Machine Learning - Elitech Systems Pvt Ltd

🤖 ChatGPT Bot API Uses Own Custom Data from Txt, CSV, PDF

elitech_idaratan — Thu, 24 Aug 2023 05:53:39 +0000

Introducing our cutting-edge code that revolutionizes the way you interact with your custom data – the Ready-to-Use API that answers your questions. With this meticulously crafted API, effortlessly obtain precise answers directly from your documents, text files, and websites.

Our innovative code empowers you to seamlessly integrate ChatGPT’s capabilities into your workflow, opening the door to a world of efficient information retrieval.

Experience the future of data interaction as you extract insights and solutions using our advanced API, simplifying the process and enhancing your access to knowledge like never before.

Get Answers from your documents, textfiles, and websites. Ready to use API for your Custom Data Using LlamaIndex and OpenAI.

This tutorial is step by step guide to setup python packages and setup API in python that can be integrated to any of your existing system.

💡 Basic knowledge of Python and Docker is required. We’ve used LlamaIndex and OpenAI. No need to expert on any of them.

⭐ LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

⭐ OpenAI offers a spectrum of models with different levels of power suitable for different tasks

Steps

Download source code attached to bottom of the page and follow steps.

Setup Docker Or Install Python
Get the OpenAI API key and replace it in the source code.
Build Model
Run Server
Call API and Ask any questions

Code Setup using Docker

Setup Docker – Refer to FAQ #1 👇
Prepare Context Data in simple TXT, CSV, or PDF format. Ability to parse multiple files, just paste files into the context_data folder.
Get your OpenAI API key and replace it in server.py and build_model.py
Build Docker

# Build a Docker image with the tag "my-bot" using the current directory (.) as the build context.
docker build --no-cache -t my-bot .

5. Run Docker

# Run a Docker container using the "my-bot" image and map port 5001 on the host to port 5001 in the container.
docker run -p 5001:5001 my-bot

Code Setup Manually

Install Python

# Linux:
# Ubuntu/Debian:

sudo apt update
sudo apt install python3

# Fedora:
sudo dnf install python3

# Windows:
# Download the Python installer executable from the official website (https://www.python.org/downloads/windows/).
# Run the installer executable and follow the installation wizard.

# macOS:
# Homebrew:
brew install python
sudo port install python

2. Install Dependencies

pip install Flask==2.0.1
pip install requests==2.26.0
pip install llama-index==0.5.6
pip install langchain==0.0.148
pip install flask-basicauth
pip install PyPDF2

3. Prepare Context Data in simple TXT, CSV, or PDF format. Ability to parse multiple files, just paste files into the context_data folder.

4. Get your OpenAI API key and replace it in server.py and build_model.py

5. Build Model

python build_model.py

6. Run Server

python server.py

Run API – Node JS

Get ready to get GPT based answers from your document using node js api.

I’ve included Postman collection in this bundle so you can export code in your programming language.

const axios = require('axios');
let data = JSON.stringify({
  "query": "Can you summarize this judgement?"
});

let config = {
  method: 'post',
  maxBodyLength: Infinity,
  url: 'http://127.0.0.1:5001/ask_ai',
  headers: { 
    'Content-Type': 'application/json', 
    'Authorization': 'Basic ZWxpdGVjaDplbGl0ZWNoMTIzKg=='
  },
  data : data
};

axios.request(config)
.then((response) => {
  console.log(JSON.stringify(response.data));
})
.catch((error) => {
  console.log(error);
});

FAQ

How to set up Docker?

To install Docker on various operating systems, follow the instructions below:

Install Docker on Linux: For Linux distributions, Docker provides installation scripts that you can use to install Docker Engine. Here’s a general method to install Docker on most Linux distributions: Open a terminal and run the following commands:

sudo apt update
sudo apt install -y curl
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh

After installation, start and enable the Docker service:

sudo systemctl start docker
sudo systemctl enable docker

Install Docker on macOS: Docker Desktop is the recommended way to install Docker on macOS. You can download it from the official Docker website and follow the installation instructions provided: Download Docker Desktop for macOS

Install Docker on Windows: Similarly, Docker Desktop is the preferred method for installing Docker on Windows. Download it from the official Docker website and follow the installation instructions: Download Docker Desktop for Windows Note that Docker Desktop requires Windows 10 Pro or Enterprise (64-bit) for installation.

After installing Docker, you can verify the installation by running the following command in your terminal:

docker --version

This should display the version of the Docker that was installed.

Hey! We specialize in assisting you with the seamless integration of GPT into your product.

Let’s discuss.

The post 🤖 ChatGPT Bot API Uses Own Custom Data from Txt, CSV, PDF first appeared on Elitech Systems Pvt Ltd.

Python Vs Go, Which one to choose?

