Agentic AI Private

RAG Pipelines with LlamaIndex and Vector Databases (AGNTDV102)

3 days
Agentic AIPythonAI

Build production RAG pipelines with LlamaIndex and vector databases, covering chunking, hybrid search, reranking, permissions, ingestion, and evaluation.

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  • Customizable agenda
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Course Overview

Retrieval is where most enterprise LLM projects actually succeed or fail. A demo that answers questions over ten PDFs rarely survives contact with ten million documents, permission boundaries, tables, stale content, and users who ask questions the corpus cannot answer.

This course builds RAG the way it has to be built for production: measure first, then improve. Students start with a naive pipeline and an evaluation set, then systematically address each weakness — chunking, embedding choice, hybrid search, metadata filtering, reranking, query transformation, and multi-document routing. Students work hands-on with multiple vector stores so they can reason about index types, recall/latency trade-offs, filtering support, and operational cost rather than picking by brand.

The course closes on the hard parts: ingestion pipelines that keep an index fresh, permission-aware retrieval, structured and tabular data, agentic retrieval, and the economics of an index at scale.

Who should attend: Data engineers, backend and ML engineers, and architects responsible for search or knowledge-access features.

Course Benefits

  • Build an end-to-end RAG pipeline with LlamaIndex and defend each design choice with measurements
  • Select chunking, embedding, and index strategies that fit a given corpus
  • Choose and operate a vector database, including index tuning and metadata filtering
  • Implement hybrid search, reranking, and query rewriting, and quantify the gain from each
  • Build incremental ingestion with change detection, deletion, and reindexing
  • Enforce document-level permissions in retrieval
  • Measure retrieval and answer quality, and detect regressions
  • Estimate and control the cost of an index at scale

Delivery Methods

Private Class
Delivered for your team at your site or online.

Course Outline

  • Day 1 — Pipeline Fundamentals and Measurement
    • Module 1: Why Retrieval, and Why It Breaks
      • Grounding vs. fine-tuning vs. long context: choosing by constraint, not fashion
      • The RAG pipeline end to end: ingest, chunk, embed, index, retrieve, rerank, synthesize
      • The failure taxonomy: missed retrieval, wrong chunk, lost context, contradiction, confident nonsense
      • Setting up for honest evaluation before writing pipeline code
    • Module 2: Documents, Parsing, and Chunking
      • Loaders and readers; PDFs, HTML, Office documents, code, and transcripts
      • Layout-aware parsing; tables, headers, and figures
      • Chunking strategies: fixed, sentence, semantic, hierarchical, and document-structure-aware
      • Metadata extraction and why metadata drives retrieval quality
    • Module 3: Embeddings and Vector Stores
      • Embedding model selection: dimension, domain fit, multilingual, cost, and self-hosted options
      • Similarity metrics and normalization
      • Index types: flat, IVF, HNSW, and quantization trade-offs
      • Vector store landscape: pgvector, Qdrant, and a managed option — filtering, scale, and operations
  • Day 2 — Making Retrieval Actually Good
    • Module 4: Hybrid Search and Reranking
      • Where dense retrieval fails: identifiers, acronyms, rare terms, exact phrases
      • BM25 and keyword search; fusion strategies
      • Cross-encoder reranking and the latency it buys you
      • Choosing top-k honestly
    • Module 5: Query Understanding
      • Query rewriting, expansion, and decomposition
      • Metadata filter extraction from natural language
      • Routing across multiple indexes and data sources
      • Handling questions the corpus cannot answer, and refusing well
    • Module 6: Response Synthesis and Citations
      • Synthesis modes: compact, refine, tree summarize, and their cost profiles
      • Citation and provenance that survives summarization
      • Context ordering and the lost-in-the-middle effect
      • Groundedness checks and hallucination mitigation
  • Day 3 — Production Concerns
    • Module 7: Ingestion at Scale
      • Ingestion pipelines, caching, and idempotent document IDs
      • Change detection, upserts, deletions, and index drift
      • Backfills and zero-downtime reindexing
      • Scheduling and orchestration options
    • Module 8: Security and Multi-Tenancy
      • Document-level and row-level permissions in retrieval
      • Pre-filter vs. post-filter, and why post-filtering leaks
      • Tenant isolation strategies: namespace, filter, or separate index
      • PII handling, redaction, and audit trails
    • Module 9: Beyond Text Chunks
      • Structured data: text-to-SQL alongside vector retrieval
      • Tabular and spreadsheet content
      • Knowledge graph and property-graph indexes
      • Agentic retrieval: retrieval as a tool rather than a fixed pipeline stage

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following is required for this Agentic AI class:

  • Python proficiency
  • Basic SQL

Experience in the following would be useful for this Agentic AI class:

Building AI Agents with LangGraph and LangChain (AGNTDV101) or equivalent LLM familiarity is helpful but not required.

Prerequisite Courses

Courses that can help you meet these prerequisites:

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