Chat with MySQL Database with Python | LangChain Tutorial

Discover how to interact with a MySQL database using Python and LangChain in our latest tutorial. This comprehensive guide walks you through the process of creating a LangChain chain, detailing every step with a helpful diagram for a clearer understanding. Whether you're a beginner or an experienced developer, this video will equip you with the knowledge to execute SQL queries using an innovative approach.

IMPORTANT: Remember to NOT use a MySQL user with WRITE privileges. Use only READ and limit the scope. Otherwise your user could ask your chain to delete data.

USEFUL LINKS:

Timestamps:
0:00 Intro
1:02 How this works
2:59 Our test data
4:58 Load the test data
6:47 Notebook setup
9:23 Create SQL Chain Prompt
13:00 Load MySQL Database in Python
17:01 Create SQL Chain
26:02 Create run_query Function & Final Prompt
29:45 Create Full Chain
35:54 Conclusion

In this tutorial, you'll learn:

- How to set up LangChain to communicate with a MySQL database.
- The intricacies of creating and utilizing SQL queries within LangChain.
- Implementing a full chain that includes SQL query generation and natural language response construction.
- Best practices for interacting with databases using Python and LangChain.

Key Highlights:

- LangChain Integration: Learn how to create a LangChain chain for database queries.
- SQL Query Generation: Understand the process behind generating SQL queries from natural language questions.
- Interactive Diagram: A detailed diagram explains the architecture and process flow.
- Comprehensive Code Walkthrough: From setting up your environment to executing queries, every step is covered.

Who Should Watch?
This tutorial is perfect for developers, data scientists, and tech enthusiasts interested in leveraging LangChain for database interactions. Whether you're looking to enhance your projects or explore new technologies, this guide has something for everyone.

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Happy Coding! 🚀

[imranonthenet]

I really love your tutorials, you are teaching us to create such powerful AI tools in Python that are really useful. I'm surprised that you have only 26K subscribers, you should have millions.

[tannerdio]

Dude, incredible tutorial, right on the money for what i needed.

[student]

Awesome. Looking forward to part 2....

[davidtindell]

Thanks for including MySQL and not just SQLite.

[krisograbek]

Loving this video, Alejandro! 2 things I'd like to add:
1. I removed the {schema} part from the full_chain prompt and it works great. Only the sql_chain needs schema (to produce the sql query).
2. Your website is down :(

Thanks for explaining every bit of your code so well!

[davidtindell]

Hi, I reviewed this "Chat with MySQL DB" tutorial yet again and decided to try a more difficult SQL query: "determine the most popular artist in the database based upon total sales.". Of course, I manually ran the full Select -- with three table joins -- to make sure that this new query would work and produce the correct answer(s). To my surprise, the "natural language query" was properly processed by my modified version of your program and produced the correct response: " Iron Maiden with total sales of $138.60 " ! To further test the program, I changed the question to "top three most popular artists". The correct result was again returned: "Iron Maiden, U2, and Metallica" ! Glad to see 'U2' near the top ! Since "langchain-openai" is only at version 0.0.8 and "SQLDatabase" at 0.0.26, we may expect even more NLP Query improvements in the near future [i.e., if 'Altman' & 'Musk' do not mess everything up for all of us !?!]. P.S. Looking forward to the next MySQL vid(s) that you post !

[heaton]

Love to watch your tutorials. It's very details.

[funmiemore]

First time here and I'm glad I gound your channel.Thanks for sharing!

[mazinzain]

I love your content, thanks for all your efforts ❤

[teddyperera]

This is a great tutorial. Thanks for explaining it in a way that's easy to understand

[adnank]

Love your videos, started watching all your langchain and it really has helped me and I wanted to say thanks. I would also like to see the use of agents if it isnt too much to ask

[msssouza]

Hi Alejandro. Great post! It helped me a lot. I was trying to find a Gemini alternative to a solution that I learned from a Udemy course, using LangChain and OpenAI Agents to access a SQLite database and pass the results to the OpenAI LLM. I searched for days and found nothing, until I saw your video. Now my code is running and I can see many possibilities for accessing enterprise databases to enable users to obtain results using generative AI. Thank you and greetings from Brazil.

[dgm]

Thank you for your videos. As a subscriber to your channel, I look forward to each new one. I would like to make a comment, and perhaps a suggestion for a future video. As a Finance Manager for the last 40+ years, I have come to find these three areas important as it relates to the retrieval of information. What every office need is a way to search:
1) URL's, EXCEL, and PDF on the internet.
2) EXCEL and CSV files locally. (and Securely)
3) PDF and DOC documents locally. (and Securely)
And nowadays, you could never get a NON GUI program adopted by the office staff. And of course, all three of the search types would be incorporated into the same GUI.
Python based program would be the preferred language. OLLAMA based. NO DOCKER. No wrapper programs like streamlit, etc. Thanks for your time, and keep up the good work.

[chibuzoemelike]

Thank you so much for this video, this is really helpful!! Looking forward to using huggingface models

[Aidev]

Thanks. What about long ot short term memory during conversation?

[victorchrist]

for the GUI, what tool would you suggest one can use to return a table, just like in mysql

[abhaypkyek]

You really made my day. i was trying to figure out this code from Langchain templates from many days. But you gave a perfect clarity giving step by step understanding. Thanks alot for that. Can u further enhance this in your next video as said in the end of the video with ollama & vizualization from the response using PandasAI or LIDA AI or something better please.

[av]

Thanks for posting this. Very helpful. is there any open source LLM which can convert Natural language to SQL ? would Llama, Flan T5 etc work instead of gpt ?

[fbravoc]

Amazing video!! Thanks for creating it!! Is there a way to apply the same principles but with another LLMs (open-source)?? If I had gone over all the database I am working with and I had identified the queries related to the most frequent questions, how can I finetune my queries to these frequent questions? ..should I think about adding RAG logic to it?

[guanjwcn]

Awesome. Could you include streaming in part 2 as well?