Key insights

• ⚙️ Retrieval Augmented Generation (RAG) enhances large language models with real-world knowledge
• 🔍 RAG ensures AI operates with the same facts, history, and resources as the business's most seasoned employees
• 📊 RAG consists of three main components: vector embeddings, vector search, and augmentation of models with multiple inputs for generating cohesive answers
• 🔠 Embeddings allow representing the meaning behind words as numerical vectors for various types of data including text, images, audio, video, and code snippets
• 🔎 Vector search using embeddings enables smarter and scalable retrieval of relevant information
• 🔗 RAG augments LLMs with external knowledge base for comprehensive and up-to-date responses
• 🗂️ Using a collab notebook to extract, summarize, and store embeddings in a vector database
• 🌐 Multimodal rag technology combines information retrieval with generative large language models, showcasing its application in various industries

Q&A

• What is the application of multimodal rag technology?

  Multimodal rag technology combines information retrieval with generative large language models, showcasing its application in various industries such as automotive, technology, retail, and media/entertainment. It can analyze text, images, and other media formats to provide relevant answers and recommendations.

• How is a rag chain used in the context of RAG?

  The video demonstrates constructing a rag chain to perform multimodal search using embeddings and Vector database for Q&A, providing coherent responses from Gemini model to textual and image queries.

• What does the video demonstrate about storing and retrieving embeddings?

  The video discusses the process of storing and retrieving embeddings from a vector database using a collab notebook. It demonstrates extracting text, images, and tables from a PDF, summarizing the content, generating embeddings, and storing them in a vector database.

• What is Gemini 1.5 Pro and how is it used in RAG?

  Gemini 1.5 Pro is demonstrated for RAG usage with a new car example. It is a large language model that augments and generates human-readable responses using multimodal data, providing coherent answers from a multimodal search using embeddings and Vector database for Q&A.

• How does RAG incorporate multimodal data?

  RAG augments LLMs with an external knowledge base, incorporating multimodal data for comprehensive and up-to-date responses. Multimodal RAG can use text-based or multimodal embeddings for retrieval, depending on accuracy and information loss.

• How do embeddings and vector search work in RAG?

  Embeddings allow representing the meaning behind words as numerical vectors, which can be generated for various types of data including text, images, audio, video, and code snippets. Vector search uses embeddings for smarter and scalable retrieval of relevant information.

• What are the three main components of RAG?

  RAG consists of three main components: vector embeddings for capturing semantic meaning, vector search for retrieval, and augmentation of models with multiple inputs for generating cohesive answers.

• What is Retrieval Augmented Generation (RAG)?

  Retrieval Augmented Generation (RAG) enhances large language models with real-world knowledge from private or first-party sources. It ensures AI operates with the same facts, history, and resources as the business's most seasoned employees. RAG differs from fine-tuning or training new models, enabling rapid adaptation and highly focused responses.

• 00:08 Today's session discusses retrieval augmented generation (RAG) and its ability to enhance large language models with real-world knowledge, adapting to specific business needs and enabling highly focused responses.
• 05:42 Embeddings allow representing the meaning behind words as numerical vectors, and they can be generated for text, images, audio, video, and code snippets. Vector search using embeddings enables smarter and scalable retrieval of relevant information. Large language models like Gemini augment and generate human-readable responses using multimodal data.
• 11:29 RAG augments LLMs with external knowledge base, incorporating multimodal data for comprehensive responses. Multimodal RAG can use text-based or multimodal embeddings for retrieval, depending on accuracy and information loss. Gemini 1.5 Pro is demonstrated for rag usage with a new car example.
• 17:03 The video discusses the process of storing and retrieving embeddings from a vector database using a collab notebook. It demonstrates extracting text, images, and tables from a PDF, summarizing the content, generating embeddings, and storing them in a vector database.
• 22:40 A demonstration of constructing a rag chain to perform multimodal search using embeddings and Vector database for Q&A, providing coherent responses from Gemini model to textual and image queries.
• 28:29 A demonstration of multimodal rag technology that combines information retrieval with generative large language models, showcasing its application in various industries such as automotive, technology, retail, and media/entertainment. The technology can analyze text, images, and other media formats to provide relevant answers and recommendations.