Anyscale’s Post

I tried this #webscraping tool powered by #LLMs to extract product information from #marketplaces. It's so powerful that you can just say what information you want from a product page.

ScrapeGraphAI: You Only Scrape Once Neat little web scraping tool powered by LLMs. LLMs are powerful information extractors so it's not surprising to see the popularity of this Python library and many others. It works with ollama and other LLM providers. (links in the comments)

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

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

The open-source Python tool stack is used to scale, test, deploy, and keep an eye on Large Language Models (LLMs) . To manage every step of an LLM project, from scalability to testing, evaluation, deployment, and logging, we have a variety of specialised Python libraries and tools. Some of the top Python libraries to accelerate LLM building are given below- 1) Scaling: - DeepSpeed - Megatron-DeepSpeed - YaFSDP - Megatron-LM - Fairscale 2) Testing/Evaluation: - Deepchecks - lm-evaluation-harness - Giskard - MLflow - truera 3) Serving/Deployment: - vLLM - FlowiseAI - CTranslate2 4) Logging: - MLflow - truera - Weights&Biases Photo Credit: Mr. Avi Chawla #Python_Libraries #LLMs

Can GPT-4o find bugs in large Python codebases? In collaboration with Andy Lee (project lead) and Bing Hu (advisor) from WAT.ai & University of Waterloo, we built a new benchmark called "Bug In The Code Stack" (BICS) to test how well LLMs find syntactic bugs in large Python codebases. This is similar to a classic needle-in-the-haystack test but much harder for LLMs. The hype is real - GPT-4o showed the best performance. The GPT-4-Series was superior to other models at long context lengths. Google's Gemini 1.0 Pro performed the worst, surprisingly inferior to Llama3-70B. See full results here: https://lnkd.in/gVnA3tPV

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

A large snake such as a python can represent overcoming obstacles or facing deep internal fears in your dreams. Pythons are heavy-bodied snakes known for their powerful constricting grip. To dream of a python may suggest you are dealing with overwhelming problems or strong negative emotions that feel constricting. It could mean you are grappling with internal issues that you find hard to control. The python in your dream may symbolize an unwanted situation in your life that you feel trapped by or trapped within. It could signify deep-rooted issues, perhaps from your past, that still influence you today.pythons are also associated with wisdom and regeneration. Dreaming of a large snake like a python could mean you are undergoing an internal process of self-discovery, insight, or personal transformation. Facing a python may represent confronting difficult truths about yourself or gaining a d - https://lnkd.in/gtn4gn2m

Elevate your search game with Retrieval-augmented generation (RAG) - where information retrieval meets advanced #AI for precise answers ✅ Here's the tutorial you need to implement an end-to-end #RAG system using the OpenAI, LlamaIndex, and MongoDB or Python, OpenAI, LlamaIndex, MongoDB #AI Stack https://lnkd.in/emnuS_Jc

AI Agents in Databutton are key to building the backend, and here prompting is crucial. Databutton Agents (+ its tools) are designed for building such robust Python backend (FastAPI routers). What we have learned over time is crucial - the way we pass prompts to the agent makes a big difference. With Databutton, you can set up any endpoint in no time if you clearly explain the input and output arguments right from the start. This clarity in prompt is super essential.