DataLens

DataLens is a retrieval-augmented generation (RAG) application built for exploring uploaded documents through conversational search and grounded answers. The project combines document ingestion, indexing, retrieval, and chat into a single workflow-oriented product.

Project Overview

The goal of DataLens is to make document knowledge easier to work with once files have been uploaded. Instead of relying on keyword search alone, the system prepares content for retrieval and uses that retrieved context to support more focused question-answering across a document set.

Key Features

• Document upload and ingestion for preparing source material for later retrieval
• Knowledge indexing to turn uploaded content into a queryable document collection
• Retrieval-augmented chat for asking questions against indexed content
• Grounded answer flow designed around retrieved context rather than free-form response generation
• Document management workflow for iterating on indexing and knowledge exploration

Technical Highlights

Full-Stack RAG Architecture

The project is structured as a separate frontend and backend system, which makes it easier to evolve the user workflow independently from the retrieval and indexing pipeline. That split also reflects a more production-oriented architecture than a single-script demo.

Retrieval Pipeline Thinking

The most interesting part of the project is not only the chat interface, but the operational thinking around retrieval quality: ingestion, indexing state, query behavior, and the feedback loop needed to make document answers more useful and reliable.

AI Application Design

DataLens sits in the space between traditional software and applied AI systems. It is useful as a portfolio project because it shows an understanding of how retrieval, UX, and system behavior need to work together for RAG applications to feel practical.