Neural Digest

Building a NL2GraphQL using Lyzr SDK As technology continues to evolve, the need for seamless communication between humans and machines becomes increasingly crucial. In the realm of databases, particularly GraphQL, translating human intent into machine-readable queries can often be challenging. Here at Lyzr, we aim to bridge that gap with our innovative application, NL2GraphQL. Built using the Lyzr SDK, this app effortlessly converts natural language prompts into accurate GraphQL queries, making database interactions smoother than ever before. GraphQL, a query language for APIs, provides a powerful and flexible way to interact with your data. However, constructing GraphQL queries requires a certain level of expertise, which not everyone possesses. NL2GraphQL is designed to democratize access to GraphQL by allowing users to input natural language prompts and receive precise GraphQL queries in return. Whether you’re a developer looking to save time or a non-technical user needing to access data, NL2GraphQL is here to help. Why use Lyzr SDK’s? With Lyzr SDKs, crafting your own GenAI application is a breeze, requiring only a few lines of code to get up and running swiftly. Checkout the Lyzr SDK’s Lets get Started! Create an app.py file import streamlit as st from lyzr_automata.ai_models.openai import OpenAIModel from lyzr_automata import Agent, Task from lyzr_automata.pipelines.linear_sync_pipeline import LinearSyncPipeline from PIL import Image from lyzr_automata.tasks.task_literals import InputType, OutputType import os The code sets up a Streamlit web application for translating natural language prompts to GraphQL queries using Lyzr Automata and OpenAI’s GPT-4-turbo model. It imports necessary libraries, sets up the OpenAI API key, adjusts CSS styles for the app’s appearance, displays a logo, and introduces the app with a title and input field. An OpenAIModel instance is configured, and a function (generation) is defined to process user input into GraphQL queries. # Set the OpenAI API key os.environ["OPENAI_API_KEY"] = st.secrets["apikey"] This line sets the OpenAI API key as an environment variable, retrieving it securely from Streamlit’s secrets for authentication. input = st.text_input("Please enter your natural language prompt:",placeholder=f"""Type here""") This line creates a text input field using Streamlit’s text_input function, prompting the user to enter their natural language prompt. open_ai_text_completion_model = OpenAIModel( api_key=st.secrets["apikey"], parameters={ "model": "gpt-4-turbo-preview", "temperature": 0.2, "max_tokens": 1500, }, ) This segment initializes an instance of the OpenAIModel class, which will be used to interact with OpenAI’s GPT-4-turbo-preview model. It requires an API key, retrieved from Streamlit’s secrets management using st.secrets["apikey"]. Additionally, it specifies parameters for the model, including the model type ("gpt-4-turbo-preview"), the temperature for generating text (set to 0.2 for moderate creativity), and the maximum number of tokens in the generated text (limited to 1500 to prevent overly lengthy responses). def generation(input): generator_agent = Agent( role=" Expert GRAPH DATABASE ENGINEER and ANALYST", prompt_persona=f"Your task is to TRANSLATE a natural language prompt into the corresponding GRAPHQL query, ensuring that it FULLY CAPTURES the user's intent expressed in natural language.") prompt = f""" You are an Expert GRAPH DATABASE ENGINEER and ANALYST. Your task is to TRANSLATE a natural language prompt into the corresponding GRAPHQL query, ensuring that it FULLY CAPTURES the user's intent expressed in natural language. [Prompts here] """ The generation function is designed to facilitate the translation of natural language prompts into GraphQL queries. It begins by creating an Agent instance named generator_agent, which is configured with a specific role as an “Expert GRAPH DATABASE ENGINEER and AN... https://dev.to/akshay007/building-a-nl2graphql-using-lyzr-sdk-148o

Supercharge Your Fitness Journey: 5 Ways AI Can Transform Your Workout Routine Photo by Jonathan Borba on UnsplashContinue reading on . » @neural_digest https://medium.com/aimonks/supercharge-your-fitness-journey-5-ways-ai-can-transform-your-workout-routine-4c727ea89bf7?source=rss------chatgpt-5

6 Cutting-Edge AI Tools that will transform Your Workflow In the fast-paced world of technology, staying ahead means embracing innovation that can streamline your workflow and boost productivity. Without further ado, here is the curated list: 1. AIHumanizer.ai AIHumanizer is an AI tool that transforms your AI text into human content instantly. 2. PopAI PopAI is a game-changer for productivity. This AI tool serves as your personal AI workspace for boosting productivity. -> Chat with document. -> AI Presentations -> Chat w/image -> AI Chat 3. Text Blaze Text-Blaze is an AI tool for anyone looking to eliminate repetitive typing tasks. By using text-shortcuts, text-blaze can save users over 28 hours a month, which translates to more than $10,000 a year in savings through productivity improvements. 4. TypeDream TypeDream is a innovative AI tool that simplifies the process of building websites. With a single prompt you can get complete content structure and style done for you, ready to edit and publish instantly. 5. CircleBack CircleBack is an AI-powered tool designed to automate the process, from transcriptions to actions. By automatically updates your CRM, Notion, Teams, Slack and more.This automation saves time and allows you to focus on more strategic tasks. 6. Simplified Simplified is an AI tool used to create compelling and effective copy in minutes. This will will generate marketing copy: emails, social media posts and more. Recap If you've read this far, first of all, thank you! Please let me know if I have missed any or if you believe that you have your own AI websites that should be listed here. @neural_digest https://dev.to/hr21don/6-cutting-edge-ai-tools-that-will-transform-your-workflow-1am0

