Python tool for Claude AI skills generation

🚀 Set in Python - A Set in Python is a collection data type that is unordered, unindexed, and contains unique elements. It is mainly used when you want to store non-duplicate items and perform mathematical set operations like union, intersection, and difference. 🧩 Key Features: ▪️ Unordered: Elements have no defined order. ▪️ Mutable: You can add or remove items after creation. ▪️ No duplicates: Automatically removes repeated elements. ▪️ Supports set operations like union(), intersection(), difference(), etc. 💡 When to Use: 🔸 You need unique values. 🔸 You want to perform fast membership testing. 🔸 You need set-based operations (like finding common elements). #Python #PythonLearning #PythonBasics #DataStructures #Coding #LearnPython #SetInPython

Python MarkItDown: Convert Documents Into LLM-Ready Markdown Get started with Python MarkItDown to turn PDFs, Office files, images, and URLs into clean, LLM-ready Markdown in seconds. https://lnkd.in/eUbCyEiF

Python MarkItDown: Convert Documents Into LLM-Ready Markdown Get started with Python MarkItDown to turn PDFs, Office files, images, and URLs into clean, LLM-ready Markdown in seconds. https://lnkd.in/eUbCyEiF

🐍📰 Python MarkItDown: Convert Documents Into LLM-Ready Markdown Start using Python MarkItDown to quickly convert PDFs, Office files, images, and URLs into clean, Markdown format suitable for LLMs. https://lnkd.in/d8cXnVt8

CodeAlpha Task 3: Basic Chatbot using Python Built a simple rule-based chatbot that responds to user inputs like greetings, jokes, facts, quotes, and even tells the time and date! This project helped me practice if-elif conditions, loops, and user interaction in Python. 🐍 Github Link:https://lnkd.in/eh9ziPeX #Python #CodeAlpha #Chatbot #Automation #Project #Learning

https://lnkd.in/gx7MyUvu Tom explained how to build a Chatbot that responds based on given document (rather than LLM's inbuilt knowledge) He outline the training part (with given document) consists of:- -Load -Split -Embed -Store Inference or generative includes -Question (by user of chatbot) -Retrieval -LLM to infer -Output To tell chatbot not to use its own LLM embedded knowledge simply includes the following in python :- "rag_prompt = f""" You are an assistent which answers questions based on knowledge which is provided to you. While answering, you don't use your internal knowledge, but solely the information in the "The knowledge" section. You don't mention anything to the user about the povided knowledge." There is even an video #Chatbot; #LLM; #OpenAI; #LangChain; #ChromaDB; #RAG https://lnkd.in/gJkYs9ba

Code to demonstrates how to interact with multiple Large Language Models (OpenAI, Gemini, Groq, and Ollama) in Python. It takes a user question as input, sends it to each model, and compares their responses side by side.

Python Data Cleaning: Essential Steps for Dataset Preparation Transform chaotic data into pure gold: Get practical Python cleaning steps that turn statistical nightmares into your next breakthrough insight. https://lnkd.in/gqZSkRHy

The advice that I always to anyone wanting to better leverage Generative AI for data analysis is to learn a little Python. As an example, GPT creates dynamic graphs using the package matplotlib. Without any knowledge of Python, you wouldn't realize that matplotlib has animation capabilities through the matplotlib.animate module. In essence, a basic understanding of Python enables you to ask GPT the right questions, which is half the battle in prompting.

It's fascinating how language models are used without much output validation. The raw output is often fed directly into renderers. Tools like Jinja, a Python templating language, offer immense power by allowing Python code execution within templates. This means calculations can be done directly in the template, avoiding the need for pre-computed placeholder variables. This capability, however, also opens the door to executing potentially dangerous commands like os.popen within a template. #LanguageModels #Python #Jinja #Templating #Security