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.

Production inference for encoder models - ColBERT, GLiNER, ColPali, embeddings etc. - as vLLM plugins for online and in-process deployment

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

High-performance late-interaction retrieval engine for on-prem AI. ColBERT/ColPali multi-vector search with Rust fused MaxSim, Triton GPU kernels, ROQ quantization, LEMUR routing, WAL-backed CRUD, and a FastAPI server — single machine, CPU or GPU.

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

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

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

OpenAI-compatible multimodal embedding server for Qwen3-VL-Embedding-2B — embed text, images, or both via a simple REST API.

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

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.

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

Repository for team Devs

LumiCite is a multimodal RAG system for academic papers, designed for multimodal evidence retrieval and citation-aware question answering.

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

Local-first multimodal Graph RAG with Qwen3-VL embeddings, Neo4j vector search, and a lightweight visual retrieval console.

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.

A doctor-assistive AI system that interprets medical knowledge and patient images simultaneously. It utilizes a Dual-Encoder architecture to cross-reference textbook theory with visual pathology, generating clinically grounded diagnoses.