RAG to SQL chatbot with streamlit || RAG use case || Data Analytics with LLM

Рет қаралды 974

Күн бұрын

Unlock the full potential of your data analytics projects by learning how to efficiently generate SQL queries using the revolutionary Retrieval-Augmented Generation (RAG) technique. In this comprehensive tutorial, we delve into the practical applications of RAG for enhancing data retrieval and manipulation, making it an invaluable tool for data analysts, developers, and anyone looking to streamline their data processes.
Throughout the video, we provide a step-by-step guide on:
Understanding the basics of Retrieval-Augmented Generation (RAG) and its importance in data analytics.
Setting up the necessary environment and tools to implement RAG for generating SQL queries.
Demonstrating real-world examples of how RAG can optimize your data analysis tasks by automating the creation of complex SQL queries.
Tips and best practices for integrating RAG into your data analytics workflow to improve efficiency and accuracy.
Whether you're a beginner aiming to enhance your data analytics skills or an experienced professional looking to incorporate advanced techniques into your repertoire, this tutorial offers valuable insights and practical advice to elevate your data management capabilities.
Don't forget to like, share, and subscribe for more insightful tutorials on leveraging cutting-edge technologies in data analytics. Join our community to stay updated with the latest trends and techniques that can transform your data handling strategies.
GITHUB:
github.com/JYTDemo/RAG2SQL
#DataAnalytics #SQLGeneration #RAGTechnique #DataScience #SQLQueries #Tutorial

Пікірлер: 9

@AliSidi865 Ай бұрын

Thank you sir , have you used open source LLMS such as Claude 3 , Llama 3 or Mistral ? what do you think of a fine tuned model ? some companies don't like closed source models (security ...)

@joymaitra5414 Ай бұрын

Thank you for commenting ! I have tried to do it with Llama 2 and Mistral , well with 7B models, the response was not so good. However, I think with the higher parameter model, it must be possible.

@AliSidi865 Ай бұрын

@@joymaitra5414 Thanks for the response 🙂

@TechU-Turn 2 ай бұрын

Darun session

@joymaitra5414 2 ай бұрын

Thank you !!

@wah866sky7 Ай бұрын

Thank you, Joy. I am a new learner on RAG. Would you mind show us a demo on using module with OpenAI (not AzureChatOpenAI) and use MySQL (with full database tables provided)? I hope can follow with you to build up the chatbot step by step successfully. Thanks!

@joymaitra5414 Ай бұрын

I think the same steps will work, only instead of the sqlite make the connection to MySQL (pythonic way) and change the API key for OpenAI.

@rajkumar8it Ай бұрын

Thanks Joy. Once the vector DB is loaded with schema and if we have some sample questions and corresponding SQLs how can we add it to the vector DB for better results ? Also does the vector DB keeps updating itself with more interaction or how can we improve by manually adding more questions and SQLs?

@joymaitra5414 Ай бұрын

Thanks for reaching out ! You can update the metadata of the records in vectordb. I have another video on Vector DB, that might be of help. Vector DBs don't have any intelligence to update itself. But we can build logics around it to handle it