Best AI tools for< Tag Exams >

mapEDU is an AI-powered curriculum mapping and exam tagging software designed specifically for healthcare professions schools. It uses natural language processing and machine learning to automatically extract relevant MeSH tags from existing digital content, map events/courses/programs with outcomes, and auto-tag exam questions. This provides healthcare professions schools with objective, actionable data to improve curriculum design, validate revisions, and enhance student performance analytics.

Bibit AI is a real estate marketing AI designed to enhance the efficiency and effectiveness of real estate marketing and sales. It can help create listings, descriptions, and property content, and offers a host of other features. Bibit AI is the world's first AI for Real Estate. We are transforming the real estate industry by boosting efficiency and simplifying tasks like listing creation and content generation.

AI Tag Generator is a free and powerful tool designed to help users generate optimized tags for their YouTube and Instagram content. It utilizes the latest AI technology to quickly identify content topics and generate relevant tags, enhancing content visibility and reach. The tool offers smart tag generation, large model technology for accuracy, user-friendly interface, real-time optimization, and multilingual support. With different pricing tiers, users can access various features like tag records, unlimited generations, and intelligent tag tracking. The tool is suitable for beginners, standard users, and professional users looking to improve their tagging system.

TagifyNow is a free AI YouTube video tag generator and hashtag generator tool designed to simplify the process of selecting the perfect keywords for YouTube videos. It helps content creators reach a wider audience, save time, and boost visibility by generating SEO-friendly tags effortlessly. The tool offers features like brainstorming relevant keywords, trendspotting, competition analysis, and time-saving capabilities. TagifyNow ensures that users choose tags wisely to enhance their video's discoverability and avoid penalties from YouTube.

EtsyGenerator is an AI-powered tool designed to assist Etsy sellers in creating high-quality product listings effortlessly. It offers a range of features such as generating product descriptions, titles, tags, and SEO content using intelligent machine learning models. The tool helps sellers save time and effort by automating the listing creation process, ultimately improving Etsy search rankings and attracting more potential customers. With a user-friendly interface, EtsyGenerator is a game-changer for beginners and experienced sellers alike, providing valuable ideas and simplifying the listing process.

Nero Platinum Suite is a comprehensive software collection for Windows PCs that provides a wide range of multimedia capabilities, including burning, managing, optimizing, and editing photos, videos, and music files. It includes various AI-powered features such as the Nero AI Image Upscaler, Nero AI Video Upscaler, and Nero AI Photo Tagger, which enhance and simplify multimedia tasks.

Lang.ai is an AI-powered customer experience (CX) insights and automation platform designed for mid-market businesses. It helps businesses unlock CX data, increase automation beyond chatbots, drive decisions based on relevant and accurate CX insights, and improve the overall customer experience. Lang.ai offers a range of features, including intelligent triage of complex requests, email automation, continuous improvement of chatbots, granular tagging, proactive alerts, automated discovery of new topics, and custom taxonomies. It integrates seamlessly with popular helpdesks such as Zendesk, Salesforce, Intercom, Kustomer, Dixa, and Freshworks.

AltTextGenerate is a free online tool for generating alt text for images, which can boost your images' SEO in SERP. The tool uses AI-powered descriptions to provide suitable alt text for images, enhancing user experience and accessibility of websites. AltTextGenerate offers a comprehensive solution for generating alt text across various platforms, including WordPress, Shopify, and CMSs. It utilizes Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to understand image content and context, providing descriptive text for images.

PhotoTag.ai is an AI-powered platform that helps users generate tags, titles, and descriptions for photos and videos using cutting-edge AI technology. It enables users to save time by automating the keyword generation process, making it ideal for stock photography, e-commerce, marketing, and more. With features like customizable upload settings, batch processing, and multilingual support, PhotoTag.ai offers a seamless experience for content creators looking to enhance their workflow.

MLflow is an open source platform for managing the end-to-end machine learning (ML) lifecycle, including tracking experiments, packaging models, deploying models, and managing model registries. It provides a unified platform for both traditional ML and generative AI applications.

ChatGPT is a leading Chinese learning website that offers a comprehensive AI learning experience. It provides tutorials on ChatGPT, GPTs, and AI applications, guiding users from basic principles to advanced usage. The platform also offers ChatGPT Prompt words for various professions and life scenarios, inspiring creativity and productivity. Additionally, MidJourney tutorials focus on AI drawing, particularly suitable for beginners. With AI tools like AI Reading Assistant and GPT Finder, ChatGPT aims to enhance learning, work efficiency, and business success.

Playbook is an AI-powered file manager for creatives, by creatives. It is the world's first collaborative creative space that combines the features of Dropbox and Pinterest, with 4TB of starter space. Playbook helps users organize, share, and collaborate on creative files and projects with their clients and team. It uses AI to organize work in a way that makes sense, and allows users to find files 10x faster than traditional cloud storage. Playbook also has a beautiful gallery feature that makes it easy to share work with clients and gather feedback.

