Train Models Contrastively in Pytorch
Agentic RAG for local and self-hosted document search: hybrid retrieval, reranking and multimodal RAG on embedded LanceDB, with Docling parsing and an MCP server
Radient turns many data types (not just text) into vectors for similarity search, RAG, regression analysis, and more.
EMKG: multimodal memory and retrieval for open-world object-goal navigation in dynamic environments.
Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval
Production inference for encoder models - ColBERT, GLiNER, ColPali, embeddings etc. - as vLLM plugins for online and in-process deployment
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.
Awesome Memory Papers in Vision-Language Models
Scientific paper figure generation Skill for agents: web research-figure conventions, controlled colour, topology planning, and image generation. 科研论文图生成 Skill：联网参考、配色与拓扑规划。
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.
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.
Local multimodal RAG for PDFs: MinerU, Jina CLIP, FAISS, BM25, BGE reranking and Ollama. Runs on your hardware via web and desktop; summaries, outlines and quizzes of any document.
🧠 Multimodal Retrieval-Augmented Generation that "weaves" together text and images seamlessly. 🪡
🔰 A Comprehensive RAG repository covering basic vanilla RAG techniques, advanced retrieval methods, hybrid search fusion approaches, hands-on reranking techniques with code + explanation 📚✨
[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
Official ICML 2026 implementation. A framework for training highly efficient list-wise multimodal rerankers for long documents.
OpenAI-compatible multimodal embedding server for Qwen3-VL-Embedding-2B — embed text, images, or both via a simple REST API.
[EMNLP 2025] M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework
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