RAG Training with LlamaIndex and Vector DBs (Pinecone, Chroma, Weaviate, Qdrant)

This training is designed for AI engineers, data engineers, ML engineers, knowledge management architects, and platform developers who want to build production-grade enterprise knowledge bases and RAG systems using LlamaIndex with its data-first paradigm. At the heart of the program is the following approach: learning LlamaIndex is not simply 'putting a PDF into a vector DB and running top-k retrieval.' Real engineering value comes from production-grade document parsing with LlamaParse, the right chunk size and metadata enrichment, sharding/replication/scaling decisions in vector DB selection, precision/recall optimization with advanced retrieval patterns (hybrid, recursive, auto-retrieval, reranking), structured retrieval with Knowledge Graph + Property Graph Index, event-driven Workflows architecture for agentic RAG, image-text alignment for multi-modal RAG, and KVKK-compliant multi-tenant production deployment.

The LlamaIndex ecosystem has matured rapidly over the past three years and as of 2026 is structured around five main products: LlamaIndex Core (index taxonomy, retriever, query engine, response synthesizer), LlamaCloud (managed parsing + indexing), LlamaParse (production-grade document parsing), LlamaHub (100+ connectors), LlamaIndex Workflows (event-driven multi-step pipelines), and LlamaIndex Agents (FunctionAgent, ReActAgent, AgentWorkflow). This training addresses these five products not as separate silos but as an integrated data engineering + RAG framework. Comprehensive LlamaIndex-specific training in Turkey is virtually nonexistent; LangChain courses exist, but distinguishing features like LlamaIndex's data-first paradigm, LlamaParse production parsing, Knowledge Graph Index, and LlamaIndex Workflows are generally not covered. This program is designed to fill that gap as Turkey's most comprehensive LlamaIndex + Vector DB reference training.

A strategic dimension of the program is positioning LlamaIndex's place in the agentic AI ecosystem by comparing it with other frameworks and approaches. The comparison with LangChain is particularly important: while LangChain is a general-purpose application-first framework, LlamaIndex is a data-first-focused tool optimized for very large corpora (1M+ documents). The index taxonomy (VectorStoreIndex, SummaryIndex, TreeIndex, KnowledgeGraphIndex, DocumentSummaryIndex, CompositeIndex) does not exist in LangChain; this is LlamaIndex's unique architectural differentiator. Comparisons with Haystack and raw LLM SDKs are also made; the strengths and weaknesses of each approach and which project type calls for which are analyzed in detail.

The backbone of the program is the vector DB comparison module. The 5 leading vector DBs of 2026 — Pinecone (serverless managed), Chroma (embedded + client-server), Weaviate (open-source + hybrid + native multi-modal), Qdrant (open-source + performance focused), pgvector (Postgres native) — are compared head-to-head. Indexing algorithms like HNSW, IVF, and ScaNN; sharding, replication, and scaling characteristics; managed vs self-hosted cost trade-offs; KVKK-compliant self-hosted vs cloud decisions for Turkey; namespace and metadata-filtering structures are addressed in detail. Alternatives like Milvus, LanceDB, and MongoDB Atlas Vector Search are also included in the decision matrix. This is the only training in Turkey that performs vector DB comparison at this depth.

The LlamaParse module is one of the strongest differentiators of the LlamaIndex ecosystem. It is compared head-to-head with alternatives like PyPDF, Unstructured.io, and AWS Textract; LlamaParse's superiority in complex table extraction, image OCR, formula parsing, and markdown output is shown through hands-on exercises. In production scenarios — insurance policies, financial reports, academic papers, engineering documents — the right parameter selection and cost-effective usage of LlamaParse are addressed in detail. The nuances of Turkish document parsing, morphology-aware chunking strategies, and token counting are also part of this module.

The index taxonomy module reveals LlamaIndex's unique architectural distinction. VectorStoreIndex (classic semantic search), SummaryIndex (full-document iteration), TreeIndex (hierarchical recursive summarization), KeywordTableIndex (keyword-based filtering), DocumentSummaryIndex (hybrid retrieval), and CompositeIndex (multi-index orchestration) are addressed in detail. Dynamic index selection via Router Query Engine, the multi-document Doc Agent pattern, the Storage Context (DocStore, IndexStore, VectorStore) layers, and persistent-index + incremental-update strategies are covered comprehensively. The discipline of orchestrating multiple indices provides architectural maturity beyond the classic single-vector-DB approach.

