unstructured.io

🎉 We’re Live: Unstructured Serverless API is Here! We’re excited to announce that Unstructured Serverless API delivers: 💥 Simplified Onboarding and User Dashboard: Easily manage your keys, billing options, and monitor usage through an intuitive dashboard. 💥 New Per-Page Pricing: Enjoy reduced costs with a transparent and predictable pricing model. 💥 Improved Processing Throughput and Latency: Our latest generation of file transformation pipelines deliver a 5x speedup over our previous API. 💥 Enhanced Extraction Performance: Our new document transformation models deliver industry-leading extraction performance for over 25 file types. 💥 Revamped Documentation: We’ve completely rewritten our documentation, making it easier than ever to render your data RAG-ready. 👉 Sign up in seconds and get started today for FREE: https://lnkd.in/djRT-R_n #WhateverItIsWeCanStructureIt

How significant is the improvement of Llama3.1 over Llama3 in retrieval-augmented generation (RAG) tasks with unstructured text? Our small-scale experiment indicates substantial enhancements in faithfulness, answer similarity, and answer correctness. To hear about the results and methodology, join Nina Lopatina and Yujian Tang for a Tuesday tech talk at OSS4AI, July 30 at 9 am PT, registration link below. Or try out the linked colab notebook on any URL of your choice, and share your results in a comment below! 📆: https://lnkd.in/gViBb62u 📓: https://lnkd.in/gXg2ZGqj tech stack: #RAG #Llama3.1 #Ragas #LangChain #GPT4o #Unstructured

If you want to turn unstructured documents into a knowledge graph at production scale, check out this Neo4j Unstructured blog by Neo4j's Fanghua (Joshua) Yu and summary by Daniel Bukowski ✍ : https://lnkd.in/gVrwTJZV

I'm thrilled to share our latest piece, which dives deep into the challenges and opportunities in harnessing human-generated data with Generative AI (GenAI). We are at a critical inflection point in technological development, reminiscent of the transformative emergence of the Modern Data Stack a decade ago. Back then, enterprises poured resources into ETL tools, data warehouses, and BI tools to mine value from the vast volumes of structured data they generated daily. Fast forward to today, and we find ourselves at a similar juncture with GenAI. Enterprises are now grappling with the task of leveraging their exponentially larger (4-5x) stores of unstructured data in tandem with large language models (LLMs). The complexity of this endeavor cannot be understated. The diverse array of file formats, document layouts, and the complex "cocktails" of models required to render data "RAG ready" present formidable challenges. Yet, with these challenges come extraordinary opportunities. Data scientists and data engineers stand on the brink of unlocking this new category of data. The potential for growth and advancement with GenAI is immense but it requires effortlessly and rapidly joining human generated data with LLMs. This is an exciting era of discovery and we're just getting started.

Watch Tracy Lee and Unstructured's Nina Lopatina flip the script on live demos, where Tracy uses the Unstructured library to embed her podcast transcripts in a DataStax AstraDB. We go through the source/destination set up and file transfer so Tracy can access the information from her Engineering Leadership podcast in her vector database.

💡  GenAI is poised to transform business operations, from marketing and customer service to product development and back office automation. Yet, a significant gap exists between the hype and ROI. In a recent Forbes article, Brian S. Raymond, CEO of Unstructured, highlights this challenge, emphasizing, "As important as the algorithms are, they’re only as good as the data available to them." Key takeaways: 💡GenAI is transforming marketing, customer service, HR, supply chain, and regulatory compliance. 💡Despite its potential, many companies struggle to leverage their unstructured data. 💡Overcoming this requires investment in GenAI-native preprocessing tools and skilled data engineering teams. GenAI's promise is immense, but unlocking its full potential anchors on rendering unstructured data GenAI-ready. Read the full article: https://lnkd.in/eqGZV_Rs

Check out this great blog post by Fateh Ali Aamir 👇 How to Set up a Free (Local) Generative AI Application The article walks you through building a local RAG application using LangChain, Qdrant, Ollama : https://lnkd.in/gQisjmzy

⭐️Community Content 💻How to Set up a Free (Local) Generative AI Application Great article walking through using Qdrant , unstructured.io and Ollama to do RAG locally! https://lnkd.in/gQisjmzy

Did you miss the SingleStore webinar featuring Unstructured?  Don’t worry, you can still watch the recording and learn about: 💡 Challenges of preprocessing unstructured data 💡 Building ETL pipelines for unstructured data 💡 What’s under the hood of Unstructured 💡 Data ingestion, preprocessing and loading results into SingleStoreDB Recording: https://lnkd.in/eewXjCKK

Thanks for including us in the Future 50 list Mario Gabriele! #WhateverItIsWeCanStructureIt Give us a try for FREE: app.unstructured.io