Showing 4 open source projects for "cloud mini project"

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    MongoDB Atlas runs apps anywhere

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  • 1
    QMD

    QMD

    mini cli search engine for your docs, knowledge bases, etc.

    QMD is a powerful and lightweight command-line tool that acts as an on-device search engine for your personal knowledge base, allowing you to index and search files like Markdown notes, meeting transcripts, technical documentation, and other text collections without depending on cloud services. Designed to keep all search activity local, it combines classic full-text search techniques with modern semantic features such as vector similarity and hybrid ranking so that queries return not just...
    Downloads: 1 This Week
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  • 2
    Eigenfocus

    Eigenfocus

    Self-Hosted - Project Management, Planning and Time Tracker

    Eigenfocus is an AI-powered personal knowledge management system that uses embeddings and semantic search to help users organize and retrieve ideas across documents. Designed for researchers and creatives, it enables deep linking between notes and supports querying based on meaning rather than keywords.
    Downloads: 0 This Week
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  • 3
    RAG from Scratch

    RAG from Scratch

    Demystify RAG by building it from scratch

    RAG From Scratch is an educational open-source project designed to teach developers how retrieval-augmented generation systems work by building them step by step. Instead of relying on complex frameworks or cloud services, the repository demonstrates the entire RAG pipeline using transparent and minimal implementations. The project walks through key concepts such as generating embeddings, building vector databases, retrieving relevant documents, and integrating the retrieved context into language model prompts. ...
    Downloads: 0 This Week
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  • 4
    Language Models

    Language Models

    Explore large language models in 512MB of RAM

    languagemodels is a lightweight Python library designed to simplify experimentation with large language models while maintaining extremely low hardware requirements. The project focuses on enabling developers and students to explore language model capabilities without needing expensive GPUs or large cloud infrastructures. By using small and optimized models, the library allows LLM inference to run in environments with limited resources, sometimes requiring only a few hundred megabytes of memory. The package provides simple APIs that allow developers to generate text, perform semantic search, classify text, and answer questions using local models. ...
    Downloads: 0 This Week
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