Enterprise ServiceNow instances accumulate large volumes of documents, making precise, timely retrieval difficult through keyword search alone. A Retrieval-Augmented Generation (RAG) approach—combining embeddings, a vector store, semantic search, and a large language model (LLM)—enables users to upload a document and ask natural-language questions grounded in its content. This paper describes an end-to-end implementation using ServiceNow Virtual Agent (VA) for interaction, a PDF-to-text extraction step, Gemini-AI embeddings for vectorization, Qdrant as the vector database, and an Gemini- AI chat model for answer generation, following an eight-step pipeline from document capture to response delivery . We also propose an evaluation framework, borrowing the “confusion matrix as foundation” measurement mindset from the provided demo paper , and adapt it to RAG quality, faithfulness, and operational performance.
ServiceNow, Virtual Agent, RAG, embeddings, semantic search, vector database, Qdrant, Gemini-AI
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