Document-QA Assistant
Interactive document question-answering assistant on real-time tabular and textual data.
1. Problem Context & Objectives
Finding an answer inside a long, multi-format enterprise document is tedious manual work — scanning pages for a single figure, table, or clause.
2. Technical Architecture & Implementation
Built an interactive Streamlit QA system that parses unstructured documents into FAISS vector representations, keeps conversational memory, and returns answers with source attribution.
3. Outcomes, Benchmarks & Empirical Validation
Turned document search into conversation: upload a document, ask a question, and get a cited answer in seconds instead of scanning for minutes — an early, clean demonstration of the RAG pattern the research later formalizes.
Source Code Inspection & Local Replication
This system is maintained in an open-source repository with full git commit history, environment configuration templates (.env.example), and dependency manifests.