Train Models Contrastively in Pytorch

Radient turns many data types (not just text) into vectors for similarity search, RAG, regression analysis, and more.

Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval

A sample app for the Multimodal Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power Q&A experiences.

[NAACL 2024] Official Implementation of paper "Self-Adaptive Sampling for Efficient Video Question Answering on Image--Text Models"

🚀 HAG: Next-Gen AI | Neo4j + Weaviate Fusion | Dual-Similarity Retrieval | 100% Local & Private | Graph Intelligence Meets Vector Search

🧠 Multimodal Retrieval-Augmented Generation that "weaves" together text and images seamlessly. 🪡

Self-adaptive Planning Agent。自适应规划代理的多模态检索增强生成技术。

📄 Multimodal RAG pipeline combining ColPALI visual retrieval, YOLO-DocLayNet layout detection, sentence embedding-based text retrieval, and LLaMA-4 completion for document question answering.

🔰 A Comprehensive RAG repository covering basic vanilla RAG techniques, advanced retrieval methods, hybrid search fusion approaches, hands-on reranking techniques with code + explanation 📚✨

Anaya is a Content Engine that specializes in analyzing and comparing multiple PDF documents. It uses Retrieval Augmented Generation (RAG) techniques to effectively retrieve, assess, and generate insights from the documents.

Repository for team Devs

Multimodal RAG and comparisons between language models. (Project for Deep Learning Module at the FHSWF)

A comprehensive Multimodal Retrieval-Augmented Generation (RAG) application that combines FastAPI backend with Streamlit frontend, supporting multiple AI models, advanced OCR capabilities, and intelligent document processing.

Multimodal RAG powered by Milvus, Visualized BGE model, and GPT-4o.

Multimodal RAG using Colsmolvlm in colab free-tier GPU

Multimodal RAG system for generating test cases and use cases from documents using hybrid retrieval, safety guards, and LLMs.

A robust, production-grade pipeline converting complex Medical PDFs into structured, RAG-ready JSONL datasets. Features smart table merging, multimodal extraction, and dynamic layout analysis using Detectron2 & PaddleOCR.