The multimodal Retrieval-Augmented Generation (RAG) system integrates several advanced AI technologies to provide highly accurate and contextually relevant responses. It utilizes LLaMA 3.3 70B, a powerful instruction-tuned model, to generate human-like responses to user queries. The system employs Weaviate, a vector database, for efficient management and retrieval of multimodal content. Ollama embeddings are used to process various content types, including text, images, audio, and tables, ensuring the system can handle diverse data formats. Additionally, Nomic-Embed-Text embeddings transform textual data into dense vector representations, enabling precise searches. The inclusion of LLaVA-v1.6 enhances the model's ability to synthesize multimodal data, providing enriched responses based on context from different sources.

System Design of Multimodal Rag

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