RAG & KNOWLEDGE BASE
Ask a question. Get an answer with sources.
A knowledge base that cites its work. Every answer traced back to the source.
Ask a question about Acme Corp, or try one of these:
Behind the Build ▼
This is a full Retrieval Augmented Generation (RAG) pipeline. Questions are first checked against pre-built expert answers, then filtered for off-topic content. Relevant queries enter the RAG pipeline: embedded using bge-base-en-v1.5 via Workers AI and compared against 25 pre-embedded knowledge base articles using cosine similarity. The top matches above 60% confidence are retrieved with source citations. Answers are generated by Gemma 4 (with Llama 3.1 fallback) grounded entirely in the retrieved article content. No hallucination: every answer is traceable to a source document.
A full RAG (Retrieval Augmented Generation) knowledge base for Acme Corp with 25 articles across 5 categories. Pre-built questions return instant expert answers. Free-text questions trigger the real-time RAG pipeline: embedding, vector search, reranking, and grounded generation. Each answer shows inline citations, source documents, and confidence scores based on actual semantic similarity. This is the same architecture used in production enterprise knowledge bases.