Llm

What I Was Trying to Do I had structured data in SQLite and didn’t want to write SQL for every question. LlamaIndex let me ask things like “which city has the most people?” in plain English — it figures out the query, runs it, and returns the answer. Key capabilities include: Translating natural language into SQL queries. Converting unstructured context into structured insights. Dynamically retrieving relevant schemas or tables for complex databases. Note: Never expose full write-access databases to LLMs. Always use read-only credentials or sandboxed environments for safety. ...

Why I Built This I wanted a chatbot that answers questions from my own documents — without sending data to a cloud API. This RAG setup runs fully offline with Ollama and ChromaDB on my machine. Privacy — your data stays on your machine No API costs — no usage fees or rate limits Speed — no network delay for each query Control — you choose what goes into the knowledge base Offline use — works without internet after setup Architecture Overview My local RAG system consists of four main components: ...

Why I Use Ollama I wanted to run LLMs on my own hardware without paying for API calls or sending data to the cloud. Ollama made that straightforward — install, pull a model, start chatting. Install Ollama Arch Linux Update your system, then install Ollama: sudo pacman -Syu sudo pacman -S ollama Enable and start the service: sudo systemctl enable ollama sudo systemctl start ollama sudo systemctl status ollama You should see active (running). ...