COURSE · OPS · A001

Enterprise-Scale RAG

Learn enterprise-scale rag through RunLocalAI's practical lens: enterprise, rag, distributed and kafka, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.

24 chapters16hOperator trackBy Fredoline Eruo
PREREQUISITES
  • I001
  • I004

Why this course matters

Enterprise-Scale RAG is for operators making local AI reliable, measurable and cheaper to run. It connects enterprise, rag, distributed, kafka and scalability to the questions RunLocalAI wants every reader to answer before they install, upgrade or scale a model: will it run, what will it cost in memory, what setting changes the result, and how do you verify the answer instead of trusting a demo?

What you will be able to do

By the end, you should be able to explain the main tradeoffs in plain language, choose a safe next experiment, and use the chapter exercises as a repeatable operator checklist. The course favors local evidence, hardware fit, context limits, latency and failure modes over generic AI vocabulary.

How to use this course

Start at chapter one if the topic is new. If you already have a working stack, scan for chapters such as Enterprise RAG Challenges, Distributed Architecture, Microservices Decomposition and Event Queue with Kafka and use those lessons as a quality-control pass before changing a workstation, team workflow or production-like local deployment.

CHAPTERS
  1. 01Enterprise RAG ChallengesEnterprise RAG failures are rarely single-component problems. They emerge from interactions between scale, freshness, access control, and latency budgets that no single pipeline can satisfy. You need an architecture designed for these constraints from day one.15 min
  2. 02Distributed ArchitectureDistributed RAG architecture is a systems design problem. You are balancing consistency, availability, partition tolerance, and latency—not just picking the best embedding model.15 min
  3. 03Microservices DecompositionMicroservices solve scaling mismatches but introduce distributed systems complexity. Every service boundary is a potential failure point that needs explicit handling through idempotency, retries with backoff, and compensating transactions.15 min
  4. 04Event Queue with KafkaKafka provides durability and fan-out at the cost of operational complexity. Consumer lag monitoring, schema registry governance, and dead letter queue processing are not optional—they are load-bearing infrastructure.15 min
  5. 05Document Ingestion PipelineDocument ingestion is a reliability engineering problem. Parser correctness, chunking quality, and error handling matter more than throughput for the first 90% of implementation. Optimize throughput only after achieving 99.9% pipeline success rate.15 min
  6. 06Real-Time IndexingReal-time indexing is achievable but requires explicit architecture. You need synchronous write confirmation, metadata-index synchronization, and index refresh monitoring. Without these, you'll ship a system where "documents uploaded today don't appear until tomorrow."15 min
  7. 07Batch vs Streaming IngestionChoose your ingestion mode based on update frequency and tolerance for latency, not convenience. Most systems need both modes working together—but the interaction between batch and streaming creates subtle consistency bugs.15 min
  8. 08Multi-Modal Enterprise RAGMulti-modal RAG multiplies system complexity. Each modality introduces its own parser, embedding strategy, and metadata schema. Start with text-only, prove retrieval quality, then expand modalities incrementally.15 min
  9. 09Document Access ControlDocument access control is not a feature you add to RAG—it is an architectural constraint that shapes every component. Get it wrong and you leak sensitive information. Get it too restrictive and users cannot do their jobs.20 min
  10. 10Row-Level SecurityRow-level security requires treating permission information as first-class index metadata. Your vector database must support flexible filtering, or you must architect indexes around permission boundaries. There is no post-hoc way to add RLS to an index designed without it.20 min
  11. 11SLA MonitoringSLA monitoring is a feedback loop. You define SLAs, measure against them, identify regressions, fix issues, and tighten SLAs as the system improves. Without this loop, you have no way to know if your system is getting better or worse.15 min
  12. 12Latency BudgetingLatency budgets transform vague "make it faster" requirements into specific engineering targets. When every team knows their budget allocation, optimization efforts focus automatically. When budgets are violated, the root cause is immediately identifiable.20 min
  13. 13Semantic CachingSemantic caching reduces LLM inference costs by 30-70% for repetitive queries, but the similarity threshold must be tuned per domain—a threshold too low returns irrelevant cached responses, too high defeats the cache purpose entirely.15 min
  14. 14Cache InvalidationSemantic cache invalidation requires explicit tag tracking between cache entries and source documents. Without this linkage, there's no reliable way to invalidate related entries when source data changes.20 min
  15. 15Geographic DistributionVector indices cannot be replicated via standard Redis replication for similarity search. Each region maintains a local index with async sync from the primary, accepting eventual consistency for search results.15 min
  16. 16Disaster RecoveryRAG disaster recovery requires backing up both the vector embeddings and the Redis metadata index separately. Vector embeddings in Parquet format on S3 provide a portable, queryable backup that exceeds the RPO window.15 min
  17. 17Cost ModelingVector embedding storage is the largest line item at scale—larger than LLM inference costs for read-heavy workloads. Cache hit rate directly reduces LLM costs but has no impact on storage costs.20 min
  18. 18Capacity PlanningFor vector databases, in-memory storage is mandatory for consistent p99 latency. Disk-based approaches (哪怕NVMe) add 5-15ms per query, making p99 targets unreachable. Plan RAM as the primary resource constraint.20 min
  19. 19Performance BenchmarkingBenchmark retrieval and generation separately. Generation latency (typically 100-2000ms) dominates end-to-end RAG latency, but retrieval improvements of even 5ms matter for p99 targets.15 min
  20. 20Load TestingLoad test with realistic query distributions, not uniform queries. A 1%hot queries pattern (same queries repeated) reveals cache effectiveness that uniform random queries miss.15 min
  21. 21Production MigrationNever migrate all traffic at once. The canary phase validates that the target system handles production load without errors before any significant traffic shift. Errors per minute thresholds trigger automatic rollback.20 min
  22. 22Incident ResponseVector search latency spikes and LLM generation failures require different responses. Embedding service failures block ingestion; LLM failures only affect generation. Always diagnose the retrieval layer first—it affects every query.20 min
  23. 23Compliance AuditingAudit logging must happen before potential failures, not after. Log writers using synchronous writes to immutable storage guarantee audit trail integrity even under system failure.20 min
  24. 24Enterprise RAG Platform ProjectEnterprise-grade RAG isn't a single system—it's a platform combining retrieval, generation, caching, compliance, and observability. Each component failure mode must be planned for, and graceful degradation ensures the platform remains functional under partial failures.20 min
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