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In this article, I’ll explain about how we can pass an audio file to LLM and I’m taking OpenAI as our LLM . There are many people who prefer audio and video tutorials over reading along with our podcast lovers as listening seems to be more effective for them as compared to reading a book, an e-book or an article, and it is quite common that after a certain period of time, we may forget some of the portions of our tutorial. Now, in order to get the insights again, re-watching or re-listening is the only option, which could be very time-consuming. So, the best solution is to come up with a small AI-based application by writing just a few lines of code which can analyze the audio and respond to all the questions that are asked by the user. Here, utilizing generative AI could be the best option, but the problem is, we can’t pass audio directly as it is text-based. Let’s deep dive into this article, to understand how we can make this work in a step-by-step fashion. If this is what that int

In this article, I’ll explain how you can integrate ChatGPT inside Google docs and utilize the capabilities of any text based OpenAI model of your choice. If you are not aware what Google docs is — it is an offering by Google, where in you can create and collaborate documents online. Setting Up Google Docs You can go to https://docs.google.com/, login with your Gmail id and you are all set. If you have already created any document, you can open that otherwise you can go ahead and open a new document. New document will look something like this: In the above window, click on Extensions button and select Apps Script: On click of Apps Script will open up an editor wherein we will write our code. If you're looking for complete source code and explanation, then feel free to check out my article on Medium . Alternatively you can watch the video here .

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

If you want to explore Azure OpenAI but not sure how to get started, then you are at the right place. I've created a video, which explains everything about get your journey started. Have a look:

In this article, I’ll show you how you can use your locally stored text files to get response using GPT-3 . You can ask questions and get response like ChatGPT . On technology front, we will be using: OpenAI  Langchain Python Input files You can take bunch of text files and store them in a directory on your local machine. I’ve grabbed input data from here and created 5 text files. My files are all about ‘ Cause And Effect Of Homelessness ’ and are placed in a directory named Store. Import Required Packages As we are using Python , let’s go ahead and import the required packages. If you do not have above packages installed on your machine, then please go ahead and install these packages before importing. Once required packages are imported, we need to get OpenAI API key. Get OpenAI API Key To get the OpenAI key, you need to go to https://openai.com/, login and then grab the keys using highlighted way: Once you got the key, set that inside an environment variable(I’m using Windows). Load

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 https://openai.com/, login and then grab the keys using highlighted way: Once you got the key

Can you imagine a life of a programmer using an editor having no intellisense support? In today’s era where most of us are working in many programming languages at the same time, it is very difficult to remember the syntax of all the programming languages? The one thing which is most important is the underlying concept of any programming language and if you are good in those fundamentals, there are various ways to get help in writing code. It could be by using a search engine, by going through some book, by lending fellow coder’s hand, by using editor’s or IDEs capabilities, etc. My personal favorite way is by taking the help of IDE. I prefer the IDE which provides very good support for syntax and coding guidelines. In today’s article, I’m focusing on Jupyter notebook inside VS Code, which is a very common IDE these days when you are coding in Python . By default, syntax support or say intellisense support is not enabled in Jupyter Notebook in VS Code and hence we need to enable it us

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

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