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To implement the multimodal RAG solution, several key components are required, excluding the Dell Data Lakehouse storage and DDAE. The system leverages advanced machine learning models, vector database, and a robust development environment to facilitate seamless data interaction. The multimodal RAG process involves the use of pre-trained LLMs and embedding models such as CLIP for handling diverse data types, including text, images, audio, and video. Hugging Face is employed to access and fine-tune these models, while LangChain is used to orchestrate the integration and flow of data across components. A vector database, such as ChromaDB, is essential for storing and retrieving data embeddings, enabling efficient similarity search operations. Additionally, a framework like Streamlit is utilized for developing an interactive chatbot UI, allowing users to input queries and receive responses. The solution requires a high-performance computing environment with GPU support to handle model inference efficiently, ensuring quick and accurate responses to user queries.
Note: Users can choose any alternative VectorDBs or GraphDB solution for storing vector embeddings.
Software Components used in the validation:
Hardware Components used in the validation:
The detailed flow of the architecture is as follows: