RT_RAG

RT_RAG is a Retrieval-Augmented Generation (RAG) system designed to perform real-time information retrieval and question-answering based on specific document datasets. It combines advanced search capabilities with large language models to provide accurate, context-aware responses to user queries.

The system acts as an intelligent document assistant that can 'read' and discuss a collection of text data. It takes raw text data and breaks it down into smaller, manageable segments or 'chunks' to ensure the AI can process information efficiently without losing context. Unlike traditional search engines that look for exact keywords, this project converts text into mathematical vectors to understand the meaning behind words, allowing it to find relevant information even if the user uses different terminology than the source document.

When a user asks a question, the system retrieves the most relevant portions of the uploaded documents and feeds them to a language model. The AI then generates an answer based strictly on the retrieved facts, reducing the likelihood of 'hallucinations' or false information. The system is designed to handle queries and return evidence-backed answers quickly, making it suitable for exploring large technical manuals, research papers, or internal knowledge bases.

The project is implemented using a specialized AI development stack focused on high-speed retrieval and language generation. The core logic is developed in Jupyter Notebook, allowing for step-by-step data processing, model testing, and visualization. It utilizes powerful pre-trained language models from providers like Google or OpenAI to interpret queries and synthesize final answers.

The project uses the LangChain library to manage the complex workflow between document loading, vector storage, and the language model. A specialized vector database (ChromaDB) is used to store and index the processed document chunks, enabling lightning-fast similarity searches. Advanced embedding models are used to transform text into high-dimensional numerical data, which is the foundation for the system's 'understanding' of content similarity.

• Developed a Retrieval-Augmented Generation system for real-time information retrieval and question-answering
• Implemented document ingestion and processing system breaking text into manageable chunks
• Built semantic search using vector embeddings to understand meaning rather than exact keyword matching
• Created contextual question answering system generating evidence-backed answers from retrieved documents
• Designed real-time interaction system handling queries and returning answers quickly
• Integrated LangChain framework for managing complex workflow between document loading and language models
• Utilized ChromaDB vector database for lightning-fast similarity searches across document chunks
• Implemented advanced embedding models to transform text into high-dimensional numerical representations
• Developed in Jupyter Notebook environment for interactive data processing and model testing
• Enabled exploration of large technical manuals, research papers, and knowledge bases with accurate responses

Document Processing

Document ingestion and processing system breaking text into manageable chunks for efficient AI processing

Semantic Search

Vector-based semantic search converting text to mathematical vectors for meaning-based information retrieval

Question Answering

Contextual question answering system retrieving relevant document portions and generating evidence-backed answers