Showing 4 open source projects for "sql sorting queries"

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    Databend

    Databend

    Cloud-native open source data warehouse for analytics and AI queries

    Databend is an open source cloud-native data warehouse designed for large-scale analytics and modern data workloads. Built in Rust, the system focuses on high performance, scalability, and efficient data processing for analytical queries. It is designed with a separation of compute and storage, allowing compute nodes to scale independently while storing data in object storage systems. This architecture enables cost-efficient storage and elastic scaling for workloads that involve large datasets and complex queries. Databend provides a unified engine capable of handling analytics, vector search, and full-text search within a single platform. ...
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  • 2
    Spice.ai OSS

    Spice.ai OSS

    A self-hostable CDN for databases

    ...Spice makes it easy and fast to query data from one or more sources using SQL. You can co-locate a managed dataset with your application or machine learning model, and accelerate it with Arrow in-memory, SQLite/DuckDB, or with attached PostgreSQL for fast, high-concurrency, low-latency queries. Accelerated engines give you flexibility and control over query cost and performance.
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  • 3
    PostgresML

    PostgresML

    The GPU-powered AI application database

    PostgresML is a complete platform in a PostgreSQL extension. Build simpler, faster, and more scalable models right inside your database. Explore the SDK and test open source models in our hosted database. Combine and automate the entire workflow from embedding generation to indexing and querying for the simplest (and fastest) knowledge-based chatbot implementation. Leverage multiple types of natural language processing and machine learning models such as vector search and personalization...
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  • 4
    Korvus

    Korvus

    Korvus is a search SDK that unifies the entire RAG pipeline

    Korvus is an open-source retrieval-augmented generation (RAG) pipeline designed to run entirely inside PostgreSQL, allowing developers to build AI search and knowledge systems directly within a database environment. The project consolidates the typical steps of a RAG pipeline—including embedding generation, document retrieval, reranking, and text generation—into a single query executed within the Postgres ecosystem. By leveraging PostgresML and vector extensions such as pgvector, Korvus...
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