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Chat with Multiple PDFs | LangChain App Tutorial in Python (Free LLMs and Embeddings) 121gamers 2 Views • 2 years ago

In this video you will learn to create a Langchain App to chat with multiple PDF files using the ChatGPT API and Huggingface Language Models.

Welcome to our comprehensive step-by-step tutorial on building a powerful chatbot that allows you to ask questions about your multiple PDFs using LangChain and ChatGPT API. In this project-based video tutorial, we will guide you through the process of harnessing the capabilities of LangChain, a cutting-edge framework for developing language model-powered applications.

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Powered by ChatGPT, an advanced AI language model, our chatbot implementation enables you to interact with your PDF documents in a whole new way. We will also explore the utilization of Huggingface language models to enhance the chatbot's performance.

Throughout this Python tutorial, you'll learn how to integrate LangChain into your application, leveraging its data-awareness and agentic features, allowing your language model to tap into various data sources and interact with its environment seamlessly.

Key Topics Covered:
- Introduction to LangChain and its principles of data awareness and agency
- Exploring the capabilities of ChatGPT API and its potential for artificial intelligence applications
- Step-by-step walkthrough on setting up the LangChain framework in Python
- Integration of Huggingface language models for enhanced chatbot functionality
- Building a project-based chatbot application that answers questions based on your PDFs
- Optimizing your chatbot for efficient and accurate responses
- Best practices for leveraging open-source GPT models, including GPT-3.5 and GPT-4
- Unlocking the potential of ChatGPT and LangChain to create innovative applications

Whether you're a developer, AI enthusiast, or simply curious about the world of artificial intelligence, this tutorial is perfect for you. Join us on this exciting journey to develop your very own PDF-powered chatbot using LangChain, ChatGPT API, and the power of Python.

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TIMESTAMPS
0:00 Intro
1:31 Setup
3:26 Create GUI
8:50 Add your API Keys
11:46 How this works (Diagram)
16:41 Handle process button
19:43 Extract text from PDFs
24:33 Split text into Chunks
29:26 Embedings
32:30 OpenAI Embeddings
36:24 Instructor Embeddings
40:57 Create ConversationChain
46:18 Make conversation persistent
50:14 HTML templates
55:04 Display Chat History
1:02:05 Free Huggingface LLM
1:06:10 Conclusion

Keywords:
#ChatGPT #streamlit #langchain #OpenAI

Streamlit Python Course: Build a Machine Learning App to Predict Cancer 121gamers 2 Views • 2 years ago

Welcome to this Streamlit Python tutorial, where we will dive into creating an interactive machine-learning web application using the Streamlit framework! In this comprehensive project-based course, you'll learn how to build a machine-learning dashboard to predict whether a cell cluster is benign or malignant using Python and Streamlit. This tutorial is perfect for beginners, experienced developers, and data scientists looking to learn how to create powerful machine-learning projects that integrate with web applications.

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🔥 Useful links:
- Github project: https://github.com/alejandro-a....o/streamlit-cancer-p
- Blog post: https://alejandro-ao.com/posts..../data-science/stream

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------------------------

🚀 What you'll learn in this tutorial:

How to create a Streamlit dashboard
Training a logistic regression model for machine learning
Visualizing data using Plotly
Displaying predictions based on user input
Creating an AI full-stack app using Python

In this video tutorial, we train a logistic regression model using machine learning in Python. We will be using a dataset to predict whether a cell cluster is benign or malignant. Once our model is ready, we will integrate it into a Streamlit application that runs in the web browser. You'll see how easy it is to create an interactive dashboard using Streamlit and Python!

Next, we will visualize our data using Plotly to create informative and interactive charts. These visualizations will help users understand the predictions made by our machine-learning model. We'll then display the predictions based on the user's input, making our dashboard not only visually appealing but also highly functional.

