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6 repositorios

Awesome GitHub RepositoriesData Querying and Selection

Retrieving specific subsets of information from data structures using various selection methods.

Distinct from Select Query Builders: Shortlist candidates focused on UI interaction or SQL query builders rather than general data structure selection.

Explore 6 awesome GitHub repositories matching data & databases · Data Querying and Selection. Refine with filters or upvote what's useful.

Awesome Data Querying and Selection GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • morvanzhou/tutorialsAvatar de MorvanZhou

    MorvanZhou/tutorials

    12,952Ver en GitHub↗

    This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

    Provides techniques for retrieving specific subsets of information from data structures.

    Pythonmachine-learningmultiprocessingneural-network
    Ver en GitHub↗12,952
  • airweave-ai/airweaveAvatar de airweave-ai

    airweave-ai/airweave

    6,453Ver en GitHub↗

    Airweave is a unified AI knowledge base platform that syncs data from external APIs into a searchable layer for retrieval-augmented generation. It provides a pre-built data connector library and a framework for building custom connectors, enabling the extraction, transformation, and synchronization of structured and unstructured data from SaaS applications. The platform includes a hybrid vector retrieval system that combines semantic, neural, and keyword search strategies to deliver grounded context for AI agents. The platform distinguishes itself through an agentic search engine that iterati

    Retrieves updates and subitems linked to parent items from Monday.com, maintaining full hierarchy.

    Pythonagent-infrastructureaiai-agents
    Ver en GitHub↗6,453
  • j3ssie/osmedeusAvatar de j3ssie

    j3ssie/Osmedeus

    6,425Ver en GitHub↗

    Osmedeus is a security workflow orchestration engine that coordinates AI agents, shell commands, and scanning tools through declarative YAML pipelines. It functions as a distributed security scanner, a declarative workflow automator, and an AI agent framework for security, enabling automated multi-step security analysis with conditional branching, parallel execution, and distributed workers. The engine distinguishes itself through a hybrid runner model that executes workflow steps on the local host, inside Docker containers, or over SSH to remote machines, selected per step or module. It supp

    Provides functions to import, query, and update scan data in the database.

    Go
    Ver en GitHub↗6,425
  • apache/pinotAvatar de apache

    apache/pinot

    6,098Ver en GitHub↗

    Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

    Stores data segments on local hardware and executes analytical queries directly against the stored data.

    Java
    Ver en GitHub↗6,098
  • infiniflow/infinityAvatar de infiniflow

    infiniflow/infinity

    4,570Ver en GitHub↗

    Infinity es una base de datos vectorial distribuida y un almacén vectorial multimodal diseñado para gestionar datasets a gran escala para recuperación y búsqueda por similitud. Sirve como backend para aplicaciones de modelos de lenguaje grandes y pipelines de generación aumentada por recuperación (RAG) almacenando y recuperando vectores densos, vectores dispersos y datos de texto completo. El sistema funciona como un motor de búsqueda híbrido, combinando embeddings vectoriales y búsqueda de texto completo con algoritmos de reranking para identificar los documentos más relevantes. Admite el almacenamiento de datos multimodal, permitiendo el mantenimiento de diversos tipos de datos, incluyendo tensores, cadenas y numéricos, dentro de un único entorno. La base de datos ofrece capacidades para gestionar esquemas y registros, incluyendo importación, exportación y consultas estructuradas. Incluye herramientas para la gestión de índices y optimización de almacenamiento, y ofrece recuperación de estado mediante snapshots del sistema o de tablas. La base de datos puede desplegarse como un binario único o mediante Docker, y es accesible a través de una API HTTP y un SDK de Python.

    Provides capabilities for querying structured data using filters, sorting, grouping, and aggregation.

    C++ai-nativeapproximate-nearest-neighbor-searchbm25
    Ver en GitHub↗4,570
  • datlechin/tableproAvatar de datlechin

    datlechin/TablePro

    4,471Ver en GitHub↗

    TablePro is a cross-platform database management client designed for browsing, querying, and administering both SQL and NoSQL databases. It functions as a unified workspace that integrates a code-centric SQL editor with schema visualization tools, allowing developers to manage complex data models and execute queries across diverse database engines. The application distinguishes itself through an agentic AI integration layer that connects language models directly to database tools, enabling automated query generation, optimization, and error fixing with configurable approval gates. It features

    Explores databases, schemas, and tables through a sidebar UI and executes queries with support for semi-structured data.

    Swift
    Ver en GitHub↗4,471
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  2. Data & Databases
  3. Data Querying and Selection

Explorar subetiquetas

  • Scan Data Query and Update Functions1 sub-etiquetaProvides functions to import assets and vulnerabilities, update fields, run SELECT queries, and retrieve counts and diffs. **Distinct from Data Querying and Selection:** Distinct from Data Querying and Selection: specifically for importing and updating scan data, not general data selection.
  • Segment Query Engines1 sub-etiquetaExecution engines that run analytical queries directly against locally stored data segments. **Distinct from Data Querying and Selection:** Distinct from Data Querying and Selection: focuses on the architectural capability of hosting and querying segments locally rather than general selection methods.