How-To Tutorials - ChatGPT

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights and books. Don't miss out – sign up today!IntroductionOne of the most important activities for an investor is to always keep up to date with the latest and relevant news. Usually, it’s done by reading at least a dozen news articles starting from macroeconomic issues, political issues, news related to the sector of the corresponding stock, analyst reports, and whatnot. This, of course, takes a lot of time and also sometimes can be overwhelming for new investors since the amount of information to be processed is too much.Many ML developers have tried to solve this issue by building a traditional ML workflow usually called the sentiment analyzer. This system will take text from the news as the input and return the sentiment score as the output. This is no doubt helpful for the investor, but it doesn’t solve the bigger problem which is the need to curate relevant articles and also knowing what’s the impact of each news toward their investment decision. In other words, it’s lacking of broader insight. What if there’s an AI assistant that can act as our personal investment news analyst? What if there’s an AI assistant that is able to analyze dozens of news articles and generate the insights summary along with the investment recommendation? And, what if I told you that this AI assistant is personalized toward your risk appetite and investment portfolio allocation? In this article, I’ll guide you on how to build an AI assistant that can do all the above-mentioned things with only a few lines of code - thanks to GPT4! We’ll discuss several ways to get the news data in bulk and in real-time. We’ll discuss what are the important search keywords we need to use to get relevant news data. We’ll also discuss how to construct the prompt to fulfill all of the above-mentioned criteria while also getting a great generated output. Finally, we’ll see how to put all of this together to build our AI assistant!Without wasting any more time, let’s take a deep breath, make yourselves comfortable, and be ready to learn how to build your personal AI investment news analyst!News Data SourcesGetting as much news data as possible is important since we don’t want to miss any important information out there. Once we get all the information, we just need to filter them out with the help of our AI assistant.SerpAPI is one of the best all-in-one scraping tools that we can utilize to get news data from Google, Yahoo, Bing, DuckDuckGo, and many other search engines. It also provides a free plan with a 100 searches/month limit. However, this limit is surely not enough for our use case. If you don’t mind spending some money and want to get multiple search results from different search engines, then this tool is suitable for you.Another solution that is more budget-friendly is by utilizing DuckDuckGo search engine API directly. DuckDuckGo is a search engine that offers data privacy as their main unique selling point. No search history will be stored. Moreover, they also open their search engine API for free. We will use DuckDuckGo in this article and learn how to utilize it via Python!The more effective way to widen our search results is actually not by using different search engines but by having a diverse yet mutually exclusive set of search keywords. The goal of our AI investment assistant is to summarize the important insights that are relevant to a particular stock that we’re interested in. Hence, we need to provide relevant news data to be able to achieve our goal.The following are some of the search keywords that we can use. Note that this list is not exhaustive, you can surely expand the search keywords based on your own needs. We’ll use AAPL as the ticker example. You can change it to any ticker you want.$AAPL stock $AAPL industry and competitors $AAPL business model and strategy $AAPL management and leadershipBesides ticker-specific search keywords, we can also search for more general information that is not ticker-specific. The following is an example list of such keywords.economic growth this yearmonetary and fiscal policies todaypolitic todayeconomic todayinflation rate todayinterest rate todayreal estate todayDuckDuckGo APIOnce we have the keywords list, we can easily get the news data using DuckDuckGo via Python. First, we need to install the duckduckgo package by running the following command. pip install duckduckgo-searchOnce it is installed we can create the general Python function that can take the search keyword as the input and return the search results.from duckduckgo_search import DDGS import json ddgs = DDGS() def web_search(query: str, num_results: int = 4,debug=True) -> str:    """Useful for general internet search queries."""    if debug:        print("Searching with query {0}...".format(query))    search_results = []    if not query:        return json.dumps(search_results)    results = ddgs.text(query)    if not results:        return json.dumps(search_results)    total_added = 0    for j in results:        search_results.append(j.get('body',''))        total_added += 1        if total_added >= num_results:            break    return search_resultsUsing this function is very simple. We just need to pass the search keyword along with the number of search results to this function and get the list of search results.apple_competitors_news = web_search(“$AAPL industry and competitors”, num_results = 10)Prompt EngineeringThe next important thing to do is to build our AI assistant. Here, we’ll utilize GPT4 to build our assistant. Since it’s an LLM, we just need to provide the prompt without the need to train it from scratch. However, creating the prompt itself is indeed not an easy task. I have published another article regarding prompt engineering if you’re interested to learn more about it.Remember that the goal of our assistant is to analyze the provided news data dump and return the summary insights along with the recommendation as the output. However, to be able to give a recommendation, our assistant needs to know our risk appetite along with our portfolio condition. The following is an example of the system prompt that we can give to GPT4.system_prompt = “””You are an expert in giving recommendation to BUY / SELL / HOLD for {} ({}). You can only return in JSON format with 5 fields: "Investment Thesis" (dictionary of string. Consist of elaborated decision reasoning (in bullet points) based on the risk profile of the investor, unrealized profit, and all of the factors as the basis of your recommendation. Provide numbers to justify your assertions, a lot ideally. The deeper the analysis the better.), "Investor Profiling" (dictionary of string. Connect the investment thesis with each of the investor profiles, including risk profile and unrealized profit.) "Summary Thesis" (string. Summary of your all investment thesis as the basis of the given recommendation.  You have to take into account all factors in the investment thesis as well as the investor profiles.), "recommendation" ("BUY"/"SELL"/"HOLD") In the investment thesis, please cover the following factors. If a particular factor needed to write the investment thesis does not exist, don't try to make up the answer, just write "The information needed is unavailable". (1) Industry and Competitive Analysis: Assess the company's position within its industry and analyze industry trends, competition, barriers to entry, and market dynamics. (2) News and Events: Stay updated on relevant news, earnings announcements, product launches, regulatory changes, and other events that can impact the company or the overall market. (3) Market and Economic Conditions: Assess broader macroeconomic factors from news, including economic growth, interest rates, inflation, monetary and fiscal policies, geopolitical events, gold price, bond price, index price, real estate.”””And here’s an example of the user prompt that consists of all necessary data points. Risk profiles can be “Moderate”, “Aggresive”, or “Conservative”. user_prompt = “””<INVESTOR PROFILE> Risk Profile: {} Unrealized Profit: {}% {}”””Putting All TogetherNow, we just need to create the main function that will act as our personal AI investment assistant. def personal_investment_assistant(company_name:str, ticker:str, risk_profile: str,  unrealized_profit_perc: float):    news_data = []    for search_keyword in search_kwrds_lst:          news_data.extend(web_search(search_keyword))    news_data = "\n".join(news_data)            messages = [                        {                            "role": "system",                            "content": system_prompt.format(company_name,ticker)                        },                        {                            "role": "user",                            "content": user_prompt.format(risk_profile,unrealized_profit_perc,news_data)                }            ]    response = get_gpt_response("gpt-4",                                temperature = 0.0,                                messages = messages                                                )    return response["choices"][0]["message"]["content"].strip() import requests import json import os def get_gpt_response(model: str,temperature: float,messages: list): headers = {                       'content-type': "application/json",                       'Authorization': "Bearer " + os.environ["OPENAI_API_KEY"]                       } endpoint = 'https://api.openai.com/v1/chat/completions'           data = json.dumps({                                   "model": model, "messages": messages,                                   "temperature": temperature,                                   })             try: data = requests.post(endpoint, data=data, headers=headers)                       openai_response = json.loads(data.text)                       return openai_response           except Exception as e:                       print(e)                       return ""ConclusionCongratulations on keeping up to this point! Throughout this article, you have learned how to build your own personal AI investment analyst based on news data. You have learned how to get the news data, a list of useful search keywords, also the code implementation to build the AI assistant. Hope the best for your investment journey and see you in the next article!Author BioLouis Owen is a data scientist/AI engineer from Indonesia who is always hungry for new knowledge. Throughout his career journey, he has worked in various fields of industry, including NGOs, e-commerce, conversational AI, OTA, Smart City, and FinTech. Outside of work, he loves to spend his time helping data science enthusiasts to become data scientists, either through his articles or through mentoring sessions. He also loves to spend his spare time doing his hobbies: watching movies and conducting side projects. Currently, Louis is an NLP Research Engineer at Yellow.ai, the world’s leading CX automation platform. Check out Louis’ website to learn more about him! Lastly, if you have any queries or any topics to be discussed, please reach out to Louis via LinkedIn.

This article is an excerpt from the book, "Principles of Data Science", by Sinan Ozdemir. This book provides an end-to-end framework for cultivating critical thinking about data, performing practical data science, building performant machine learning models, and mitigating bias in AI pipelines. Learn the fundamentals of computational math and stats while exploring modern machine learning and large pre-trained models.IntroductionTransfer learning (TL) has revolutionized the field of deep learning by enabling pre-trained models to adapt their broad, generalized knowledge to specific tasks with minimal labeled data. This article delves into TL with BERT and GPT, demonstrating how to fine-tune these advanced models for text classification and image classification tasks. Through hands-on examples, we illustrate how TL leverages pre-trained architectures to simplify complex problems and achieve high accuracy with limited data.TL with BERT and GPTIn this article, we will take some models that have already learned a lot from their pre-training and fine-tune them to perform a new, related task. This process involves adjusting the model’s parameters to better suit the new task, much like fine-tuning a musical instrument:Figure 12.8 – ITLITL takes a pre-trained model that was generally trained on a semi-supervised (or unsupervised) task and then is given labeled data to learn a specific task.Examples of TLLet’s take a look at some examples of TL with specific pre-trained models.Example – Fine-tuning a pre-trained model for text classificationConsider a simple text classification problem. Suppose we need to analyze customer reviews and determine whether they’re positive or negative. We have a dataset of reviews, but it’s not nearly large enough to train a deep learning (DL) model from scratch. We will fine-tune BERT on a text classification task, allowing the model to adapt its existing knowledge to our specific problem.We will have to move away from the popular scikit-learn library to another popular library called transformers, which was created by HuggingFace (the pre-trained model repository I mentioned earlier) as scikit-learn does not (yet) support Transformer models.Figure 12.9 shows how we will have to take the original BERT model and make some minor modifications to it to perform text classification. Luckily, the transformers package has a built-in class to do this for  us called BertForSequenceClassification:Figure 12.9 – Simplest text classification caseIn many TL cases, we need to architect additional layers. In the simplest text classification case, we add a classification layer on top of a pre-trained BERT model so that it can perform the kind of classification we want.The following code block shows an end-to-end code example of fine-tuning BERT on a text classification task. Note that we are also using a package called datasets, also made by HuggingFace, to load a sentiment classification task from IMDb reviews. Let’s  begin by loading up the dataset:# Import necessary libraries from datasets import load_dataset from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments # Load the dataset imdb_data = load_dataset('imdb', split='train[:1000]') # Loading only 1000 samples for a toy example # Define the tokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # Preprocess the data def encode(examples): return tokenizer(examples['text'], truncation=True, padding='max_ length', max_length=512) imdb_data = imdb_data.map(encode, batched=True) # Format the dataset to PyTorch tensors imdb_data.set_format(type='torch', columns=['input_ids', 'attention_ mask', 'label'])With our dataset loaded up, we can run some training code to update our BERT model on our labeled data:# Define the model model = BertForSequenceClassification.from_pretrained( 'bert-base-uncased', num_labels=2) # Define the training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=1, per_device_train_batch_size=4 ) # Define the trainer trainer = Trainer(model=model, args=training_args, train_dataset=imdb_ data) # Train the model trainer.train() # Save the model model.save_pretrained('./my_bert_model')Once we have our saved model, we can use the following code to run the model against unseen data:from transformers import pipeline # Define the sentiment analysis pipeline nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) # Use the pipeline to predict the sentiment of a new review review = "The movie was fantastic! I enjoyed every moment of it." result = nlp(review) # Print the result print(f"label: {result[0]['label']}, with score: {round(result[0] ['score'], 4)}") # "The movie was fantastic! I enjoyed every moment of it." # POSITIVE: 99%Example – TL for image classificationWe could take a pre-trained model such as ResNet or the Vision Transformer (shown in Figure 12.10), initially trained on a large-scale image dataset such as ImageNet. This model has already learned to detect various features from images, from simple shapes to complex objects. We can take advantage of this knowledge, fi ne-tuning  the model on a custom image classification task:Figure 12.10 – The Vision TransformerThe Vision Transformer is like a BERT model for images. It relies on many of the same principles, except instead of text tokens, it uses segments of images as “tokens” instead.The following code block shows an end-to-end code example of fine-tuning the Vision Transformer on an image classification task. The code should look very similar to the BERT code from the previous section because the aim of the transformers library is to standardize training and usage of modern pre-trained models so that no matter what task you are performing, they can offer a relatively unified training and inference experience.Let’s begin by loading up our data and taking a look at the kinds of images we have (seen in Figure 12.11). Note that we are only going to use 1% of the dataset to show that you really don’t need that much data to get a lot out of pre-trained models!