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How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

Flipboard

During the embeddings experiment, the dataset was converted into embeddings, stored in a vector database, and then matched with the embeddings of the question to extract context. The generated query is then run against the database to fetch the relevant context. Based on the initial tests, this method showed great results.

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What are the Biggest Challenges with Migrating to Snowflake?

phData

Ensuring a seamless transition while accommodating business requirements, security considerations, and performance optimizations further complicates the information architecture setup during the migration process to Snowflake. Once the information architecture is created on paper, the work of implementing it can be equally challenging.

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Generative AI for agriculture: How Agmatix is improving agriculture with Amazon Bedrock

AWS Machine Learning Blog

Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.

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