Hacker News

MARCH 20, 2025

We introduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a new universal markup format that captures all page elements in their full context with location.

120

120

Analytics Vidhya

JULY 27, 2024

This is where the term frequency-inverse document frequency (TF-IDF) technique in Natural Language Processing (NLP) comes into play. Introduction Understanding the significance of a word in a text is crucial for analyzing and interpreting large volumes of data. appeared first on Analytics Vidhya.

Natural Language Processing 312

Natural Language Processing Analytics Analytics Python 312

insideBIGDATA

FEBRUARY 26, 2025

models include: A new vision language model (VLM) for document understanding tasks that IBM said demonstrates performance that matches or exceeds that of significantly larger models IBM (NYSE: IBM) today announced additions to its Granite portfolio of large language models intended to deliver small, efficient enterprise AI.

AI 402

AI AI 402

AWS Machine Learning Blog

DECEMBER 12, 2024

A large portion of that information is found in text narratives stored in various document formats such as PDFs, Word files, and HTML pages. Some information is also stored in tables (such as price or product specification tables) embedded in those same document types, CSVs, or spreadsheets.

AWS 99

AWS Machine Learning Machine Learning AI 99

Flipboard

APRIL 23, 2025

Traditional keyword-based search mechanisms are often insufficient for locating relevant documents efficiently, requiring extensive manual review to extract meaningful insights. This solution improves the findability and accessibility of archival records by automating metadata enrichment, document classification, and summarization.

AWS 96

AWS ML ML AI 96

Analytics Vidhya

OCTOBER 30, 2024

Imagine trying to navigate through hundreds of pages in a dense document filled with tables, charts, and paragraphs. Finding a specific figure or analyzing a trend would be challenging enough for a human; now imagine building a system to do it.

Analytics 245

Analytics Analytics AI AI 245

Towards AI

FEBRUARY 25, 2025

In my previous blog, I explored building a Retrieval-Augmented Generation (RAG) chatbot using DeepSeek and Ollama for privacy-focused document interactions on a local machine here. Image generated using napkin.ai Now, Im elevating that concept with an Agentic RAG approach powered by CrewAI.

Python 93

Python AI AI Data Science 93

Analytics Vidhya

APRIL 24, 2025

Handling documents is no longer just about opening files in your AI projects, its about transforming chaos into clarity. Retrieving structured content from these documents has become a big task today. Docs such as PDFs, PowerPoints, and Word flood our workflows in every shape and size.

Analytics 162

Analytics Analytics AI AI 162

Dataconomy

APRIL 21, 2025

Victims choose one: Documented loss payment, up to $10,000. Cash fund payment, prorated with no documentation. Documented loss payment This option reimburses verifiable outofpocket expenses connected to the breach, capped at $10,000 per person. Select Documented Loss or Cash Fund. Choose your payment type.

174

174

Dataconomy

APRIL 28, 2025

Retrieval-Augmented Generation, or RAG, has been hailed as a way to make large language models more reliable by grounding their answers in real documents. Even the safest models, paired with safe documents, became noticeably more dangerous when using RAG. Adding more retrieved documents only worsened the problem.

AI 113

AI AI 113

Analytics Vidhya

NOVEMBER 18, 2024

Have you ever been curious about what powers some of the best Search Applications such as Elasticsearch and Solr across use cases such e-commerce and several other document retrieval systems that are highly performant? Apache Lucene is a powerful search library in Java and performs super-fast searches on large volumes of data.

Analytics 223

Analytics Analytics Data Mining Data Mining 223

Analytics Vidhya

DECEMBER 21, 2024

It is one thing to detect text on images on documents and another thing when the text is in an image on a person’s T-shirt. Scene text recognition (STR) continues challenging researchers due to the diversity of text appearances in natural environments.

