Business challenge
- People need answers from long documents quickly
- Manual reading takes time
- Generic AI may answer without document context
Case Study
AI Document Assistant
Document upload and question-answering system using RAG.
PythonDjangoLangChainVector DBLLM API
What was built
- Designed upload-to-chat flow
- Prepared embeddings and vector DB architecture
- Built chatbot UI concept with citations-ready layout
Key functionality
- Upload PDF/Word/Text files
- Ask questions from uploaded content
- Vector search based retrieval
- Chat-style interface
Architecture Thinking
How I would build this production-ready
The production version should include authentication, role-based access, environment-based configuration, audit logs, database backups, CI/CD pipeline, and deployment monitoring. For AI-enabled projects, I would add document parsing, chunking, embeddings, vector search, prompt templates, and guardrails around user access.
LessManual work
BetterWorkflow visibility
ReadyFor cloud deployment