RAG for Structured Data: Querying SQL Databases with LLMs

Text-to-SQL (NL2SQL) for structured data: LangChain SQL chains, agent toolkits, few-shot examples, schema curation, read-only security, and execution accuracy evaluation.

RAG for PDFs: Extracting and Retrieving from Complex Documents

RAG for PDFs: extraction with pdfplumber, PyMuPDF, OCR, and vision LLMs — heading-based chunking, atomic table handling, metadata, hybrid search, and extraction-first diagnostics.

GraphRAG Explained: Knowledge Graphs Meet Retrieval-Augmented Generation

GraphRAG explained: knowledge graphs for multi-hop and global RAG queries with Microsoft GraphRAG, Neo4j + LlamaIndex, community summarization, and hybrid agentic routing.

Agentic RAG: Combining AI Agents with Retrieval

Agentic RAG tutorial: build grade-and-retry retrieval loops with LangGraph, Corrective RAG (CRAG), LlamaIndex ReActAgent, and production guardrails for cost and latency.

Query Expansion and Rewriting for Better RAG Retrieval

Query expansion and rewriting for RAG: LLM query rewriting, MultiQueryRetriever, HyDE, query decomposition, and step-back prompting — measured with RAGAS.

Parent-Child Chunking: Advanced Document Splitting for RAG

Parent-child chunking tutorial for RAG: small-to-big retrieval with LlamaIndex HierarchicalNodeParser, AutoMergingRetriever, and LangChain ParentDocumentRetriever.

Contextual Compression in RAG: Retrieve Less, Answer Better

Contextual compression RAG tutorial with LangChain: LLMChainExtractor, EmbeddingsFilter, reranker pipelines, and measuring gains with RAGAS context precision.

RAGAS Tutorial: Automated Evaluation for RAG Pipelines

RAGAS tutorial: evaluate RAG pipelines with faithfulness, answer relevancy, context precision, and context recall metrics using LLM-as-a-judge and synthetic testsets.

Haystack Tutorial: Build a RAG Pipeline with Deepset's Framework

Haystack 2.x tutorial: build a full RAG pipeline with indexing (DocumentSplitter, embedder, writer) and query (retriever, PromptBuilder, OpenAIGenerator) components from deepset.

Supabase Vector Tutorial: pgvector Made Easy

Supabase pgvector tutorial: enable the vector extension, create a VECTOR table, add an HNSW index, generate embeddings, and build semantic search with match_documents in SQL.