Vector Insight Engine

Vector Insight Engine is a project aware AI memory system designed to solve a real pain point: information inside reports, research notes, and PDFs remains difficult to search, link, or reason over. Traditional chatbots can summarize, but they cannot tell you where their answers came from. This project ingests raw text or PDFs, breaks them into semantic chunks, and embeds them into Qdrant. When a user asks a question, the engine retrieves the most relevant evidence and asks Gemini to generate an answer grounded strictly in that context. The interface displays both the final answer and the supporting snippets, creating a transparent and verifiable reasoning chain. The result is a lightweight but powerful workflow for analysts, students, and teams working across multiple clients or topics. Each project becomes a persistent AI memory layer where documents are connected, searchable, and interpretable instead of static files. This system demonstrates practical RAG design, clean engineering, and real world usefulness beyond a typical chatbot demo.