Train Models Contrastively in Pytorch
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Updated
Mar 26, 2025 - Python
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
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. 🪡
[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
Build sovereign RAG systems with MAS‑RAG, Dual‑RAG, GraphRAG, Spatial‑RAG, multimodal pipelines, and vector search directly inside Oracle AI Database 26ai and Exadata.
🔰 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
Local-first multimodal Graph RAG with Qwen3-VL embeddings, Neo4j vector search, and a lightweight visual retrieval console.
30 production-focused examples: covering RAG patterns, hybrid search, routing, agentic RAG, knowledge graphs, multimodal RAG, and debugging.
LumiCite is a multimodal RAG system for academic papers, designed for multimodal evidence retrieval and citation-aware question answering.
Vision-first multimodal RAG pipeline that extracts structured knowledge (formulas, tables, diagrams) from PDFs using GPT-4o Vision and enables hybrid search (semantic + BM25 + section-aware) with PostgreSQL + pgvector.
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