# Import necessary libraries from datasets import load_dataset from transformers import ViTImageProcessor, ViTForImageClassification from torch.utils.data import DataLoader import matplotlib.pyplot as plt import torch from torchvision.transforms.functional import to_pil_image # Load the CIFAR10 dataset using Hugging Face datasets # Load only the first 1% of the train and test sets train_dataset = load_dataset("cifar10", split="train[:1%]") test_dataset = load_dataset("cifar10", split="test[:1%]") # Define the feature extractor feature_extractor = ViTImageProcessor.from_pretrained('google/vitbase-patch16-224') # Preprocess the data def transform(examples): # print(examples) # Convert to list of PIL Images examples['pixel_values'] = feature_ extractor(images=examples["img"], return_tensors="pt")["pixel_values"] return examples # Apply the transformations train_dataset = train_dataset.map( transform, batched=True, batch_size=32 ).with_format('pt') test_dataset = test_dataset.map( transform, batched=True, batch_size=32 ).with_format('pt')We can similarly use the model using the following code:Figure 12.11 – A single example from CIFAR10 showing an airplaneNow, we can train our pre-trained Vision Transformer:# Define the model model = ViTForImageClassification.from_pretrained( 'google/vit-base-patch16-224', num_labels=10, ignore_mismatched_sizes=True ) LABELS = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] model.config.id2label = LABELS # Define a function for computing metrics def compute_metrics(p): predictions, labels = p preds = np.argmax(predictions, axis=1) return {"accuracy": accuracy_score(labels, preds)} # Define the training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=5, per_device_train_batch_size=4, load_best_model_at_end=True, # Save and evaluate at the end of each epoch evaluation_strategy='epoch', save_strategy='epoch' ) # Define the trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=test_dataset )Our final model has about 95% accuracy on 1% of the test set. We can now use our new classifier on unseen images, as in this next code block:from PIL import Image from transformers import pipeline # Define an image classification pipeline classification_pipeline = pipeline( 'image-classification', model=model, feature_extractor=feature_extractor ) # Load an image image = Image.open('stock_image_plane.jpg') # Use the pipeline to classify the image result = classification_pipeline(image)Figure 12.12 shows the result of this single classification, and it looks like it did pretty well:Figure 12.12 – Our classifier predicting a stock image of a plane correctlyWith minimal labeled data, we can leverage TL to turn models off the shelf into powerhouse predictive models.ConclusionTransfer learning is a transformative technique in deep learning, empowering developers to harness the power of pre-trained models like BERT and the Vision Transformer for specialized tasks. From sentiment analysis to image classification, these models can be fine-tuned with minimal labeled data, offering impressive performance and adaptability. By using libraries like HuggingFace’s transformers, TL streamlines model training, making state-of-the-art AI accessible and versatile across domains. As demonstrated in this article, TL is not only efficient but also a practical way to achieve powerful predictive capabilities with limited resources.Author BioSinan is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including "Quick Start Guide to LLMs". Sinan is currently the founder of LoopGenius which uses AI to help people and businesses boost their sales and was previously the founder of the acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a Master’s Degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights and books. Don't miss out – sign up today!IntroductionThe emergence of a massive language model is a watershed moment in the field of artificial intelligence (AI) and natural language processing (NLP). Because of their extraordinary capacity to write human-like text and perform a range of language-related tasks, these models, which are based on deep learning techniques, have earned considerable interest and acceptance. This field has undergone significant scientific developments in recent years. Researchers all over the world have been developing better and more domain-specific LLMs to meet the needs of various use cases.Large Language Models (LLMs) such as GPT-3 and its descendants, like any technology or strategy, have downsides and limits. And, in order to use LLMs properly, ethically, and to their maximum capacity, it is critical to grasp their downsides and limitations. Unlike large language models such as GPT-4, which can follow the majority of commands. Language models that are not equivalently large enough (such as GPT-2, LLaMa, and its derivatives) frequently suffer from the difficulty of not following instructions adequately, particularly the part of instruction that asks for generating output in a specific structure. This causes a bottleneck when constructing a pipeline in which the output of LLMs is fed to other downstream functions.Introducing Guidance - an effective and efficient means of controlling modern language models compared to conventional prompting methods. It supports both open (LLaMa, GPT-2, Alpaca, and so on) and closed LLMs (ChatGPT, GPT-4, and so on). It can be considered as a part of a larger ecosystem of tools for expanding the capabilities of language models.Guidance uses Handlebars - a templating language. Handlebars allow us to build semantic templates effectively by compiling templates into JavaScript functions. Making it’s execution faster than other templating engines. Guidance also integrates well with Jsonformer - a bulletproof way to generate structured JSON from language models. Here’s a detailed notebook on the same. Also, in case you were to use OpenAI from Azure AI then Guidance has you covered - notebook.Moving on to some of the outstanding features that Guidance offers. Feel free to check out the entire list of features.Features1. Guidance Acceleration - This addition significantly improves inference performance by efficiently utilizing the Key/Value caches as we proceed through the prompt by keeping a session state with the LLM inference. Benchmarking revealed a 50% reduction in runtime when compared to standard prompting approaches. Here’s the link to one of the benchmarking exercises. The below image shows an example of generating a character profile of an RPG game in JSON format. The green highlights are the generations done by the model, whereas the blue and no highlights are the ones that are copied as it is from the input prompt, unlike the traditional method that tries to generate every bit of it.SourceNote: As of now, the Guidance Acceleration feature is implemented for open LLMs. We can soon expect to see if working with closed LLMs as well.2.  Token Healing - This feature attempts to correct tokenization artifacts that commonly occur at the border between the end of a prompt and the start of a group of generated tokens.For example - If we ask LLM to auto-complete a URL with the below-mentioned Input, it’s likely to produce the shown output. Apart from the obvious limitation that the URL might not be valid. I'd like to draw your attention to the extra space it creates (highlighted in red). Such considerations make it difficult to construct a dependable parsing function and robustly absorb its result into subsequent phases.Input: “The link is <a href=http:”Actual Output: “The link is <a href=http: //www.google.com/search?q”Expected Output: “The link is <a href=http://www.google.com/search?q” This is the exact bucket of problems that Token Healing tries to solve using the backtracking method. Feel free to check out this jupyter notebook for more examples.3. Guaranteed Output Structure - Large language models are fantastic at producing useful outputs, but not so much at producing outputs in a specified format (especially open-source ones like LLaMa, GPT-2, and so on). When we want to use the output of a language model as input to another system, this is frequently an issue. With Handlebars, guidance guarantees the output format to be the same as what was being asked for.Let’s now see Guidance in action -InstallationInstalling guidance is a breeze, just do a pip :$ pip install guidanceAssume we are now creating a product description for an e-commerce website. Here's how the traditional generation compares to the guidance generation. Feel free to play with this colab notebook with both the below examples.Traditional GenerationInput:Consider you are an e-commerce expert. You need to write a product description for a product to be listed on the e-commerce website. The product description consists of fixed set of fields to be filled in the JSON. The below shown JSON is the structure of the description with constraints for each of the attributes enclosed within < > brackets. Please follow the mentioned constraint and format diligently. { prod_id: <numeric value of 5 digits>, prod_name: <name starts with the prefix 'p_'>, prod_price: <should be an integer between 1 and 16. Should end with suffix '$'> } The product description isOutput:Consider you are an e-commerce expert. You need to write a product description for a product to be listed on the e-commerce website. The product description consists of fixed set of fields to be filled in the JSON. The below shown JSON is the structure of the description with constraints for each of the attributes enclosed within < > brackets. Please follow the mentioned constraint and format diligently. {   prod_id: <numeric value of 5 digits>,     prod_name: <name starts with the prefix 'p_'>, prod_price: <should be an integer between 1 and 16. Should end with suffix '$'> } The product description is { resentprod_id: <numeric value of 5 digits>, resentprod_name: <name begins with the prefix 'p_'>, resentprod_price: <should be an integer between 1 and 16. Should end with suffix '$'> } In the above example, the product description has 5 constraint fields and 5 attribute fields. The constraints are as follows: resentprod_id: - value of 5 digits, resentprod_name: - name of the product, resentprod_price: - price of the product, resentprod_price_suffix: - suffix of the product price, resentprod_id: - the product id, resentpro diabetic_id: value of 4 digits, resentprod_ astronomer_id: - value of 4 digits, resentprod_ star_id: - value of 4 digits, resentprod_is_generic: - if the product is generic and not the generic type, resentprod_type: - the type of the product, resentprod_is_generic_typeHere’s the code for the above example with GPT-2 language model -``` from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("gpt2-large") model = AutoModelForCausalLM.from_pretrained("gpt2-large") inputs = tokenizer(Input, return_tensors="pt") tokens = model.generate( **inputs, max_new_tokens=256, temperature=0.7, do_sample=True, )Output:tokenizer.decode(tokens[0], skip_special_tokens=True)) ```Guidance GenerationInput w/ code:guidance.llm = guidance.llms.Transformers("gpt-large") # define the prompt program = guidance("""Consider you are an e-commerce expert. You need to write a product description for a product to be listed on the e-commerce website. The product description consists of fixed set of fields to be filled in the JSON. The following is the format ```json { "prod_id": "{{gen 'id' pattern='[0-9]{5}' stop=','}}", "prod_name": "{{gen 'name' pattern='p_[A-Za-z]+' stop=','}}", "prod_price": "{{gen 'price' pattern='\b([1-9]|1[0-6])\b\$' stop=','}}" }```""") # execute the prompt Output = program()Output:Consider you are an e-commerce expert. You need to write a product description for a product to be listed on the e-commerce website. The product description consists of a fixed set of fields to be filled in the JSON. The following is the format```json { "prod_id": "11231", "prod_name": "p_pizzas", "prod_price": "11$" }```As seen in the preceding instances, with guidance, we can be certain that the output format will be followed within the given restrictions no matter how many times we execute the identical prompt. This capability makes it an excellent choice for constructing any dependable and strong multi-step LLM pipeline.I hope this overview of Guidance has helped you realize the value it may provide to your daily prompt development cycle. Also, here’s a consolidated notebook showcasing all the features of Guidance, feel free to check it out.Author BioPrakhar has a Master’s in Data Science with over 4 years of experience in industry across various sectors like Retail, Healthcare, Consumer Analytics, etc. His research interests include Natural Language Understanding and generation, and has published multiple research papers in reputed international publications in the relevant domain. Feel free to reach out to him on LinkedIn

IntroductionIn the ever-evolving landscape of artificial intelligence, a groundbreaking collaboration has emerged between Wolfram Alpha and ChatGPT, giving birth to an extraordinary plugin: the AI Advantage. This partnership bridges the gap between ChatGPT's proficiency in natural language processing and Wolfram Alpha's computational prowess. The result? A fusion that unlocks an array of new possibilities, revolutionizing the way we interact with AI. In this hands-on tutorial, we're embarking on a journey to explore the power of the Wolfram Alpha API, demonstrate its integration with Python and ChatGPT, and empower you to tap into this dynamic duo for tasks ranging from complex calculations to real-time data retrieval.Understanding Wolfram Alpha APIImagine having an intelligent assistant at your fingertips, capable of not only understanding your questions but also providing detailed computational insights. That's where Wolfram Alpha shines. It's more than just a search engine; it's a computational knowledge engine. Whether you need to solve a math problem, retrieve real-time data, or generate visual content, Wolfram Alpha has you covered. Its unique ability to compute answers based on structured data sets it apart from traditional search engines.So, how can you tap into this treasure trove of computational knowledge? Enter the Wolfram Alpha API. This API exposes Wolfram Alpha's capabilities for developers to harness in their applications. Whether you're building a chatbot, a data analysis tool, or an educational resource, the Wolfram Alpha API can provide you with instant access to accurate and in-depth information. The API supports a wide range of queries, from straightforward calculations to complex data retrievals, making it a versatile tool for various use cases.Integrating Wolfram Alpha API with ChatGPTChatGPT's strength lies in its ability to understand and generate human-like text based on input. However, when it comes to intricate calculations or pulling real-time data, it benefits from a partner like Wolfram Alpha. By integrating the two, you create a dynamic synergy where ChatGPT can effortlessly tap into Wolfram Alpha's computational engine to provide accurate and data-driven responses. This collaboration bridges the gap between language understanding and computation, resulting in a well-rounded AI interaction.Before we dive into the technical implementation, let's get you set up to take advantage of the Wolfram Alpha plugin for ChatGPT. First, ensure you have access to ChatGPT+. To enable the Wolfram plugin, follow these steps:Open the ChatGPT interface.Navigate to "Settings."Look for the "Beta Features" section.Enable "Plugins" under the GPT-4 options.Once "Plugins" is enabled, locate and activate the Wolfram plugin.With the plugin enabled you're ready to harness the combined capabilities of ChatGPT and Wolfram Alpha API, making your AI interactions more robust and informative.In the next sections, we'll dive into practical applications and walk you through implementing the integration using Python and ChatGPT.Practical Applications with Code ExamplesLet's start by exploring how the Wolfram Alpha API can assist with complex mathematical tasks. Below are code examples that demonstrate the integration between ChatGPT and Wolfram Alpha to solve intricate math problems. In these scenarios, ChatGPT serves as the bridge between you and Wolfram Alpha, seamlessly delivering accurate solutions.Before diving into the code implementation, let's ensure your environment is ready to go. Follow these steps to set up the necessary components:Install the required packages: Make sure you have the necessary Python packages installed. You can use pip to install them:pip install langchain openai wolframalphaNow, let's walk through implementing the code example you provided earlier. This code integrates the Wolfram Alpha API with ChatGPT to provide accurate and informative responses:Wolfram Alpha can solve simple arithmetic queries:# User input question = "Solve for x: 2x + 5 = 15" # Let ChatGPT interact with Wolfram Alpha response = agent_chain.run(input=question) # Extracting and displaying the result from the response result = response['text'] print("Solution:", result)Or mode complex ones like calculating integrals:# User input question = "Calculate the integral of x^2 from 0 to 5" # Let ChatGPT interact with Wolfram Alpha response = agent_chain.run(input=question) # Extracting and displaying the result from the response print("Integral:", response) Real-time Data RetrievalIncorporating real-time data into conversations can greatly enhance the value of AI interactions. Here are code examples showcasing how to retrieve up-to-date information using the Serper API and integrate it seamlessly into the conversation:# User input question = "What's the current exchange rate between USD and EUR?" # Let ChatGPT interact with Wolfram Alpha response = agent_chain.run(input=question) # Extracting and displaying the result from the response print("Exchange Rate:", response)We can also ask for the current weather forecast:# User input question = "What's the weather forecast for London tomorrow?" # Let ChatGPT interact with Wolfram Alpha response = agent_chain.run(input=question) # Extracting and displaying the result from the response print("Weather Forecast:", response)Now we can put everything together into a single block including all the required library imports and use both real time data with Serper and use the reasoning skills of Wolfram Alpha.