[20 Days of DynamoDB] Day 16 - Enhancing Write Performance with Batching
Posted: 2/Feb/2024The DynamoDB BatchWriteItem operation can provide a performance boost by allowing you to squeeze in 25 individual PutItem and DeleteItem requests in a single API call - this can be...
View Article[20 Days of DynamoDB] Day 17 - BatchGetItem operation
Posted: 5/Feb/2024You can club multiple (up to 100) GetItem requests in a single BatchGetItem operation - this can be done across multiple tables.Here is an example that fetches includes fourGetItem...
View Article[20 Days of DynamoDB] Day 18 - Using a SQL-compatible query language
Posted: 6/Feb/2024DynamoDB supports PartiQL to execute SQL-like select, insert, update, and delete operations. Here is an example of how you would use PartiQL based queries for a simple URL shortener...
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Run and test DynamoDB applications locally using Docker and Testcontainers
Use the DynamoDB Local Testcontainers module for your Go applications!DynamoDB Local is a version of Amazon DynamoDB that you can run locally as a Docker container (or other forms). It's super easy to...
View Article[20 Days of DynamoDB] Day 19 - PartiQL Batch Operations
Posted: 12/Feb/2024You can use batched operations with PartiQL as well, thanks to BatchExecuteStatement. It allows you to batch reads as well as write requests.Here is an example (note that you cannot...
View Article[20 Days of DynamoDB] Day 20 - Converting between Go and DynamoDB types
Posted: 13/Feb/2024The DynamoDB attributevalue in the AWS SDK for Go package can save you a lot of time, thanks to the Marshal and Unmarshal family of utility functions that can be used to convert...
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How to use Retrieval Augmented Generation (RAG) for Go applications
Implement RAG (using LangChain and PostgreSQL) to improve the accuracy and relevance of LLM outputsGenerative AI development has been democratised, thanks to powerful Machine Learning models...
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Vector Databases for generative AI applications
How to overcome LLM limitations using Vector databases and RAGI first shared this blog from my session (at GIDS 2024). If you attended it, thank you for coming and I hope you found it useful! If not,...
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Deploy an AWS Lambda function using Amazon Q
No browser tabs were used to get this done (except when searching for funny pics for this blog)Amazon Q is a generative AI powered assistant that you can use in the AWS Management Console, command line...
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Use Anthropic Claude 3 models to build generative AI applications with Go
Anthropic's Claude 3 is a family of AI models with different capabilities and cost for a variety of tasks:Claude 3 Haiku is a compact and fast model that provides near-instant responsivenessClaude 3...
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Simplify RAG application with MongoDB Atlas and Amazon Bedrock
By fetching data from the organization’s internal or proprietary sources, Retrieval Augmented Generation (RAG) extends the capabilities of FMs to specific domains, without needing to retrain the model....
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How to add flexibility to your RAG applications by choosing the right...
Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you implement the entire RAG workflow from ingestion to retrieval and prompt augmentation without having to build custom...
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A single API for all your conversational generative AI applications
Use the Converse API in Amazon Bedrock to create generative AI applications using single API across multiple foundation modelsYou can now use the Converse API in Amazon Bedrock to create conversational...
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Getting started with Valkey using JavaScript
Run existing Redis apps with Valkey and learn how to use it with LangChainValkey is an open source alternative to Redis. It's a community-driven, Linux Foundation project created to keep the project...
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Use AWS Generative AI CDK constructs to speed up app development
Assemble and deploy the infrastructure for a RAG solution using AWS CDK for PythonIn this blog, we will use the AWS Generative AI Constructs Library to deploy a complete RAG application composed of the...
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Maintain chat history in generative AI apps with Valkey
Integrate Valkey with LangChainA while back I wrote up a blog post on how to use Redis as a chat history component with LangChain. Since LangChain already had Redis chat history available as a...
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Use Guardrails to prevent hallucinations in generative AI applications
With Contextual grounding check, you can prevent hallucinations by detecting irrelevant and ungrounded LLM responses.Guardrails for Amazon Bedrock enables you to implement safeguards for your...
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Use Guardrails for safeguarding generative AI applications built using custom...
Learn how the ApplyGuardrail API can provide a flexible way to integrate Guardrails with your generative AI applicationsGuardrails for Amazon Bedrock enables you to implement safeguards for your...
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Use Mistral AI to build generative AI applications with Go
Learn how to use Mistral AI on Amazon Bedrock with AWS SDK for GoMistral AI offers models with varying characteristics across performance, cost, and more:Mistral 7B - The first dense model released by...
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Build generative AI applications in Go using Amazon Titan Text Premier model
Use Amazon Titan Text Premier model with the langchaingo packageIn this blog I will walk you through how to use the Amazon Titan Text Premier model in your Go applications with langchaingo which is a...
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