DBeaver's Data Editor gives you the freedom to work with document databases the way you prefer. Explore and modify your data in a familiar grid view, or dive into the details with a dedicated JSON view that provides a clear, structured format for easy analysis. Seamlessly switch between these modes to efficiently manage and update your data.
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Digital Productivity Advisor- offer tools and software to improve workflows, automate tasks, and boost productivity
The Ultimate Data Scraping Checklist for Beginners Ready to dive into data scraping but unsure where to start? Here’s a step-by-step checklist to help you set up your first data scrape like a pro, even if you’re new. Identify data sources Define the data you need Choose the right tools Set scraping intervals (how often to pull data) Organize and clean your data Save this checklist and tackle each step confidently with Scraper API or your favorite tool! 📌 Bonus Tip: Don’t forget to review scraping rules for each site! #DataScraping #ScraperAPI #Checklist
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📃 Real World Data Prep for LLMs: Why is PDF parsing so difficult? 👩💻 Shuveb Hussain, as the co-founder of Unstract, which addresses structured data extraction problems using LLMs, is well-versed in the various challenges of data extraction. Watch this short tutorial by Shuveb Hussain with Yujian Tang with OSS4AI, focusing on the challenges and solutions in real-world data preparation for LLMs. In this video, watch as Shuveb shares the problems that are prevalent throughout the PDF extraction landscape, and how you can extract structured data from complex documents. Here are some of the most common text extraction challenges: ❌ PDFs with Tables ❌Non-linear Text Flow: PDFs often organize text in columns or around images, confusing extraction tools. ❌Quality of the PDF: Issues like lighting conditions, rotation, skew, and compression levels of the original photo can degrade text extraction quality. ❌Page Orientation: Extracting text from PDFs with both portrait and landscape modes is more complex than uniform page orientations. ❌Handwritten Forms: Not all OCRs can recognize handwritten text. ❌Checkboxes and Radio Buttons: Many text extractors struggle with these elements, though pdf.js does a good job, it’s not always feasible to use third-party services. 👨💻 When faced with these challenges, manual methods often become necessary. In the video, you'll learn: how we take unstructured documents, use a text extraction library/service to extract raw text, and then employ a combination of Pydantic and LangChain to create structured JSON. We will examine different document formats, including tables, PDF forms, and scanned documents, using various libraries and services: ✅ PDF Plumber ✅ Camelot ✅ Tabula ✅ unstructured.io ✅ LlamaParse ✅ Unstract's LLMWhisperer Join us as we delve into these challenges and explore the tools that can make text extraction more efficient and accurate. https://lnkd.in/gnSxfMnE #TextExtraction #pdfextraction #LLMs #datapreparation
Real-world Data Prep for LLMs: Challenges and Solutions
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Senior Software Engineer | Cloud -AWS & Azure | Gen AI, RAG, LLMs, Agent | Microservice, RESTful APIs | Dynamics 365 F&O, Field Service & PowerApps
RAG is saver for hallucination but need to be carefully handled to have better result
GenAI Evangelist | Developer Advocate | Tech Content Creator | 30k Newsletter Subscribers | Empowering AI/ML/Data Startups
Struggling to Optimize Your #RAG Setup? Not sure if you chunked your data right to enable optimal context retrieval? Not sure which embedding model will work best for your data? Don’t worry, you’re not alone.. A RAG has several moving parts: data ingestion, retrieval, re-ranking, generation etc.. Each part comes with numerous options. If we consider a toy example, where you could choose from: 5 different chunking methods, 5 different chunk sizes, 5 different embedding models, 5 different retrievers, 5 different re-rankers/ compressors 5 different prompts 5 different LLMs That’s 78,125 distinct RAG configurations! If you could try evaluating each one in just 5 mins, that’d still take 271 days of non-stop trial-and-error effort! In short, it’s kinda impossible to find your optimal RAG setup manually. So, how do you determine the most optimal RAG configuration for your data and use-case? Use hyperparameter tuning - an ML technique for identifying the optimal values for your parameters when there’s a large set of possible values. But, how do you do it without writing a bunch of code to do hyperparameter tuning? I stumbled upon this tool ‘RAGBuilder’ that takes your data as an input, and runs hyperparameter tuning on the various RAG parameters (like chunk size, embedding etc.) evaluating multiple configs, and shows you a dashboard where you can see the top performing RAG setup, and in 1-click generate the code for that RAG setup. So you can go from your RAG use-case to production-grade RAG setup in just minutes. Best part, it’s open source with active contributors. Check out the RAGBuilder Github repo: https://lnkd.in/gTZZqbrQ ----------------------------------------------------------- Get started with RAGBuilder using SingleStore as your vector database. Try SingleStore database for free: https://lnkd.in/gCAbwtTC
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Data Science Leader | 28,300+ followers | Transforming businesses with AI / Data Science / Machine Learning / LLMs
how do you determine the most optimal RAG configuration for your data and use-case?