# Import required libraries from langchain.agents import load_tools, initialize_agent from langchain.llms import OpenAI from langchain.memory import ConversationBufferMemory from langchain.chat_models import ChatOpenAI # Set environment variables import os os.environ['OPENAI_API_KEY'] = 'your-key' os.environ['WOLFRAM_ALPHA_APPID'] = 'your-key' os.environ["SERPER_API_KEY"] = 'your-key' # Initialize the ChatGPT model llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo") # Load tools and set up memory tools = load_tools(["google-serper", "wolfram-alpha"], llm=llm) memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) # Initialize the agent agent_chain = initialize_agent(tools, llm, handle_parsing_errors=True, verbose=True, memory=memory) # Interact with the agent response_weather = agent_chain.run(input="what is the weather in Amsterdam right now in celcius? Don't make assumptions.") response_flight = agent_chain.run(input="What's a good price for a flight from JFK to AMS this weekend? Express the price in Euros. Don't make assumptions.")ConclusionIn this tutorial, we've delved into the exciting realm of integrating the Wolfram Alpha API with Python and ChatGPT. We've explored how this collaboration empowers you to tackle complex mathematical tasks and retrieve real-time data seamlessly. By harnessing the capabilities of both Wolfram Alpha and ChatGPT, you've unlocked a powerful synergy that's capable of transforming your AI interactions. As you continue to explore and experiment with this integration, you'll discover new ways to enhance your interactions and leverage the strengths of each tool. So, why wait? Start your journey toward more informative and engaging AI interactions today.Author BioAlan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a Mechanical Engineering degree from the National University of Tucuman in 2015, participated as the founder of startups, and later on earned a Master's degree from the faculty of Mathematics in the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.LinkedIn 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights and books. Don't miss out – sign up today!IntroductionIn the era of artificial intelligence, ChatGPT stands as a remarkable example of natural language understanding and generation. Developed by OpenAI, ChatGPT is an advanced language model designed to comprehend and generate human-like text, making it a versatile tool for a wide range of applications.One of the critical domains where ChatGPT can make a significant impact is time series analysis. Time series data, consisting of sequential observations over time, is fundamental across industries such as finance, healthcare, and energy. It enables organizations to uncover trends, forecast future values, and detect anomalies, all of which are invaluable for data-driven decision-making. Whether it's predicting stock prices, monitoring patient health, or optimizing energy consumption, the ability to analyze time series data accurately is paramount.The purpose of this article is to explore the synergy between ChatGPT and time series analysis. We will delve into how ChatGPT's natural language capabilities can be harnessed to streamline data preparation, improve forecasting accuracy, and enhance anomaly detection in time series data. Through practical examples and code demonstrations, we aim to illustrate how ChatGPT can be a powerful ally for data scientists and analysts in their quest for actionable insights from time series data.1. Understanding Time Series Data Time series data is a specialized type of data that records observations, measurements, or events at successive time intervals. Unlike cross-sectional data, which captures information at a single point in time, time series data captures data points in a sequential order, often with a regular time interval between them. This temporal aspect makes time series data unique and valuable for various applications. Characteristics of Time Series Data:Temporal Order: Time series data is ordered chronologically, with each data point associated with a specific timestamp or time period.Dependency: Data points in a time series are often dependent on previous observations, making them suitable for trend analysis and forecasting.Seasonality: Many time series exhibit repetitive patterns or seasonality, which can be daily, weekly, monthly, or annual, depending on the domain.Noise and Anomalies: Time series data may contain noise, irregularities, and occasional anomalies that need to be identified and addressed. Real-World Applications of Time Series Analysis:Time series analysis is a crucial tool in numerous domains, including:Finance: Predicting stock prices, currency exchange rates, and market trends.Healthcare: Monitoring patient vital signs, disease progression, and healthcare resource optimization.Energy: Forecasting energy consumption, renewable energy generation, and grid management.Climate Science: Analyzing temperature, precipitation, and climate patterns.Manufacturing: Quality control, demand forecasting, and process optimization.Economics: Studying economic indicators like GDP, inflation rates, and unemployment rates. Emphasis on Powerful Tools and Techniques:The complexity of time series data necessitates the use of powerful tools and techniques. Effective time series analysis often involves statistical methods, machine learning models, and data preprocessing steps to extract meaningful insights. In this article, we will explore how ChatGPT can complement these techniques to facilitate various aspects of time series analysis, from data preparation to forecasting and anomaly detection. 2. ChatGPT OverviewChatGPT, developed by OpenAI, represents a groundbreaking advancement in natural language processing. It builds upon the success of its predecessors, like GPT-3, with a focus on generating human-like text and facilitating interactive conversations.Background: ChatGPT is powered by a deep neural network architecture called the Transformer, which excels at processing sequences of data, such as text. It has been pre-trained on a massive corpus of text from the internet, giving it a broad understanding of language and context. Capabilities: ChatGPT possesses exceptional natural language understanding and generation abilities. It can comprehend and generate text in a wide range of languages and styles, making it a versatile tool for communication, content generation, and now, data analysis. Aiding Data Scientists: For data scientists, ChatGPT offers invaluable assistance. Its ability to understand and generate text allows it to assist in data interpretation, data preprocessing, report generation, and even generating code snippets. In the context of time series analysis, ChatGPT can help streamline tasks, enhance communication, and contribute to more effective analysis by providing human-like interactions with data and insights. This article will explore how data scientists can harness ChatGPT's capabilities to their advantage in the realm of time series data. 3. Preparing Time Series DataData preprocessing is a critical step in time series analysis, as the quality of your input data greatly influences the accuracy of your results. Inaccurate or incomplete data can lead to flawed forecasts and unreliable insights. Therefore, it's essential to carefully clean and prepare time series data before analysis.Importance of Data Preprocessing:1. Missing Data Handling: Time series data often contains missing values, which need to be addressed. Missing data can disrupt calculations and lead to biased results.2. Noise Reduction: Raw time series data can be noisy, making it challenging to discern underlying patterns. Data preprocessing techniques can help reduce noise and enhance signal clarity.3. Outlier Detection: Identifying and handling outliers is crucial, as they can significantly impact analysis and forecasting.4. Normalization and Scaling: Scaling data to a consistent range is important, especially when using machine learning algorithms that are sensitive to the magnitude of input features.5. Feature Engineering: Creating relevant features, such as lag values or rolling statistics, can provide additional information for analysis.Code Examples for Data Preprocessing:Here's an example of how to load, clean, and prepare time series data using Python libraries like Pandas and NumPy:import pandas as pd import numpy as np # Load time series data data = pd.read_csv("time_series_data.csv") # Clean and preprocess data data['Date'] = pd.to_datetime(data['Date']) data.set_index('Date', inplace=True) # Resample data to handle missing values (assuming daily data) data_resampled = data.resample('D').mean() data_resampled.fillna(method='ffill', inplace=True) # Feature engineering (e.g., adding lag features) data_resampled['lag_1'] = data_resampled['Value'].shift(1) data_resampled['lag_7'] = data_resampled['Value'].shift(7) # Split data into training and testing sets train_data = data_resampled['Value'][:-30] test_data = data_resampled['Value'][-30:]4. ChatGPT for Time Series ForecastingChatGPT's natural language understanding and generation capabilities can be harnessed effectively for time series forecasting tasks. It can serve as a powerful tool to streamline forecasting processes, provide interactive insights, and facilitate communication within a data science team.Assisting in Time Series Forecasting:1. Generating Forecast Narratives: ChatGPT can generate descriptive narratives explaining forecast results in plain language. This helps in understanding and communicating forecasts to non-technical stakeholders.2. Interactive Forecasting: Data scientists can interact with ChatGPT to explore different forecasting scenarios. By providing ChatGPT with context and queries, you can receive forecasts for various time horizons and conditions.3. Forecast Sensitivity Analysis: You can use ChatGPT to explore the sensitivity of forecasts to different input parameters or assumptions. This interactive analysis can aid in robust decision-making.Code Example for Using ChatGPT in Forecasting:Below is a code example demonstrating how to use ChatGPT to generate forecasts based on prepared time series data. In this example, we use the OpenAI API to interact with ChatGPT for forecasting:import openai openai.api_key = "YOUR_API_KEY" def generate_forecast(query, historical_data):    prompt = f"Forecast the next data point in the time series: '{historical_data}'. The trend appears to be {query}."    response = openai.Completion.create(        engine="text-davinci-002",        prompt=prompt,        max_tokens=20,  # Adjust for desired output length        n=1,  # Number of responses to generate        stop=None,  # Stop criteria    )    forecast = response.choices[0].text.strip()    return forecast # Example usage query = "increasing" forecast = generate_forecast(query, train_data) print(f"Next data point in the time series: {forecast}")5. ChatGPT for Anomaly DetectionChatGPT can play a valuable role in identifying anomalies in time series data by leveraging its natural language understanding capabilities. Anomalies, which represent unexpected and potentially important events or errors, are crucial to detect in various domains, including finance, healthcare, and manufacturing. ChatGPT can assist in this process in the following ways:Contextual Anomaly Descriptions: ChatGPT can provide human-like descriptions of anomalies, making it easier for data scientists and analysts to understand the nature and potential impact of detected anomalies.Interactive Anomaly Detection: Data scientists can interact with ChatGPT to explore potential anomalies and receive explanations for detected outliers. This interactive approach can aid in identifying false positives and false negatives, enhancing the accuracy of anomaly detection.Code Example for Using ChatGPT in Anomaly Detection:Below is a code example demonstrating how to use ChatGPT to detect anomalies based on prepared time series data: import openai openai.api_key = "YOUR_API_KEY" def detect_anomalies(query, historical_data):    prompt = f"Determine if there are any anomalies in the time series: '{historical_data}'. The trend appears to be {query}."    response = openai.Completion.create(        engine="text-davinci-002",        prompt=prompt,        max_tokens=20,  # Adjust for desired output length        n=1,  # Number of responses to generate        stop=None,  # Stop criteria    )    anomaly_detection_result = response.choices[0].text.strip()    return anomaly_detection_result # Example usage query = "increasing with a sudden jump" anomaly_detection_result = detect_anomalies(query, train_data) print(f"Anomaly detection result: {anomaly_detection_result}")6. Limitations and ConsiderationsWhile ChatGPT offers significant advantages in time series analysis, it is essential to be aware of its limitations and consider certain precautions for its effective utilization: 1. Lack of Domain-Specific Knowledge: ChatGPT lacks domain-specific knowledge. It may generate plausible-sounding but incorrect insights, especially in specialized fields. Data scientists should always validate its responses with domain expertise.2. Sensitivity to Input Wording: ChatGPT's responses can vary based on the phrasing of input queries. Data scientists must carefully frame questions to obtain accurate and consistent results.3. Biases in Training Data: ChatGPT can inadvertently perpetuate biases present in its training data. When interpreting its outputs, users should remain vigilant about potential biases and errors.4. Limited Understanding of Context: ChatGPT's understanding of context has limitations. It may not remember information provided earlier in a conversation, which can lead to incomplete or contradictory responses.5. Uncertainty Handling: ChatGPT does not provide uncertainty estimates for its responses. Data scientists should use it as an assistant and rely on robust statistical techniques for decision-making. Best PracticesDomain Expertise: Combine ChatGPT's insights with domain expertise to ensure the accuracy and relevance of its recommendations.Consistency Checks: Ask ChatGPT multiple variations of the same question to assess the consistency of its responses.Fact-Checking: Verify critical information and predictions generated by ChatGPT with reliable external sources.Iterative Usage: Incorporate ChatGPT iteratively into your workflow, using it to generate ideas and hypotheses that can be tested and refined with traditional time series analysis methods.Bias Mitigation: Implement bias mitigation techniques when using ChatGPT in sensitive applications to reduce the risk of biased responses.Understanding the strengths and weaknesses of ChatGPT and taking appropriate precautions will help data scientists harness its capabilities effectively while mitigating potential errors and biases in time series analysis tasks.ConclusionIn summary, ChatGPT offers a transformative approach to time series analysis. It bridges the gap between natural language understanding and data analytics, providing data scientists with interactive insights, forecasting assistance, and anomaly detection capabilities. Its potential to generate human-readable narratives, explain anomalies, and explore diverse scenarios makes it a valuable tool in various domains. However, users must remain cautious of its limitations, verify critical information, and employ it as a supportive resource alongside established analytical methods. As technology evolves, ChatGPT continues to demonstrate its promise as a versatile and collaborative companion in the pursuit of actionable insights from time series data.Author BioBhavishya Pandit is a Data Scientist at Rakuten! He has been extensively exploring GPT to find use cases and build products that solve real-world problems.