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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

With this solution, you can interact directly with the chat assistant powered by AWS from your Google Chat environment, as shown in the following example. The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint. Run the script init-script.bash : chmod u+x init-script.bash./init-script.bash

APIs 123
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Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

AWS Machine Learning

For enterprise data, a major difficulty stems from the common case of database tables having embedded structures that require specific knowledge or highly nuanced processing (for example, an embedded XML formatted string). As a result, NL2SQL solutions for enterprise data are often incomplete or inaccurate.

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Build a Multi-Agent System with LangGraph and Mistral on AWS

AWS Machine Learning

By using the power of LLMs and combining them with specialized tools and APIs, agents can tackle complex, multistep tasks that were previously beyond the reach of traditional AI systems. Whenever local database information is unavailable, it triggers an online search using the Tavily API. Its used by the weather_agent() function.

APIs 126
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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

The following table provides example questions with their domain and question type. Amazon Bedrock APIs make it straightforward to use Amazon Titan Text Embeddings V2 for embedding data. The eight different question types are simple , simple_w_condition , comparison , aggregation , set , false_premise , post-processing , and multi-hop.

Benchmark 111
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. With this launch, customers can now seamlessly share and access ML models registered in SageMaker Model Registry between different AWS accounts.

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Secure a generative AI assistant with OWASP Top 10 mitigation

AWS Machine Learning

In this post, we show you an example of a generative AI assistant application and demonstrate how to assess its security posture using the OWASP Top 10 for Large Language Model Applications , as well as how to apply mitigations for common threats. These steps might involve both the use of an LLM and external data sources and APIs.

APIs 116
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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

APIs 111