MySQL database with PandasAI & Ollama & Streamlit

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Today, we'll cover how to analyze data in a MySQL database with local Llama 3 using Pandas AI, Ollama and Streamlit for free. Happy learning.
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00:01 Introduction
01:06 Setup
02:47 Create a new database
04:43 Load the model with Ollama
06:41 Initialize the model
08:29 Connect to the database
10:45 Build the app
12:41 Inference
13:31 Data visualization
RELATED VIDEOS:
▶ PandasAI Tutorials: bit.ly/pandasai
▶ Ollama Tutorials: bit.ly/ollama-tutorials
▶ LangChain Tutorials: bit.ly/langchain-tutorials
▶ Generative AI for DS: bit.ly/genai-for-data-science
▶ HuggingFace Tutorials: bit.ly/hugging-face-tutorials
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▶ Project files: github.com/TirendazAcademy/Pa...
▶ MySQL Download: dev.mysql.com/downloads/
▶ Dataset: www.kaggle.com/datasets/uom19...
Hi there, I'm a content creator on generative AI & data science, PhD. My goal is to make the latest technologies understandable for everyone.
#ai #generativeai #datascience

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Опубликовано:

10 май 2024

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Комментарии : 23

@Airsoftshowoffs 9 дней назад

Great video. Thank you for making this easy.

@jackgaleras 18 дней назад

12:26 Gracias mil, la animación es muy adecuada cuando se intenta estas cosas y se consiguen mil errores hasta que por fin se logra el objetivo ja

@nguyenngothuong Месяц назад

Can you make a video tutorial on role configuration for Botchat?. For example, only respond in English, and focus on using SQL to query data and respond to users.....

@nguyenngothuong Месяц назад

Added features to save chat history, suggest prompts after asking, etc.

@zainmunir5628 Месяц назад

Hi. I love your videos. Is there anyway to do same with MongoDB or any NoSQL database?

@TirendazAI Месяц назад

Thanks for your feedback. PandasAI provides connectors for the following SQL databases: PostgreSQL, MySQL, Generic SQL, Snowflake, DataBricks, GoogleBigQuery, Yahoo Finance and Airtable.

@DarkNessShadow- Месяц назад

Hi, awesome videos, can you make a video using agent pandasai with the explain and clarification questions features, i try to use it but doesn't work for me

@TirendazAI Месяц назад

Thanks, noted!

@erdemkoraysanl7547 Месяц назад

Hello, I wrote the same code, it works, but it is very slow, for example, when I say bring the first 4 data, it responds in 2 minutes 3 minutes, but it is very very fast in you. It is very fast when chatting normally with llama3. It is very fast in my database, but when I write this code, it works very slowly.

@TirendazAI Месяц назад

The app answered quickly a response in the video because I used cache. Cache allows PandasAI to store the results of previous queries. My queries took some time. Which graphic card do you use?

@erdemkoraysanl7547 Месяц назад

@@TirendazAI I use the Amd Rodeon rx580 graphics card. Actually, my goal is to use AI search on my website. Algorithms almost most of the time can not give correct results. I really thought that artificial intelligence would be very useful to think like a human, but it works very slowly.

@suffympm1601 Месяц назад

thanks for your video. from indonesia.. but why in my laptop result data is so long.. any idea ?. my spec is asus ryzen 7 gen 5

@TirendazAI Месяц назад

Hi, to get a quick response, it is important to have a powerful graphics card.

@ByteBop911 Месяц назад

Is it possible to use agents without the pandasapi and use ollama?

@TirendazAI Месяц назад

You can use many agents with LangChain.

@HimanshuSaxena16november Месяц назад

Can we get data from multiple tables using prompts?

@TirendazAI Месяц назад

PandasAI supports queries with multiple dataframes. You can utilize a SmartDatalake to implement such queries.

@HimanshuSaxena16november Месяц назад

@@TirendazAI what about joins in tables,what will be the prompt in that case?

@Airsoftshowoffs 9 дней назад

@@HimanshuSaxena16november Totally agree, if a DB with its ERD and relationships are loaded , it would be amazing.

@DanielMouttapa 2 дня назад

@@TirendazAI when i used smartdatalake to test with the 2 datasets employee and salary table, with llama3 in local, for the question who gets paid the most, the code produced by the llm provoked error and no result, however with pandas ai api the result is ok (but limited quota), what local model can we use to get good result ?