Certifiedunder the RAG-Based AI Assistant module in the Agentic AI Essentials program.Certifiedunder the RAG-Based AI Assistant module in the Agentic AI Essentials program.
This project implements a Retrieval-Augmented Generation (RAG) chat assistant that allows users to upload documents and interact with them conversationally through a professional interface. Each document maintains its own persistent chat history, ensuring context isolation and accurate retrieval. The system combines vector search, metadata-filtered retrieval, and LLM-based reasoning to deliver grounded, document-specific answers through a clean web UI.
The purpose of this project is to demonstrate a clean, reliable, and user-centric RAG system that allows users to:
The system is intentionally scoped to single-document conversations to maximize correctness and interpretability.
Core Techniques:
Many RAG demos fail in real usage due to:
This project addresses those issues by aligning backend architecture, retrieval logic, and UI design around clarity and determinism.
Category:
🔹 AI Application / System Design
This is a full-stack AI system, not just a model demo. It showcases practical integration of LLMs into a real user-facing workflow.
Instead of supporting unrestricted multi-file chat, this project deliberately limits conversations to one document at a time.
Benefits:
This mirrors how professional document assistants are often designed in enterprise settings.
file metadatafile = selected_file)This ensures answers remain relevant, concise, and grounded.
The system prompt enforces:
This significantly reduces hallucinations and improves reliability.
FileChatMessageHistoryThis design mirrors scoped memory in professional AI tools.
Every UI element reflects the system’s internal state.
These checks improve robustness and user trust.
These are intentional design trade-offs to maintain clarity and correctness.
This section ensures reproducibility and usability, which are essential for both academic rigor and open-source collaboration. It allows reviewers, users, and contributors to easily install, run, and evaluate the system.
The project is implemented in Python 3.10+ and follows a modular, service-oriented architecture.
Steps to install and run the system:
git clone <repository-url> cd <project-directory>
2.Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
3.install dependencies:
pip install -r requirements.txt
4.Configure environment variables:
set GROQ_API_KEY for llm inference
start the FastAPI server:
uvicorn app:app --reload
This project demonstrates how to build a production-minded RAG assistant by:
A good RAG system is defined not by how much it answers, but by how well it knows its limits.