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionChatGPT is an efficient language that may be used in a range of tasks, including the creation of SQL queries. In this article, you will get to know how effectively you will be able to use SQL queries by using ChatGPT to optimize and craft them correctly to get perfect results.It is necessary to have sufficient SQL knowledge before you can use ChatGPT for the creation of SQL queries. The language that the databases are communicating with is SQL. This is meant to be used for the production, reading, updating, and deletion of data from databases. SQL is the most specialized language in this domain. It's one of the main components in a lot of existing applications because it deals with structured data that can be retrieved from tables.There are a number of different SQL queries, but some more common ones include the following:SELECT: It will select data from a database.INSERT: It will insert new data into a database.UPDATE: This query will update the existing data in a database.DELETE: This query is used to delete data from a database.Using ChatGPT to write SQL queriesOnce you have a basic understanding of SQL, you can start using ChatGPT to write SQL queries. To do this, you need to provide ChatGPT with a description of the query that you want to write. After that, ChatGPT will generate the SQL code for you.For example, you could just give ChatGPT the query below to write an SQL query to select all of the customers in your database.Select all of the customers in my databaseFollowing that, ChatGPT will provide the SQL code shown below:SELECT * FROM customers;The customer table's entire set of columns will be selected by this query. Additionally, ChatGPT can be used to create more complex SQL statements.How to Use ChatGPT to Describe Your IntentionsNow let’s have a look at some examples where we will ask ChatGPT to generate SQL code by asking it queries from our side.For Example:We'll be creating a sample database for ChatGPT, so we can ask them to set up restaurant databases and two tables.ChatGPT prompt:Create a sample database with two tables: GuestInfo and OrderRecords. The GuestInfo table should have the following columns: guest_id, first_name, last_name, email_address, and contact_number. The OrderRecords table should have the following columns: order_id, guest_id, product_id, quantity_ordered, and order_date.ChatGPT SQL Query Output:We requested that ChatGPT create a database and two tables in this example. After it generated a SQL query. The following SQL code is to be executed on the Management Studio software for SQL Server. As we are able to see the code which we got from ChatGPT successfully got executed in the SSMS Database software.How ChatGPT Can Be Used for Optimizing, Crafting, and Debugging Your QueriesSQL is an efficient tool to manipulate and interrogate data in the database. However, in particular, for very complex datasets it may be difficult to write efficient SQL queries. The ChatGPT Language Model is a robust model to help you with many tasks, such as optimizing SQL queries.Generating SQL queriesThe creation of SQL queries from Natural Language Statements is one of the most common ways that ChatGPT can be used for SQL optimization. Users who don't know SQL may find this helpful, as well as users who want to quickly create the query for a specific task.For example, you could ask for ChatGPT in the following way:Generate an SQL query to select all customers who have placed an order in the last month.ChatGPT would then generate the following query:SELECT * FROM customers WHERE order_date >= CURRENT_DATE - INTERVAL 1 MONTH;Optimizing existing queriesThe optimization of current SQL queries can also be achieved with ChatGPT. You can do this by giving ChatGPT the query that you want improved performance of and it will then suggest improvements to your query.For example, you could ask for ChatGPT in the following way:SELECT * FROM products WHERE product_name LIKE '%shirt%';ChatGPT might suggest the following optimizations:Add an index to the products table on the product_name column.Use a full-text search index on the product_name column.Use a more specific LIKE clause, such as WHERE product_name = 'shirt' if you know that the product name will be an exact match.Crafting queriesBy providing an interface between SQL and Natural Language, ChatGPT will be able to help with the drafting of complicated SQL queries. For users who are not familiar with SQL and need to create a quick query for a specific task, it can be helpful.For Example:Let's say we want to know which customers have placed an order within the last month, and spent more than $100 on it, then write a SQL query. The following query could be generated by using ChatGPT:SELECT * FROM customers WHERE order_date >= CURRENT_DATE - INTERVAL 1 MONTH AND order_total > 100;This query is relatively easy to perform, but ChatGPT can also be used for the creation of more complicated queries. For example, to select all customers who have placed an order in the last month and who have purchased a specific product, we could use ChatGPT to generate a query.SELECT * FROM customers WHERE order_date >= CURRENT_DATE - INTERVAL 1 MONTH AND order_items LIKE '%product_name%';Generating queries for which more than one table is involved can also be done with ChatGPT. For example, to select all customers who have placed an order in the last month and have also purchased a specific product from a specific category, we could use ChatGPT to generate a query.SELECT customers.*FROM customersINNER JOIN orders ON customers.id = orders.customer_idINNER JOIN order_items ON orders.id = order_items.order_idWHERE order_date >= CURRENT_DATE - INTERVAL 1 MONTHAND order_items_product_id = (SELECT id FROM products WHERE product_name = 'product_name')AND product_category_id = (SELECT id FROM product_categories WHERE category_name = 'category_name');The ChatGPT tool is capable of providing assistance with the creation of complex SQL queries. The ChatGPT feature facilitates users' writing efficient and accurate queries by providing an interface to SQL in a natural language.Debugging SQL queriesFor debugging SQL queries, the ChatGPT can also be used. To get started, you can ask ChatGPT to deliver a query that does not return the anticipated results. It will try to figure out why this is happening.For example, you could ask for ChatGPT in the following way:SELECT * FROM customers WHERE country = 'United States';Let's say that more results than expected are returned by this query. If there are multiple rows in a customer table, or the country column isn't being populated correctly for all clients, ChatGPT may suggest that something is wrong.How ChatGPT can help diagnose SQL query errors and suggest potential fixesYou may find that ChatGPT is useful for diagnosing and identifying problems, as well as suggesting possible remedies when you encounter errors or unexpected results in your SQL queries.To illustrate how ChatGPT could help you diagnose and correct SQL queries, we'll go over a hands-on example.Scenario: You'll be working with a database for Internet store transactions. The 'Products' table is where you would like to see the total revenue from a specific product named "Laptop". But you'll get unexpected results while running a SQL query.Your SQL Query:SELECT SUM(price) AS total_revenue FROM Products WHERE product_name = 'Laptop'; Issue: The query is not providing the expected results. You're not sure what went wrong.ChatGPT Assistance:Diagnosing the Issue:You can ask ChatGPT something like, "What could be the issue with my SQL query to calculate the total revenue of 'Laptop' from the Products table?"ChatGPT’s Response:The ChatGPT believes that the problem may arise from a WHERE clause. It suggests that because the names of products may not be distinctive, and there might be a lot of entries called 'Laptops', it is suggested to use ProductID rather than the product name. This query could be modified as follows:SELECT SUM(price) AS total_revenue FROM Products WHERE product_id = (SELECT product_id FROM Products WHERE product_name = 'Laptop');Explanation and Hands-on Practice:The reasoning behind this adjustment is explained by ChatGPT. In order to check if the revised query is likely to lead to an expected overall profit for a 'Laptop' product, you can then try running it.SELECT SUM(price) AS total_revenue FROM Products WHERE product_id = (SELECT product_id FROM Products WHERE product_name = 'Laptop');We have obtained the correct overall revenue from a 'Laptop' product with this query, which has resolved your unanticipated results issue.This hands-on example demonstrates how ChatGPT can help you diagnose and resolve your SQL problems, provide tailored suggestions, explain the solutions to fix them, and guide you through the process of strengthening your SQL skills by using practical applications.ConclusionIn conclusion, this article provides insight into the important role that ChatGPT plays when it comes to generating efficient SQL queries. In view of the key role played by SQL in database management for structured data, which is essential to modern applications, it stressed that there should be a solid knowledge base on SQL so as to effectively use ChatGPT when creating queries. We explored how ChatGPT could help you generate, optimize, and analyze SQL queries by presenting practical examples and use cases.It explains to users how ChatGPT is able to diagnose SQL errors and propose a solution, which in the end can help them solve unforeseen results and improve their ability to use SQL. In today's data-driven world where effective data manipulation is a necessity, ChatGPT becomes an essential ally for those who seek to speed up the SQL query development process, enhance accuracy, and increase productivity. It will open up new possibilities for data professionals and developers, allowing them to interact more effectively with databases.Author BioChaitanya Yadav is a data analyst, machine learning, and cloud computing expert with a passion for technology and education. He has a proven track record of success in using technology to solve real-world problems and help others to learn and grow. He is skilled in a wide range of technologies, including SQL, Python, data visualization tools like Power BI, and cloud computing platforms like Google Cloud Platform. He is also 22x Multicloud Certified.In addition to his technical skills, he is also a brilliant content creator, blog writer, and book reviewer. He is the Co-founder of a tech community called "CS Infostics" which is dedicated to sharing opportunities to learn and grow in the field of IT.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn the evolving world of digital creativity, the collaboration between Canva and ChatGPT ushers a new era. Canva is a popular graphic design platform that allows users to create a wide variety of visual content, such as social media posts, presentations, posters, videos, banners, and many more. Whereas ChatGPT is an extensive language model that is capable of writing many types of creative material like poems, stories, essays, and songs, generating code snippets, translating languages, and providing you with helpful answers to your queries.In this article, we examine the compelling reasons for embracing these two cutting-edge platforms and reveal the endless possibilities they offer.Why use Canva on ChatGPT?Using Canva and ChatGPT individually can be a great way to create content, but there are several benefits to using them.You can get the best of both platforms by integrating Canva on ChatGPT. The creativity and flexibility of ChatGPT are dynamic while the functionality and simplicity of Canva are user-friendly.You can optimize your workflow and save time and effort by integrating Canva with ChatGPT. When you submit your design query to ChatGPT, It will quickly start the process of locating and producing the best output within less time.You can get ideas and get creative by using Canva on ChatGPT. By altering the description or the parameters in ChatGPT, you can experiment with various options and styles for your graphic.How to use Canva on ChatGPT?Follow the below steps to get started for the Canva plugin:Step-1:To use GPT-4 and Canva Plugin you will need to upgrade to the Plus version. So for that go to the ChatGPT website and log in to your account. Then navigate to top of your screen, then you will be able to find the GPT-4 button.Step-2:Once clicked, then press the Upgrade to Plus button. On the Subscription page, enter your email address, payment method, and billing address. Click the Subscribe button. Once your payment has been processed, you will be upgraded to ChatGPT Plus. Step-3:Now, move to the “GPT-4” model and choose “Plugins” from the drop-down menu.Step-4:After that, you will be able to see “Plugin store” in which you can access different kinds of plugins and explore them.Step-5:Here, you must search “Canva” and click on the install button to download the plugin in ChatGPT.  Step-6:Once installed, make sure the “Canva” plugin is enabled via the drop-down menu.  Step-7:Now, go ahead and enter the prompt for the image, video, banner, poster, and presentation you wish to create. For example, you can ask ChatGPT to generate, “I'm performing a keynote speech presentation about advancements in Al technology. Create a futuristic, modern, and innovative presentation template for me to use” and it generated some impressive results within a minute.  Step-8:By clicking the link in ChatGPT's response you will be redirected toward the Canva editing page then you can customize the design, without even signing in. Once you are finished editing your visual content, you can download it from Canva and share it with others.So overall, you may utilize the Canva plugin in ChatGPT to quickly realize your ideas if you want to create an automated Instagram or YouTube channel with unique stuff. The user's engagement is minimal and effortless.Here are some specific examples of how you can use the Canva plugin on ChatGPT to create amazing content: Create presentations: Using your topic and audience, ChatGPT can generate presentation outlines for you. Once you have an outline, Canva can be used to make interactive and informative presentations.Generate social media posts: Using ChatGPT, you can come up with ideas for social media posts depending on your objectives and target audience. Once you have a few ideas, you may use Canva to make visually beautiful and interesting social media posts.Design marketing materials: You may utilize ChatGPT to come up with concepts for blog articles, infographics, and e-books, among other types of marketing materials. You may use Canva to create visually appealing and informative marketing materials.Make educational resources: ChatGPT can be used to create worksheets, flashcards, and lesson plans, among other types of educational materials. Once you've collected some resources, you can utilize Canva to make interesting and visually appealing educational materials.Things you must know about Canva on ChatGPTBe specific in your prompts. The more specific you are in your prompts, the better ChatGPT will be able to generate the type of visual content you want. Use words and phrases that are appropriate for your visual material. In order to come up with visual content ideas, ChatGPT searches for terms that are relevant to your prompt.Test out several templates and prompts. You may use Canva in a variety of ways on ChatGPT, so don't be hesitant to try out various prompts and templates to see what works best for you.Use ChatGPT's other features. ChatGPT can do more than just generate visual content. You can also use it to translate languages, write different kinds of creative content, and answer your questions in an informative way.ConclusionOverall, using Canva on ChatGPT has a number of advantages, including simplicity, strength, and adaptability. You can save a tonne of time and work by using the Canva plugin to create and update graphic material without using ChatGPT. With ChatGPT's AI capabilities, you can produce more inventive and interesting visual material than you could on your own. You also have a lot of versatility when generating visual material because to Canva's wide variety of templates and creative tools. So we got to know that, whether you are a content creator, a marketing manager, or a teacher, using the Canva plugin on ChatGPT can help you create amazing content that will engage the audience and help you to achieve your goals.Author BioSangita Mahala is a passionate IT professional with an outstanding track record, having an impressive array of certifications, including 12x Microsoft, 11x GCP, 2x Oracle, and LinkedIn Marketing Insider Certified. She is a Google Crowdsource Influencer and IBM champion learner gold. She also possesses extensive experience as a technical content writer and accomplished book blogger. She is always Committed to staying with emerging trends and technologies in the IT sector.