OSMOSIS Association’s Post

8B parameters SQL code generation model (Llama 3 based) very closed to much bigger models (~ GPT-4-Turbo, Claude-3-Opus) on SQLEval Available with all weights with commercial license https://lnkd.in/gQsA2ChZ

I’ve just released a new tool! 🚀 ✨ This Text-to-SQL agent, built with LlamaIndex workflows, transforms natural language questions into SQL commands, making data access simpler and more secure. With features like intent recognition and automated SQL generation, it’s perfect for users without deep SQL knowledge 💻🔍 ✨ Read the full article here: https://lnkd.in/dtzFRUiv ✨ Explore the code on GitHub: https://lnkd.in/d2WmB_kj #Text2SQL #TextToSQL #SQL #LlamaIndex

🌟 Day 19: Conquering Data Structures & Algorithms! 🚀 📋 Problem Statement: Count distinct elements in every window 💡 My Solution: Using HashMap and Sliding Window. 📈 Time Complexity: O(N), Space Complexity: O(N) 🔍 Looking for Feedback: If you have any valuable insights, alternative solutions, or suggestions for optimising the code, please feel free to share them in the comments. Let's collaborate to find the most efficient solution together! 💬 🔗 Link to Github solution: https://lnkd.in/di_aYgQb 🌟 Check out my solution below: #dsachallenge  #algorithms #dailylearning #dsa 

Hannes speaks about the work of Péter Király to develop Shacl4Bib, a custom tool for the validation of library data. - Shapes Constraint Language (SHACL) is a formal language for validating RDF graphs against a set of conditions. Shacl4Bib is a mechanism to define SHACL-like rules for data sources in non-RDF based formats, such as XML, CSV and JSON. Cf. https://lnkd.in/efMijQVg #MDGAGM2024

⚙️ 𝗖𝗿𝗲𝗮𝘁𝗲 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗼𝗿 ⚙️ Build a dataset for Named Entity Recognition (NER) using schema-based extraction. This helps in performing semantic searches based on the entities. Pydantic, the backbone of Instructor, enables high customization and utilizes return datatype hints for seamless schema validation. It seamlessly integrates with #LanceDB and directly inserts data into tables. 🔨 Code Implementation - https://lnkd.in/gmjepTcg Checkout for related examples and tutorials https://lnkd.in/gWYgJD8z

I found this tool last Night, it visualises the JSON data, making it easier to work with. If you work with nested JSON files, this one’s for you. Check JSONCRACK — a tool that transforms JSON files into clear, readable graph diagrams that can be downloaded as images too. ToDiagram #JsonTools #TechTools #ProductivityHack #DeveloperTools #PakistanTech #Ujjandotcom

Here's a concise overview of the key concepts of GraphQL query language in one great definition: - GraphQL's schema-driven approach defines types and relationships, empowering clients to request specific data through queries. - Clients can modify data using mutations, while fields determine the data to retrieve. - Enhancing query flexibility, arguments, aliases, and fragments play crucial roles, with variables adding dynamism to queries. - Directives enable conditional execution, and introspection allows clients to explore schema structures and capabilities, making GraphQL a powerful and versatile querying language. #GraphQL #QueryLanguage #Schema #Mutations #Directives #Introspection

This two libraries can save your parsing time while scraping 1. Extracting datetime from text : https://lnkd.in/dKxfeTAC 2. Extracting price from text: https://lnkd.in/dujVeQQB There so many more in scrapinghub

To future-proof SurrealQL we have introduced an updated syntax for KNN. The old syntax will be supported for the lifespan of SurrealDB 1.x.x https://lnkd.in/eAfRfUjJ #SurrealDB #documentation

🎉 Synthetic dataset made with Llama 3.1 405B. See my comments in the associated post about what I personally think about dataset and model versioning. Please feel free to leave your comments there as well. #huggingface #models #datasets #versioning #llama #syntheticDataset