Optimizing LLM Applications with Vector Embeddings, affordable alternatives to OpenAI’s API and how we move from LlamaIndex to Langchain
So you may think that I’m gonna write part 2 of the series on how to build a great chatbot app that is different from 99% of tutorials on the internet. Guess what, it is not gonna happen in this post. I’m sorry in advance but there is a reason why I’m not rushing into part 2 yet and I shall explain to you.
Zero to One: A Guide to Building a First PDF Chatbot with LangChain & LlamaIndex — Part 1
Welcome to Part 1 of our engineering series on building a PDF chatbot with LangChain and LlamaIndex. Don’t worry, you…
Yep, I know you’ve been eagerly awaiting the second part of our journey in building an amazing AI-powered chatbot using Large Language Models (LLMs). But, you know what? Something happened while I was working on my very own LLM app. I discovered that each embedding model I experimented with produced different and intriguing results. Some were simply so good, while others fell a bit short of expectations. That got me thinking:
how can we truly grasp the power of these embedding models and understand their impact on chatbot performance?
So, I couldn’t resist the urge to share my insights with you through this article. Trust me, it’s well worth your time to equip yourself with this essential knowledge before diving headfirst into your own projects. After all, every great house is built upon a solid foundation, right? Now, don’t worry, I promise this won’t be a dull university lecture. I’ve made sure to include plenty of practical tutorials and engaging examples to keep you excited throughout the read. Similar to the post on how to use Llamaindex’s Index correctly, I will take the same approach for this article
LlamaIndex: How to use Index correctly.
and understanding what use case for what type of index
So, without further ado, let’s embark on this fascinating journey together and uncover the secrets of embedding models. Let’s get started!