# Ragit – chat with any folder of documents using a local LLM > Source: > Published: 2026-07-01 06:50:52+00:00 Local RAG CLI to chat with any folder of documents using Ollama. ``` cd ~/ragit python3 -m pip install -e . ``` If your default Python is 3.14+, use Python 3.10–3.13 (recommended: 3.12) because some vector DB dependencies may not publish wheels for very new Python versions yet. Make sure Ollama is installed and running: ``` ollama pull nomic-embed-text ollama serve ``` Index a folder: ``` ragit index ./docs ``` Start chat: ``` ragit chat ./docs ``` List available Ollama models: ``` ragit models ``` Clear an index: ``` ragit clear ./docs ``` `ragit` implements Retrieval-Augmented Generation (RAG): - It loads supported documents ( `.txt` ,`.md` ,`.pdf` ,`.docx` ) recursively. - It splits text into overlapping chunks (about 500 words with 50-word overlap). - It creates embeddings using Ollama ( `nomic-embed-text` ) and stores vectors in local ChromaDB at`~/.ragit//` . - During chat, it embeds each query, retrieves the top relevant chunks, and injects them into a prompt. - It streams an answer from a local Ollama chat model (prefers `llama3.2` if available), then shows source chunks used. - All data stays local on your machine (Ollama + Chroma local persistence). - Indexes are stored under `~/.ragit//` . - Files that cannot be parsed are skipped with a clear error message.