Showing 2 open source projects for "engine"

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  • Digital business card + lead capture + contact enrichment Icon
    Digital business card + lead capture + contact enrichment

    Your complete in-person marketing platform

    Share digital business cards, capture leads, and enrich validated contact info - at events, in the field, and beyond. Powered by AI and our proprietary data engine, Popl drives growth for companies around the world, turning every handshake into an opportunity.
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  • CompanyCam is a photo-based solution created for contractors, by contractors. Icon
    CompanyCam is a photo-based solution created for contractors, by contractors.

    Take photos, track progress, and collaborate on tasks with job site management tools and AI shortcuts for every phase of any project.

    Take unlimited photos—which are location and time-stamped, sent to the cloud, and stored securely. Every photo is organized by project and instantly available to your team, allowing you to see what’s going on anytime, anywhere. Annotate photos with drawings, arrows, comments, tags, and voice notes, and create project timelines, photo galleries, reports, and transformation photos through the app. Sharing photos with customers and insurance adjusters has never been easier, and keeping your entire process organized has never been simpler.
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    MCP Server Qdrant

    MCP Server Qdrant

    An official Qdrant Model Context Protocol (MCP) server implementation

    The Qdrant MCP Server is an official Model Context Protocol server that integrates with the Qdrant vector search engine. It acts as a semantic memory layer, allowing for the storage and retrieval of vector-based data, enhancing the capabilities of AI applications requiring semantic search functionalities. ​
    Downloads: 4 This Week
    Last Update:
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  • 2
    LEANN

    LEANN

    Local RAG engine for private multimodal knowledge search on devices

    LEANN is an open source system designed to enable retrieval-augmented generation (RAG) and semantic search across personal data while running entirely on local devices. It focuses on dramatically reducing the storage overhead typically required for vector search and embedding indexes, enabling efficient large-scale knowledge retrieval on consumer hardware. LEANN introduces a storage-efficient approximate nearest neighbor index combined with on-the-fly embedding recomputation to avoid storing...
    Downloads: 0 This Week
    Last Update:
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