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Knowledge Bases for Amazon Bedrock now supports hybrid search

AWS Machine Learning

For example, if you have want to build a chatbot for an ecommerce website to handle customer queries such as the return policy or details of the product, using hybrid search will be most suitable. Contextual-based chatbots – Conversations can rapidly change direction and cover unpredictable topics.

APIs 111
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Knowledge Bases for Amazon Bedrock now supports metadata filtering to improve retrieval accuracy

AWS Machine Learning

The following are common use cases for metadata filtering: Document chatbot for a software company – This allows users to find product information and troubleshooting guides. Filters on the release version, document type (such as code, API reference, or issue) can help pinpoint relevant documents. Virginia) and US West (Oregon).

APIs 107
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Deploy self-service question answering with the QnABot on AWS solution powered by Amazon Lex with Amazon Kendra and large language models

AWS Machine Learning

Powered by Amazon Lex , the QnABot on AWS solution is an open-source, multi-channel, multi-language conversational chatbot. This includes automatically generating accurate answers from existing company documents and knowledge bases, and making their self-service chatbots more conversational. For example, when asked “What is Amazon Lex?”,

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Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat

AWS Machine Learning

RAG allows models to tap into vast knowledge bases and deliver human-like dialogue for applications like chatbots and enterprise search assistants. It provides tools that offer data connectors to ingest your existing data with various sources and formats (PDFs, docs, APIs, SQL, and more). Choose Deploy again to create the endpoint.

APIs 101
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The Future of Debt Collection Agencies: Contact Center Technology and Customer-Centric Strategies

NobelBiz

Account Setup and Verification : Upon receiving a debt, the agency sets up an account for the debtor and verifies all the details. Call centers are equipped with tools that allow agents to quickly access a debtor’s full account information, ensuring that every interaction is informed and constructive.

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Automate chatbot for document and data retrieval using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Numerous customers face challenges in managing diverse data sources and seek a chatbot solution capable of orchestrating these sources to offer comprehensive answers. This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

Each business unit has each own set of development (automated model training and building), preproduction (automatic testing), and production (model deployment and serving) accounts to productionize ML use cases, which retrieve data from a centralized or decentralized data lake or data mesh, respectively.