In the retriever and query engine module, top-k retrieval, hybrid (sparse + dense), reciprocal rank fusion with QueryFusionRetriever, Auto Merging Retriever, query decomposition with SubQuestion QueryEngine, and Router Query Engine are addressed. The Response Synthesizer's refine, compact, tree_summarize, and accumulate modes; streaming response; and production topics like token-by-token output are shown hands-on. In the following advanced retrieval module, cross-encoder rerankers (bge-reranker-v2, Cohere Rerank, Voyage Rerank), LLM-as-reranker, recursive retrieval (chunk → parent document), auto-retrieval (LLM-generated metadata filters), and sentence-window and parent-document patterns are addressed. This discipline improves the retrieval quality of basic RAG by 30–50%.

Perhaps the most distinguishing module of the program is dedicated to Knowledge Graph + Property Graph Index. LlamaIndex is the only mainstream RAG framework that performs automatic entity-relation extraction from documents and builds schema-aware knowledge bases. This training covers in detail: automatic entity and relation extraction; schema-free vs schema-aware KG setup; hybrid KG + vector retrieval with KGTableRetriever; rich schema-aware knowledge bases with the Property Graph Index; the GraphRAG approach and comparison with Microsoft GraphRAG; and graph backend integrations like Neo4j / FalkorDB / Nebula. This discipline dramatically improves RAG quality especially in structured-data-rich sectors like finance, healthcare, and law.

The LlamaIndex Workflows module teaches the framework's agentic RAG paradigm. Event-driven pipeline design with the @step decorator, context / branch / join / loop patterns, streaming workflows, and mid-flight observability are addressed. In agent types, FunctionAgent (structured tool use), ReActAgent (thought-action-observation loop), and AgentWorkflow (multi-agent orchestration) are addressed in detail. As agentic RAG patterns, query router agent, dynamic index selection, self-correcting RAG, and replan-on-failure mechanics are shown.

The multi-modal RAG module addresses LlamaIndex's mature multi-modal capabilities as of 2026. MultiModalVectorStoreIndex and image-text alignment, CLIP / ImageBind / Voyage multi-modal embedding models, the comparison of GPT-5 Vision / Claude Opus 4.7 Vision / Gemini 2.5 Pro Vision, recursive node parsing with LlamaParse table extraction, nested tables, the table-as-image fallback approach, Whisper transcript + LlamaIndex pipeline, scene detection, and timestamp-aware retrieval topics are addressed hands-on. Multi-modal RAG is directly applicable to real production scenarios like insurance claims, e-commerce product catalogs, engineering drawings, and medical imaging.

The evaluation module represents the production-discipline dimension of the training. RAGAS framework metrics (faithfulness, answer relevancy, context recall, context precision); synthetic test sets with RAGAS testset.synthesize; TruLens feedback functions and trace anatomy; the TruLens dashboard and A/B comparison; LlamaIndex's native CorrectnessEvaluator, FaithfulnessEvaluator, RelevancyEvaluator; and regression-test pipelines with BatchEvalRunner are addressed in detail. In the production deployment module, FastAPI + LlamaIndex endpoints, LlamaCloud managed deployment, comparison of Vercel / AWS / GCP / Kubernetes, tenant-aware indexing with namespace isolation, tenant access control via vector DB metadata filtering, KVKK-compliant multi-tenant strategy, embedding caching, query cache, semantic cache, and model routing (GPT-5 + Sonnet + Haiku + Local hybrid) are covered end to end.

In the capstone project, each participant designs an end-to-end production-grade enterprise knowledge base and RAG system for their own company: a LlamaParse → chunking → embedding → vector DB → retrieval → eval → deployment pipeline; vector DB selection and multi-tenant deployment topology; a Knowledge Graph + vector hybrid architecture; an eval report and cost projection. By the end of the training, participants reach a level of technical and architectural competence to manage the LlamaIndex ecosystem in an integrated way within the data-first paradigm, make architectural decisions among the 5 main vector DBs, perform production-grade parsing with LlamaParse, optimize precision/recall with advanced retrieval patterns, build structured RAG with Knowledge Graph + Property Graph Index, design agentic RAG architecture with LlamaIndex Workflows, perform multi-modal RAG, set up production evaluation with RAGAS/TruLens, and perform KVKK-compliant multi-tenant deployment. The training consists of 3 days, 12 modules, and over 80 hands-on lessons.