awesome-repositories.com
المدونة
awesome-repositories.com

اكتشف أفضل مستودعات المصادر المفتوحة باستخدام بحث مدعوم بالذكاء الاصطناعي.

استكشفعمليات بحث منسقةبدائل مفتوحة المصدربرمجيات ذاتية الاستضافةالمدونةخريطة الموقع
المشروعحولكيفية ترتيب النتائجالصحافةخادم MCP
قانونيالخصوصيةالشروط
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

4 مستودعات

Awesome GitHub RepositoriesEmbedding Generation

Generation of numerical vector representations from document content for semantic analysis.

Distinct from Document Extraction Tools: Focuses on creating feature vectors (embeddings) rather than just extracting structured text or fields.

Explore 4 awesome GitHub repositories matching data & databases · Embedding Generation. Refine with filters or upvote what's useful.

Awesome Embedding Generation GitHub Repositories

اعثر على أفضل المستودعات باستخدام الذكاء الاصطناعي.سنبحث عن أفضل المستودعات المطابقة باستخدام الذكاء الاصطناعي.
  • autogluon/autogluonالصورة الرمزية لـ autogluon

    autogluon/autogluon

    9,997عرض على GitHub↗

    AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

    Provides the ability to generate N-dimensional feature representations of documents for downstream similarity searches.

    Pythonautogluonautomated-machine-learningautoml
    عرض على GitHub↗9,997
  • kreuzberg-dev/kreuzbergالصورة الرمزية لـ kreuzberg-dev

    kreuzberg-dev/kreuzberg

    8,527عرض على GitHub↗

    Kreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into clean, structured text and metadata. It is built around a compiled Rust core that can be used as a native library, a command-line tool, a REST API server, or a WebAssembly module for browser-based processing. The system is designed to run entirely on self-hosted infrastructure, with no data leaving the user's environment. What distinguishes Kreuzberg is its breadth of integration surfaces and its pipeline architecture. It exposes extraction capabilities through native bindings fo

    Creates vector embeddings from document content using built-in ONNX models.

    Rustdocument-intelligenceelixirffi
    عرض على GitHub↗8,527
  • tkipf/gcnالصورة الرمزية لـ tkipf

    tkipf/gcn

    7,361عرض على GitHub↗

    This is a graph convolutional network library designed for performing node and graph classification on graph-structured data. It functions as a framework for generating graph embeddings and implementing spectral convolutional neural networks to predict labels for nodes and entire graph structures. The library provides specialized tools for spectral graph convolutions, utilizing Chebyshev polynomial approximations to perform feature aggregation. It includes a multi-graph processing framework that manages batches of different graph instances through block-diagonal adjacency matrices and pooling

    Generates low-dimensional vector representations of nodes based on their structural connectivity within a graph.

    Python
    عرض على GitHub↗7,361
  • a16z-infra/ai-getting-startedالصورة الرمزية لـ a16z-infra

    a16z-infra/ai-getting-started

    4,133عرض على GitHub↗

    This project is an artificial intelligence application starter kit and cloud deployment framework. It provides a pre-configured foundation for building AI applications, featuring integrated authentication, orchestration, and vector database connectivity. The framework serves as an implementation template for retrieval augmented generation systems. It includes a pipeline to convert markdown documents into vector embeddings and store them in a database to enable question-and-answer functionality. The system covers the coordination of communication between user interfaces and backend AI service

    Generates numerical vector representations from markdown document content to power question-and-answer systems.

    TypeScript
    عرض على GitHub↗4,133
  1. Home
  2. Data & Databases
  3. Document Extraction Tools
  4. Embedding Generation