Skip to main content

Ambuj Kumar Tripathi

Production RAG.
Engineered for reality.

READ THE DOCS→FLAGSHIP PROJECT→
System Metrics
â—Ź LIVE
TOTAL CHUNKS
31,528
across 20+ legal acts
CHILD VECTORS
28,352
Pinecone & Qdrant
PARENT CHUNKS
3,176
text in Supabase
CACHE STRATEGY
SHA-256
sub-100ms repeat
TOKENS SAVED
300K+
per deployment
COMPLIANCE
GDPR
30-day TTL
Production RAG Pipelinev2.0
NEW PLAYBOOKFeatured Playbook

INTRODUCING THE ARCHITECTURE

Designed for scale, this platform leverages autonomous agentic workflows to plan, generate, and maintain production-grade RAG pipelines, seamlessly integrating with lightweight embeddings and strict 512MB RAM constraints.

ACTIVE INTEGRATIONS:

  • LangGraph StateMachines
  • Qdrant Vector Database
  • FastAPI (Render Cloud)
  • Jina AI Embeddings
  • Google Gemini Flash

TRUSTED ENGINEERING

Engineered for the modern AI stack, this architecture solves the hardest problems in generative AI: Out-Of-Memory crashes, API quota exhaustion, and state management under heavy load.

GitHubLinkedInPortfolio
©️
Copyright & IP NoticeThe architecture, diagrams, and written content provided on this website and in the downloadable playbooks are the original intellectual property of Ambuj Kumar Tripathi. You may read, reference, and learn from these materials. However, reproducing, republishing, or claiming this architecture/content as your own work—without explicit written permission and proper attribution—is strictly prohibited.
⚠️
Engineering Portfolio DisclaimerI don't claim to be a professor, nor am I pretending to be an 'industry visionary'. These aren't theoretical tutorials or polished bootcamp projects. They are simply my raw, field-tested engineering notes provided "as is" from building production RAG systems under strict constraints (512MB RAM, $0 budget). Use these insights at your own discretion as I do not guarantee their suitability for every production environment.