Nishan JaininGoPenAIUnlocking the Power of Embeddings: How to Choose the Best Embedding Model for RAGA Comprehensive Guide to Choosing the Right Embedding Model for RAG ApplicationsJun 29Jun 29
Nishan JaininGoPenAIRAG: Loading Multimodal Documents into Vector Databases with Microsoft Phi-3, Phi-3 Vision, and…Learn to load multimodal documents with text, images, and tables into a vector database using Microsoft Phi-3 for text, Phi-3 Vision for…Jun 161Jun 161
Nishan JaininGoPenAIChunking PDFs and Multimodal Documents: Efficient Methods for Handling Text, Tables, and Images for…In this article, you will learn how to chunk documents like PDF, Word, and other multimodal documents for RAG applications.Jun 101Jun 101
Nishan JaininGoPenAIOptimizing RAG With Document Chunking: Beginner’s Guide to Chunking TechniquesThis blog will explore Fixed Size Chunking, Recursive Chunking, and Document Chunking methods.Jun 2Jun 2
Nishan JaininGoPenAIIntroduction to Retrieval-Augmented Generation (RAG): A Beginner’s GuideWhat is Retrieval Augmented Generation (RAG)May 251May 251
Nishan JainFinancial QA with LangChain and OpenAIThis project implements FinQA using OpenAI’s embedding models and LangChain’s Python library. The aim is to make a user-friendly Financial…Mar 2Mar 2
Nishan JaininGoPenAIK-Means++: The Next Generation Clustering Algorithm for Efficient Data SegmentationMay 14, 2023May 14, 2023
Nishan JaininGoPenAIEssential Machine Learning Terms Every Beginner Should KnowSupervised LearningMay 6, 2023May 6, 2023