Text Embedding Guide: Query PDFs & Retrieve Relevant Info Without Vector DB скачать в хорошем качестве

Text Embedding Guide: Query PDFs & Retrieve Relevant Info Without Vector DB

In this step-by-step tutorial, you'll learn how to perform text embedding and similarity search on PDFs without using any vector database! We’ll parse PDFs using pdfPlumber with page chunking, convert text chunks into embeddings using the Mini-LM-L6-v2 model, and then perform similarity search with cosine similarity and dot products to query and retrieve relevant information. What you'll learn: How to extract and chunk text from PDFs using pdfPlumber Generating text embeddings with Mini-LM-L6-v2 Performing similarity search without a vector database Querying PDFs to find the most relevant information using cosine similarity and dot product calculations This tutorial is perfect for anyone wanting to build a simple, efficient search system on PDF documents without relying on external vector databases! Github Code: https://github.com/mohitdb7/Youtube-T... #textEmbedding #genai #rag #vectordb #embeddings #aichatbot #aidevelopment #generativeai