Document-QA Assistant

Interactive document question-answering assistant on real-time tabular and textual data.

Architectural Role: Sole Developer

Verifiable Metrics & Performance Receipts

Embeddings:FAISS Vector Index

Pipeline:LangChain Orchestrator

Technology Stack & Libraries

StreamlitLangChainFAISSOpenAI APIPython

1. Problem Context & Objectives

Extracting factual answers from complex, multi-format enterprise PDF documents requires tedious manual search.

2. Technical Architecture & Implementation

Constructed interactive Streamlit QA system parsing unstructured documents into FAISS vector representations with conversational memory.

3. Outcomes, Benchmarks & Empirical Validation

Provides real-time citations and source attribution for uploaded enterprise documents.

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.

View repository README & installation guide on GitHub