4 مستودعات
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.
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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.
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.
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.
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.