Chromadb

What I Built Basic RAG retrieves docs and generates an answer. I wanted something smarter — a system that picks the right strategy and retries with heavier techniques when the first answer isn’t good enough. This project uses three agents: Router Agent — reads the question and checks answer quality Basic Generator Agent — fast retrieval for simple questions Advanced Generator Agent — uses heavier techniques for hard questions The system tries the fast path first. If the answer is weak, it automatically switches to advanced methods like query decomposition, HyDE, and multi-query retrieval. ...

What I Built I hate writing SQL for simple questions. This project lets me ask things like “how many customers are from Brazil?” in plain English — it generates the SQL, runs it, and gives me a readable answer. All local, using Ollama and ChromaDB. Easy to use — ask questions in everyday language Private — everything runs locally on your machine Fast feedback — generates and runs SQL in seconds Schema-aware — understands table names and relationships Interactive — ask follow-up questions in a chat-style flow Error handling — catches common issues like wrong table name casing System Architecture My RAG Text-to-SQL system consists of five core components: ...

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: ...