This project presents a practical implementation of a Retrieval-Augmented Generation (RAG)–based AI assistant designed to answer user queries using custom document collections. The system combines document retrieval with large language models (LLMs) to generate context-aware and grounded responses.
The assistant loads domain-specific documents, processes them into searchable text chunks, and stores vector embeddings in a vector database. When a user submits a query, the system retrieves the most relevant document chunks and uses them as contextual input for the language model to generate accurate and informative answers.
This project serves as a hands-on introduction to RAG architecture and agentic AI concepts, focusing on real-world implementation using Python, vector databases, embeddings, and LLM frameworks. The resulting system demonstrates how retrieval-enhanced AI assistants can reduce hallucinations and improve response relevance when working with private or domain-specific data.
The system follows a standard Retrieval-Augmented Generation (RAG) pipeline, implemented in a modular and extensible manner.
Text documents are stored in the data/ directory and loaded into the system at runtime. Each document is read from disk and represented with its content and metadata, enabling traceability during retrieval.
To improve retrieval accuracy, documents are split into smaller text chunks. Chunking is performed using a fixed character-length strategy, ensuring that each chunk remains semantically meaningful while fitting within embedding model constraints.
Each text chunk is converted into a numerical vector using a pre-trained embedding model. These embeddings capture semantic meaning and enable similarity-based search.
All embeddings, along with their associated text and metadata, are stored in a vector database (ChromaDB). This allows efficient similarity search across large document collections.
When a user submits a query, the query is embedded using the same embedding model. The vector database is then queried to retrieve the most relevant document chunks based on semantic similarity.
A prompt template is designed to combine the retrieved context with the user’s question. The prompt explicitly instructs the language model to rely on the provided context when generating responses.
The final response is generated by passing the constructed prompt to a large language model. The output includes both the generated answer and the supporting retrieved context, ensuring transparency and explainability.
The implemented RAG-based AI assistant successfully answers user queries using information retrieved from custom documents. Testing showed that the system is able to identify relevant document sections and generate responses that remain grounded in the provided data.
Compared to a standard LLM without retrieval, the RAG approach significantly improves factual accuracy and reduces hallucinated responses, especially for domain-specific questions. The modular architecture also allows easy replacement of components such as the embedding model, vector database, or language model.
Overall, the project demonstrates the effectiveness of Retrieval-Augmented Generation as a practical solution for building document-aware AI assistants and provides a strong foundation for more advanced agentic AI systems.