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Showing posts with the label Python

How To Handle Large Response From An API In Python

Handling large responses from APIs in Python can be challenging, especially when dealing with substantial amounts of data. Here are some strategies to efficiently manage such scenarios: Pagination If the API supports it, use pagination to retrieve data in smaller chunks. Fetch a limited number of records per request, and then iterate through subsequent pages. This approach prevents overwhelming your system with a massive response all at once. import requests def fetch_data_from_api ( url , page_size = 50 ):     all_data = []     page = 1     while True :         response = requests . get ( url , params ={ " page " : page , " per_page " : page_size })         if not response . ok :             break         data = response . json ()         all_data . extend ( data )         page += 1     return all_data # Example usage: api_url = " " result = fetch_data_from_api ( api_url ) print ( result ) Asynchro

How To Hide Sensitive Data Before Passing To LLM-OpenAI

In my previous article “ Passing An Audio File To LLM ”, I explained how one can pass an audio file to LLM. In continuation to that, I’m extending this article by adding more value to it by addressing the use case of sensitive information. Let’s say, an audio file contains information about your bank account number, your secure id, your pin, your passcode, your date of birth, or any such information which has to be kept secured. You will find this kind of information if you are dealing with customer facing audio calls, specifically in finance sector. As these details, which are also known as PII (Personal Identifiable Information), are very sensitive and it is not at all safe to keep them only on any server. Hence, one should be very careful while dealing with such kind of data. Now, when it comes to using PII with generative AI based application, we need a way wherein we can just remove such information from data before passing that to LLM and that’s what this article is all about. In

How To Search Content Which ChatGPT Can’t Find Today — OpenAI | Python

In this article, I’ll show you how can you get your hands dirty with Langchain agents. If you are not aware what Langchain is, I would recommend you to watch my recording here, wherein I just briefed about it. Langchain agents are the ones who uses LLM to determine what needs to be done and in which order. You can have a quick look at the available agents in the documentation, but I’ll list them here too. zero-shot-react-description react-docstore self-ask-with-search conversational-react-description Here agents work via tools and tools are nothing but those are functions which will be used by agents to interact with outside world. List of tools which are available today are: python_repl serpapi wolfram-alpha requests terminal pal-math pal-colored-objects llm-math open-meteo-api news-api tmdb-api google-search searx-search google-serper, etc In this article, I’m covering serpapi . Import Required Packages In order to get started, we need to import these below packages: from langchain

Create Chatbot Based Using GPT-Index/LlamaIndex | OpenAI | Python

In this article, I’ll show you how can you create a basic chat bot which utilizes the data provided by you. Here we will be using GPT-Index/LlamaIndex, OpenAI and Pytho n. Let’s get started by installing the required Python module. Install modules/packages We need to install, two packages named  llama-index and langchain and this can be done using below lines: pip install llama-index pip install langchain Importing packages Next, we need to import those packages so that we can use them: from llama_index import SimpleDirectoryReader , GPTListIndex , GPTVectorStoreIndex , LLMPredictor , PromptHelper , ServiceContext , StorageContext ,load_index_from_storage from langchain import OpenAI import sys import os Please note that, here, we don’t need an GPU because we are not doing anything local. All we are doing is using OpenAI server. Grab OpenAI Key To grab the OpenAI key, you need to go to, login and then grab the keys using highlighted way: Once you got the key

How To Simplify IF In Python

Using conditionals or say IF-ELSE statement is quite common when developing any application and based on the programming language, the syntax to handle such conditionals varies but the underline concept remains the same. In this article, I’ll show you how to write an IF statement in Python to check if a given item is present in the collection or not. Have a look at the traditional way to achieve the same: fruits = [‘apple’,’orange’,’banana’,’mango’] fruit = ‘mango’ if (fruit==’apple’ or fruit==’orange’ or fruit==’banana’ or fruit==’mango’): print (‘Found the fruit’) In the above snippet, we have a collection holding multiple fruits and a variable holding one fruit. The idea here is to check whether the given fruit exists in the collection or not.  There is no problem with the above code but we do have a way to optimize it and by optimizing the code, we can achieve the below benefits: less number of lines of code less error prone no need to change code, if more items are added

Generate Pivot Table Using Python

Python is nowadays a very common language to use, especially when you want to automate something. Hence, we will go with Python to automate our today’s flow wherein we will generate a pivot table using Python and then we will save it back to Microsoft Excel. Input Data I’m considering CSV file as an input, which holds multiple columns as shown in the below sample: Scenario Let’s consider a scenario, wherein we want to generate a pivot table which depicts the interest of students based on the residence. For example, say you want to know how many students are falling under Uncertain category from Rural, then pivot table should be able to provide us with this data. Generating Pivot Table The very first thing we need to do is to grab the CSV data and bring it into the memory, so that we can perform operations on it. Yes, you guessed it right. We can go for a pandas data frame. Once data is available in a data frame, we can filter out the required columns and start our pivoting process.  He

How To Schedule A Python Script On Windows

Whenever we think about automating something, there are many questions which come to our mind. Like, How will we schedule it? How many times we want to execute it? Is it possible to automate this scheduling part? Well, in this article I’m going to walk you through all those various steps which are required to schedule a Python script on Windows. Step 1: Prepare The Python script Automation begins with the piece of code which will automate something. So, the first step here is to get ready with a Python script which must be in working condition. There is no constraint on how big or small a script has to be, but we need to make sure that the script is doing what it is intended to do. Step 2: Create An Executable Once the script is verified, we need to create an executable or EXE file as this executable file we are going to schedule in our next step. In order to create an executable in Python, we need to install a package named pyinstaller using pip: pip install pyinstaller Once the packa

How To Print Calendar Using Python

In this article, I’ll show you those two lines of Python code, using which you can print calendar of any year. Required Package The pre-requisite to generate calendar is to import the required package named calendar as shown below: from calendar import * Generate Calendar Now to generate a calendar, we need to call constructor and pass in four parameters as shown below: print(calendar(2018,2,1,4)) Here, 2018 is the year for which calendar will be printed 2 signifies width of each character 1 signifies number of lines per week 4 signifies column separation value You can adjust the last three parameters for better spacing in between the text. Output This is how the calendar looks like: I hope you enjoyed generating a calendar of your choice. Make sure to check out my recording explaining each and every parameter discussed in this article:

Sort A Python Dictionary By Value

It is quite common to sort a Python dictionary based on key. But what if you want to perform sorting on the value? In this article, I’m going to show you multiple ways to sort dictionary value and then return the resultant dictionary. Method 1: Using operator The very first method to sort dictionary value is by using sorted(…) function along with operator. So, let’s go ahead and do it as shown below: import operator students = {‘Shweta’:25,’Andy’:30,’Maddy’:3} students = sorted(students.items(), key=operator.itemgetter(1)) print (students) Method 2: Using lambda The second method to sort dictionary value is by using sorted(…) function along with lambda and the code looks as shown below: : import operator students = {‘Shweta’:25,’Andy’:30,’Maddy’:3} students = sorted(students.items(), key=lambda stud:stud[1]) print (students) Output On execution of above two methods, you will get exactly same output. I hope you enjoyed sorting your dictionary values in Python. If you have reached t