RLAMA

Hey! I developed RLAMA to solve a straightforward but frustrating problem: how to easily query my own documents with a local LLM without using cloud services. What it actually is RLAMA is a command-line tool that bridges your local documents and Ollama models. It implements RAG (Retrieval-Augmented Generation) in a minimalist way: RLAMA extracts text from the documents and generates embeddings via Ollama. When you ask a question, it retrieves relevant passages and sends them to the model. The tool handles many formats automatically. For PDFs, it first tries pdftotext, then tesseract if necessary. For binary files, it has several fallback methods to extract what it can. Problems it solves I use it daily for: Finding information in old technical documents without having to reread everything Exploring code I'm not familiar with (e.g., "explain how part X works") Creating summaries of long documents Querying my research or meeting notes The real time-saver comes from being able to ask questions instead of searching for keywords. For example, I can ask "What are the possible errors in the authentication API?" and get consolidated answers from multiple files. Why use it? It's simple: four commands are enough (rag, run, list, delete) It's local: no data is sent over the internet It's lightweight: no need for Docker or a complete stack It's flexible: compatible with all Ollama models I created it because other solutions were either too complex to configure or required sending my documents to external services. If you already have Ollama installed and are looking for a simple way to query your documents, this might be useful for you. In conclusion I've found that in discussions on r/ollama point to several pressing needs for local RAG without cloud dependencies: we need to simplify the ingestion of data (PDFs, web pages, videos...) via tools that can automatically transform them into usable text, reduce hardware requirements or better leverage common hardware (model quantization, multi-GPU support) to improve performance, and integrate advanced retrieval methods (hybrid search, rerankers, etc.) to increase answer reliability. The emergence of integrated solutions (OpenWebUI, LangChain/Langroid, RAGStack, etc.) moves in this direction: the ultimate goal is a tool where users only need to provide their local files to benefit from an AI assistant trained on their own knowledge, while remaining 100% private and local so I wanted to develop something easy to use!