Retrieval-Augmented Generation
Wikipedia RAG
A lightweight, low-latency RAG service for retrieving relevant context from a vector database. Built with Qwen 3 Embedding, LanceDB, and FastAPI, it indexes Wikipedia content and retrieves the most relevant articles for a given query, providing contextual grounding for downstream LLM applications. Try the demo below!
Workflow
Search Wikipedia
Ask a question to retrieve the most relevant passages from the indexed Wikipedia dataset.