RAG Pipeline

Retrieval-Augmented Generation system — document ingestion, vector store management, and query analytics.

Documents Ingested
24,580
Vector Store Size
2.4 GB
Query Accuracy
94.5%
Avg Query Latency
340ms
Pipeline Architecture
All Stages Operational
Document Ingestion
24.5K docs
Chunking & Embedding
128-dim vectors
Vector Store
2.4 GB index
Semantic Retrieval
Top-K=5
LLM Generation
Mistral-7B
Response Delivery
94.5% accuracy
Document Sources
Source Composition
Policy Documents
Knowledge Base Articles
Technical Manuals
Compliance Regulations
Incident History
Recent Queries
QueryConfidenceLatencySourceStatus
SLA requirements for P1 incidents
96%
280msPolicy DocumentsResolved
GDPR data retention policy
94%
310msCompliance RegulationsResolved
BullSequana server maintenance schedule
89%
420msTechnical ManualsResolved
Onboarding process for new hires
92%
350msKnowledge Base ArticlesResolved
Azure Stack HCI failover procedures
91%
380msTechnical ManualsResolved
Knowledge Base Sources
Index Freshness: 97%
3,420
Policy Documents
Updated: 2026-05-06
8,950
Knowledge Base Articles
Updated: 2026-05-03
2,100
Technical Manuals
Updated: 2026-05-01
1,240
Compliance Regulations
Updated: 2026-04-28
8,870
Incident History
Updated: 2026-05-06