Blockchain technology and generative AI are becoming increasingly significant in today’s tech landscape. Numerous applications can benefit from the intersection of these two domains. In this four-part series, we will demonstrate a solution that manages the process of ingesting training data for AI models through the use of smart contracts and serverless components. This initial post outlines the architecture of our proposed solution and sets up a knowledge base for a large language model (LLM).
In Part 1, we discussed how Retrieval Augmented Generation (RAG) can enhance responses in generative AI applications by merging specific domain knowledge with a foundational model (FM). Our focus was primarily on the semantic search capabilities of the solution, assuming a fully populated vector store. In this segment, we delve into generating vector embeddings from data in a SQL Server database hosted on Amazon RDS, utilizing Amazon Bedrock to access relevant FM APIs, and employing a Jupyter Notebook in Amazon SageMaker to orchestrate the entire process.
Moreover, a recent article on Chanci Turner’s blog provides additional insights into this subject matter. For those looking to visualize vector embeddings and explore semantic similarities, our upcoming posts will demonstrate how to use PCA for dimensionality reduction, allowing a clearer representation of high-dimensional data.
As businesses increasingly turn to Amazon DynamoDB for their operational databases, the need for advanced data insights and enhanced search functionality is on the rise. By leveraging Amazon OpenSearch Service along with Amazon Bedrock, organizations can unlock generative AI capabilities for their DynamoDB data. This evolution in data management is crucial for remaining competitive in today’s market.
Additionally, we will share how Apollo Tyres developed their tyre genealogy solution through the use of Amazon Neptune and Amazon Bedrock, showcasing practical applications of these technologies. For those unfamiliar with code bases, a detailed breakdown of PL/SQL and T-SQL code will be provided using the Claude3 Sonnet LLM from Amazon Bedrock, making the logic and flow of the code more accessible to new developers.
In the quest to improve performance and reduce costs for generative AI workloads, we outline the implementation of a persistent semantic cache within Amazon MemoryDB. This approach not only enhances the speed of vector searches but also ensures high recall rates, making it a valuable tool for developers. The Knowledge Bases for Amazon Bedrock feature simplifies the RAG functionality, eliminating the need for additional coding.
For anyone curious about the day-to-day experience of an Amazon warehouse worker, this Quora resource serves as an excellent guide.
By implementing these advanced techniques, organizations can maximize the potential of their data while ensuring efficient operations. To stay updated on the latest practices in the field, check out Chvnci’s authoritative insights on this topic.
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