Navigating the Testing Challenges in Machine Learning Systems Hey there! Let's dive into the world of Testing Machine Learning (ML) Systems. It's a bit different from what you might be used to with regular software testing. With ML, we're dealing with complex algorithms that use data to make decisions, making it tricky to figure out what's right and wrong. Imagine trying to understand how an ML model makes decisions—it's like trying to solve a maze. Knowing how these decisions are made is super important for testing, but it's not easy. Then there's the problem of finding realistic test scenarios. ML models can handle tons of different inputs, and finding the right ones to really put the system to the test is like finding a needle in a haystack. And let's not forget about creating reliable test benchmarks. In regular software, we know exactly what the output should be. But with ML, it's all about probabilities and uncertainties, so it's hard to say what's right or wrong. Oh, and running tests on ML systems can be really expensive. Especially for big, complex models, it takes a lot of computing power. We need to find ways to keep costs down without skimping on testing. And speaking of challenges, one big one is the lack of clear benchmarks to measure ML systems against. Normally, we have standards to compare our software to, but with ML, it's like trying to hit a moving target. Then there's the issue of dealing with the huge range of possible inputs. ML systems work with all kinds of data, and trying to cover everything can feel impossible. It's like testing a car not just in a vacuum but on real roads with real traffic. And let's not forget about hyper-parameters. These settings can make or break an ML model's performance, so having a systematic methodology for tweaking them is crucial. Plus, having common frameworks and benchmarks can help standardize testing practices across the board. Oh, and there's this cool concept called metamorphic relationships. It's about finding how inputs relate to outputs in ways that remain consistent even if the model changes. Pretty neat, right? Some potential directions for future research in the domain of testing machine learning based systems include: Automated Generation of Metamorphic Relationships: Further exploration and development of techniques using machine learning to automate the creation of metamorphic relationships for testing ML-based systems. Enhanced Adversarial Testing: Continued research on generating adversarial examples for non-image data, such as text and audio, to improve the effectiveness of detecting errors in real scenarios for ML-based system testing. Efficient Sampling of Input Data: Further investigation into techniques for efficiently sampling input data with combinatorial interaction coverage to address the challenges of large input spaces in ML-based systems. Adaptation of Traditional Testing Techniques: Research efforts focused on adapting traditional testing techniques with machine learning support to enhance the cost-effectiveness and scalability of testing ML-based systems. By exploring these research directions, the field of testing machine learning based systems can advance towards more effective and efficient testing practices, ensuring the quality and reliability of software systems supported by machine learning. So yeah, testing ML systems might be a bit of a challenge, but by understanding the ins and outs of ML algorithms and harnessing advanced testing techniques, we can build systems that are not just reliable and robust but also perform like champs in real-world scenarios. It's a journey of discovery and improvement, and with everyone pitching in—from researchers to practitioners to industry players—we're bound to keep pushing the boundaries of what's possible. REFERENCES: https://link.springer.com/article/10.1007/s10664-020-09881-0 https://ieeexplore.ieee.org/abstract/document/8718214 @neural_digest https://dev.to/pravinkumar/navigating-the-testing-challenges-in-machine-learning-systems-1o5c

Earn $1090 Hassle-Free: Leverage ChatGPT to Generate Passive Income Online Earn $1090 with ChatGPT: A Beginner’s Guide to Effortless Online IncomeContinue reading on Medium » @neural_digest https://medium.com/@moneytent/earn-1090-hassle-free-leverage-chatgpt-to-generate-passive-income-online-eda9d61be505?source=rss------chatgpt-5

AI Projects in Python for Beginners: A Step-by-Step Introduction to Artificial Intelligence Start your journey into artificial intelligence with our beginner-friendly AI projects in Python. These projects are designed to make learning AI accessible, guiding you through the development of simple applications like chatbots, image classifiers, and sentiment analysis tools. With comprehensive tutorials and easy-to-follow code examples, you'll gain hands-on experience and a solid understanding of fundamental AI concepts. Perfect for those new to Python and AI, our resources provide a practical and engaging way to start building your skills in machine learning and data science. Dive in and explore the exciting world of AI today! @neural_digest https://dev.to/hazradec0a5b51c71236/ai-projects-in-python-for-beginners-a-step-by-step-introduction-to-artificial-intelligence-41de

How ChatGPT Work We are amazed at the way Large Language Models generate responses. LLM can generate such human-like responses and it is fine-tuned to…Continue reading on Medium » @neural_digest https://medium.com/@vivekkgupta/how-chatgpt-work-5c3010f47cb7?source=rss------chatgpt-5

An Introduction to Prompt Engineering Generative Artificial Intelligence systems (GenAI) are artificial intelligence systems that can generate content for users. In late 2022…Continue reading on Medium » @neural_digest https://medium.com/@J.R.Ingram/an-introduction-to-prompt-engineering-2fcb0eab3c91?source=rss------chatgpt-5

La dritta via Palestro a Genova Continue reading on Medium » @neural_digest https://medium.com/@lucadeangelis_33906/la-dritta-via-palestro-a-genova-ff7c0767a8b6?source=rss------chatgpt-5

How to Use ChatGPT’s Highlighting for Context ChatGPT now lets you highlight parts of its responses for easy follow-up questions, rewrites, reusing old context, and more.Continue reading on Medium » @neural_digest https://medium.com/@alaminhossainim/how-to-use-chatgpts-highlighting-for-context-c8d6e2154bc4?source=rss------chatgpt-5