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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 = " https://spmeapi.abc.com/invoices " result = fetch_data_from_api ( api_url ) print ( result ) Asynchro

Large Language Models (LLMs) are powerful AI systems that can understand and generate natural language. They have many applications in various domains, such as natural language processing, machine translation, and healthcare. However, building LLM-based applications is not a trivial task. It requires careful consideration of several factors, such as the choice of the LLM, the data quality, the evaluation metrics, and the ethical implications.  In this blog post, I will share some tips to solve most common problems. How to extract correct content from LLM Problem says that, although the answer is present in the content, but model fails to extract that.  Here are the quick tips to resolve this problem: Prompt compression Remove irrelevant data Rectify typos and grammatical errors Remove duplicate data Use data cleaning libraries Problem of missing top ranked documents Problem states that correct document was not rankled while ranking the documents. Here are the few suggestions, which can

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

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: