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The RAG (Retrieval-Augmented Generation) Assistant is an innovative AI-driven tool designed to enhance information retrieval and response generation. By leveraging state-of-the-art vector databases and large language models (LLMs), the RAG Assistant provides users with comprehensive answers to their queries based on a curated knowledge base.
The RAG Assistant enables users to ask questions and receive detailed answers enriched with relevant context from a database of documents. Whether for educational purposes, research, or casual inquiry, this tool streamlines the process of obtaining information, ensuring that responses are both accurate and contextually relevant.
Document Loading: The assistant can load documents from a specified directory or utilize sample documents if no directory is found. These documents are chunked for efficient processing.
Vector Database Integration: Using the VectorDB class, the assistant stores and retrieves document chunks through a persistent client, employing ChromaDB to manage the underlying data.
Embedding Generation: The assistant utilizes Hugging Face's Sentence Transformers to convert text into embeddings, enabling semantic search capabilities.
Query Processing: When a user submits a question, the assistant searches the vector database for relevant document chunks. It compiles these chunks as context for generating a response.
Response Generation: The assistant employs a prompt template to frame the context and question, invoking the LLM (currently integrated with Groq) to generate a comprehensive answer.
User Interaction: The system supports an interactive query loop, allowing users to ask multiple questions in succession.
The RAG Assistant represents a significant advancement in AI-driven information retrieval. By combining the power of vector databases and large language models, it offers an intuitive and effective way to access information, making it an invaluable tool for students, researchers, and anyone seeking knowledge.