Building a Local SQLite Vector Database for Multi-Modal Image Search
Build a SQLite vector database for image search using embeddings and GPT Vision, enabling cosine similarity queries to find relevant images based on user input.
Live Code Review - Pinecone Vercel Starter Template and Retrieval Augmented Generation
Explore Pinecone Vercel starter template for building AI chatbots with Retrieval Augmented Generation. Learn about embeddings, vector databases, and web crawling to create more accurate AI applications.
Pinecone Vercel Starter Template and RAG - Live Code Review Part 2
Explore Retrieval Augmented Generation in a production app using TypeScript and Next.js. Learn about document splitting, embedding, context injection, and UI design for improved relevance and auditability.
Live Code Review - Pinecone Semantic Search
Build a semantic search engine with TypeScript, exploring concepts and diving into code. Learn about embeddings, compare search methods, and implement key components like CSV loading and indexing.
The Evolution of Milvus - A Cloud-Native Vector Database
Explore Milvus 2.0, a cloud-native vector database for unstructured data. Learn its architecture, features, and real-world applications in art, security, real estate, and e-commerce.
Vector Database Explained - What is Vector Database and How to Use It
Explore vector databases: storage, similarity comparison, and applications. Learn Euclidean Distance for feature similarity, see real-world uses, and follow a demo using Bollywood movie plots on Replit.
A Fun and Absurd Introduction to Vector Databases by Alex Chatzizacharias
Explore vector databases for AI applications, natural language interfaces, and advanced search capabilities. Learn to translate and query data for text, image, and generative search.
Python RAG Tutorial - AI for PDFs with Local LLMs
Build a Python RAG app to query PDFs using AI. Learn advanced topics like local execution, vector DB updates, file handling, and response quality testing.
Vector Databases - The Simple and Powerful Future of Machine Learning After ChatGPT
Explore vector databases' potential in machine learning, their real-world applications, and how they complement ChatGPT. Includes hands-on demonstration using open-source tools to showcase practical implementation.
Using Vector Databases for Multimodal Embeddings and Search
Explore multimodal machine learning for cross-modal search at scale. Learn to implement natural search interfaces using open-source models and vector databases for billion-object retrieval.
Python AI Agent Tutorial - Build a Coding Assistant with RAG and LangChain
Build a custom AI coding assistant using Python, RAG, and LangChain. Learn to create a GitHub helper that can query information and assist with coding tasks in under an hour.
Building a Petabyte-Scale Vector Store - Powering Future AGI
Gain insights for planning and scaling AI applications effectively, focusing on petabyte-scale vector stores to power future artificial general intelligence.
Building Scalable Cross-Modal Search with Ray
Discover how to build a powerful fashion image retrieval system using Ray and Pinecone, enabling efficient text-to-image and image-to-image search with CLIP embeddings and vector similarity.
Retrieval Augmented Generation - Understanding Semantic Search and LLM-Based Text Generation
Discover how Retrieval Augmented Generation (RAG) combines semantic search with LLM-based generation to enhance accuracy and reliability in AI responses, featuring practical implementations and tools.
Retrieval Augmented Generation - Bridging the Gap Between Information Retrieval and NLG
Explore the fundamentals and applications of Retrieval Augmented Generation (RAG), focusing on how it enhances LLMs through external knowledge integration for improved document processing.
Under the Hood: Key Algorithms Powering Vector Search - HNSW, LSH, and PQ
Dive into fundamental algorithms like HNSW, LSH, and PQ that power vector search and AI-driven databases, exploring their technical mechanisms and practical applications.
Chunking Strategies for Retrieval Augmented Generation (RAG) with LangChain and LlamaIndex
Master text chunking techniques for RAG systems using LangChain and LlamaIndex, covering fixed-size, recursive, document, and semantic approaches to optimize LLM performance.
Building a RAG Pipeline with LangChain and ChromaDB for Email-Based Chat Applications
Master practical RAG implementation with LangChain and ChromaDB to build an intelligent email-based chatbot system using vector stores, text splitters, and LLM integration.
Advanced Search and AI Enhancements for E-commerce Websites
Discover how to enhance e-commerce websites with AI-powered search capabilities, from semantic search and vector databases to personalization and dynamic pricing implementations.
Boosting LLM Accuracy with Entity Resolution-based RAG
Discover how Entity Resolution enhances RAG systems for LLMs, improving accuracy when handling structured enterprise data like customer records through real-time unified data processing.
Exploring Vector Search at Scale with Milvus - Building Billion-Scale Solutions
Explore vector search at billion-scale through Milvus, understanding distributed systems architecture, indexing strategies, and scalability solutions for unstructured data processing.