Tag: rag

Claude Code skill that builds knowledge graphs of entire codebases using Leiden community detection, giving Claude persistent memory at 71x fewer tokens per query vs reading raw files. Viral success (450k+ downloads, ~40k GitHub stars) demonstrates demand for better codebase context management. People building on top without the author's involvement.

A clear architectural distinction between traditional RAG (linear: query → search → respond) and agentic RAG (non-linear: aggregator agent plans, delegates to specialized sub-agents for local data, APIs, web search, then synthesizes). The post is practical, includes a concrete architecture diagram in prose, and is directly relevant to anyone building production retrieval systems that need to handle complex, multi-source queries.

Built from Karpathy's workflow, the Graphify tool compiles raw folders into structured knowledge graphs, achieving 71.5× token reduction. Instead of reloading raw files every session, it creates a queryable wiki structure that Claude Code can navigate efficiently.

A practical approach to giving Claude Code persistent memory using Obsidian as a knowledge base. The author built custom commands and agent personas that reference a structured vault, enabling Claude to maintain context across sessions. The setup will be open-sourced, offering a blueprint for others to implement persistent agent memory.

The author published a structured failure-mode checklist for RAG systems covering 16 reproducible failure categories — and LlamaIndex adopted it into their official RAG troubleshooting docs. The post walks through each failure mode with concrete LlamaIndex examples. For anyone building production RAG pipelines, this is a structured diagnostic tool worth bookmarking.

Onyx is a self-hostable AI chat platform supporting any LLM, with built-in support for custom agents, knowledge source connections, and hybrid search/retrieval workflows. This is squarely in the intersection of self-hosted AI and RAG interests — a production-grade platform, not a toy demo.

A single-image RAG debugging reference that can be uploaded directly into any LLM alongside a failing run to get structured diagnostic suggestions — no install required. The "upload to LLM" use pattern is a clever zero-friction distribution mechanism for debugging tools.