Embeddings — Topic Summaries
AI-powered summaries of 21 videos about Embeddings.
21 summaries
Transformers, the tech behind LLMs | Deep Learning Chapter 5
Transformer-based models—behind systems like ChatGPT—turn text into a stream of vectors, mix information across tokens with attention, and then...
Vector databases are so hot right now. WTF are they?
Vector databases are surging because they turn raw text, images, and audio into searchable “meaning” using embeddings—and then use that similarity...
GPT-4 & LangChain Tutorial: How to Chat With A 56-Page PDF Document (w/Pinecone)
A practical architecture for turning a long PDF into a chat-ready assistant hinges on two phases: ingest the document into a vector database, then...
Introduction To Undertsanding RAG(Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is positioned as a practical way to make large language models more reliable and more useful for an...
2-Build RAG Pipeline From Scratch-Data Ingestion to Vector DB Pipeline-Part 1
A practical RAG pipeline is built end-to-end: raw files get parsed into a structured “document” format, split into chunks that fit model context...
LangChain - Using Hugging Face Models locally (code walkthrough)
Running Hugging Face models locally inside LangChain is the practical workaround when Hugging Face Hub access fails—especially for conversational...
Master PDF Chat with LangChain - Your essential guide to queries on documents
Building a “chat with your PDF” system hinges on one practical fix: plain prompting can’t reliably handle long books because the context window is...
LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!
Getting rid of OpenAI entirely for Retrieval QA with LangChain is feasible, but the quality hinges on the local LLM’s context limits, prompt format...
Augmented Language Models (LLM Bootcamp)
Augmented language models hinge on a simple constraint: modern LLMs are strong at language and instruction-following, but they lack up-to-date world...
Smart Second Brain for Obsidian(Free & Offline)
A privacy-first “second brain” for Obsidian can run entirely offline by pairing the Obsidian plugin Smart Second Brain with locally hosted large...
Advanced RAG with Llama 3 in Langchain | Chat with PDF using Free Embeddings, Reranker & LlamaParse
Building a high-quality “chat with your PDF” system hinges less on the language model and more on the pipeline around it: parsing complex documents...
How to Build a Notion ChatBot For Your Knowledge Base Using LangChain (Code Template Included)
A practical pipeline turns Notion pages into a searchable “chatbot” knowledge base by combining text chunking, vector embeddings, and a...
37% Better Output with 15 Lines of Code - Llama 3 8B (Ollama) & 70B (Groq)
A simple query-rewriting step inside a local RAG (retrieval-augmented generation) pipeline can materially improve answers—often by roughly 37%—even...
Chunking 101: The Invisible Bottleneck Killing Enterprise AI Projects
Chunking—how text is cut into retrieval-ready pieces—is a major, often invisible failure point for enterprise AI systems, and it can directly cause...
The A-to-Z AI Literacy Guide (2025 Edition)
AI literacy in 2025 comes down to understanding how language models turn text into tokens, map those tokens into mathematical meaning, and then...
Local Gemma 4 with OpenCode & llama.cpp | Build a Local RAG with LangChain | 🔴 Live
A local RAG app built around Gemma 4 can work surprisingly well on a single machine—but getting reliable retrieval depends less on the chat model and...
Make with Notion 2024: Taking Notion AI to the max (Michelle Hilzinger, Shir Yehoshua)
Notion AI’s big pitch is simple: one AI system that can search, write, and coordinate multi-step workflows across everything stored in a workspace—so...
Loaders, Indexes & Vectorstores in LangChain: Question Answering on PDF files with ChatGPT
A practical LangChain pipeline for turning PDFs, YouTube transcripts, and plain text into question-answering over embeddings is the core takeaway—and...
LangChain Models: ChatGPT, Flan Alpaca, OpenAI Embeddings, Prompt Templates & Streaming
LangChain can unify three major building blocks—text generation models, embeddings, and chat interfaces—so the same workflow (prompting, formatting,...
ToDo list Embeddings with TensorFlow in JavaScript
A practical path to “icon suggestions” for a to-do app hinges on turning short task text into numeric embeddings and then measuring similarity...
How RAG Finds Answers in Millions of Documents | Embeddings, Vector Databases, LangChain & Supabase
Retrieval in RAG hinges on one practical step: turning a user question into a vector and then finding the most semantically similar document chunks...