RLAMA is a command-line tool for building and managing local Retrieval-Augmented Generation (RAG) systems. It empowers users to create AI-powered question-answering tools over their documents by integrating with local Ollama models, offering advanced document processing, chunking, and retrieval capabilities.

How It Works

RLAMA leverages a clean architecture, processing documents from various sources (local files, websites) into text. It then generates vector embeddings using Ollama and stores them locally. When a query is made, RLAMA performs a semantic search against the stored embeddings to retrieve relevant document chunks, which are then passed to Ollama along with the query to generate a contextually informed answer. This approach prioritizes local processing, privacy, and minimal dependencies.

Quick Start & Requirements

• Install: curl -fsSL https://raw.githubusercontent.com/dontizi/rlama/main/install.sh | sh
• Prerequisites: Ollama installed and running.
• Docs: https://github.com/DonTizi/rlama

Highlighted Details

• Supports multiple document formats (.txt, .md, .pdf, .docx, .pptx, .xlsx, code files, etc.).
• Offers advanced chunking strategies: fixed, semantic, hybrid, and hierarchical.
• Integrates directly with Hugging Face GGUF models via Ollama.
• Includes features for directory watching and website monitoring for automatic RAG updates.
• Provides an API server for integrating RAG capabilities into other applications.

Maintenance & Community

The project is actively developed by DonTizi. Further community engagement details are not explicitly provided in the README.

Licensing & Compatibility

The project does not explicitly state a license in the provided README. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The README does not specify a license, which may impact commercial adoption. While it mentions supporting OpenAI models for inference, it also states that document embeddings still use Ollama, implying a hybrid approach for model usage. The project is primarily CLI-based, with a lightweight web interface planned for the future.