Show HN: LLMNet – The Offline Internet, Search the web without the web

Summary

🌐 LLMNet The Offline Internet. A premium, private, and AI-powered search experience that lives entirely on your machine. LLMNet transforms your local LLMs into a structured search engine. It combines the power of local generative AI with a high-performance Vector Database (RAG) to provide instant, offline answers from your own knowledge base. Home Page - Screenshot ✨ Key Features 🔒 100% Private: Your queries and data never leave your local network. Your queries and data never leave your local network. 🧠 Local RAG: Index any website or wiki into a persistent Postgres Vector DB. Index any website or wiki into a persistent Postgres Vector DB. ⚡ Instant Results: Sub-second semantic search using pgvector & HNSW indexing. Sub-second semantic search using pgvector & HNSW indexing. 🎨 Premium UI: A glassmorphic, dark-mode interface inspired by modern search engines. A glassmorphic, dark-mode interface inspired by modern search engines. 🌐 No Internet Required: Once indexed, your knowledge stays available offline. 🛠️ Tech Stack Frontend: Next.js, Tailwind CSS Next.js, Tailwind CSS Intelligence: Local LLMs (via OpenAI-compatible APIs) Local LLMs (via OpenAI-compatible APIs) Database: PostgreSQL with pgvector PostgreSQL with Orchestration: Bun, Cheerio (Crawl), Turndown (Markdown) 🚀 Quick Start 1. Requirements Ensure you have the following running locally: LLM Server: Port configured in .env (e.g., Llama.cpp, Ollama) Port configured in (e.g., Llama.cpp, Ollama) Embedding Server: Port configured in .env Port configured in Database: Postgres with the vector extension (see postgres-pgvector/ ) 2. Configuration Configure your environment variables in .env : # Example configuration API_BASE_URL=http://localhost:8888/v1 EMBEDDING_URL=http://localhost:8889/v1/embeddings 3. Setup & Run # Install dependencies bun install # Initialize Database bun postgres-pgvector/migrate.ts # Start the engine bun dev Visit localhost:3000 to start searching. ⊕ Add Knowledge LLMNet features a recursive in...