This project presents a Retrieval Augmented Generation (RAG) based AI Knowledge Assistant designed to provide accurate, context-aware responses using external knowledge sources. The system combines vector-based document retrieval with a Large Language Model (LLM) to answer user queries based on domain-specific documents rather than relying solely on pretrained model knowledge. This approach improves response accuracy, reduces hallucinations, and enables real-world applications such as document assistants, internal knowledge bases, and AI-powered support systems.
The system follows a Retrieval Augmented Generation pipeline. First, documents are collected and preprocessed by splitting them into smaller text chunks. Each chunk is converted into vector embeddings using an embedding model and stored in a vector database. When a user submits a query, the query is embedded and compared against stored vectors to retrieve the most relevant document segments. These retrieved contexts are then passed to a Large Language Model, which generates a final response grounded in the retrieved information. This architecture ensures that responses are accurate, relevant, and based on verified data sources.
The AI Knowledge Assistant successfully generates accurate and context-aware answers by leveraging document-based retrieval. Compared to traditional LLM-only responses, the RAG-based system demonstrates improved relevance and reduced hallucination. The project validates the effectiveness of combining vector databases with large language models for real-world knowledge-intensive applications.