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights and books. Don't miss out – sign up today!IntroductionFine-tuning with OpenAI is a new feature that might become a crucial aspect of enhancing AI language models for specific tasks and contexts. It holds significant importance as it allows these models to be adapted to perform tasks beyond their initial capabilities in a different way that could be done just with Prompt Engineering. In this article, we will use traditional fine-tuning, which involves training a model on a specialized dataset. The dataset we will be using consists of conversations formatted in a JSON lines structure, where each exchange is a sequence of chat message dictionaries. Each dictionary includes role assignments (system, user, or assistant) and the corresponding content of the message. This approach aims to adapt the model to better understand and generate human-like conversations. Let’s start by taking a look at the different alternatives to adapt a Large Language Model for custom tasks. Fine-tuning versus Prompt Engineering There are two distinct approaches for adapting a model to work with custom data: prompt engineering and traditional fine-tuning. While both methods aim to customize LLMs for specific tasks, they differ in their approaches and objectives. Prompt engineering entails crafting precise input prompts to guide the AI's responses effectively. It involves tailoring the prompts to elicit desired outcomes from the AI. This technique requires developers to experiment with different prompts, instructions, and formats to achieve precise control over the model's behavior. By providing explicit instructions within prompts, developers can elicit specific answers for tasks like code generation or translation. Prompt engineering is particularly valuable when clear guidance is essential, but finding the optimal prompts might require iterative testing. On the other hand, fine-tuning focuses on adapting a pre-trained LLM to perform better on a particular task or context. This process involves training the model on custom datasets that align with the desired application. Fine-tuning allows LLMs to develop a deeper understanding of context and language nuances, making them more adaptable to diverse prompts and human-like conversations. While it offers less direct control compared to prompt engineering, fine-tuning improves the model's ability to generate coherent responses across a broader range of scenarios. In essence, prompt engineering emphasizes precision and specific instruction within prompts, while fine-tuning enhances the LLM's adaptability and comprehension of context. Both prompt engineering and traditional fine-tuning serve as techniques to enhance the AI's conversational abilities. Prompt engineering emphasizes precise instruction, while traditional fine-tuning focuses on training the model to comprehend and generate conversations more effectively. Looking at the Training Data Before training a model, we need to understand the required data format for the OpenAI fine-tuning endpoints. This format utilizes JSON lines and consists of a primary key "messages," followed by an array of dictionaries representing chat messages. These dictionaries collectively form a complete conversation. The expected structure to train an Open AI model looks like this: {"messages": [{"role": "system", "content": "..."}, ...]} {"messages": [{"role": "system", "content": "..."}, ...]} {"messages": [{"role": "system", "content": "..."}, ...]} {"messages": [{"role": "system", "content": "..."}, ...]} Each chat message dictionary includes two essential components: The "role" field: This identifies the source of the message, which can be system, user, or assistant. It indicates the origin of the message. The "content" field: This contains the actual textual content of the message. In this article, we will be using an already available training dataset that complies with this structure within the Hugging Face datasets repository. Before we get this data, let’s first install the datasets package alongside the open ai and langchain modules using pip. !pip install datasets==2.14.4 openai==0.27.9 langchain==0.0.274Next, we can download the dataset using the datasets library and write it into a JSON file.from datasets import load_dataset # data = load_dataset( "jamescalam/agent-conversations-retrieval-tool", split="train" ) data.to_json("conversations.jsonl") To verify the structure of the file, we open it and load it into separate conversations. import json with open('conversations.jsonl', 'r') as f: conversations = f.readlines() # Assuming each line is a JSON string, you can iterate through the lines and load each JSON string parsed_conversations = [json.loads(line) for line in conversations] len(parsed_conversations)We get 270 conversations, and if we want, we can inspect the first element of the list.parsed_conversations[0] In the following code snippet, the OpenAI Python library is imported, and the API key is set using the environment variable. The script then uses the OpenAI API to create a fine-tuning job for GPT-3.5 Turbo. It reads the contents of a JSON Lines file named conversations.jsonl and sets the purpose of the file as 'fine-tune'. The resulting file ID is saved for later use.import openai import os # Set up environment variables for API keys os.environ['OPENAI_API_KEY'] = 'your-key' res = openai.File.create( file=open("conversations.jsonl", "r"), purpose='fine-tune' ) # We save the file ID for later file_id = res["id"] Now we can start the Fine-tuning job. res = openai.FineTuningJob.create( training_file=file_id, model="gpt-3.5-turbo" ) job_id = res["id"]In this part of the code, the fine-tuning job is initiated by calling the Openai.FineTuningJob.create() function. The training data file ID obtained earlier is passed as the training_file parameter, and the model to be fine-tuned is specified as "gpt-3.5-turbo". The resulting job ID is saved for monitoring the fine-tuning progress. Monitoring Fine-Tuning Progress from time import sleep while True:    print('*'*50)    res = openai.FineTuningJob.retrieve(job_id)    print(res)    if res["finished_at"] != None:        ft_model = res["fine_tuned_model"]        print('Model trained, id:',ft_model)        break    else:        print("Job still not finished, sleeping")        sleep(60) . In this section, the code enters a loop to continuously check the status of the fine-tuning job using the openai.FineTuningJob.retrieve() method. If the job has finished indicated by the "finished_at" field in the response, the ID of the fine-tuned model is extracted and printed. Otherwise, if the job is not finished yet, the script pauses or waits for a minute using the "sleep(60)" function before checking the job status again.Using the Fine-Tuned Model for Chat from langchain.chat_models import ChatOpenAI from langchain.prompts.chat import (    ChatPromptTemplate,    SystemMessagePromptTemplate,    AIMessagePromptTemplate,    HumanMessagePromptTemplate, ) from langchain.schema import AIMessage, HumanMessage, SystemMessage chat = ChatOpenAI(    temperature=0.5,    model_name=ft_model ) messages = [    SystemMessage(        content="You are a helpful assistant."    ),    HumanMessage(        content="tell me about Large Language Models"    ), ] chat(messages)  In this last part of the code, the fine-tuned model is integrated into a chat using the LangChain library. A ChatOpenAI instance is created with specified settings, including a temperature of 0.5 and the name of the fine-tuned model (ft_model). A conversation is then simulated using a sequence of messages, including a system message and a human message. The chat interaction is executed using the chat() method. The provided code is a step-by-step guide to set up, fine-tune, monitor, and utilize a chatbot model using OpenAI's API and the LangChain library. It showcases the process of creating, training, and interacting with a fine-tuned model for chat applications. ConclusionIn conclusion, fine-tuning GPT-3.5 and GPT-4 marks a significant leap in customizing AI language models for diverse applications. Whether you opt for precise prompt engineering or traditional fine-tuning, both approaches offer unique strategies to enhance conversational abilities. This step-by-step article demonstrates how to prepare data, initiate fine-tuning, monitor progress, and leverage the fine-tuned model for chat applications.As AI evolves, fine-tuning empowers language models with specialized capabilities, driving innovation across various fields. Developers can harness these techniques to excel in tasks ranging from customer support to complex problem-solving. With the power of fine-tuning at your disposal, the possibilities for AI-driven solutions are limitless, promising a brighter future for AI technology.Author BioAlan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, and Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a Mechanical Engineering degree from the National University of Tucuman in 2015, participated as the founder of startups, and later on earned a Master's degree from the faculty of Mathematics at the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.LinkedIn

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, Building AI Applications with ChatGPT APIs, by Martin Yanev. Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions IntroductionBefore we start writing our first code, it’s important to create an environment to work in and install any necessary dependencies. Fortunately, Python has an excellent tooling system for managing virtual environments. Virtual environments in Python are a complex topic, but for the purposes of this book, it’s enough to know that they are isolated Python environments that are separate from your global Python installation. This isolation allows developers to work with different Python versions, install packages within the environment, and manage project dependencies without interfering with Python’s global installation.In order to utilize the ChatGPT API in your NLP projects, you will need to set up your Python development environment. This section will guide you through the necessary steps to get started, including the following:Installing PythonInstalling the PyCharm IDEInstalling pipSetting up a virtual environmentInstalling the required Python packagesA properly configured development environment will allow you to make API requests to ChatGPT and process the resulting responses in your Python code.Installing Python and the PyCharm IDEPython is a popular programming language that is widely used for various purposes, including machine learning and data analysis. You can download and install the latest version of Python from the official website, https://www.python.org/downloads/.  Once you have downloaded the Python installer, simply follow the instructions to install Python on your computer. The next step is to choose an Integrated Development Environment (IDE) to work with (see Figure 1.7).Figure 1.7: Python InstallationOne popular choice among Python developers is PyCharm, a powerful and user-friendly IDE developed by JetBrains. PyCharm provides a wide range of features that make it easy to develop Python applications, including code completion, debugging tools, and project management capabilities.To install PyCharm, you can download the Community Edition for free from the JetBrains website, https://www.jetbrains.com/pycharm/download/ Once you have downloaded the installer, simply follow the instructions to install PyCharm on your computer.Setting Up a Python Virtual EnvironmentSetting up a Python virtual environment is a crucial step in creating an isolated development environment for your project. By creating a virtual environment, you can install specific versions of Python packages and dependencies without interfering with other projects on your system.Creating a Python virtual environment specific to your ChatGPT application project is a recommended best practice. By doing so, you can ensure that all the packages and dependencies are saved inside your project folder rather than cluttering up your computer’s global Python installation. This approach provides a more organized and isolated environment for your project’s development and execution.PyCharm allows you to set up the Python virtual environment directly during the project creation process. Once installed, you can launch PyCharm and start working with Python. Upon launching PyCharm, you will see the Welcome Window, and from there, you can create a new project. By doing so, you will be directed to the New Project window, where you can specify your desired project name and, more importantly, set up your Python virtual environment. To do this, you need to ensure that New environment using is selected. This option will create a copy of the Python version installed on your device and save it to your local project.As you can see from Figure 1.8, the Location field displays the directory path of your local Python virtual environment situated within your project directory. Beneath it, the Base interpreter displays the installed Python version on your system. Clicking the Create button will initiate the creation of your new project.Figure 1.8: PyCharm Project SetupFigure 1.9 displays the two main indicators showing that the Python virtual environment is correctly installed and activated. One of these indications is the presence of a venv folder within your PyCharm project, which proves that the environment is installed. Additionally, you should observe Python 3.11 (ChatGPTResponse) in the lower-right corner, confirming that your virtual environment has been activated successfully.Figure 1.9: Python Virtual Environment IndicationsA key component needed to install any package in Python is pip. Lets’s see how to check whether pip is already installed on your system, and how to install it if necessary.The pip Package Installerpip is a package installer for Python. It allows you to easily install and manage third-party Python libraries and packages such as openai. If you are using a recent version of Python, pip should already be installed. You can check whether pip is installed on your system by opening a command prompt or terminal and typing pip followed by the Enter key. If pip is installed, you should see some output describing its usage and commands.If pip is not installed on your system, you can install it by following these steps:1. First, download the get-pip.py script from the official Python website: https:// bootstrap.pypa.io/get-pip.py.2. Save the file to a location on your computer that you can easily access, such as your desktop or downloads folder.3. Open a command prompt or terminal and navigate to the directory where you saved the get-pip.py file.4. Run the following command to install pip: python get-pip.py5. Once the installation is complete, you can verify that pip is installed by typing pip into the command prompt or terminal and pressing Enter.You should now have pip installed on your system and be able to use it to install packages and libraries for Python.Building a Python Virtual Environment from the TerminalAlternatively, to create a Python virtual environment, you can use the built-in venv module that comes with Python. Once you create your project in PyCharm, click on the Terminal tab located at the bottom of the screen. If you don’t see the Terminal tab, you can open it by going to View | Tool Windows | Terminal in the menu bar. Then, run this command:$ python3 -m venv myenvThis will create a new directory named myenv that contains the virtual environment. You can replace myenv with any name you want.To activate the virtual environment, run the following command: On Windows:$ myenv\Scripts\activate.batOn macOS or Linux:$ source myenv/bin/activateOnce activated, you should see the name of the virtual environment in the command prompt or terminal. From here, you can install any packages or dependencies you need for your project without interfering with other Python installations on your system.This was a complete guide on how to set up a Python development environment for using the ChatGPT API in NLP projects. The steps included installing Python, the PyCharm IDE, and pip, and setting up a virtual environment. Setting up a virtual environment was a crucial step in creating an isolated development environment for your project. You are now ready to complete your first practice exercise on using the ChatGPT API with Python to interact with the OpenAI library.A Simple ChatGPT API ResponseUsing the ChatGPT API with Python is a relatively simple process. You’ll first need to make sure you create a new PyCharm project called ChatGPTResponse (see Figure 1.8). Once you have that set up, you can use the OpenAI Python library to interact with the ChatGPT API. Open a new Terminal in PyCharm, make sure that you are in your project folder, and install the openai package:$ pip install openaiNext, you need to create a new Python file in your PyCharm project. In the top-left corner,  right-click on ChatGPTResponse | New | Python File. Name the file app.py and hit Enter. You should now have a new Python file in your project directory.Figure 1.10: Create a Python FileTo get started, you’ll need to import the openai library into your Python file. Also, you’ll need to provide your OpenAI API key. You can obtain an API key from the OpenAI website by following the steps outlined in the previous sections of this book. Then you’ll need to set it as a parameter in your Python code. Once your API key is set up, you can start interacting with the ChatGPT API:import openai openai.api_key = "YOUR_API_KEY"Replace YOUR_API_KEY with the API key you obtained from the OpenAI platform page. Now, you can ask the user