RAG CLI

What it does

Local-first RAG CLI for personal documentation. Index markdown, text, and PDF files with semantic search powered by local embeddings and Upstash Vector DB. - satoshiman/rag-cli

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  Rich-CLI – A CLI toolbox for highlighting, Markdown, JSON and rich text2022 · github.com · ▲126

  Rich-cli is a command line toolbox for fancy output in the terminal - Textualize/rich-cli

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  Open-source Rule-based PDF parser for RAG2024 · github.com · ▲293

  The PDF parser is a rule based parser which uses text co-ordinates (boundary box), graphics and font data. The PDF parser works off text layer and also offers a OCR option to automatically use OCR if there are scanned pages in your PDFs. The OCR feature is based off a modified version of tika which uses tesseract underneath. The PDF Parser offers the following features: * Sections and subsections along with their levels. * Paragraphs - combines lines. * Links between sections and paragraphs. * Tables along with the section the tables are found in. * Lists and nested lists. * Join content…

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  RAG-chunk – A CLI to test RAG chunking strategiesNov 2025 · github.com · ▲10

  A Python CLI to test, benchmark, and find the best RAG chunking strategy for your Markdown documents. - messkan/rag-chunk

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  LiquidIndex 2.02025 · liquidindex.dev · ▲134

  I'm building LiquidIndex, an RAG API service that makes it easy to add personalized context to AI apps. Instead of building a whole system to upload, process, and search through a user’s files or notes, you just call a few APIs. It handles the hard parts - like connecting to Notion or Google Drive, breaking the data into usable pieces, and making it searchable. Then, when you want to answer a question using that data, you just query it, and it gives you the most relevant results - optionally with an LLM-generated answer. No pipelines, no infrastructure, no headaches. Here are the core APIs:…

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  Sisi – Semantic Image Search CLI tool, locally without third party APIs2024 · github.com · ▲128

  I wrote this tool to get familiar with CLIP model, I know many people have written similar tools with CLIP before, but I'm new to machine learning and writing a classic tool helps my study. The unusual thing with my version is, it is in pure Node.js, with the power of node-mlx, a Node.js machine learning framework. The repo in the link is mostly about implementing indexing and CLI, the code of the model implementation lives as a Node.js module: https://github.com/frost-beta/clip . Hope this helps other learners!

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  RAG in 3 Lines of PythonDec 2025 · pypi.org · ▲41

  Got tired of wiring up vector stores, embedding models, and chunking logic every time I needed RAG. So I built piragi. from piragi import Ragi kb = Ragi(\["./docs", "./code/\*\*/\*.py", "https://api.example.com/docs"\]) answer = kb.ask("How do I deploy this?") That's the entire setup. No API keys required - runs on Ollama + sentence-transformers locally. What it does: - All formats - PDF, Word, Excel, Markdown, code, URLs, images, audio - Auto-updates - watches sources, refreshes in background, zero query latency - Citations - every answer includes sources…

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  Dev tools · May 2026 · github.com