Advanced RAG Assistant – Production-Ready Document Intelligence System

Overview

Advanced RAG Assistant is a production-ready Retrieval-Augmented Generation (RAG) application that enables users to upload PDF documents and ask natural language questions to receive accurate, context-aware responses. Instead of relying solely on an LLM's internal knowledge, the system retrieves relevant information from uploaded documents before generating responses, improving factual accuracy and reducing hallucinations.

The project was designed with a production-first approach, focusing on efficient resource utilization, low-latency retrieval, and deployment under free-tier cloud constraints.

Problem Statement

Large Language Models struggle to answer questions about private or custom documents without additional context. Traditional RAG implementations often face challenges such as memory limitations, token overflows, slow document processing, and poor retrieval quality during deployment.

This project addresses these challenges by implementing a scalable and optimized RAG pipeline capable of delivering reliable document-based question answering.

Key Features

1.Upload and process PDF documents for intelligent question answering

2.Hybrid retrieval combining Semantic Search and BM25 Keyword Search

3.Query rewriting to improve retrieval accuracy

4.Lightweight reranking for production environments

5.Real-time streaming responses

6.Background document ingestion for faster user experience

7.Dynamic token control to prevent context overflow

8.Environment-aware architecture for local and cloud deployments

9.Source-aware responses for better transparency

Technology Stack

Backend

FastAPI

LangChain

ChromaDB

Groq (LLaMA 3)

Frontend

Streamlit

Retrieval

Semantic Embeddings

BM25 Keyword Search

Deployment

Docker

Render

Streamlit Cloud

System Workflow

1.Users upload a PDF document through the Streamlit interface.
2.The backend processes the document asynchronously.
3.Documents are chunked and converted into embeddings.
4.Chunks are stored in ChromaDB for semantic retrieval.
5.User queries are rewritten for better search quality.
6.Hybrid retrieval combines semantic search with BM25 keyword matching.
7.Relevant chunks are reranked and dynamically trimmed based on token limits.
8.Groq LLaMA 3 generates grounded responses along with supporting sources.

Performance Optimizations

1.To ensure smooth deployment on limited cloud resources, several optimizations were implemented:
2.Lazy loading of vector databases and embeddings
3.Dynamic context trimming to prevent token overflow
4.Chunk limiting for efficient retrieval
Background document ingestion
5.Lightweight embedding strategy for production
6.Environment-specific configuration for local and deployed environments

Challenges and Solutions

Memory Constraints

Optimized embeddings and lazy loading to operate within free-tier memory limits.

Token Overflow

Implemented dynamic context trimming and document limiting to prevent prompt size errors.

Slow Document Processing

Introduced asynchronous background ingestion to improve responsiveness.

Retrieval Accuracy

Combined semantic retrieval with BM25 and query rewriting for more relevant document retrieval.

Engineering Highlights

1.Designed a multi-stage Retrieval-Augmented Generation pipeline
2.Implemented hybrid retrieval using semantic search and BM25
3.Built an environment-aware architecture supporting both local development and cloud deployment
4.Developed dynamic token management to avoid LLM context overflow
5.Optimized the system to run efficiently on free-tier cloud infrastructure
6.Integrated streaming responses to improve user experience

Future Enhancements

1.Multi-document querying
Persistent chat history
2.Authentication and user management
RAG evaluation using RAGAS
3.Cross-encoder reranking for improved retrieval accuracy
4.Support for additional document formats

Project Outcome

This project demonstrates practical expertise in Generative AI, Retrieval-Augmented Generation, backend API development, vector databases, prompt engineering, and production deployment. It showcases the ability to design scalable AI systems while addressing real-world engineering challenges such as memory optimization, token management, and retrieval quality.