for a question using the input() function:question = input("What would you like to ask ChatGPT? ")The input() function is used to prompt the user to input a question they would like to ask the ChatGPT API. The function takes a string as an argument, which is displayed to the user when the program is run. In this case, the question string is "What would you like to ask ChatGPT?". When the user types their question and presses Enter, the input() function will return the string that the user typed. This string is then assigned to the question variable.To pass the user question from your Python script to ChatGPT, you will need to use the ChatGPT API Completion function:response = openai.Completion.create( engine="text-davinci-003", prompt=question, max_tokens=1024, n=1, stop=None, temperature=0.8, )The openai.Completion.create() function in the code is used to send a request to the ChatGPT API to generate the completion of the user’s input prompt. The engine parameter allows us to specify the specific variant or version of the GPT model we want to utilize for the request, and in this case, it is set to "text-davinci-003". The prompt parameter specifies the text prompt for the API to complete, which is the user’s input question in this case.The max_tokens parameter specifies the maximum number of tokens the request and the response should contain together. The n parameter specifies the number of completions to generate for the prompt. The stop parameter specifies the sequence where the API should stop generating the response.The temperature parameter controls the creativity of the generated response. It ranges from 0 to 1. Higher values will result in more creative but potentially less coherent responses, while lower values will result in more predictable but potentially less interesting responses. Later in the book, we will delve into how these parameters impact the responses received from ChatGPT.The function returns a JSON object containing the generated response from the ChatGPT API, which then can be accessed and printed to the console in the next line of code:print(response)In the project pane on the left-hand side of the screen, locate the Python file you want to run. Right-click on the app.py file and select Run app.py from the context menu. You should receive a message in the Run window that asks you to write a question to ChatGPT (see Figure 1.11).Figure 1.11: Asking ChatGPT a QuestionOnce you have entered your question, press the Enter key to submit your request to the ChatGPT API. The response generated by the ChatGPT API model will be displayed in the Run window as a complete JSON object:{ "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "text": "\n\n1. Start by getting in the water. If you're swimming in a pool, you can enter the water from the side, …………. } ], "created": 1681010983, "id": "cmpl-73G2JJCyBTfwCdIyZ7v5CTjxMiS6W", "model": "text-davinci-003", "object": "text_completion", "usage": { "completion_tokens": 415, "prompt_tokens": 4, "total_tokens": 419 } }This JSON response produced by the OpenAI API contains information about the response generated by the GPT-3 model. This response consists of the following fields:The choices field contains an array of objects with the generated responses, which in this case only contains one response object as the parameter n=1.The text field within the response object contains the actual response generated by the GPT-3 model.The finish_reason field indicates the reason why the response was generated; in this case, it was because the model reached the stop condition provided in the request. Since in our case stop=None, the full response from the ChatGPT API was returned.The created field specifies the Unix timestamp of when the response was created.The id field is a unique identifier for the API request that generated this response.The model field specifies the GPT-3 model that was used to generate the response.The object field specifies the type of object that was returned, which in this case is text_ completion.The usage field provides information about the resource usage of the API request. It contains information about the number of tokens used for the completion, the number of tokens in the prompt, and the total number of tokens used.The most important parameter from the response is the text field, which contains the answer to the question asked to the ChatGPT API. This is why most API users would like to access only that parameter from the JSON object. You can easily separate the text from the main body as follows:answer = response["choices"][0]["text"] print(answer)By following this approach, you can guarantee that the answer variable will hold the complete ChatGPT API text response, which you can then print to verify. Keep in mind that ChatGPT responses can significantly differ depending on the input, making each response unique.OpenAI: 1. Start by getting in the water. If you're swimming in a pool, you can enter the water from the side, ladder, or diving board. If you are swimming in the ocean or lake, you can enter the water from the shore or a dock. 2. Take a deep breath in and then exhale slowly. This will help you relax and prepare for swimming.ChatGPT can be employed for a multitude of NLP tasks across a wide array of topics accessible in the vast expanse of available data. We can utilize our script to inquire about diverse areas of expertise and receive advanced responses from the ChatGPT API, as demonstrated here:1. Mathematics:* User: What is the square root of 256?* ChatGPT: The square root of 256 is 16.2. Sentiment analysis:* User: Analyze the sentiment of the sentence “I had a great day today!”* ChatGPT: The sentiment of the sentence “I had a great day today!” is positive.3. Programming:* User: How do you declare a variable in Python?* ChatGPT: To declare a variable in Python, you can use the following syntax: variable_ name = value.4. Science:* User: What is the process of photosynthesis in plants?* ChatGPT: The process of photosynthesis in plants is the fundamental mechanism by which they convert light energy into chemical energy.5. Literature:* User: What are the major themes in Shakespeare’s play “Hamlet”?* ChatGPT: “Hamlet,” one of Shakespeare’s most renowned tragedies, explores several major themes that continue to captivate audiences and provoke thought.In this section, you learned how to use the OpenAI Python library to interact with the ChatGPT API by sending a request to generate the completion of a user’s input prompt/question. You also learned how to set up your API key and how to prompt the user to input a question, and finally, how to access the generated response from ChatGPT in the form of a JSON object containing information about the response. You are now ready to build more complex projects and integrate the ChatGPT API with other frameworks.ConclusionIn this article, we covered how to set up a Python development environment, specifically using the PyCharm IDE, and creating a virtual environment. To help you get started with using the ChatGPT API, we walked through a simple example of obtaining a ChatGPT API response.Author BioMartin Yanev is an experienced Software Engineer who has worked in the aerospace and industries for over 8 years. He specializes in developing and integrating software solutions for air traffic control and chromatography systems. Martin is a well-respected instructor with over 280,000 students worldwide, and he is skilled in using frameworks like Flask, Django, Pytest, and TensorFlow. He is an expert in building, training, and fine-tuning AI systems with the full range of OpenAI APIs. Martin has dual master's degrees in Aerospace Systems and Software Engineering, which demonstrates his commitment to both practical and theoretical aspects of the industry.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, R Bioinformatics Cookbook - Second Edition, by Dan MacLean. Discover over 80 recipes for modeling and handling real-life biological data using modern libraries from the R ecosystem.IntroductionEmbrace the power of streamlined debugging and code refinement with ChatGPT's expertise. Unravel the possibilities of effortlessly troubleshooting errors, optimizing performance, and refining code structures. This article explores how ChatGPT, armed with its extensive programming knowledge, assists in identifying and rectifying coding errors, offering tailored solutions, and fostering a deeper understanding of code logic. Dive into a journey of code enhancement, where ChatGPT becomes your indispensable companion in navigating complexities and elevating your coding experiencesDebugging and improving code with ChatGPTChatGPT is capable of debugging code due to its extensive training on programming-related text. It has acquired knowledge of common programming patterns and errors, allowing it to understand code snippets, identify potential issues, and provide suggestions or improvements. It can identify minor errors by highlighting syntax issues such as missing brackets, incorrect function calls, and invalid variable assignments. ChatGPT helps troubleshooting by asking clarifying questions to better understand the problem and guide developers toward the root cause, and can aid in code optimization by suggesting efficiency improvements, identifying performance bottlenecks, and proposing more optimized implementations. Lastly, the debugging process with ChatGPT can foster learning and exploration, enabling developers to discover new concepts, programming techniques, or functionalities of specific libraries or packages. In this recipe, we’ll walk through an example debugging session with ChatGPT and look at how it helps us to reflect better on our own code.Getting readyWe just need an account at ChatGPT: https://chat.openai.com/.How to do it…We can debug some code as follows:1. Set up some code that we expect to work and try it:data <- data.frame( predictor = c(1, 2, 3, 4, NA), response = c("A", "B", "C", "D", "E") ) model <- lm(response ~ predictor, data = data)2. Send the broken code to ChatGPT:Help me fix the following code: data <- data.frame( predictor = c(1, 2, 3, 4, NA), response = c("A", "B", "C", "D", "E") ) model <- lm(response ~ predictor, data = data)3. Read and run the resulting response in R:To fix the code, you need to handle the missing value (NA) in the predictor column. Here's the corrected code: # Fixing the code data <- data.frame( predictor = c(1, 2, 3, 4, NA), response = c("A", "B", "C", "D", "E") ) # Replace NA in the predictor column with the mean value data$predictor[is.na(data$predictor)] <- mean(data$predictor, na.rm = TRUE) # Convert the response variable to a factor data$response <- as.factor(data$response) # Perform linear regression model <- lm(response ~ predictor, data = data) summary(model) In the fixed code, I replaced the NA value in the predictor column with the mean value of the remaining numeric values using the mean() function with na.rm = TRUE argument. Then, I converted the response variable to a factor using the as.factor() function. Finally, the linear regression model is computed with the corrected data, and the summary(model) displays the model summary.4. Tell ChatGPT the code still doesn’t work:It still doesn't work - Im getting thie error: Error in as.character.factor(x) : malformed factor In addition: Warning message: In Ops.factor(r, 2) : '^' not meaningful for factors 5. Read the response and run the code:Apologies for the oversight. The error is occurring because the ^ operator is not applicable to factors in the formula used in the linear regression. To fix this, we can convert the factor to a numeric representation using numeric codes or dummy variables. Here's the updated code: # Fixing the code data <- data.frame( predictor = c(1, 2, 3, 4, NA), response = c("A", "B", "C", "D", "E") ) # Replace NA in the predictor column with the mean value data$predictor[is.na(data$predictor)] <- mean(data$predictor, na.rm = TRUE) # Convert the response variable to a factor data$response <- as.factor(data$response) # Convert the factor to numeric codes data$response <- as.numeric(data$response) # Perform linear regression model <- lm(response ~ predictor, data = data) summary(model) In the updated code, I've used as.numeric() to convert the response factor variable to numeric codes. This allows the regression model to treat it as a numerical response. Now, the lm() function should run without the error, and summary(model) will display the model summary.6. Ask it to check whether the original code was appropriately analyzed:Were the original data suitable for a linear model?7. Read the response:No, the original data provided in the code were not suitable for a linear model. Here's why: 1. The `predictor` variable was numeric, which is suitable for a linear model. 2. The `response` variable, however, was categorical with values "A", "B", "C", "D", and "E". Categorical variables cannot be directly used as the response variable in a linear regression model because they do not represent continuous numeric values. To use a categorical variable as the response variable in a linear model, you typically need to encode it into dummy variables or use techniques like multinomial logistic regression. Dummy variable encoding represents each category as a separate binary variable (0 or 1). Once the categorical variable is properly encoded, it can be used as the response variable in a linear regression model.Again, that’s how the process goes though the output can be different from user to user but the meaning should be the same.How it works…In step 1, we set up a data frame of some data as a test set to use in a linear model; we wish to examine the relationship between the two variables so assume that a linear model will work. When we run the code in R, we get this:Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : NA/NaN/Inf in 'y' In addition: Warning message: In storage.mode(v) <- "double" : NAs introduced by coercion This is a typically confounded R error message. We want help, so in step 2, we ask ChatGPT to fix the code.Step 3 shows us ChatGPT’s response, which suggests fixing the NA values that are in the predictor column. That seems reasonable, and, as it explains, ChatGPT gives us some code that imputes a new value from the mean of all the other values – again, a reasonable value to impute. When we run the code, it still doesn’t work and we get a new error, so in step 4, we tell ChatGPT about it and ask it to fix the new errors.In step 5, we see an apologetic language model attempt to correct the error. It gives us a confusing reason for doing some strange text/number conversion and fixed code. When we run this new code in the console, we get output like this:## Call: ## lm(formula = response ~ predictor, data = data) ## ## Residuals: ## 1 2 3 4 5 ## -0.5 -0.5 -0.5 -0.5 2.0 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 0.5000 1.5546 0.322 0.769 ## predictor 1.0000 0.5774 1.732 0.182 ## ## Residual standard error: 1.291 on 3 degrees of freedom ## Multiple R-squared: 0.5, Adjusted R-squared: 0.3333 ## F-statistic: 3 on 1 and 3 DF, p-value: 0.1817This looks a bit strange – the residuals are weird and the rest of the values look poor. We start to question whether this was the right thing to do in the first place.In step 6, we ask ChatGPT whether the linear model was the right sort of analysis. It responds as in step 7, telling us quite clearly that it was not appropriate.This recipe highlights that we can use ChatGPT to fix code that doesn’t work, but shows also that ChatGPT will not reason without prompting. Here, it let us pursue a piece of code that wasn’t right for the task. As a language model, it can’t know that, even though we believe it would be evident from the question setup. It didn’t try to correct our flawed assumptions or logic. We still need to be responsible for the logic and applicability of our code.ConclusionIn conclusion, ChatGPT emerges as a valuable ally in code debugging, offering insightful solutions and guiding developers toward efficient, error-free code. While it excels in identifying issues and suggesting fixes, it's crucial to recognize that ChatGPT operates within the boundaries of provided information. This journey underscores the importance of critical thinking in code analysis, reminding us that while ChatGPT empowers us with solutions, the responsibility for logical correctness and code applicability ultimately rests on the developer. Leveraging ChatGPT's expertise alongside mindful coding practices paves the way for seamless debugging and continual improvement in software development endeavors.Author BioProfessor Dan MacLean has a Ph.D. in molecular biology from the University of Cambridge and gained postdoctoral experience in genomics and bioinformatics at Stanford University in California. Dan is now Head of Bioinformatics at the world-leading Sainsbury Laboratory in Norwich, UK where he works on bioinformatics, genomics, and machine learning. He teaches undergraduates, post-graduates, and post-doctoral students in data science and computational biology. His research group has developed numerous new methods and software in R, Python, and other languages with over 100,000 downloads combined.

