Releases·Tool Release
raggy - CLI tool for RAG over your local documents built with Ollama
Developer paulknysh released raggy, a lightweight CLI tool for retrieval-augmented generation over local documents, built on Ollama. It runs vector search, BM25 indexing, and embedding generation entirely locally, with answer generation via a local LLM or a remote API key. The tool supports common document formats and applies OCR to images and scans automatically.
0 engagement1 sourceWed, Sep 9, 2026, 03:57 PM
AI Overview
- raggy is a lightweight CLI tool for RAG over local documents built on Ollama, with a hybrid vector + BM25 database running fully locally.
- raggy supports most common document formats and handles images and scans automatically via OCR.
- raggy generates answers via a local LLM or remotely using an API key; embedding generation runs locally.
Related
Products·launched
raggy: CLI tool for RAG over local documents
raggy is a lightweight command-line tool for retrieval-augmented generation over documents stored locally. It uses local embedding generation and a hybrid vector + BM25 database, with answer generation via a local LLM or a remote API key. It suits developers and users who want private, offline-capable search and Q&A over their own files.
raggy is a lightweight command-line tool for retrieval-augmented generation over documents stored locally. It uses local embedding generation and a hybrid vector + BM25 database, with answer generation via a local LLM or a remote API key. It suits developers and users who want private, offline-capable search and Q&A over their own files.
12021 engagement4 sourcesredditTue, Sep 8, 2026, 04:35 PM
Products·launched
Raggy – lightweight CLI tool for RAG over local documents
Raggy is an open-source command-line tool for running retrieval-augmented generation over documents stored locally. It uses LLMs to answer questions grounded in the user's own files without uploading them to external services. It targets developers who want a simple, self-hosted way to search and query local document collections.
Raggy is an open-source command-line tool for running retrieval-augmented generation over documents stored locally. It uses LLMs to answer questions grounded in the user's own files without uploading them to external services. It targets developers who want a simple, self-hosted way to search and query local document collections.
7 engagement1 sourcehackernewsFri, Sep 11, 2026, 04:01 PM
Products·launched
Raqule — CLI that turns a project into LLM-friendly context
Raqule is a Node.js CLI tool that packages a codebase for LLM consumption. It generates a visual project tree and gathers relevant source files into one formatted context, so developers don't have to manually explain structure or collect files before prompting a model.
Raqule is a Node.js CLI tool that packages a codebase for LLM consumption. It generates a visual project tree and gathers relevant source files into one formatted context, so developers don't have to manually explain structure or collect files before prompting a model.
6 engagement1 sourceredditFri, Sep 18, 2026, 09:59 PM
Products·building
research-rag: local CPU search over PDFs and ebooks with browser and MCP access
A local retrieval tool for searching medium-sized corpora of books and papers, inspired by UltraRAG. It returns passages with source locations instead of generating answers, letting researchers read results in context and avoid plagiarism concerns. Runs on CPU with browser and MCP access; optional answer generation may be added later.
A local retrieval tool for searching medium-sized corpora of books and papers, inspired by UltraRAG. It returns passages with source locations instead of generating answers, letting researchers read results in context and avoid plagiarism concerns. Runs on CPU with browser and MCP access; optional answer generation may be added later.
1 engagement1 sourceredditFri, Oct 16, 2026, 09:19 PM
Products·launched
Offline PDF chat with RAG using local LLMs and vector search
A production-oriented Retrieval-Augmented Generation (RAG) system for chatting with PDFs. It uses local LLMs via Ollama, ChromaDB for vector search, and LangChain to provide grounded answers from uploaded documents, fully offline. Aimed at users needing private, local document Q&A.
A production-oriented Retrieval-Augmented Generation (RAG) system for chatting with PDFs. It uses local LLMs via Ollama, ChromaDB for vector search, and LangChain to provide grounded answers from uploaded documents, fully offline. Aimed at users needing private, local document Q&A.
1 engagement1 sourcegithubSat, Jul 11, 2026, 03:21 AM