UltraRAG 2.0: Less Code, Lower Barrier, Faster Deployment! MCP-based low-code RAG framework, enabling researchers to build complex pipelines to creative innovation.

A demonstration of hybrid search with reranking using Qdrant and BGE-M3 model. A showcase of dense and sparse retrieval combined with ColBERT reranking for optimal search results

LLM API Server , OpenAI 同时支持 ChatGLM3 ，Llama, Llama-3, Firefunction, Openfunctions ，BAAI/bge-m3 ,bge-large-zh-v1.5

ONNX implementation of the BGE-M3 multilingual embedding model and tokenizer with native C#, Java, and Python implementations. Generates all three embedding types: dense, sparse, and ColBERT vectors.

Example application for using the BGE-M3 embedding model and Google's Gemma-2-9B-Instruct generation model in a LangChain-based RAG pipeline to answer Lord of the Rings trivia questions

Chinese Fake News Detection based on MacBERT

Train a pooling Head for Dense Embeddings

Herramientas para procesar datos de desaparecidos: anonimización de narrativas con IA. Búsqueda semántica de casos similares mediante embeddings de texto (BGE-M3).

Telegram chatbot FAQ untuk membantu mahasiswa Universitas Lampung mengakses informasi Siakadu menggunakan semantic search (BGE-M3 + FAISS).

Dự án MVP “Interior Design Agent” – trợ lý AI tư vấn thiết kế phòng làm việc dựa trên Mastra (Agent + RAG), LLM gpt-oss, embedding bge-m3 và vector store Chroma.