IntroductionImagine constructing a house without a blueprint.Sounds chaotic, right?Similarly, diving into software coding without a UML or sequence diagram is like building that house blindly.Just as architects rely on blueprints, developers can rely on diagrams to build a clear project architecture that guides during the coding phase.It paints a clear roadmap of what needs to be developed.It ensures everyone is on the same page.It saves time during the execution phase.Unfortunately, this phase is often overlooked.It takes time, and you quickly get overwhelmed with so many tools and ways to sketch out diagrams.Now, imagine you can quickly draft diagrams, even during team meetings, so you can visualize complex workflows on the fly.This is what we will cover today.I will share a step-by-step guide on the Little-Known secret of automating diagram creation with ChatGPT (and it can be done all with a free version)Here's how step-by-step:Step 1: Create a sample applicationThe first thing we need is an application.For this example, I will use the following prompt to generate a Java Rest application for borrowing technical books.Create a Java rest application based on Spring Boot that allows borrowing technical books to users admins have dedicated role that are able to add books, remove books users and admins can log in by using Google OAuth as an authentication provider As the first step, show me the directory structureSince I am not interested in code but still curious about how the project would look, I have added.As the first step, show me the directory structure.ChatGPT will also give an explanation for each class.If this is something you are not interested in, just add the word "only".As the first step, show me only the directory structure.Using ASCII art, ChatGPT will present a directory structure.Hint: Sometimes chatGPT will "glitch" meaning it won't give you the full result, so just hit "continue" to get the entire output.Step 2: Quick and dirty way to create a diagram in ChatGPTNow, it's time to visualize the UML diagram.Ask ChatGpt to create a UML and sequence diagram.Prompt:*Create a UML and Sequence diagram using ASCII*You will get an ASCII representation.Do you see the problem?First, there is the limitation of using text to visualize.Second, good luck with maintaining these.Step 3: Using the right tools to create UMLTo get things further, let's use the proper tools.There are many tools on the market you can use to create UML. However, not all can be represented as text.PlantUML and MermaId are both tools that use specific diagram notations in which we can render our final diagram.Personally, I prefer MermaId as it is simpler to useNow we can use the same prompt, but this time, ask for output for MermaidUsing generated directory structure, generate UML diagram for MermaId toolNow copy the output to the MermaId tool: https://mermaid.live/classDiagram    class Application {        + main(args: String[]): void    }    class WebSecurityConfig {        <<configuration>>    }    class OAuth2Config {        <<configuration>>    }    class BookController {        + addBook(bookDTO: BookDTO): ResponseEntity<?>        + removeBook(bookId: Long): ResponseEntity<?>    }    class UserController {        + addUser(userDTO: UserDTO): ResponseEntity<?>        + removeUser(userId: Long): ResponseEntity<?>    }    class AuthController {        + loginUser(): ResponseEntity<?>    }    class Book {        - id: Long        - title: String        - author: String        - available: boolean    }    class User {        - id: Long        - username: String        - password: String        - email: String        - roles: Set<UserRole>    }    class UserRole {        <<enumeration>>        - USER        - ADMIN    }    class BookRepository {        <<interface>>    }    class UserRepository {        <<interface>>    }    class BookService {        + addBook(bookDTO: BookDTO): Book        + removeBook(bookId: Long): void    }    class UserService {        + addUser(userDTO: UserDTO): User        + removeUser(userId: Long): void    }    class UserDetailsServiceImpl {        <<service>>    }    class BookDTO {        - title: String        - author: String    }    class UserDTO {        - username: String        - password: String        - email: String    }    Application --|> WebSecurityConfig    Application --|> OAuth2Config    BookController --|> Book    UserController --|> User    AuthController --|> User    Book -- BookRepository    User -- UserRepository    BookController --|> BookService    UserController --|> UserService    UserService -- UserDetailsServiceImpl    BookController --> BookDTO    UserController --> UserDTOAnd here is the final result.Step 4: Create sequence diagramA sequence diagram, also known as a swimlane diagram, is a type of diagram that visually shows request flow.Imagine an actual swimming pool divided into lanes. Each lane is dedicated to a specific swimmer (e..g class file)Now, use the following prompt to get the swimlane diagram.Create a sequence diagram for Mermaid.Copy the output to Mermaid.Here is the output:sequenceDiagram    participant User as User    participant AuthController as "Auth Controller"    participant OAuth2Config as "OAuth2 Config"    participant UserDetailsServiceImpl as "User Details Service"    participant GoogleOAuth as "Google OAuth Provider"    participant UserDatabase as "User Database"    User ->> AuthController: Initiate Login    AuthController ->> OAuth2Config: Forward to OAuth2 Provider    OAuth2Config ->> GoogleOAuth: Send OAuth Request    GoogleOAuth -->> OAuth2Config: Receive OAuth Response    OAuth2Config -->> AuthController: Receive OAuth Response    AuthController ->> UserDetailsServiceImpl: Load User Details    UserDetailsServiceImpl ->> UserDatabase: Retrieve User Info    UserDatabase -->> UserDetailsServiceImpl: User Info    UserDetailsServiceImpl -->> AuthController: User Details    AuthController -->> User: Authentication SuccessfulHere is the full conversation with ChatGPT 3.5:https://chat.openai.com/share/70157733-da64-4a12-b15b-3607f4d1afe9Step 5: Making things even more fasterEverything we have done can now be compiled into a Mega prompt, compile all the inputs into a 1 mega prompt, and just replace the content under App descriptions.For a given app description APP DESCRIPTION START Spring boot rest application that stores reservations APP DESCRIPTION END Create directory structure, then create UML and sequence diagram for Mermaid tool using it's own syntaxBonus: ChatGPT Plus users onlyIf you are on a ChatGTP Plus subscription, you get several benefits apart from the obvious GPT4.First, ChatGPT4 itself gives you a nice text output, including some nice emojis.Prompt:Create a Java rest application based on Spring Boot that allows borrowing technical books to users admins have dedicated role that are able to add books, remove books users and admins can log in by using Google OAuth as an authentication provider As the first step, show me the directory structure with file names, use emojis to represent different content typeSecond, to speed up chart creation, you can use 2 plugins:GitHub plugin “AskTheCode” (which lets you scan the GitHub repository)Draw plugin “Mermaid Chart” -> generates diagrams for you and displays images directly as part of the chat.ConclusionPretty powerful, huh?Traditional methods of creating UML and sequence diagrams are much more time-consuming.Imagine how much time we just saved.By using this approach, you'll not only save time but get valuable insight into your architecture.Feel free to use these prompts, tweak them, and make them your own.If you want to get systems like these, please connect and reach out to me over Linkedin.Author bioJakov Semenski is an IT Architect working at IBMiX with almost 20 years of experience.He is also a ChatGPT Speaker at WeAreDevelopers conference and shares valuable tech stories on LinkedIn

This article highlights how ChatGPT can create data dictionaries within minutes, aiding data professionals in documenting data items. By leveraging ChatGPT's capabilities, professionals gain deeper insights, enhancing data management. A practical example demonstrates the efficiency and effectiveness of using ChatGPT in generating data dictionaries. What is a data dictionary? Data professionals, such as data engineers, data scientists, analysts, database administrators, and developers, face various data challenges, ranging from business requirement definition to data volume and speed management. To effectively tackle these difficulties, they require a comprehensive understanding of the data. Data dictionaries play a vital role in providing deeper insights into the data. A data dictionary serves as documentation for the data, encompassing names, definitions, and attributes of the database's data items. Its main purpose is to comprehend and describe the significance of data items in relation to the application, along with including data element metadata. Data dictionaries are indispensable in data projects as they contribute to success by offering valuable insights into the data. Benefits of creating a data dictionary: Conquer data discrepancies Facilitate data exploration and analysisMaintain data standards throughout the projectEstablish uniform and consistent standards for the projectEstablish data standards to control the gathered data and explain it across the project A typical data dictionary has below components:  ComponentDescriptionData ElementName of the data elementDescription Definition of the data elementData TypeType of data stored in the attribute (ex. text, number, date)LengthMaximum number of characters stored in the attributeFormatFormat  for the data (e.g. date/currency format) Valid ValuesList of allowed values for the data elementRelationshipsRelationships between different tables in the databaseSource Origin of the data (e.g. system, department)ConstraintsRules related to the use of the dataListing of data objectsNames and DefinitionsDetailed properties of data elementsData type, Size, nullability, optionality, indexesBusiness rulesSchema validation or Data Quality Image 1 : Sample Database Schema with Data Attribute Name, Data Type & Constraints     As demonstrated in the example above, each database has a basic set of data about the data dictionary, but this information is insufficient when working with a database that has numerous tables, each of which may have multiple columns.  Creating a practical data dictionary with ChatGPT Data and natural language processing can be used by ChatGPT to produce in-depth knowledge on any subject. As a result, ChatGPT may be used to build instructive data dictionaries for any dataset.       Image 2: ChatGPT to create Data Dictionary Let’s understand the step-by-step process to create a data dictionary using ChatGPT: Finding and copying the data               Let us ask ChatGPT for one of the public datasets to create a data dictionary:    List of Data Sources recommended by ChatGPT Now, I will download the csv file named Institutions.csv from the FDIC Bank Data API. Image 4: Downloaded CSV file for FDIC Bank DataLet’s use this data to create a data dictionary using ChatGPT. Prepare ChatGPT Let’s now prompt the GPT to create a raw data dictionary for the dataset that we picked above:Image 5: Output Data Dictionary Request Data Dictionary with additional information Additionally, we can request that ChatGPT add new columns and other pertinent data to the output of the data dictionary. For instance, in the sample below, I've asked ChatGPT to add a new column called Active Loan and to provide descriptions to the columns based on its knowledge of banking. Output Data Dictionary from ChatGPT with additional columns and informationWe can now see that the data dictionary is updated which can be shared within the organization.ConclusionIn conclusion, leveraging ChatGPT's capabilities expedites the creation of data dictionaries, enhancing data management for professionals. Its efficiency and insights empower successful data projects, making ChatGPT a valuable tool in the data professional's toolkit. Author BioSagar Lad is a Cloud Data Solution Architect with a leading organization and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP. Link - Medium, Amazon, LinkedIn.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionThe ChatGPT chatbot from OpenAI is a large language model that can be used for text writing, translation, content creation, and answers to your questions in an informative way. It's still developing, but has learned to perform many tasks, such as helping with Excel. Using ChatGPT with Excel can be done in several ways. Access to ChatGPT on the OpenAI website is one way of doing this. Another way to do this would be by using a third-party add-in such as the ListenData ChatGPT for Excel Addin. Access to ChatGPT from Excel via this add-in will allow you to do that at any time.What can ChatGPT do for Excel users?ChatGPT can be used to help Excel users with a variety of tasks, including:Learning Excel concepts: It is possible to describe Excel concepts clearly and succinctly by using ChatGPT. It may be of use to newcomers as well as users with a lot of experience.Writing formulas and functions: In Excel, you can use the ChatGPT program to write sophisticated formulas and functions. It's also capable of explaining how the formulas and functions work.Analyzing data: Excel data analysis can be helped by ChatGPT. It's been able to identify trends, patterns, and outliers. Reports and charts may also be generated.Automating tasks: In Excel, you can use the ChatGPT program to perform tasks automatically. There's a lot of time and effort that can be saved.Best Practices for Using ChatGPT for ExcelBe clear and concise in your prompts: ChatGPT is very good at understanding natural language, but it is important to be as specific as possible in your requests. For example, instead of saying "Can you help me with this Excel spreadsheet?", you could say "Can you help me to write a formula to calculate the average sales for each product category?".Provide context: If you are asking ChatGPT to help you with a specific task, it is helpful to provide some context. For example, if you are asking ChatGPT to write a formula to calculate the average sales for each product category, you could provide a sample of your spreadsheet data.Break down complex tasks into smaller steps: If you have a complex task that you need help with, it is often helpful to break it down into smaller, more manageable steps.Be patient: ChatGPT is still under development, and it may not always be able to provide the perfect answer. If you are not satisfied with the response that you receive, try rephrasing your prompt or providing more context.Generating Formulas and FunctionsTo generate Excel formulas and functions, you can use ChatGPT. It may be useful when you have no idea how to create a particular formula or function, or if you need any assistance with the way formulas and functions work.You can create a function or formula with ChatGPT by simply typing the description of what you want it to do. For example, you have a spreadsheet with the following data:You want to generate a formula that will calculate the average daily sales growth rate for the five days, but excluding the weekend days (Saturday and Sunday).Steps:1. Go to ChatGPT and enter the following prompt:Write an Excel formula to calculate the average daily sales growth rate for the following data, but excluding the weekend days (Saturday and Sunday):2. ChatGPT will generate the following formula and steps:=IF(WEEKDAY(A4,2)=7,"",IF(WEEKDAY(A4,2)=1,"",(B4-B3)/B3*100))3. Copy and paste the formula into cell D3 of your Excel spreadsheet.4. Press Enter.5. The formula will calculate the average daily sales growth rate for the five days, excluding the weekend days, which is 20%.Explanation:The formula works by first checking the day of the week for the date in cell A3. If the day of the week is Saturday or Sunday, the formula returns a blank value. Otherwise, the formula calculates the difference in sales between the second and third days, divides it by the sales value in cell B2, and multiplies it by 100 to express the result as a percentage.Data Standardization, Conditional Formatting, and Dynamic Filtering in Excel with ChatGPT1. Data StandardizationWhile analyzing data during data analysis, data standardization plays an important role as the raw data that we might extract from resources may not be in a uniform way. So, we need to ask ChatGPT perfectly to make out data in a standardized manner.For Example:Question: “I have a dataset with names in highly varied formats (e.g., 'John Smith,' 'Smith, John,' 'Dr. Jane Doe'). How can I standardize them to 'First Name Last Name' in Excel while preserving titles and suffixes?"ChatGPT Response: The above image shows that once you apply the formula given by ChatGPT for your query, you will get the result in standardized form.2. Conditional FormattingA feature that enables Excel to automatically format cells according to their value or content is conditional formatting. You can look at any cells that contain a value and color code them according to the range in which they are valued, e.g. You can use any of the options available to make your data more attractive and comprehensible.For Example:Question: "I have a list of sales data in Excel, and I want to highlight cells where the sales are above $1,000 in green and below $500 in red. How can I set up conditional formatting for this?"ChatGPT Response: As you can see that once we perform the stepwise procedure given by ChatGPT, we will be successfully able to get the correct results.3. Data Sorting and FilteringData sorting and filtering are two powerful features in Excel that can help you organize and analyze your data more efficiently. Sorting allows you to arrange your data in a specific order, such as alphabetically, numerically, or by date. This can be useful for finding specific information or for identifying trends in your data. Filtering allows you to display only the data that meets certain criteria. For example, you could filter your data to show only the rows that contain a certain value in a certain column. This can be useful for focusing on the data that is most important to you or for identifying outliers.Question: "I have a large dataset in Excel, and I want to sort it by a specific column in ascending order and then apply a filter to show only rows where the value in column B is greater than 50. What's the code to do this?"ChatGPT Response: The code will display only rows where the value in column B is greater than 50, by sorting data with ascending values and filtering them.ConclusionIn conclusion, the integration of ChatGPT with Excel provides a valuable service to all users whether they are simply starting out and trying to learn Microsoft's concepts or experienced users that need assistance for specific tasks. The ChatGPT is able to help you with a variety of aspects of the use of Excel, such as making complex formulas, analyzing data, standardizing data for consistency, using configurable formatting, and automated tasks.In addition, a practical example of what ChatGPT can do for users to achieve Excel-related goals is given in the report on Data Standardization, Conditional Formatting, Data Sorting, and Filtering with ChatGPT. Overall, ChatGPT has proved to be an invaluable tool for Excel users that enables them to free up time, improve data analysis, and streamline their tasks in a more rapid and engaging way.Author BioChaitanya Yadav is a data analyst, machine learning, and cloud computing expert with a passion for technology and education. He has a proven track record of success in using technology to solve real-world problems and help others to learn and grow. He is skilled in a wide range of technologies, including SQL, Python, data visualization tools like Power BI, and cloud computing platforms like Google Cloud Platform. He is also 22x Multicloud Certified.In addition to his technical skills, he is also a brilliant content creator, blog writer, and book reviewer. He is the Co-founder of a tech community called "CS Infostics" which is dedicated to sharing opportunities to learn and grow in the field of IT.