Featured AI Engineering Project
Enterprise RAG Assistant
An end-to-end Retrieval-Augmented Generation application that enables users to ask natural-language questions across multiple PDF documents using semantic retrieval and context-grounded LLM responses.
The Challenge
The Problem
Organizations often maintain large collections of policies, manuals, handbooks, and internal documents in PDF format. Finding specific information across these documents using traditional keyword search can be slow and inefficient.
The goal of this project was to build an AI-powered knowledge assistant that allows users to ask questions in natural language and receive relevant answers grounded in the content of their documents.
Architecture
System Architecture
The application follows a modular Retrieval-Augmented Generation workflow. Documents are processed into searchable vector representations, while user questions are embedded and matched against the most relevant document chunks before the final answer is generated by the language model.
1
PDF Documents
Multiple source documents
2
Chunking
Text split with overlap
3
Embeddings
Semantic vector representation
4
FAISS
Vector storage and retrieval
5
Semantic Search
Retrieve Top-K relevant chunks
6
LLM Answer
Grounded response generation
RAG Workflow
How It Works
01 — Document Ingestion
Multiple PDF documents are loaded and their textual content is extracted along with source and page metadata.
02 — Intelligent Chunking
Extracted text is divided into manageable chunks with overlap. Word-boundary handling helps prevent text from being split at inappropriate positions.
03 — Embedding Generation
Each chunk is converted into a numerical vector representation using an embedding model, allowing semantic similarity to be measured between documents and user questions.
04 — Vector Storage & Retrieval
Embeddings are stored in a FAISS vector index. When a question is submitted, the system performs semantic search and retrieves the Top-K most relevant document chunks.
05 — Context-Grounded Generation
The retrieved chunks are assembled as context and passed to the language model together with the user's question, enabling the model to generate an answer grounded in the source documents.
Engineering Choices
Technology Stack
Core Technologies
AI Concepts
Built From First Principles
Instead of starting with a high-level RAG framework, the retrieval pipeline was implemented directly using Python, OpenAI embeddings, and FAISS. This provided hands-on understanding of document loading, chunking, vector representation, similarity search, retrieval, and context construction before introducing abstraction frameworks such as LangChain or LangGraph.
Implementation
Key Features
Multi-PDF Knowledge Base
Processes multiple PDF documents while preserving source and page metadata for retrieved content.
Intelligent Chunking
Uses configurable chunk size, overlap, and word-boundary handling to create retrieval-ready document segments.
Semantic Retrieval
Converts questions into embeddings and retrieves the most semantically relevant document chunks using FAISS.
Persistent Vector Index
Saves the FAISS index and associated metadata so the knowledge base can be reused without rebuilding it for every session.
Grounded LLM Responses
Supplies retrieved document context to the language model before generating an answer to the user's question.
Streamlit Interface
Provides an interactive interface for building the knowledge base and asking questions across uploaded documents.
Explore the Project
View the complete source code, project structure, implementation details, and setup instructions on GitHub.