Analytics 224

Analytics Analytics AI AI 224

Analytics Vidhya

MARCH 31, 2025

RAG combines the power of document retrieval with the […] The post Top 13 Advanced RAG Techniques for Your Next Project appeared first on Analytics Vidhya. And how do we keep it from confidently spitting out incorrect facts? These are the kinds of challenges that modern AI systems face, especially those built using RAG.

Analytics 214

Analytics Analytics AI AI 214

insideBIGDATA

OCTOBER 12, 2024

The solution ensures that AI models are developed using secure, compliant, and well-documented data. Alation Inc., the data intelligence company, launched its AI Governance solution to help organizations realize value from their data and AI initiatives.

Data Quality 365

Data Quality AI AI Data Governance 365

Data Science Dojo

NOVEMBER 8, 2024

Document Summarization LLaMA 3.1 Also learn about AI-powered document search Language Translation Services Translation services can use Llama 3.1 to translate complex legal documents, ensuring that the translated text maintains its original meaning and legal accuracy. For instance, a healthcare provider can use a LLaMA 3.1-powered

AI 397

AI AI 397

Flipboard

MARCH 11, 2025

For years, businesses, governments, and researchers have struggled with a persistent problem: How to extract usable data from Portable Document Format (PDF) files.

Data Analysis 174

Data Analysis Data Analysis Algorithm Machine Learning 174

Analytics Vidhya

MARCH 29, 2025

Imagine an AI that can write poetry, draft legal documents, or summarize complex research papersbut how do we truly measure its effectiveness? As Large Language Models (LLMs) blur the lines between human and machine-generated content, the quest for reliable evaluation metrics has become more critical than ever.

Analytics 199

Analytics Analytics AI AI 199

KDnuggets

AUGUST 6, 2024

Learn statistics through interactive books, code examples, cheat sheets, guides, and tools documentation.

Data Science 365

Data Science 365

Analytics Vidhya

JUNE 24, 2024

Introduction Microsoft Research has introduced a groundbreaking Document AI model called Universal Document Processing (UDOP), which represents a significant leap in AI capabilities.

Analytics 319

Analytics Analytics AI AI 319

Flipboard

NOVEMBER 19, 2024

A common adoption pattern is to introduce document search tools to internal teams, especially advanced document searches based on semantic search. In a real-world scenario, organizations want to make sure their users access only documents they are entitled to access. The following diagram depicts the solution architecture.

AWS 123

AWS AI AI Big Data 123

Data Science Dojo

DECEMBER 6, 2023

This process is typically facilitated by document loaders, which provide a “load” method for accessing and loading documents into the memory. This involves splitting lengthy documents into smaller chunks that are compatible with the model and produce accurate and clear results.

Database 370

Database Data Preparation Algorithm AI 370

Hacker News

JANUARY 30, 2025

They can act as a signature for the printer that law enforcement uses as document forensic evidence (like in. The layout of the dots are different between printer brands and some dont leave any at all. Information like serial number and sometime the print time is encoded in these dots.

181

181

insideBIGDATA

SEPTEMBER 16, 2024

This new Audio Overview feature can turn documents, slides, charts and more into engaging two-party discussions with one click. Here is a an example of a wild new experimental feature from Google called NotebookLM. Two AI hosts start up a lively “deep dive” discussion based on your sources.

AI 408

AI AI Machine Learning Machine Learning 408

AWS Machine Learning Blog

APRIL 7, 2025

The following use cases are well-suited for prompt caching: Chat with document By caching the document as input context on the first request, each user query becomes more efficient, enabling simpler architectures that avoid heavier solutions like vector databases. Please follow these detailed instructions:" "nn1.

AWS 132

AWS AI AI ML 132

Analytics Vidhya

JUNE 3, 2024

Evaluation ensures the RAG pipeline retrieves relevant documents, generates […] The post A Guide to Evaluate RAG Pipelines with LlamaIndex and TRULens appeared first on Analytics Vidhya. Over the past few months, I’ve fine-tuned my RAG pipeline and learned that effective evaluation and continuous improvement are crucial.

