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Unbundling the Graph in GraphRAG

O'Reilly Media

Store these chunks in a vector database, indexed by their embedding vectors. The various flavors of RAG borrow from recommender systems practices, such as the use of vector databases and embeddings. Here’s a simple rough sketch of RAG: Start with a collection of documents about a domain. Split each document into chunks.

Database 127
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Build a dynamic, role-based AI agent using Amazon Bedrock inline agents

AWS Machine Learning Blog

To understand how this dynamic role-based functionality works under the hood, lets examine the following system architecture diagram. As shown in preceding architecture diagram, the system works as follows: The end-user logs in and is identified as either a manager or an employee. Nitin Eusebius is a Sr.

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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning Blog

Solution overview For our custom multimodal chat assistant, we start by creating a vector database of relevant text documents that will be used to answer user queries. This script can be acquired directly from Amazon S3 using aws s3 cp s3://aws-blogs-artifacts-public/artifacts/ML-16363/deploy.sh. us-east-1 or bash deploy.sh

AWS 124
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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.

SQL 168
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

It was built using a combination of in-house and external cloud services on Microsoft Azure for large language models (LLMs), Pinecone for vectorized databases, and Amazon Elastic Compute Cloud (Amazon EC2) for embeddings. This integrated workflow provides efficient query processing while maintaining response quality and system reliability.

AWS 68
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Automating product description generation with Amazon Bedrock

AWS Machine Learning Blog

The system architecture comprises several core components: UI portal – This is the user interface (UI) designed for vendors to upload product images. Product database – The central repository stores vendor products, images, labels, and generated descriptions. This could be any database of your choice.

AWS 116
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10 industries that use distributed computing

IBM Journey to AI blog

Computing Computing is being dominated by major revolutions in artificial intelligence (AI) and machine learning (ML). The algorithms that empower AI and ML require large volumes of training data, in addition to strong and steady amounts of processing power. Relational databases put all workers on the same page instantly.