Built Powerful Multimodal RAG using Vertex AI(GCP), AstraDb and Langchain #rag #ai

AI Data Scientist Expert

The integration of multimodal embeddings into retrieval-augmented generation systems signifies a pivotal trend in AI development. By leveraging diverse data inputs, AI models can enhance their contextual understanding and output quality. For instance, using image embeddings alongside traditional text data can significantly improve the relevance of responses generated by language models. This approach allows for more dynamic interactions that cater to user needs more effectively, ultimately promoting a versatile application of AI in diverse fields.

AI Ethics and Governance Expert

As the deployment of AI systems, particularly those using multimodal embeddings, increases, ethical considerations around data usage and fairness must be prioritized. Ensuring that AI models do not inadvertently reinforce biases through the data they retrieve and generate is crucial. This necessitates robust governance frameworks that oversee the application of AI technologies, particularly in consumer-facing platforms where the implications of erroneous outputs could have significant consequences.

Retrieval-Augmented Generation (RAG)

Discussed in the context of building a multimodel system using various AI models.

Multimodal Embedding

It is integral to the AI application's capability to process and respond to user queries effectively.

Vector Database

Mentioned as a crucial component for conducting similarity searches efficiently.

Vex AI

Used extensively in the video to provide the computational backbone for the multimodel RAG system.

Mentions: 5

Astra DB

Contextualized in the video as the foundation for storing embedding data.

Mentions: 4