With Qwen3-Reranker-4B, you can turn your retrieval-augmented generation (RAG) system into a smarter assistant: it not only finds relevant documents but also reorders them based on their usefulness for your query. The model uses a cross-encoder that evaluates queries and passages together, ensuring more accurate answers with less noise.
Tag Archives: RAG
Supercharging Retrieval with Qwen and LlamaIndex: A Hands-On Guide
A step-by-step guide to building a retrieval-augmented generation (RAG) pipeline using Qwen3 and LlamaIndex, powered by regolo.ai/. Learn how to connect Qwen’s LLM and embedding models, build a document index, and create a smart query engine for scalable document analysis, search, and summarization – all with full control over your infrastructure.
Build a Chatbot using Regolo.ai and Flowise
Unlock the full potential of your AI projects by seamlessly integrating regolo.ai/ with Flowise! Discover how to effortlessly set up a powerful, interactive chatflow that transforms your documentation handling and automation processes. With just a few clicks, harness the capabilities of advanced language models, streamline your workflow, and achieve precise, reliable interactions tailored to your specific needs.
How to Use the CheshireCat Plugin with regolo.ai
Learn how to install and configure the CheshireCat plugin for regolo.ai/, enabling seamless use of Regolo’s AI models for conversations, embeddings, and advanced workflows directly within the CheshireCat environment.