Showing 10 open source projects for "databases"

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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: 7 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: 2 This Week
    Last Update:
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  • 3
    pgvector

    pgvector

    Open-source vector similarity search for Postgres

    pgvector is an open-source PostgreSQL extension that equips PostgreSQL databases with vector data storage, indexing, and similarity search capabilities—ideal for embeddings-based applications like semantic search and recommendations. You can add an index to use approximate nearest neighbor search, which trades some recall for speed. Unlike typical indexes, you will see different results for queries after adding an approximate index.
    Downloads: 41 This Week
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  • 4
    pgai

    pgai

    A suite of tools to develop RAG, semantic search, and other AI apps

    pgai is a suite of PostgreSQL extensions developed by Timescale to empower developers in building AI applications directly within their databases. It integrates tools for vector storage, advanced indexing, and AI model interactions, facilitating the development of applications like semantic search and Retrieval-Augmented Generation (RAG) without leaving the SQL environment.
    Downloads: 2 This Week
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  • 5
    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. Built on the OceanBase engine, it maintains ACID compliance and MySQL compatibility while delivering real-time analytical performance. ...
    Downloads: 10 This Week
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  • 6
    ViMax

    ViMax

    Director, Screenwriter, Producer, and Video Generator All-in-One

    ...It integrates components like visual encoders, cross-modal fusion techniques, and reasoning modules so that users can go beyond simple captioning or classification to perform tasks such as visual question answering, multi-image inference, and structured scene understanding. ViMax’s design accommodates large image sets and supports retrieval augmentation, enabling it to work with external image databases, supplementary metadata, and semantic search to enhance context awareness. The system aims to bridge foundational vision backbones and generative language models through adapters and fusion layers that maximize both signal integration and reasoning depth, and includes utility pipelines for training, evaluation, and deployment.
    Downloads: 7 This Week
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  • 7
    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: 5 This Week
    Last Update:
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  • 8
    txtai

    txtai

    Build AI-powered semantic search applications

    ...Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes). Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. The following applications are powered by txtai.
    Downloads: 8 This Week
    Last Update:
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  • 9
    Weaviate

    Weaviate

    Weaviate is a cloud-native, modular, real-time vector search engine

    Weaviate in a nutshell: Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable...
    Downloads: 5 This Week
    Last Update:
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  • 10
    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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