LLM Archives - ITNotes

Building a Production-Ready Text-to-SQL Engine with Vanna.ai and Python

October 19, 2026

Build a reliable Text-to-SQL engine using Vanna.ai and Python. Learn how to index schema DDL, business rules, and golden queries while implementing production security guardrails.

Mastering Outlines in Python: Guaranteed Structured LLM Output with Regex and CFGs

October 19, 2026

Frustrated with broken JSON from commercial LLM APIs? Discover how Outlines uses regular expressions and context-free grammars to guarantee valid structured outputs in Python.

Build Visual Document RAG with ColPali and Byaldi: Zero-OCR PDF Retrieval

October 18, 2026

Tired of broken OCR pipelines scrambling tables and charts? Learn how to implement visual document retrieval with ColPali and Byaldi to index complex PDF pages directly without text extraction.

Crawl4AI and Python: Extracting Clean Markdown for LLMs and RAG Systems

October 16, 2026

Raw HTML wrecks RAG pipelines with token bloat and noise. Here is how to use Crawl4AI and Python to scrape dynamic pages into clean, structured Markdown while reducing embedding token usage by over 50%.

Build a Lightweight Graph-Based RAG System with LightRAG and Python

October 9, 2026

Standard vector RAG fails when queries require multi-hop reasoning across docs, while enterprise GraphRAG setups run up massive token bills. Learn how to build a lean, dual-level Graph RAG pipeline in Python with LightRAG.

Deploy SGLang with Docker on Linux: Faster LLM Inference Using RadixAttention

October 8, 2026

SGLang uses RadixAttention to cache shared prefixes across requests — making it significantly faster than vLLM for chatbots, agents, and RAG pipelines. This guide covers deploying SGLang with Docker on Linux: from GPU toolkit setup to a production-ready Docker Compose configuration.

Automating JavaScript to TypeScript Migration with AST and LLMs: A Safe Pipeline for Large Codebases

October 5, 2026

Migrating large JavaScript repositories to TypeScript often fails when teams rely solely on codemods or pure LLM generation. This guide details a battle-tested hybrid pipeline that combines deterministic AST transformations with targeted LLM type inference for zero-regression migrations.