Throughout the tutorial, we'll explore various techniques and best practices used by experts like Nicholas Renotte and Patrick Loeber. This Streamlit tutorial is an excellent resource for data science dashboard projects and a great way to learn how to create a machine learning app.

By the end of this course, you will have created an AI full-stack app using Python, Streamlit, and machine learning. You'll be ready to tackle your own machine-learning projects and create amazing data science dashboards!

Don't forget to like, comment, and subscribe to stay updated with more tutorials and projects in machine learning, data science, and web app development!

⏰ Timestamps
0:00 Intro
2:56 Download the data
6:23 Project setup
8:21 Clean the data
14:00 Train the model
21:06 Test the model
26:39 Export the model and scaler
32:33 Create the Streamlit app
38:48 Create the layout
44:18 Add the sidebar inputs
58:52 Create the chart
1:17:22 Scale the values for chart
1:25:30 Display the predictions
1:39:08 Add custom CSS
1:52:00 Deploy your app

🎯 Keywords: Streamlit Python, Streamlit Tutorial, Streamlit Dashboard, Machine Learning Python, Machine Learning App Tutorial, Python App, Machine Learning Projects, Machine Learning Projects for Beginners, Nicholas Renotte, Patrick Loeber, AI App, AI Full-Stack App, Data Science Dashboard, Data Science Dashboard Projects

#python #streamlit #MachineLearning #DataScience

How to Setup RAID 10 on Raspberry Pi 4 or Raspberry Pi 5 121gamers 2 Views • 2 years ago

In this comprehensive tutorial, we'll guide you through the step-by-step process of building and setting up a RAID 10 NAS (Network Attached Storage) using the Raspberry Pi 4 or the latest Raspberry Pi 5.
Learn how to enhance your storage capabilities, improve data redundancy, and boost performance by configuring a RAID 10 array on these compact and powerful single-board computers.

🔧 Topics Covered:
- Introduction to RAID 10 and its benefits
- Choosing the right hardware components for your NAS setup
- Installing the necessary software and operating system
- Preparing and formatting your storage drives
- Configuring RAID 10 using software RAID tools (MDADM and Webmin)
- Setting up Samba Server
- Configuring network access and permissions
- Testing and optimizing your RAID 10 NAS for performance

Whether you're a beginner or an experienced Raspberry Pi enthusiast, this tutorial provides valuable insights and practical tips to create a reliable and efficient NAS solution for your home or small office.

Commands (in Sequence):
sudo apt update
sudo apt upgrade -y
sudo apt install mdadm
sudo nano /etc/apt/sources.list
Paste the following:
deb https://download.webmin.com/download/repository sarge contrib

sudo apt-key add jcameron-key.asc
sudo apt update && sudo apt install webmin -y

Format RAID10
sudo mkfs.ext4 -v -m .1 -b 4096 -E stride=32,stripe-width=64 /dev/md0
sudo mkdir /media/SambaNAS

sudo mount /dev/md0 /media/SambaNAS
sudo chown ravi:ravi /media/SambaNAS

sudo apt install samba
sudo nano /etc/samba/smb.conf
Paste this at the end:
[RAID10_NAS]
comment = RADI10 NAS on Raspberry Pi
path = /media/SambaNAS
read only = no
browsable = yes

sudo service smbd restart

sudo blkid
sudo nano /etc/fstab
UUID=(my_uuid) /media/SambaNAS ext4 defaults,nofail 0 0
cat /etc/fstab

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AWS Tutorial 3 - How to Install XAMPP Server on EC2 Windows Server at AWS EC2 Instance 121gamers 2 Views • 2 years ago

Hi guys, let's see how to install the XAMPP server on a Windows EC2 machine. Launch Windows Ec2 instance on EC2 Instance. You will also learn how to host a website on the Ec2 Windows machine.

How to Launch Windows Machine on EC2
https://youtu.be/l2q7pSz8l1E

🔊 Watch till last for a detailed description

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