Genora AI - Intelligent Knowledge System

Abstract

Knowledge management systems play a critical role in organizational efficiency and decision-making. Traditional document retrieval approaches often struggle with semantic understanding and contextual relevance. This work presents Genora AI, an intelligent knowledge management system that leverages large language models, vector embeddings, and retrieval-augmented generation (RAG) to provide contextually relevant answers to user queries. The system processes diverse document formats, builds semantic knowledge bases, and delivers accurate responses through natural language interaction. Experimental results demonstrate significant improvements in query response accuracy and user satisfaction compared to conventional keyword-based search systems.

1. Introduction

1.1 Context and Motivation

Modern organizations generate vast amounts of textual information across multiple formats and platforms. Traditional search systems rely on keyword matching and Boolean queries, which often fail to capture semantic meaning and contextual relationships between concepts. This limitation leads to information silos, reduced productivity, and suboptimal decision-making processes.

Recent advances in natural language processing, particularly large language models (LLMs) and transformer architectures, have enabled new approaches to information retrieval and question answering. Vector embeddings capture semantic similarities between documents and queries, while retrieval-augmented generation combines the power of neural language models with relevant document retrieval.

Genora AI addresses these challenges by creating an intelligent knowledge management platform that understands natural language queries, retrieves semantically relevant information, and generates coherent, contextually appropriate responses. The system serves as an intelligent assistant that can process complex questions and provide actionable insights from organizational knowledge bases.

1.2 Objectives of the Study

The primary objectives of this work are to design and implement an AI-powered knowledge management system that:

• Processes and indexes diverse document formats including PDFs, text files, and web content
• Builds semantic vector representations of knowledge for intelligent retrieval
• Provides natural language query interfaces for intuitive user interaction
• Generates accurate, contextually relevant responses using retrieval-augmented generation
• Maintains knowledge base consistency and supports real-time updates

1.3 Contributions of the Work

• A comprehensive RAG-based architecture for intelligent document processing and query answering
• Integration of multiple document processing pipelines with semantic embedding techniques
• Natural language interface enabling conversational interaction with knowledge bases
• Scalable system architecture supporting enterprise-level knowledge management

2. Related Work

2.1 Knowledge Management Systems

Traditional knowledge management systems focus on document storage, categorization, and keyword-based retrieval. While effective for structured data, these approaches struggle with unstructured text and semantic understanding. Modern enterprise search systems have incorporated machine learning techniques but often lack the contextual awareness needed for complex queries.

2.2 Retrieval-Augmented Generation

RAG architectures combine the strengths of information retrieval systems with generative language models. By retrieving relevant documents and using them as context for text generation, RAG systems can provide more accurate and factually grounded responses compared to standalone language models. This approach has shown significant success in question-answering tasks and knowledge-intensive applications.

2.3 Vector Embeddings and Semantic Search

Vector embeddings represent text as dense numerical vectors in high-dimensional spaces, enabling semantic similarity computation. Modern embedding models trained on large corpora can capture complex linguistic relationships and domain-specific knowledge. Vector databases provide efficient storage and retrieval of embeddings, enabling rapid semantic search across large document collections.

3. System Architecture

3.1 Document Processing Pipeline

The document processing pipeline handles multiple input formats including PDFs, text documents, and web content. Documents are parsed, chunked into manageable segments, and processed through embedding models to create vector representations. This pipeline ensures consistent processing regardless of input format while preserving semantic relationships.

3.2 Vector Database and Indexing

Processed document segments are stored in a vector database optimized for similarity search. The indexing system maintains metadata associations and enables rapid retrieval of relevant documents based on semantic similarity scores. The database supports incremental updates and maintains consistency across document collections.

3.3 Query Processing and Response Generation

User queries are processed through the same embedding pipeline to create query vectors. The system retrieves the most semantically similar document segments and provides them as context to a language model for response generation. This RAG approach ensures responses are grounded in the available knowledge base while maintaining natural language fluency.

4. Implementation and Results

Genora AI was implemented using modern NLP frameworks and deployed as a scalable web application. The system demonstrates high accuracy in question answering tasks and provides intuitive natural language interaction capabilities. User studies indicate significant improvements in information retrieval efficiency compared to traditional search systems.

The RAG architecture enables the system to provide factually accurate responses while maintaining the flexibility to handle diverse query types. The vector-based retrieval system ensures relevant context is always available for response generation, leading to more helpful and actionable answers.

Technology Stack