Data Mining Tools - Lazy AI

Data Mining Tools templates

AI-Based Online SQL Query Code Generator

This AI-powered application generates SQL queries code based on user retrieval requests. It helps generating SQL queries using natural language messages from users. This app allows for easier database management and helps to fulful data analytics requests.

Fast API endpoint for Text Classification using GPT 4

This API will classify incoming text items into categories using the GPT 4 model. If the model is unsure about the category of a text item, it will respond with an empty string. The categories are parameters that the API endpoint accepts. The GPT 4 model will classify the items on its own with a prompt like this: "Classify the following item {item} into one of these categories {categories}". There is no maximum number of categories a text item can belong to in the multiple categories classification. The API will use the llm_prompt ability to ask the LLM to classify the item and respond with the category. The API will take the LLM's response as is and will not handle situations where the model identifies multiple categories for a text item in the single category classification. If the model is unsure about the category of a text item in the multiple categories classification, it will respond with an empty string for that item. The API will use Python's concurrent.futures module to parallelize the classification of text items. The API will handle timeouts and exceptions by leaving the items unclassified. The API will parse the LLM's response for the multiple categories classification and match it to the list of categories provided in the API parameters. The API will convert the LLM's response and the categories to lowercase before matching them. The API will split the LLM's response on both ':' and ',' to remove the "Category" word from the response. The temperature of the GPT model is set to a minimal value to make the output more deterministic. The API will return all matching categories for a text item in the multiple categories classification. The API will strip any leading or trailing whitespace from the categories in the LLM's response before matching them to the list of categories provided in the API parameters. The API will accept lists as answers from the LLM. If the LLM responds with a string that's formatted like a list, the API will parse it and match it to the list of categories provided in the API parameters.

Get All Products with Shopify API

The Shopify Get All Products Downloader is a FastAPI application that connects to a Shopify store using the provided store URL and Shopify API credentials. It retrieves all products from the Shopify store and returns them in JSON format. The app also includes frontend JavaScript code that calls the backend API and downloads the products (all or from a specific collection) which can be used for your storefront. You need to provide SHOPIFY_API_KEY and SHOPIFY_ADMIN_API_TOKEN.

JIRA JQL Generator Slack Bot

This app, named "Slack Mention Jira Query Generator", is designed to assist you in generating Jira Query Language (JQL) queries directly from Slack. When you mention the app in a Slack message, it will generate a JQL based on your message and ask if you want to run the query. If you agree, it will execute the query on Jira and return the results in the same Slack thread. The app is designed to handle multiple users at the same time and ensures that the correct JQL is associated with the user who requested it. It also formats the JQL results to share the links of the issues instead of the actual issue object, making it easier for you to navigate to the issues directly from Slack. To use this app, you will need to provide the following environment variables: - SLACK_BOT_TOKEN: You can get this by creating a new app in your Slack workspace, adding the bot scope, and installing the app in the workspace. - SLACK_APP_TOKEN: This can be generated by enabling Socket Mode for the app in the Slack API settings and generating an App-Level token. - JIRA_API_TOKEN and JIRA_EMAIL: These can be generated from your Jira account settings. - JIRA_SERVER_URL: This is the URL of your Jira server.

DocBot: The Google Docs Chatbot

An app for analyzing team documents, providing answers to questions, and displaying important information about its capabilities and restrictions.

AI Scraper with Google Gemini Flash

Web application to help scraping specific webpages. This selenium app sends the code of the page to Gemini flash 1.5 and asks it to retrieve the answer and points out the element on the page to do so.

URL Text Extractor with BeautifulSoup

Web application for extracting and displaying text content from a given URL using BeautifulSoup.

Data Mining Tools

Lazy apps in the Data Mining Tools category enable businesses to extract valuable insights from large data sets, turning raw information into actionable intelligence. These tools automate the process of discovering patterns, trends, and relationships hidden within data, providing a competitive edge. Here’s how they can be helpful:

Automated Data Extraction: Extract relevant data from large, complex databases or external sources, saving time and reducing manual effort in data collection.

Pattern and Trend Detection: Use algorithms to identify patterns or trends within data that may not be immediately visible, helping businesses make data-driven decisions.

Predictive Analytics: Analyze historical data to forecast future outcomes, helping businesses anticipate customer behavior, market trends, or operational needs.

Anomaly Detection: Automatically flag unusual or abnormal data points, enabling businesses to quickly detect potential issues such as fraud, errors, or inefficiencies.

Customer Segmentation: Segment large customer bases into specific groups based on behavior, demographics, or preferences, allowing for more targeted marketing and personalized services.

Sentiment Analysis: Mine text data from customer reviews, social media, or surveys to gauge customer sentiment and uncover insights into public opinion or brand reputation.

Data Aggregation: Combine data from multiple sources into a single, unified set for deeper analysis and more comprehensive reporting.

Business Intelligence Integration: Integrate data mining tools with existing business intelligence platforms to enhance reporting and decision-making capabilities.

Resource Optimization: Analyze operational data to identify inefficiencies or areas for optimization, helping businesses reduce costs and improve processes.

By automating data extraction and analysis, data mining tools empower businesses to gain deeper insights, improve decision-making, and uncover opportunities hidden within their data.