NuGet NuGet Downloads GitHub Release (CLI) In Part 1 I showed the raw pipeline: manually downloading models, writing a tokenizer, wiring up ONNX inference, and decoding BIO tags by hand. Educational, but a lot of plumbing to get right. Now it's a NuGet…
NuGet NuGet Downloads GitHub Release (CLI) As I've been building lucidRAG I'm reading social media where people keep asking the same thing. 'How do you get features from scanned text?' the category error is always 'just use an LLM'...which WORKS but is very…
Status: In development as part of lucidRAG. Source: github.com/scottgal/lucidrag Where this fits: VideoSummarizer is the orchestrator of the lucidRAG family, combining five pipelines into a unified video analysis engine: DocSummarizer - Documents (entity…
Status: AudioSummarizer.Core is currently in development as part of lucidRAG, a forthcoming mostlylucid product for multi-modal RAG. The implementation is complete and working—this article documents the architecture and design. CLI integration will be…
NuGet npm .NET Node.js This is Part 4 of the DocSummarizer series. See Part 1 for the architecture, Part 2 for the CLI tool, or Part 3 for the deep dive on embeddings. The hard part of RAG isn't the LLM. It's everything before the LLM. You've probably seen the…
Small and local LLMs are often framed as the cheap alternative to frontier models. That framing is wrong. They are not a degraded version of the same thing. They are a different architectural choice, selected for control, predictability, and survivable failure…
Your RAG system is great at "needle" questions: retrieve a few relevant chunks and synthesise an answer. It struggles with two common query types: Sensemaking: "What are the main themes across this corpus?" Connective: "How does X relate to Y across different…
This is Part 3 of the DocSummarizer series: Part 1: Building a Document Summarizer with RAG - The architecture and why the pipeline approach beats naive LLM calls Part 2: Using the Tool - Quick-start guide for the CLI Part 3: Advanced Concepts (this article) -…
📖 Part of the RAG Series: This is Part 4a - core implementation: Part 1: RAG Origins and Fundamentals - What embeddings are, why they matter Part 2: RAG Architecture and Internals - Chunking, tokenisation, vector databases Part 3: RAG in Practice - Building…
📖 Part of the RAG Series: This is Part 5 - production integration patterns: Part 1: RAG Origins and Fundamentals - What embeddings are, why they matter Part 2: RAG Architecture and Internals - Chunking, tokenisation, vector databases Part 3: RAG in Practice -…