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Enhancing RAG with Hypothetical Document Embedding

Analytics Vidhya

RAG is replacing the traditional search-based approaches and creating a chat with a document environment. The biggest hurdle in RAG is to retrieve the right document. Only when we get […] The post Enhancing RAG with Hypothetical Document Embedding appeared first on Analytics Vidhya.

Analytics 241
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RAG and Streamlit Chatbot: Chat with Documents Using LLM

Analytics Vidhya

Introduction This article aims to create an AI-powered RAG and Streamlit chatbot that can answer users questions based on custom documents. Users can upload documents, and the chatbot can answer questions by referring to those documents.

Analytics 245
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'Shake' Design Documents

Hacker News

But over on LinkedIn, someone with a perverse interest in ancient history (hi Tim) asked if I still had copies of some UI design documents that I did for the now-defunct compositing package Shake. Okay, this will be of interest to a VERY SMALL number of people. So, see below for a lot of rough…

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Revolutionizing Document Processing Through DocVQA

Analytics Vidhya

Introduction DocVQA (Document Visual Question Answering) is a research field in computer vision and natural language processing that focuses on developing algorithms to answer questions related to the content of a document, like a scanned document or an image of a text document.

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JPMorgan’s Latest AI DocLLM is Revolutionizing Document Understanding

Analytics Vidhya

JPMorgan has unveiled its latest AI – DocLLM, an extension to large language models (LLMs) designed for comprehensive document understanding. Thus, providing an efficient solution for processing visually complex documents.

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Creating a bespoke LLM for AI-generated documentation

databricks

We recently announced our AI-generated documentation feature, which uses large language models (LLMs) to automatically generate documentation for tables and columns in Unity.

AI 320
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Document Information Extraction Using Pix2Struct

Analytics Vidhya

Introduction Document information extraction involves using computer algorithms to extract structured data (like employee name, address, designation, phone number, etc.) from unstructured or semi-structured documents, such as reports, emails, and web pages.

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