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Understanding Context in Prompt Engineering Context in prompt engineering refers to the background information or circumstances surrounding a prompt that helps the AI understand and generate more relevant and accurate responses. Providing context ensures that the AI has all the necessary information to address your query appropriately. The Importance of Context Relevance: Context helps the AI generate responses that are directly related to your question or instruction. Specificity: It narrows down the scope of the response, making it more precise. Accuracy: Context ensures that the AI interprets your prompt correctly, reducing the chances of misunderstandings. Role and Persona in Defining Context Role and persona are crucial elements in defining context. They help shape how the AI understands your prompt and what kind of response it generates. Role The role is the assumed position or function you want the AI to take on when responding to your prompt. It guides the AI on how to appro...

Let's break down the concepts of clarity, context, specification, and iteration process in prompt engineering in simple terms: Prompt Engineering Basics Prompt engineering is the process of designing and refining the instructions or questions (prompts) you give to an AI model to get the best possible response. Think of it like asking a very knowledgeable assistant to give you information, and you want to make sure your question is clear and detailed enough for a good answer. 1. Clarity 📜 Clarity means making your prompt easy to understand. If your question or instruction is clear, the AI is more likely to give you a useful response. Example: Unclear Prompt: "Tell me about Python." Clear Prompt: "Explain the main features of Python programming language and its common use cases." 2. Context 🌍 Context is the background information you provide to the AI so it understands what you're asking about. Including context helps the AI give more relevant and accurate a...

Cython is a programming language that makes it easy to write C extensions for Python. It helps you write code that is as easy as Python but runs as fast as C. Key Features of Cython Speed Boost: 🚀 Cython compiles your Python code to C, making it much faster. Combines Python and C: 🤝 You can write Python code and mix it with C-like syntax for optimization. Easy Integration: 🔗 Integrates seamlessly with existing Python code and libraries. Why Use Cython? Performance: 🏃‍♂️ If you need your Python code to run faster, Cython is a great choice. Efficient: 💪 Helps in computationally intensive tasks like numerical computations, data processing, and more. Compatibility: 🔄 Works with Python code and extends it with C-like performance. How to Use Cython? Install Cython: 📦 pip install cython Write a Cython File: 📝 Save your code in a .pyx file. # example.pyx def say_hello():     print("Hello, World!") Compile the Cython Code: 🛠️ Create a setup.py file to compile the .pyx file. f...

CLOC (Count Lines of Code) is a popular tool used to count lines of code in various programming languages. It provides a detailed breakdown of source code, comments, and blank lines. Here's how you can use it: Installing CLOC First, you need to install CLOC. You can install it using various methods, depending on your operating system. Using apt on Debian/Ubuntu: sudo apt-get install cloc Using brew on macOS: brew install cloc Using chocolatey on Windows: choco install cloc Using npm (Node.js package manager): npm install -g cloc Using CLOC Once installed, you can use CLOC to analyze a directory or file. Here are some common commands: Analyzing a Directory To count lines of code in a directory, run: cloc /path/to/your/project Analyzing a Single File To count lines of code in a single file, run: cloc /path/to/your/file Analyzing Multiple Files You can also specify multiple files: cloc file1.py file2.js file3.cpp Excluding Files or Directories To exclude certain files or directories,...

PDF Extraction 🗂️📄 Step: Use PyPDF to extract text from PDF documents. Process: def extract_text_from_pdf(pdf_path):     reader = PdfReader(pdf_path)     text = ""     for page in reader.pages:         text += page.extract_text()     return text Explanation: PyPDF 📄🔍 goes through each page and extracts the text 📝 from the PDF 📂. Document Indexing 🗂️📚 Step: Index the extracted text for efficient retrieval. def index_text(text):     index = faiss.IndexFlatL2(512)  # Creating an index     embeddings = embed_text(text)   # Convert text to embeddings     index.add(embeddings)           # Add embeddings to the index     return index Explanation: The text 📝 is converted to embeddings (vector representations) 🔢 and indexed 📚 using FAISS for quick retrieval 🔍. Query Processing 🤖🔍 Step: Use LangChain to handle the sequence of operations: q...

Example 1: Extracting All Paragraphs from a Web Page 📄 ``` url = 'http://example.com' response = requests.get(url) soup = BeautifulSoup(response.content, 'html.parser') paragraphs = soup.find_all('p') for p in paragraphs:     print(p.get_text()) ``` Example 2: Extracting Table Data 📊 ``` url = 'http://example.com/tablepage' response = requests.get(url) soup = BeautifulSoup(response.content, 'html.parser') table = soup.find('table') rows = table.find_all('tr') for row in rows:     cells = row.find_all('td')     for cell in cells:         print(cell.get_text()) ``` Example 3: Extracting Data from a Specific Class 🎯 ``` url = 'http://example.com' response = requests.get(url) soup = BeautifulSoup(response.content, 'html.parser') items = soup.find_all(class_='classname') for item in items:     print(item.get_text()) ```

 Here are the basic steps to use Beautiful Soup for web scraping: 1. **Install Beautiful Soup** 💻📦:    ```    !pip install beautifulsoup4    !pip install lxml    ``` 2. **Import the Necessary Libraries** 📚:    ```    from bs4 import BeautifulSoup    import requests    ``` 3. **Fetch the Web Page** 🌐⬇️:    ```     url = 'http://example.com'    response = requests.get(url)    html_content = response.content    ``` 4. **Parse the HTML Content** 🗂️🔍:    ```    soup = BeautifulSoup(html_content, 'lxml')  # or 'html.parser'    ``` 5. **Extract Data** 📄➡️🔢:    - Extract specific elements like titles, links, tables, etc.      Example - Extracting all the links 🔗:    ```     for link in soup.find_all('a'):        print(link.get('href'))    ``` ...