retrieval Augmented Generation for Manufacturing Question Answering
arXiv
Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis
Advanced Engineering Informatics