Analytics 318

Analytics Analytics Algorithm Python 318

databricks

OCTOBER 22, 2024

Over the years, organizations have amassed a vast amount of unstructured text data—documents, reports, and emails—but extracting meaningful insights has remained a challenge.

AI 344

AI AI 344

AWS Machine Learning Blog

NOVEMBER 13, 2024

The market size for multilingual content extraction and the gathering of relevant insights from unstructured documents (such as images, forms, and receipts) for information processing is rapidly increasing. These languages might not be supported out of the box by existing document extraction software.

AWS 104

AWS Machine Learning Machine Learning ML 104

Flipboard

APRIL 23, 2025

RAG workflow: Converting data to actionable knowledge RAG consists of two major steps: Ingestion Preprocessing unstructured data, which includes converting the data into text documents and splitting the documents into chunks. Document chunks are then encoded with an embedding model to convert them to document embeddings.

AWS 127

AWS ML ML AI 127

Analytics Vidhya

JANUARY 6, 2025

It combines document processing and web search integration to simplify information retrieval and analysis. With so much happening in the Generative AI space, the need for tools that can efficiently process and retrieve information has never been greater.

AI 208

AI AI Analytics Analytics 208

Analytics Vidhya

APRIL 30, 2024

Chat with Multiple Documents using Gemini LLM is the project use case on which we will build this RAG pipeline. Introduction Retriever is the most important part of the RAG(Retrieval Augmented Generation) pipeline. In this article, you will implement a custom retriever combining Keyword and Vector search retriever using LlamaIndex.

Analytics 318

Analytics Analytics Database AI 318

Data Science Dojo

SEPTEMBER 28, 2023

The data is converted into a simple document format that is easy for LlamaIndex to process. Our example code will illustrate the development of a PDF Q&A chatbot that incorporates the OpenAI language model, VectorStoreIndex for document indexing and Streamlit for user interface design.

Natural Language Processing 435

Natural Language Processing Database Data Science Analytics 435

AWS Machine Learning Blog

APRIL 7, 2025

For example, imagine a consulting firm that manages documentation for multiple healthcare providerseach customers sensitive patient records and operational documents must remain strictly separated. Using the query embedding and the metadata filter, relevant documents are retrieved from the knowledge base.

Database 119

Database AWS Natural Language Processing AI 119

Analytics Vidhya

JUNE 2, 2024

We have implemented a simple RAG pipeline using them to generate responses to user’s questions on ingested documents. Introduction In the previous article, we experimented with Cohere’s Command-R model and Rerank model to generate responses and rerank doc sources.

Analytics 343

Analytics Analytics 343

Flipboard

FEBRUARY 11, 2025

Large-scale data ingestion is crucial for applications such as document analysis, summarization, research, and knowledge management. These tasks often involve processing vast amounts of documents, which can be time-consuming and labor-intensive. This solution uses the powerful capabilities of Amazon Q Business.

AWS 133

AWS ML ML Machine Learning 133

Machine Learning Mastery

APRIL 23, 2025

This post is divided into five parts: Understanding the RAG architecture Building the Document Indexing System Implementing the Retrieval System Implementing the Generator Building the Complete RAG System An RAG system consists of two main components: Retriever: Responsible for finding relevant documents or passages from a knowledge base given (..)

232

232

Flipboard

DECEMBER 5, 2024

Get a group of primary care physicians together, and there’s a pretty good chance they will start talking about the potential of AI scribes to reduce documentation burden and improve the clinician-patient office interaction.

AI 145

AI AI Artificial Intelligence Artificial Intelligence 145

Towards AI

APRIL 28, 2025

Models like Sentence Transformers map words, sentences, or documents into high-dimensional vectors. To find relevant text, you compare vectors using metrics like cosine similarity, retrieving documents whose embeddings are closest to the query embedding. It scores documents based on: 1. My workbook is here: [link] 5.2

Python 103

Python Database AI AI 103