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn an era dominated by instantaneous digital communication, businesses are constantly on the hunt for ways to refine and amplify their sales operations. Chatbots and platforms like ChatGPT stand at the forefront of this revolution, offering a blend of speed, accuracy, and personalized service. As companies look to the horizon, it becomes evident that AI-driven tools aren't just an option; they're a necessity. In this guide, we'll navigate through the transformative impacts of chatbots and ChatGPT on sales and offer a blueprint for leveraging their capabilities to their fullest potential. Let’s have a look at the advantages of implementing a chatbot as part of your sales channel1. Lead Generation Made EasyHow it works: Chatbots can be positioned on your website, social media pages, or even within apps. They initiate conversations, engage potential customers, and capture essential contact details.Benefits:Higher Engagement: Visitors are more likely to interact with an interactive bot than fill out a static form.Immediate Interaction: Chatbots can engage visitors instantly, increasing the likelihood of capturing leads.2. Efficient Lead QualificationHow it works: Instead of manual surveys or questionnaires, chatbots can quickly determine the potential of leads by asking relevant questions.Benefits:Time-saving: Automated lead qualification reduces manual efforts.Accuracy: Chatbots can consistently gather data and categorize leads based on predefined criteria.3. Instant Customer Queries Resolution with ChatGPTHow it works: ChatGPT uses advanced AI algorithms to comprehend user queries and provide precise answers.Benefits:Fast Responses: Instant answers mean no waiting time for customers.Consistency: AI ensures uniformity in responses, maintaining brand messaging.4. Personalized Product RecommendationsHow it works: ChatGPT can analyze past customer interactions, purchase histories, and preferences to suggest products or services.Benefits:Increased Sales: Personalized recommendations often resonate better with customers, leading to increased purchases.Enhanced Customer Experience: Customers appreciate tailored suggestions, enhancing their overall interaction with your brand.5. Elevating Customer ServiceHow it works: ChatGPT can address a wide range of customer concerns, from basic FAQs to complex product inquiries.Benefits:24/7 Support: AI doesn't sleep, ensuring round-the-clock customer assistance.Improved Satisfaction: Quick and accurate responses lead to happier, more loyal customers.6. Automation and Workload ReductionHow it works: Beyond answering questions, ChatGPT can manage routine tasks like booking appointments, sending reminders, or following up on leads.Benefits:Focus on Strategy: Sales teams can redirect their attention to more significant, high-value tasks.Consistent Follow-ups: Automated reminders and follow-ups ensure no potential lead is overlooked.7. Time-saving and Sales AugmentationHow it works: By automating repetitive tasks and offering intelligent recommendations, ChatGPT streamlines the sales process.Benefits:Faster Deal Closures: Efficient processes lead to quicker sales.Increased Revenue: More sales in less time amplify revenue generation.The fusion of chatbots and AI-driven platforms like ChatGPT offers an unprecedented advantage in the sales domain. By integrating these technologies into your sales strategy, you not only optimize operations but also provide an enhanced customer experience, setting the stage for sustainable business growth. Embrace the future of sales, and watch your business thrive!Let’s look at how to create a sales channel framework for chatbot using ChatGPTStep 1: Setting Up for Lead GenerationObjective: Engage visitors and gather essential contact details.Actions:Integrate the chatbot on prominent areas of your website, social media pages, and apps.Design conversation starters to engage users, such as: "Hello! How can I assist you today?"Prompt for ChatGPT: "Hello [User's Name], let's start by defining your sales objectives. Think about your main goals in sales. Is it to generate more leads? Maybe you want to qualify them more efficiently? Or perhaps you're looking to elevate your customer service game?Please list down all your sales objectives. Once you have them listed, we'll work together to prioritize them based on your immediate business requirements.Remember, the clearer your objectives, the more effective your tech solution deployment will be!"This prompt provides a clear directive and offers a little guidance, making it easier for the user to start listing their objectives.Step 2: Implement Efficient Lead QualificationObjective: Determine the potential of leads without manual surveys.Actions:Predefine criteria for categorizing leads (e.g., high potential, low interest).Create a set of relevant questions to gauge the user's interest and intent.Prompt: "Welcome, user! For a tailored experience with our SaaS collaboration tools, please answer a few questions. This automated process saves time, ensures accuracy, and helps us better understand your needs. From team size to current tools, your responses will guide our recommendations. Let's begin!"Step 3: Incorporate Instant Customer Query Resolution with AIObjective: Provide immediate and consistent answers.Actions:Train your chatbot using AI platforms like ChatGPT on frequently asked questions and typical user concerns.Continuously update the bot based on new questions and feedback to improve precision.Prompt: "Hello [User's Name], I'm here to assist you with your questions or concerns. Please provide a detailed description of your query or issue, and I'll do my best to give you a precise and accurate answer. Remember, the more specific you are, the better I can assist. Let's get started!"Step 4: Personalized Product RecommendationsObjective: Enhance user experience by suggesting tailored products/services.Actions:Integrate the chatbot with your CRM to analyze past customer interactions and purchase histories.Design the bot to recommend products based on user preferences and past behavior.Prompt: “Hello [User's Name], based on your history with us, I'd like to suggest fitting products or services. Can you mention the last item you engaged with? Also, are you seeking an upgrade, complementary items, or exploring new areas? Any specific preferences for the recommendation?"Step 5: Elevate Customer ServiceObjective: Provide round-the-clock support and address a wide range of concerns.Actions:Implement a 24/7 availability feature.Train the bot on both basic FAQs and in-depth product inquiries.Prompt: “Hello [User's Name], I'm here to assist you with any questions or concerns you may have about our products or services. From basic FAQs to in-depth product inquiries, I've got you covered.Please detail your query, and I'll provide a quick and accurate response. Remember, I'm available 24/7, so don't hesitate to reach out at any time. Your satisfaction is our priority. Let's get your questions answered!"ConclusionThe evolution of sales operations hinges on the symbiotic relationship between human expertise and AI efficiency. As evidenced in this guide, chatbots, especially powerhouse models like ChatGPT, aren't just transforming sales—they're redefining the entire customer journey. By integrating these technologies, businesses don't just streamline operations; they usher in a new era of customer interaction, marked by personalized experiences, swift resolutions, and enhanced satisfaction. The future of sales is here, and it beckons businesses to adapt, innovate, and thrive. Embracing these tools is no longer a luxury; it's an imperative for sustainable growth in a digital-first world.Author BioDr. Anshul Saxena is an author, corporate consultant, inventor, and educator who assists clients in finding financial solutions using quantum computing and generative AI. He has filed over three Indian patents and has been granted an Australian Innovation Patent. Anshul is the author of two best-selling books in the realm of HR Analytics and Quantum Computing (Packt Publications). He has been instrumental in setting up new-age specializations like decision sciences and business analytics in multiple business schools across India. Currently, he is working as Assistant Professor and Coordinator – Center for Emerging Business Technologies at CHRIST (Deemed to be University), Pune Lavasa Campus. Dr. Anshul has also worked with reputed companies like IBM as a curriculum designer and trainer and has been instrumental in training 1000+ academicians and working professionals from universities and corporate houses like UPES, CRMIT, and NITTE Mangalore, Vishwakarma University, Pune & Kaziranga University, and KPMG, IBM, Altran, TCS, Metro CASH & Carry, HPCL & IOC. With a work experience of 5 years in the domain of financial risk analytics with TCS and Northern Trust, Dr. Anshul has guided master's students in creating projects on emerging business technologies, which have resulted in 8+ Scopus-indexed papers. Dr. Anshul holds a PhD in Applied AI (Management), an MBA in Finance, and a BSc in Chemistry. He possesses multiple certificates in the field of Generative AI and Quantum Computing from organizations like SAS, IBM, IISC, Harvard, and BIMTECH.Author of the book: Financial Modeling Using Quantum Computing

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights and books. Don't miss out – sign up today!IntroductionWe are living in the era of Generative AI and Chatbots have evolved from simple rule-based systems to sophisticated AI-driven entities capable of holding intricate conversations. With tools like OpenAI's ChatGPT, creating a chatbot has never been more accessible. This article dives deep into how one can specialize these chatbots by fine-tuning them for specific industries and applications, using the Science QA dataset as an example.PrerequisitesBefore diving in, please ensure you have the necessary tools installed:OpenAI - Create an account on OpenAI and Generate API keysInstall Streamlit.pip install streamlitA dataset in CSV format for trainingFor this article, we have used the publicly available Science QA dataset for training and validation.Train dataset - ScienceQA-trainValidation dataset- ScienceQA-valWhile OpenAI powers the chatbot's brain, Streamlit will serve as the platform to interact with it.Understanding Fine-tuningFine-tuning is the process of training a pre-trained model on a new dataset to adapt to specific tasks. It's like teaching a general doctor to specialize in a field. The nuances and specific knowledge in the specialized field would aid and enhance to create your specialized dataset.Fine-tuning with OpenAIOpenAI's API simplifies the fine-tuning process. By providing your dataset, OpenAI trains your model to specialize in the subject of your data. First, we download the two CSV files from Science QA and save them in your project folder.Next, we process these CSV files and convert them into JSONL format which is required by OpenAI.import openai import pandas as pd # Use your Open AI access key openai.api_key = "sk-**************************************" # Load the training and validation datasets train_df = pd.read_csv("science-train.csv") val_df = pd.read_csv("science-val.csv") def convert_dataset_to_jsonl(df, file_name): df["conversation"] = df.apply(format_chat, axis=1)   with open(file_name, 'w') as jsonl_file:      for example in df["conversation"]:         jsonl_file.write(example + '\n') # Convert the training and validation datasets convert_dataset_to_jsonl(train_df, "fine_tune_train_data.jsonl") convert_dataset_to_jsonl(val_df, "fine_tune_val_data.jsonl")After converting the datasets to JSONL format, we will upload these datasets to OpenAI for further consumption for model fine-tuning.train = openai.File.create( file=open("fine_tune_train_data.jsonl", "rb"),purpose='fine-tune',) val = openai.File.create( file=open("fine_tune_val_data.jsonl", "rb"),purpose='fine-tune',) print(train) print(val) After printing, save these files names for train and validation sets. Next, we will create the fine-tuned model with your train and test data files. Pass the train and test data file names that we printed in step 3. model = openai.FineTuningJob.create( model = "gpt-3.5-turbo", training_file = train_data, validation_file = val_data, suffix = "scienceqa") print(model)Now, you have successfully created your fine-tuned model. You can also check the status in your OpenAI account.Building the Streamlit AppStreamlit is a game-changer for Python enthusiasts looking to deploy applications without the intricacies of web development. Integrating the fine-tuned model involves invoking OpenAI's API within the Streamlit interface. With customization features, tailor your app to resonate with the theme of your chatbot.You will also add some styling to your web app to create a visually appealing platform for your fine-tuned model.import streamlit as st # Set OpenAI API key openai.api_key = "sk-**********************" # Use your fine-tuned model ID here: FINE_TUNED_MODEL = "ft:gpt-3.5-turbo-****************" # Message system setup messages = [{ "role": "system", "content": "You are an AI specialized in Science and Tech.. "},] def get_chat(): return messages def chatbot(input): if input: chat = get_chat() chat.append({"role": "user", "content": input}) response = openai.ChatCompletion.create( model="ft:gpt-3.5-turbo-*****************", messages=chat, max_tokens=150, ) message = response['choices'][0]['message']['content'] chat.append({"role": "assistant", "content": message}) return message # Custom vibrant styling st.title('AI Chatbot Specialized in Science') st.write("Ask me about Science and Technology) # Sidebar for additional information or actions with st.sidebar: st.write("Instructions:") st.write("1. Ask anything related to Science.") st.write("2. Wait for the AI to provide an insightful answer!") # Main Chat Interface user_input = st.text_area("You: ", "") if st.button("Ask"): user_output = chatbot(user_input) st.text_area("AI's Response:", user_output, height=200)After saving the code, run the code using the following command-strealmit run 'yourpyfilename.py'Voila!! The app is ready!!The app opens on a new web page in your browser. Here is how it looks- Deployment ConsiderationsEnsure your API keys remain confidential. As for scalability, remember each query costs money; caching frequent queries can be a cost-effective strategy.Happy Fine Tuning!!ConclusionFine-tuning a model like GPT-3.5 Turbo for specific industries can immensely boost its effectiveness in those domains. Using tools like Streamlit, the deployment becomes hassle-free. Experiment, iterate, and watch your specialized chatbot come to life!Author BioSwagata Ashwani serves as a Principal Data Scientist at Boomi, where she leads the charge in deploying cutting-edge AI solutions, with a particular emphasis on Natural Language Processing (NLP). With a stellar track record in AI research, she is always on the lookout for the next state-of-the-art tool or technique to revolutionize the industry. Beyond her technical expertise, Swagata is a fervent advocate for women in tech. She believes in giving back to the community, regularly contributing to open-source initiatives that drive the democratization of technology.Swagata's passion isn't limited to the world of AI; she is a nature enthusiast, often wandering beaches and indulging in the serenity they offer. With a cup of coffee in hand, she finds joy in the rhythm of dance and the tranquility of the great outdoors.