An open source web crawler that searches the internet. It's a minimal, real-time web search CLI that searches the internet for you. Enter a query and get search results as JSON (title, url, published_date), sorted by recency.
Render any git repo into a single static HTML page for humans or LLMs. Flatten any GitHub repository into a single, searchable HTML page with syntax highlighting, markdown rendering, and a clean sidebar navigation.
GitHub - kantord/SeaGOAT: local-first semantic code search engine
Turn any Kiwix ZIM archive (offline Wikipedia, Stack Exchange, DevDocs, etc.) into an instant knowledge source for LLMs with a tiny CLI + Python server exposing searchable chunks, metadata and citations.
This article discusses the importance of knowledge graphs in providing context for AI agents, highlighting their advantages over traditional retrieval systems in terms of precision, reasoning, and explainability.
Google's new multi-vector retrieval algorithm (MUVERA) improves search speed and performs better on complex queries. It addresses limitations of previous multi-vector systems like ColBERT and could be a successor to Google’s RankEmbed technology. SEOs should focus on aligning with query context and intent rather than exact keyword matching.
Lightweight CLI agent to semantically search and ask your emails. Downloads inbox, generates embeddings using local (or external) LLMs, and stores everything in a vector database on your machine. Supports incremental sync for fast updates.
A Google engineer's testimony shows how page quality is scored and confirms the existence of a popularity signal that uses Chrome data.
This blog post details an experiment testing the ability of LLMs (Gemini, ChatGPT, Perplexity) to accurately retrieve and summarize recent blog posts from a specific URL (searchresearch1.blogspot.com). The author found significant issues with hallucinations and inaccuracies, even in models claiming live web access, highlighting the unreliability of LLMs for even simple research tasks.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
