These series of videos on Spring AI are amazing. Thank you for the efforts you have put in. If possible, please add a video about unit testing these applications and best practices while writing Junits for Spring AI applications.

This is so cool - now the EmbeddingClient is making sense! Just for the life of me could not understand why you need so many different embedding clients. I was putting this embedding client in the wrong place in the picture. Time to start building some data consumption stuff and see what is going into the DB. Can't wait for the next video!

Lovely! God bless you! got this - Based on the documents provided, there are 32 sports included in the Olympic Games Paris 2024.

Thank you so much Dan for the RAG video

Thanks a lot, Dan! These series are truly impressive and impactful. I have a question about RAG. What are the benefits of supplying the questions and answers ourselves? Where does the AI's strength lie in this scenario? For instance, when you asked about counting sports, the response was 'NA' and it provided the list we inputted. We envision being able to ask you about anything within the text I've included. I might sound a bit eager, but I'm keen to explore its capabilities and potential applications 😁😁

It's so cool, thank you very much for this series. This is a subject that i'm currently studying and this specific technical content and with Java is being very clear and giving awesome examples

Thanks Mr Dan.

my favorite mentor dan vega . sir keep uploading new things. plz share chat system

Create a Basic RAG model AI chatbot using any open-source model available. Database: Zilliz/Supabase or any Vector DB, of your choice. Backend: Java Frontend: HTML/CSS/JS (or anything you are familiar with) Backend should haveAPI for: • Upload a file or text - text should then be converted into chunks and then embeddings (You can use any open-source embedding model or paid one too). Embedding should then be stored in a vector DB with vector index of your choice (Cosine/ L2/KNN). • Chat API -> prompt or user queries should be passed in this API and using prompt engineering response should be derived from any Language model. Frontend: UI does not have to look pretty. basic pages should be there - 1. For uploading docs or text for training the RAG model. 2. Basic chat interface where user can send message and receive the response. Sir Please Help me doing this project 🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏

Nice one. Appreciate if you create also a tutorial to read on the database using Rest Api

How can I create a new Implementation of ChatClient? The goal is to use it internally/privately in a company.

So Spring AI is basically Spring version of LangChain4J

Dan, I have one doubt. Can we ask follow-up questions related to the previous question I asked? Will it give a relevant answer, or will it consider two questions as completely new?

Is there a way of logging the tokens used?

Hay dan vega could you please use any opensource llm instead of openAi

I could not able to run this program with open ai free limit.

all we need is aiove

I have a question: When I use embed, I use openai by default, the file that I can use would become in vectorstore but using openai to convert a embedding..I want to say that the file to convert in embbeding use Opeani ?

How to add frontend sir please tell me html