With the increasing adoption of Large Language Models (LLMs) in production for Chat and RAG, it is more and more important to ensure safe and controlled interactions. Today, we’ll dive deep into LLM guardrails – what they are, how they…
You have created an application using LangChain and AWS Bedrock and you are wondering how to have better performances and how to be more resilient ? Say no more, in this blog post we will see AWS Bedrock newest features:…
You’ve probably heard a lot about large language models (LLMs) these days—OpenAI’s GPT models, Google’s Bard, or maybe even Meta’s LLaMA. But what if I told you there’s a model that takes things to the next level by making these…
You want to learn how OpenAI’s newest model o1 works and why it is a revolution in a simple way ? You also want to know why it matters for RAG and Agentic ? Say no more, this is exactly…
If you are using RAG in your use cases, at some point, you will see that most of the answers are not domain specific but only depend on your vector stores. In this post, we are going to see a…
When beginning with RAG and vector store creation, one question will come back soon: How can you choose the correct vector for each user in a simple way? If you have this question, then you are in the right place…
Do you have a great idea for an app and need a powerful but affordable vector store solution? You already have something but the cost of your current vector store is too much ?Then you are in the right place…
So you’ve got some nice LangChain chains and you want to expose them to the world as an API? Or you need to create some internal LLM-based APIs for your projects ? Are you looking for something robust, scalable, production-ready,…