How to automatically vectorize content and create LangChain-like mechanisms to efficiently query a corpus of documents
All tech-savvy people around the globe have been playing for a while with ChatGPT…
- Many of them used it as a very clever knowledge database 🔎,
- Some explored the “Art of Prompting” (or “Prompt Engineering”) to get more relevant results, sometimes using their own data 🤖,
- But only a few went further and leveraged solutions such as LangChain to build complex workflows and create real-life applications 📚.
And it is true that mastering concepts like “embeddings” or “vector stores”, combined with programming requirements can seem complex for many and prevent them from actually unlocking the power of LLMs.
This is where “Prompt Flow” comes to the rescue!
Let’s discover how building a powerful Q&A tool in low code is now possible in Azure!
I will assume that you have the necessary rights to create the resources needed for this tutorial, the most important one is having an “Azure Machine Learning Studio Workspace”.
The “Prompt Flow” functionality, as well as the “Models Catalog” (allowing you to deploy LLMs curated by Azure, Hugging Face, Meta, etc.), are currently in private or public preview so you’ll have to join the waiting list before being able to activate and use it.
To efficiently process a large corpus and overcome the tokens limitation of current models, you need to split each document into chunks (ex. each page) and convert the…