About the Book
Build enterprise RAG that survives production with NVIDIA NeMo
Many teams can demo retrieval augmented generation, far fewer can ship a stable stack that parses messy documents, enforces permissions, meets latency targets, and produces grounded answers users can trust. This book gives you a complete, practical path from ingestion to day two operations using NeMo Retriever and NIM services.
You will map real business questions to measurable objectives, wire a pipeline that holds up under load, and deploy it on Kubernetes with clear guardrails, observability, and runbooks. Every concept is backed by working requests, scripts, and configs so you can adopt what you need without guesswork.
Plan a production RAG blueprint, connect business objectives to retrieval quality, latency budgets, availability, and cost
Parse complex PDFs with tables, figures, and OCR, choose between nemoretriever parse and pdfium, and standardize output schemas
Build ingestion jobs with run ids, tracing, and audits, deduplicate at scale with simhash and minhash before embedding
Remove or mask PII and enrich metadata with NeMo Curator for provenance, permissions, recency, and tags
Select embedding models and dimensions, apply normalization and pooling, and balance multilingual coverage with storage trade offs
Index at scale in Milvus, Qdrant, or pgvector, use GPU options and cuVS with CAGRA or IVF PQ, and choose parameters with evidence
Retrieve with dense vectors and filters, add sparse BM25 when it matters, and fuse with RRF including score scaling and filtering
Rerank with NeMo Text Reranking, control truncation and token budgets, and assemble context windows that keep citations intact
Rewrite queries and add multi hop retrieval when needed, introduce lightweight agents only where they reduce failure cases
Add NeMo Guardrails with Colang, enforce safety, privacy, grounding checks, and clear refusal policies
Measure with NeMo Evaluator, define datasets and metrics, use LLM as judge carefully, and wire CI with golden sets and regression tests
Observe Triton and NIM with Prometheus, Grafana, and Datadog, track p95 and p99, error codes, and queue time
Deploy with the NIM Operator and Helm, set GPU Operator and MIG profiles, scale with autoscaling and in flight batching
Support air gapped installs and private registries, pin NIM images by digest, canary safely, and roll back fast
Integrate frameworks, call NIM endpoints from LangChain, LlamaIndex, and Haystack, and connect to Milvus, Qdrant, Elastic, and pgvector
Operate with resilience, add semantic and result caching, run chaos tests, set retries and timeouts, and follow day two runbooks
This is a code heavy guide, you get runnable Python clients, Shell scripts, Systemd units, YAML and JSON configs you can adapt to real projects.
Grab your copy today and ship RAG with confidence
