Microsoft has open sourced GraphRAG - an open source Python library for extracting insights from unstructured data using LLMs. GraphRAG is an AI-based content interpretation and search capability. Using LLMs, it parses data to create a knowledge graph and answer user questions about a user-provided private dataset. If you have been exploring or consider exploring knowledge graphs for your RAG based application - this might provide a good starting point. Do check the GraphRAG Solution Accelerator page as well for an example on how to implement this in production. Might not exactly what you are looking for nevertheless, a good starting point in this regard. GraphRAG -> aka.ms/graphrag Github -> https://lnkd.in/dcdSAnes
Naqqash Abbassi’s Post
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Manager II - Machine Learning Product Discovery @ Bed Bath & Beyond | Machine Learning, Computer Vision, NLP, LLMs
Vector databases play a crucial role in today's ML use cases by efficiently handling and retrieving high-dimensional data. Their significance lies in optimizing similarity searches, enabling faster and more accurate operations in various machine learning applications. I've been utilizing approximate neighbor search algorithms for a while and experimenting with the vector database lately. Although there's a lack of comprehensive comparison benchmarks, I found some valuable resources and wanted to share: 1. Benchmarks of approximate nearest neighbor libraries in Python : https://lnkd.in/gVGZ7Syf 2. Framework for evaluating ANNS algorithms on billion scale datasets.: https://lnkd.in/gUcdPpQB 3. Framework for benchmarking vector search engines: https://lnkd.in/g3wVKDPz 4. Comparison of different vector databases: https://lnkd.in/g2U8bkpX
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🚀 𝗡𝗲𝘄 𝗕𝗹𝗼𝗴 𝗣𝗼𝘀𝘁 𝗔𝗹𝗲𝗿𝘁! 🚀 Today I've learned Queue and I'm excited to share my latest blog on Key Concepts of 𝗤𝘂𝗲𝘂𝗲𝘀 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀! I have learned and explained : 🔹 Fundamentals of queues 🔹 Implementing queues using Python 🔹 Using linked lists for queue operations 🔹 Detailed explanations of operations like enqueue, dequeue, and peek Whether you're a beginner looking to understand the basics or an experienced developer wanting to refresh your knowledge, this blog has something for everyone. 🔗 Read the full article here https://lnkd.in/ezatCpzy PS : Don't forget to stay hydrated ! #DataStructures #Python #Coding #Programming #TechBlog #Queues #LinkedLists #Enqueue #Dequeue #Peek #Learning #TechCommunity
What is a Queue in Data Structures? Key Concepts Explained
mahia.hashnode.dev
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Python || Django || Pandas || Python developer || Assembly Language || Reverse Engineering ||
Hello Everyone, Today I completed my Pandas, Pandas is a Python library used for working with data sets. It has functions for analyzing, cleaning, exploring, and manipulating data. In this library, I gained knowledge of Pandas DataFrame and Series, read JSON and CSV files, and analyzed data.
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|Ph.D. Scholar at BITS Pilani, Hyderabad Campus| |Data Scientist at datAIsm Services| |Learner|
Successfully completed the EDA in Python.
SHASWATI DASH's Statement of Accomplishment | DataCamp
datacamp.com
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Microsoft MVP | Software Engineer | Gold Microsoft student ambassador | Youth Leader | SDGs Actor
Very very useful
📚💻 Excited to share a Python script that transforms your PDFs into valuable insights! Using the PyPDFLoader from LangChain, we can extract text from PDFs and create a chatbot to interact with your documents. Here's what this script does: 1. **Loads PDFs**: The script checks for PDF files in a specified directory and loads them using the PyPDFLoader. 2. **Extracts Text**: It then extracts the text from each page of the PDF and combines it into a single string. 3. **Creates a Dictionary**: The script creates a dictionary containing the file name and extracted text for each PDF. This powerful tool can be used for various applications, such as: - Creating a chatbot to interact with your PDFs - Performing text analysis on your PDFs - Extracting information for data storage and processing GitHub: https://lnkd.in/gBjaRY-s Kaggle: https://lnkd.in/gkfiUzDc Twitter: https://lnkd.in/gWe9S5Kc Join me in exploring the world of Python and LangChain, and let's unlock the potential of our PDFs together! 🚀📈 #LangChain #Python #PDFs #DataScience #AI
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Just completed the "Web Scraping with Python" project, offered by Duke University on Coursera! Through this project, I was introduced to Scrapy, a robust web scraping framework. This hands-on experience has enhanced my ability to efficiently automate data extraction and handle complex scraping tasks, which will be a great addition to my data science toolkit. Excited to apply these skills to uncover valuable insights and streamline data collection. #WebScraping #Python #Scrapy #DataScience #Automation #ProfessionalDevelopment #Coursera
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Business Analyst Intern at KultureHire | Data Analyst | Python, SQL, Power BI, Excel | Expertise in Data Cleaning, Statistical Analysis, Predictive Analytics | Driving Business Insights and Strategy
#Python is paramount in data analysis and data science due to its versatility, robust libraries, and ease of use. With libraries like NumPy, Pandas, and Matplotlib, Python empowers analysts and scientists to efficiently manipulate, analyze, and visualize data. Its simplicity fosters rapid development and experimentation, while its integration with machine learning frameworks like TensorFlow and scikit-learn facilitates advanced analytics and predictive modeling. Python's widespread adoption in the data science community also means a vast ecosystem of resources and community support, making it the go-to choice for tackling complex data challenges across various industries. Cheat Sheet is made by #DataCamp
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In this blog I covered Continuous Uniform Distribution, highlighted distinctions from its discrete counterpart, explained CDF and PDF calculations, and provided practical Python examples with accompanying visualisations. It offers a concise yet comprehensive exploration of these fundamental concepts in probability and statistics. Read the full article here: https://lnkd.in/eS87eJpr #datascience #UniformDistribution #pythonfordatascience
Continuous Uniform Distribution with Python
medium.com
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Aspiring Data Scientist | Specialized in Data Analysis with Excel & Power BI at KRySP Services | Expanding Skills in Python, Pandas, Numpy, Matplotlib, Seaborn
🔍 Excited to share a new Python tool I've developed for searching patterns in text files! 🚀 Introducing a powerful search tool powered by Python's os module, regex, and list comprehension concepts. 🐍 With this tool, you can effortlessly search for specific patterns within a collection of text files, making file exploration and analysis a breeze. Whether you're looking for specific phrases, keywords, or complex patterns, this tool has got you covered. Key features include: ✔️ Utilizes Python's os module for file and directory operations. ✔️ Harnesses the power of regex to perform pattern matching with precision. ✔️ Leverages list comprehension for efficient data processing. Using this tool is simple: 1️⃣ Specify the directory containing your text files. 2️⃣ Define the pattern you want to search for using regex. 3️⃣ Run the script, sit back, and let it do the searching for you! Whether you're a data scientist, developer, or just someone who loves exploring data, this tool will streamline your workflow and help you uncover valuable insights hidden within your text files. Ready to dive in? Reach out to me for more details on how to get started with this powerful text search tool! 💻 #Python #DataAnalysis #Regex #TextProcessing #DataExploration
