Title

AWS re:Invent 2023 - Rethinking your data stack: The future of AI apps and vector databases (DAT103)

Summary

The speaker is a co-founder of Weviate, an open-source vector database company founded in 2019.
Vector embeddings, which began gaining prominence around 2015, allow for a new way of searching data by numerical representation, enabling more relational and contextual search capabilities.
Initially, vector databases were primarily used for search and recommendation, but with the advent of generative models, new use cases have emerged.
Retrieval augmented generation is a new application where data retrieved from vector databases is used to generate outputs from generative models, exemplified by the launch of JetGPT.
The speaker introduces the concept of the "Rack Stack," which intertwines vector databases with generative and vectorization models.
Weviate integrates with AWS services like Bedrock and SageMaker, as well as open-source models from OpenAI and Cohere.
A new concept of generative feedback loops is discussed, where data is retrieved, processed by generative models, and then stored back into the database with new vector embeddings, enhancing search capabilities.
The speaker emphasizes the potential for vector databases to extend beyond language to images and multimodal data.
Weviate focuses on helping users build AI-native applications without needing in-depth knowledge of the underlying models or databases.
The speaker invites attendees to visit their booth (1620) for demos, training, and the chance to win prizes.

Vector embeddings represent a significant shift in how data can be searched and utilized, moving from keyword-based retrieval to contextually rich, relational searches.
The emergence of generative models has expanded the utility of vector databases, enabling new applications like retrieval augmented generation, which can enhance the capabilities of AI applications using proprietary data.
The concept of generative feedback loops suggests a future where AI systems can iteratively improve and expand their own datasets, potentially leading to more autonomous and intelligent systems.
The integration of vector databases with AWS services and open-source models indicates a trend towards more accessible and interoperable AI infrastructure, which can accelerate the development of AI-native applications.
The focus on user-friendly tools and educational resources reflects an industry-wide effort to democratize AI and make it accessible to a broader range of users, regardless of their technical expertise.