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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

This post assesses two primary approaches for developing AI assistants: using managed services such as Agents for Amazon Bedrock , and employing open source technologies like LangChain. It uses the provided conversation history, action groups, and knowledge bases to understand the context and determine the necessary tasks.

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Build generative AI agents with Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain

AWS Machine Learning - AI

Amazon Lex supplies the natural language understanding (NLU) and natural language processing (NLP) interface for the open source LangChain conversational agent embedded within an AWS Amplify website. Amazon Lex then invokes an AWS Lambda handler for user intent fulfillment. ConversationIndexTable – Tracks the conversation state.

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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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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. To understand these limitations, let’s consider again the example of deciding where to invest based on financial reports.

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Deploy foundation models with Amazon SageMaker, iterate and monitor with TruEra

AWS Machine Learning - AI

We start off with a baseline foundation model from SageMaker JumpStart and evaluate it with TruLens , an open source library for evaluating and tracking large language model (LLM) apps. In development, you can use open source TruLens to quickly evaluate, debug, and iterate on your LLM apps in your environment.