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7 Repos

Awesome GitHub RepositoriesMongoDB Connectors

Integrations for retrieving documents and collections from database instances.

Distinct from Data Ingestion: Focuses on MongoDB-specific database ingestion, distinct from general data ingestion.

Explore 7 awesome GitHub repositories matching data & databases · MongoDB Connectors. Refine with filters or upvote what's useful.

Awesome MongoDB Connectors GitHub Repositories

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  • automattic/mongooseAvatar von Automattic

    Automattic/mongoose

    27,479Auf GitHub ansehen↗

    Mongoose is an object data modeling library and framework for Node.js that maps application objects to MongoDB documents. It functions as a document mapper and schema validator, ensuring consistent data types and validation rules for records stored in MongoDB. The project provides a system for defining structured schemas to model application data, including the ability to create hierarchical data structures through nested schema composition. It implements a middleware-based hook system that allows for the interception and modification of data states during the lifecycle of database operations

    Functions as an object data modeling library that maps application objects to MongoDB documents using structured schemas.

    JavaScript
    Auf GitHub ansehen↗27,479
  • winstonjs/winstonAvatar von winstonjs

    winstonjs/winston

    24,478Auf GitHub ansehen↗

    Winston is a versatile logging library for Node.js designed to record system events and metadata. It functions as a multi-transport log manager that routes data to various destinations and a structured log formatter that transforms entries into JSON or plain text. The project is distinguished by its pluggable transport architecture, which decouples the logging interface from delivery mechanisms. This allows for the creation of custom transport extensions and the use of hierarchical logger instances to inherit configurations while attaching persistent metadata to downstream messages. The libr

    Persists log entries into MongoDB collections with support for capped collections and TTL expiration.

    JavaScript
    Auf GitHub ansehen↗24,478
  • unstructured-io/unstructuredAvatar von Unstructured-IO

    Unstructured-IO/unstructured

    14,019Auf GitHub ansehen↗

    Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t

    Connects to databases to retrieve documents and collections for processing into structured formats.

    HTMLdata-pipelinesdeep-learningdocument-image-analysis
    Auf GitHub ansehen↗14,019
  • studio3t/robomongoAvatar von Studio3T

    Studio3T/robomongo

    9,369Auf GitHub ansehen↗

    RoboMongo is a cross-platform database manager and graphical interface for administering MongoDB databases. It serves as a shell integrated development environment and query tool for managing NoSQL data stores and exploring collections across multiple server instances. The application provides a visual environment for writing and executing modern JavaScript scripts and native shell commands. It includes capabilities for running aggregate queries with paginated results and supports side-by-side views for comparing data outputs. The tool covers remote database connectivity through SSH tunnelin

    Serves as a graphical interface for managing MongoDB databases and exploring collections across server instances.

    C++
    Auf GitHub ansehen↗9,369
  • pyeve/eveAvatar von pyeve

    pyeve/eve

    6,738Auf GitHub ansehen↗

    Eve is a REST API framework that maps database collections to web resources through declarative configuration files. It functions as a database-to-API mapper, automatically exposing data as RESTful endpoints with built-in support for CRUD operations and schema-based request validation. The project distinguishes itself through a HATEOAS API engine that generates hypermedia links and resource schemas for dynamic client discovery. It also includes an automated Swagger documentation generator that produces interactive specifications for client SDK generation and testing. The framework provides a

    Provides native connectors to MongoDB for automated collection management and data serving.

    Python
    Auf GitHub ansehen↗6,738
  • hazelcast/hazelcastAvatar von hazelcast

    hazelcast/hazelcast

    6,570Auf GitHub ansehen↗

    Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis

    Integrates MongoDB collections into processing pipelines for batch or stream-based data ingestion.

    Javabig-datacachingdata-in-motion
    Auf GitHub ansehen↗6,570
  • chonkie-inc/chonkieAvatar von chonkie-inc

    chonkie-inc/chonkie

    4,170Auf GitHub ansehen↗

    Chonkie ist eine Text-Chunking-Bibliothek, die für Retrieval-Augmented-Generation-Pipelines (RAG) konzipiert wurde. Sie fungiert als semantischer Text-Splitter und RAG-Ingestion-Pipeline und transformiert Rohtext in eingebettete Segmente für die Speicherung in Vektordatenbanken. Das Projekt zeichnet sich durch spezialisierte Splitting-Strategien aus, einschließlich eines AST-basierten Code-Splitters zur Bewahrung logischer Grenzen im Quellcode und eines semantischen Text-Splitters, der Embedding-Modelle verwendet, um Grenzen basierend auf der Bedeutung zu bestimmen. Es bietet zudem einen Vektordatenbank-Ingestor, um die Generierung von Embeddings und deren Export in verschiedene Speicher zu automatisieren. Die Bibliothek deckt ein breites Spektrum an Funktionen ab, einschließlich Dokumenten-Parsing via OCR und Markdown-Extraktion, einer Vielzahl von Splitting-Methoden wie Token-Count und hierarchische Segmentierung sowie Workflow-Orchestrierung durch wiederverwendbare Pipelines. Sie unterstützt eine breite Palette an Vektorspeicher-Integrationen, einschließlich Qdrant, Milvus, Weaviate und Elasticsearch, sowie den Datenexport in JSON- und Hugging-Face-Datensätze. Nutzer können diese Operationen über eine Kommandozeilenschnittstelle ausführen oder das System als containerisierten API-Dienst bereitstellen.

    Writes processed text segments and embeddings into MongoDB NoSQL collections.

    Pythonaichonkiechunker
    Auf GitHub ansehen↗4,170
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Unter-Tags erkunden

  • MongoDB ExportersConnectors for writing processed document data into MongoDB databases and collections. **Distinct from MongoDB Connectors:** Distinct from MongoDB Connectors: focuses on data export/egress rather than ingestion.