Ankit Patel — Tue, 12 May 2020 10:53:22 +0000

Before starting a project, most development teams meet several times to decide the best language for their software.

Often this discussion comes down to Python and Golang.

Today Elitech Systems offers you a comparison between Golang and Python, on different parameters that will help you decide as a developer which language is best for you.

What is Python?

Python is one of the fastest growing programming languages. Python favors simplicity, easy-to-read code, and uses these features to attract people who are hesitant to learn complicated programming languages.

An important feature of Python is that it is an interpreted language.

Therefore, Python does not need to be compiled into machine code before it is run.

However, code interpretation generally takes longer, which means that Python is slower than compiled languages.

Many of today’s most successful tech companies choose Python for the back-end of their website.

In this list, there are notably Instagram, Spotify or even Netflix to name a few.

What is Golang?

Golang, also known as Go, is a computer programming language developed by Google.

Its development started in 2007 at Google, and it was presented to the public in 2009.

The objective was to create a language, freely based on the syntax of the C programming language, which would eliminate “foreign waste” from languages such as C ++.

As a result, Go instills many characteristics of other modern languages, such as overloading of methods as well as that of operators, pointer arithmetic and type inheritance.

Go, is a static typing language with a library that has nothing to envy to other languages, as well as performance and increased speed.

Docker, the most active open-source project in the world, is coded in Go as well as Uber and in particular Twitch, which shows that Golang is gaining more and more weight on the market.

Key Differences Between Python and Go

Below are the key differences between Python and Go:

Python being a scripting language has to be interpreted whereas Go is faster most of the time since it does not have to consider anything at runtime.
Python is a great language with an easy to understand syntax and hence more readable, flexible whereas Go is also in the prime league when it comes to clear syntax which holds zero unnecessary components.
Python does not provide built-in concurrency mechanism whereas Go has built-in concurrency mechanism.
When it comes to safety, Python is a strongly typed language which is compiled, hence adding a layer of security whereas Go is very decent since every variable must have a type associated with it. It means a developer cannot let away the details which will further lead to bugs.
Python is less verbose than Go to achieve the same functionality.
Python has tons of libraries as compared to Go but gradually this situation with Go is improving.
Python still gains an upper hand when it comes syntax and thus user-friendly.
Python is still a favorite language when it comes to solving data science problems whereas Go is more ideal for system programming.
Python is dynamically typed language whereas Go is a statically typed language, which actually helps catch bugs at compile time which can further reduce serious bugs later in the production.
Python is great for basic programming, using it can become complicated if one wishes to build complex systems whereas, with Go, the same task can be accomplished rapidly without going into subtleties of programming language.
Python is more concise than Go. Dynamic typing helps but overall, one can also write very compact code with Go.

Python and Go Comparison Table

Below is the list of points, Describe the comparison Between Python and Go.

COMPARISON	Python	GO
Python	Object-oriented, imperative, functional, procedural, reflective	Procedural, functional and concurrent
Execution	Interpreted	Compiled
Typed	Dynamically typed language	Statically typed language
Usage	Python is more focused to write web applications	Go is more focused on being a system language, still, it’s eating away python share of web apps
Memory	Python offers no memory management	With Go you can go dirty with memory management
Syntax	Syntax uses indentation to indicate code blocks	Syntax is based on opening and closing braces
Concurrency	Lacks inbuilt concurrency	Concurrency is inbuilt
Object Orientation	First class Object Oriented programming, support for functional concepts	Mild support for Object Orientation and functional concepts, but is strongly typed

Speed

Go is definitely faster than Python. This makes sense, as it was designed specifically for high performance competition in the construction and rapid deployment of server-side scripts.

If you are programming systems, then Go might be the best choice. In addition, Python is used for just about everything in the world of software development and data science.

Over the next 20 years, as more developers and businesses implement Go, it could become a stronger competitor to Python. For now, Python still reigns supreme even if Go is faster.

Scalability

Go was specially designed for scalability the size of Google. The language makes it possible to manage several tasks and quickly increase the size of the application according to demand. It is designed to be scaled. Go is therefore very scalable over time and is a great force for him.

Python was not originally designed to be very scalable.

In terms of comparison to Go, Go is going to be the winner. Python has multiprocessing capabilities and a rich library, but Go being a little more modern wins hands down

Conclusion

It is difficult to draw conclusions in the Go vs. discussion. Python because comparing a mature language with a relatively young language does not seem fair.

Python is the leading language not only in machine learning, data analysis, but also in web development.

Golang has only been in existence for a decade, and it has not yet built a solid ecosystem or community.

An obvious advantage of Go is its speed. While Python bows to Go in terms of code execution, Go admits defeat in production speed.

The post Python Vs Go, Which one to choose? first appeared on Elitech Systems Pvt Ltd.