Remove Knowledge Base Remove Lambda Remove Machine Learning Remove Storage
article thumbnail

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.

article thumbnail

Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

You can now use Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. Knowledge Bases for Amazon Bedrock provides fully managed RAG to supply the agent with access to your data.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Incorporate offline and online human – machine workflows into your generative AI applications on AWS

AWS Machine Learning - AI

An important aspect of developing effective generative AI application is Reinforcement Learning from Human Feedback (RLHF). RLHF is a technique that combines rewards and comparisons, with human feedback to pre-train or fine-tune a machine learning (ML) model. You can build such chatbots following the same process.

article thumbnail

Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning - AI

This standardization is made possible by using advanced prompts in conjunction with Knowledge Bases for Amazon Bedrock , which stores information on organization-specific Terraform modules. In parallel, the AVM layer invokes a Lambda function to generate Terraform code. For creating lambda function, please follow instructions.

AWS 98
article thumbnail

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.

article thumbnail

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/.

article thumbnail

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

AWS Machine Learning - AI

Amazon Lex then invokes an AWS Lambda handler for user intent fulfillment. The Lambda function associated with the Amazon Lex chatbot contains the logic and business rules required to process the user’s intent. A Lambda layer for Amazon Bedrock Boto3, LangChain, and pdfrw libraries.