Showing 6 open source projects for "multi-system"

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  • Custom VMs From 1 to 96 vCPUs With 99.95% Uptime Icon
    Custom VMs From 1 to 96 vCPUs With 99.95% Uptime

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    Error to trace to log to deploy. One click. No SSH.

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    AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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  • 1
    UForm

    UForm

    Multi-Modal Neural Networks for Semantic Search, based on Mid-Fusion

    UForm is a Multi-Modal Modal Inference package, designed to encode Multi-Lingual Texts, Images, and, soon, Audio, Video, and Documents, into a shared vector space! It comes with a set of homonymous pre-trained networks available on HuggingFace portal and extends the transfromers package to support Mid-fusion Models. Late-fusion models encode each modality independently, but into one shared vector space.
    Downloads: 0 This Week
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  • 2
    Jina

    Jina

    Build cross-modal and multimodal applications on the cloud

    Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray.
    Downloads: 0 This Week
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  • 3
    Vearch

    Vearch

    A distributed system for embedding-based vector retrieval

    Vearch is the vector search infrastructure for deep learning and AI applications. Vearch is a distributed vector storage and retrieval system which can be easily extended to billions scale. Vearch implements a high-performance, lockless real-time vector indexing subsystem that utilizes various optimization techniques to support millisecond vector update and retrieval. End-to-end one-click deployment. Through the module of the plugin, a complete default visual search system can be deployed just with one click. ...
    Downloads: 1 This Week
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  • 4
    OceanBase seekdb

    OceanBase seekdb

    The AI-Native Search Database

    seekdb is an AI-native search database from OceanBase that unifies vector, full-text, relational, JSON, and GIS data into a single query engine. The system is designed to support hybrid search workloads and in-database AI workflows without requiring multiple specialized databases. It enables developers to perform semantic search, keyword search, and structured SQL queries within the same platform, simplifying modern AI application stacks. seekdb also embeds AI capabilities directly in the database layer, including embedding generation, reranking, and LLM inference for end-to-end RAG pipelines. ...
    Downloads: 0 This Week
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  • Build Agents and Models on One Platform Icon
    Build Agents and Models on One Platform

    Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.

    Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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  • 5
    Aquila X

    Aquila X

    Easy build your personal search engine with Aquila Network

    Easy build your personal search engine with Aquila Network. Aquila X is the gateway to Aquila Network and it's applications. AquilaX is a smart bookmarking tool. You can keep your bookmarks and search through it's contents. Choose to keep all your data in a local server or in the cloud. This is an open source software and thus is auditable.
    Downloads: 0 This Week
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  • 6
    Vector AI

    Vector AI

    A platform for building vector based applications

    Vector AI is a framework designed to make the process of building production-grade vector-based applications as quick and easily as possible. Create, store, manipulate, search and analyze vectors alongside json documents to power applications such as neural search, semantic search, personalized recommendations etc. Image2Vec, Audio2Vec, etc (Any data can be turned into vectors through machine learning). Store your vectors alongside documents without having to do a db lookup for metadata...
    Downloads: 1 This Week
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