GenAI Evangelist | Developer Advocate | Tech Content Creator | 30k Newsletter Subscribers | Empowering AI/ML/Data Startups
Struggling to Optimize Your #RAG Setup? Not sure if you chunked your data right to enable optimal context retrieval? Not sure which embedding model will work best for your data? Don’t worry, you’re not alone.. A RAG has several moving parts: data ingestion, retrieval, re-ranking, generation etc.. Each part comes with numerous options. If we consider a toy example, where you could choose from: 5 different chunking methods, 5 different chunk sizes, 5 different embedding models, 5 different retrievers, 5 different re-rankers/ compressors 5 different prompts 5 different LLMs That’s 78,125 distinct RAG configurations! If you could try evaluating each one in just 5 mins, that’d still take 271 days of non-stop trial-and-error effort! In short, it’s kinda impossible to find your optimal RAG setup manually. So, how do you determine the most optimal RAG configuration for your data and use-case? Use hyperparameter tuning - an ML technique for identifying the optimal values for your parameters when there’s a large set of possible values. But, how do you do it without writing a bunch of code to do hyperparameter tuning? I stumbled upon this tool ‘RAGBuilder’ that takes your data as an input, and runs hyperparameter tuning on the various RAG parameters (like chunk size, embedding etc.) evaluating multiple configs, and shows you a dashboard where you can see the top performing RAG setup, and in 1-click generate the code for that RAG setup. So you can go from your RAG use-case to production-grade RAG setup in just minutes. Best part, it’s open source with active contributors. Check out the RAGBuilder Github repo: https://lnkd.in/gTZZqbrQ ----------------------------------------------------------- Get started with RAGBuilder using SingleStore as your vector database. Try SingleStore database for free: https://lnkd.in/gCAbwtTC
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𝗔 𝘃𝗶𝘀𝘂𝗮𝗹 𝗴𝘂𝗶𝗱𝗲 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗰𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 With so many options, choosing the right data store can be confusing. This diagram shows a datastore selection choice based on a use case. Data can be 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 (𝗦𝗤𝗟 𝘁𝗮𝗯𝗹𝗲 𝘀𝗰𝗵𝗲𝗺𝗮), 𝘀𝗲𝗺𝗶-𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 (𝗝𝗦𝗢𝗡, 𝗫𝗠𝗟, 𝗲𝘁𝗰.), 𝗮𝗻𝗱 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 (𝗕𝗹𝗼𝗯). In the case of structured, they can be relational or columnar, while in the case of semi-structured, there is a wide range of possibilities, from key-value to graph. Which database have you used for which workload? Credit: Dr Milan Milanović
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Let’s talk about Models. In Metabase, you can create derived datasets, known as models, to make data more intuitive for your teams. Models allow you to pull together data from different tables, build on the results of Metabase questions, and even add custom, calculated columns. Plus, you can annotate all columns with metadata, making it easier for your team to explore and manipulate the data in the query builder as a starting point. In this new video, Alex shows us how to create a model, using it as a data source, the difference between a model and a saved question, and more: 📼 https://buff.ly/3Yd60da
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Director, Technical Architecture at AWP Safety | Certified Microsoft Power Platform Solution Architect | Dynamics 365 Custom Solution Builder | Experienced Software Application Developer & Consultant
REALLY digging the Data Transporter tool by Bram Colpaert. This thing is making moving data from 4 different #dataverse environments all the way up to production a sinch! The beauty of it is it keeps the same GUIDS across all environments with options to create/update/delete.
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Head of Sales - We offer a lock-in free technology that designs data warehouses 10 times faster than hand-coding, boosting efficiency and unlocking businesses' data potential.
🔍 Consistent Code, Zero Errors with AnalyticsCreator Generate flawless code that scales with your data needs. With AnalyticsCreator, you always own the error-free code generated. Elevate your data management today! 💡 #DataIntegrity #Scalability #CodeOwnership No-Code Data Pipeline Solution https://hubs.ly/Q02BxsGL0
No-Code Data Pipeline Solution
analyticscreator.com
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𝐂𝐀𝐒𝐓(𝐞𝐱𝐩𝐫 𝐀𝐒 𝐭𝐲𝐩𝐞 ) Wrangling data often involves converting it from one format to another in a data analysis process whether you're transforming integers to strings, dates to timestamps, or vice versa, ensuring your data is in the right format is crucial for accurate analysis. That's the case where the CAST() function is for you!! The CAST() function takes an expression of any type and produces a result value of the specified kind. this operation simplifies data manipulation and enhances flexibility in SQL queries. Plus, it's very straightforward to use. 👉 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐒𝐲𝐧𝐭𝐚𝐱: 𝐂𝐀𝐒𝐓(𝐞𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐀𝐒 𝐭𝐲𝐩𝐞) Example ; 𝐒𝐄𝐋𝐄𝐂𝐓 𝐂𝐀𝐒𝐓(𝐞𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐀𝐒 𝐭𝐲𝐩𝐞) 𝐅𝐑𝐎𝐌 𝐲𝐨𝐮𝐫_𝐭𝐚𝐛𝐥𝐞; Type that support in cast functions are : 🔢Numbers (e.g., INTEGER, FLOAT) 🔤Strings (VARCHAR, CHAR) ⏰Dates and Times (DATE, DATETIME, TIME, TIMESTAMP) And some others also like BINARY, DECIMAL, DOUBLE, JSON, spatial_type 🌐. 𝗡𝗼𝘁𝗲: 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗮𝗹𝘀𝗼 𝗲𝘅𝗽𝗿𝗲𝘀𝘀 𝘁𝗵𝗶𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝘀 𝗖𝗢𝗡𝗩𝗘𝗥𝗧(𝗲𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻, 𝘁𝘆𝗽𝗲), 𝘄𝗵𝗶𝗰𝗵 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆. #DataWrangling #DataAnalysis #CASTFunction
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Associate Director in Global Real Estate
3wAwesome product