4 projects for "databases" with 2 filters applied:

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  • 1
    yt-fts

    yt-fts

    Search all of YouTube from the command line

    ...The tool returns search results with timestamps and direct links to the exact moment in the video where the phrase occurs. In addition to traditional keyword search, the system supports experimental semantic search capabilities using embeddings from AI services and vector databases. This allows users to search videos by meaning rather than only exact keywords.
    Downloads: 8 This Week
    Last Update:
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  • 2
    Zvec

    Zvec

    A lightweight, lightning-fast, in-process vector database

    ...Its performance benchmarks show it achieving high queries-per-second and fast index build times compared to similar tools. Because it runs in-process, developers can embed it in native apps, microservices, or edge computing scenarios where traditional server-based vector databases might be overkill.
    Downloads: 4 This Week
    Last Update:
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  • 3
    Pixeltable

    Pixeltable

    Data Infrastructure providing an approach to multimodal AI workloads

    ...The system provides a declarative interface for managing the entire lifecycle of AI data pipelines, including storage, transformation, indexing, retrieval, and orchestration of datasets. Unlike traditional architectures that require multiple tools such as databases, vector stores, and workflow orchestrators, Pixeltable unifies these functions within a table-based abstraction. Developers define data transformations and AI operations using computed columns on tables, allowing pipelines to evolve incrementally as new data or models are added. The framework supports multimodal content including images, video, text, and audio, enabling applications such as retrieval-augmented generation systems, semantic search, and multimedia analytics.
    Downloads: 4 This Week
    Last Update:
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  • 4
    RAG from Scratch

    RAG from Scratch

    Demystify RAG by building it from scratch

    ...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. Each example is written with detailed explanations so that developers can understand the internal mechanics of semantic search and context-aware language generation. The repository emphasizes learning through direct implementation, allowing users to see how each component of the RAG architecture functions independently.
    Downloads: 0 This Week
    Last Update:
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