PromptPanda is an AI Prompt Management System designed to streamline workflow by securely managing prompts. It centralizes company prompts, allowing for efficient retrieval and comparison of new prompts. Users can explore and optimize market-tested prompts, ensuring consistent high-quality outcomes. The tool offers a central prompt repository for easy organization and clarity in AI usage.

Poly is a next-generation intelligent cloud storage platform that is built for the generative age. It offers a better cloud hosting service for your personal files, with features such as AI-enabled multimodal search, customizable layouts, dynamic collections, and one-click asset conversion. Poly is also designed to support outputs from your preferred generative AI models, including Automatic1111, ComfyUI, DALL-E, and Midjourney. With Poly, you can browse, manage, and navigate all your media generated by AI, and seamlessly connect and auto-import your files from your favorite apps.

EtsyHunt is an AI-powered platform designed to assist Etsy sellers in improving their shop ranking and visibility. With a comprehensive set of tools for product research, keyword analysis, shop optimization, and competitor tracking, EtsyHunt offers valuable insights and solutions to enhance the efficiency of Etsy operations. The platform boasts the world's largest database of ecommerce products, including millions of Etsy products, tags, and shops. By leveraging AI technology, EtsyHunt empowers sellers to make data-driven decisions and stay ahead in the competitive Etsy marketplace.

Zivy is an AI-powered communication tool designed to help Engineering and Product Leads manage and prioritize messages effectively. It transforms the chaotic Slack environment into organized stacks of cards, ensuring that users focus on what truly matters. Zivy's AI capabilities learn user preferences, prioritize important messages, and continuously improve efficiency. The application also emphasizes data security, encrypting messages, and adhering to strict privacy standards. Zivy aims to streamline communication processes and enhance productivity by reducing noise and optimizing message delivery.

Cyanite.ai is an AI application designed for music tagging and similarity search. It offers a comprehensive set of features to analyze and categorize music, providing users with detailed tags, descriptions, and search capabilities. The platform leverages AI algorithms to enhance music discovery and catalog management, catering to musicians, music publishers, and other industry professionals. Cyanite.ai aims to revolutionize the way music is searched, discovered, and managed by combining cutting-edge technology with user-friendly interfaces.

Imagga is a leading provider of image recognition solutions for developers and businesses. Its API empowers intelligent apps with customizable machine learning technology. Imagga's solutions include tagging, categorization, cropping, color extraction, visual search, facial recognition, custom training, and content moderation. These solutions are used by over 30K startups, developers, and students, and trusted by over 200 business customers in more than 82 countries worldwide.

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum. Thanks to a hands-on guide introducing programming fundamentals alongside topics in computational linguistics, plus comprehensive API documentation, NLTK is suitable for linguists, engineers, students, educators, researchers, and industry users alike.

SONOTELLER.AI is an AI song analyzer that analyzes song lyrics and music to provide a comprehensive summary about the song. It can identify musical attributes such as genres, subgenres, moods, instruments, BPM, and key. The tool is designed to simplify the way music lovers understand and organize their music collections, making it easier to discover and manage music across various platforms. SONOTELLER.AI is in beta phase, offering features like lyric analysis, music analysis, and automatic tagging to enhance music discovery and distribution.

Quiz Generator is a plugin for Obsidian that uses AI models to create interactive exam-style questions from notes. It supports various question types and provides real-time feedback. Users can save questions, generate in multiple languages, and use math support. The tool is suitable for students preparing for exams and educators designing assessments.

LLMs is a Chinese large language model technology stack for practical use. It includes high-availability pre-training, SFT, and DPO preference alignment code framework. The repository covers pre-training data cleaning, high-concurrency framework, SFT dataset cleaning, data quality improvement, and security alignment work for Chinese large language models. It also provides open-source SFT dataset construction, pre-training from scratch, and various tools and frameworks for data cleaning, quality optimization, and task alignment.

This NVIDIA AI Workbench project is designed for developing a Retrieval Augmented Generation application with a customizable Gradio Chat app. It allows users to embed documents into a locally running vector database and run inference locally on a Hugging Face TGI server, in the cloud using NVIDIA inference endpoints, or using microservices via NVIDIA Inference Microservices (NIMs). The project supports various models with different quantization options and provides tutorials for using different inference modes. Users can troubleshoot issues, customize the Gradio app, and access advanced tutorials for specific tasks.

AutoScraper is a smart, automatic, fast, and lightweight web scraping tool for Python. It simplifies the process of web scraping by learning scraping rules based on sample data provided by the user. The tool can extract text, URLs, or HTML tag values from web pages and return similar elements. Users can utilize the learned object to scrape similar content or exact elements from new pages. AutoScraper is compatible with Python 3 and offers easy installation from various sources. It provides functionalities for fetching similar and exact results from web pages, such as extracting post titles from Stack Overflow or live stock prices from Yahoo Finance. The tool allows customization with custom requests module parameters like proxies or headers. Users can save and load models for future use and explore advanced usages through tutorials and examples.

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=