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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

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Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning - AI

It uses the provided conversation history, action groups, and knowledge bases to understand the context and determine the necessary tasks. This is based on the instructions that are interpreted by the assistant as per the system prompt and user’s input. Additionally, you can access device historical data or device metrics.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning - AI

To create AI assistants that are capable of having discussions grounded in specialized enterprise knowledge, we need to connect these powerful but generic LLMs to internal knowledge bases of documents. Then we introduce you to a more versatile architecture that overcomes these limitations.

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Boost employee productivity with automated meeting summaries using Amazon Transcribe, Amazon SageMaker, and LLMs from Hugging Face

AWS Machine Learning - AI

For a generative AI powered Live Meeting Assistant that creates post call summaries, but also provides live transcripts, translations, and contextual assistance based on your own company knowledge base, see our new LMA solution. Transcripts are then stored in the project’s S3 bucket under /transcriptions/TranscribeOutput/.

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The Five Pillars of AWS Well-Architected Framework?—?Understanding What Makes the Cloud Secure

Cloud Conformity

The framework underpins our entire platform and forms our Knowledge Base to ensure your cloud infrastructure is the most resilient, secure and efficient for your needs. Not only does it involve recovery from failure or service disruptions, but it also includes the issue of capacity management and scalability.

AWS 40
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The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype

AWS Machine Learning - AI

We use AWS Lambda as our orchestration function responsible for interacting with various data sources, LLMs and error correction based on the user query. The following figure illustrates this architecture. The following figures shows the step-by-step procedure of how a query is processed for the text-to-SQL pipeline.