Explore the best tutorial and best practices for using Langchain and make production-ready code for your LLM applications.
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…
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,…
Do you have or want to create a Langchain with AWS Bedrock and are concerned about monitoring costs? Say no more! You are in the right place. Introduction In this post, you will learn: As usual, we will be as…
You have created a nice Langchain application using the latest best practices (that you read on this blog, right? Of course!) and you are wondering how you are going add monitoring to it ?You have come to the right place.…
