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Learn what a vector database is and why it functions differently from traditional relational databases like MySQL or PostgreSQL.

Want to understand how vector databases power modern AI applications? In this video, you'll learn how to Build a Semantic ...

In this NVIDIA GTC Taipei session, NVIDIA VP of Storage Technology Jason Hardy shares how we are working with leading ...

Traditional RAG pipelines usually depend on embeddings, vector databases, FAISS, Chroma, Pinecone, or semantic similarity ...

embeddings #llm #largelanguagemodels How does ChatGPT know that "King" and "Queen" are related, but "King" and "Potato" ...

How do AI agents "understand" relationships between concepts, memories, and files? The answer is Vector Embeddings.

Build the retrieval and grounding core of an agentic RAG app on Supabase that can ingest text, PDFs, or URLs and answer ...

Part 2 of the series where we build a real AI assistant step by step — no fluff, just the actual code and decisions. This episode: how ...

07:50 - Basic RAG Pipeline 14:40 - Document Chunking Strategy 18:58 - Retrieval and Ranking 22:20 - Vector Databases 24:32 ...

... Onboard: Day 1 Join this deep dive into Vertex AI Vector Search 2.0, Google Cloud's fully managed, self-tuning vector database ...

Learn how combining OpenAI/HuggingFace embeddings with vector databases like Pinecone, FAISS, Weaviate, and pgvector ...

Learn how AI understands images using CLIP (Contrastive Language–Image Pretraining)! In this hands-on tutorial, you'll learn ...

Dive deep into Whisper transcription, semantic chunking for intent extraction, and deploying a vector database memory layer.

Vector Databases vs Graph Databases: When to use embeddings vs explicit relationships. • Open Knowledge Format (OKF): The ...

... Elasticsearch operations, GraphQL read queries, AI-powered search, vector database queries and other APIs that need large or ...

Got some vector data? Want to know how to manage it in PostGIS? We'll go over few tips and tricks from getting started to scaling ...

Embeddings explained for AI builders: what changed, why it matters, and what to evaluate before using it. This source-led ...

... Kafka / Redpanda • Vector Database: Qdrant • Architecture: Event-Driven Microservices / Decoupled Background Workers If you ...

Matryoshka Embedding Dimension Compression: truncate Matryoshka vectors to shrink indices while tracking retrieval quality.

Every AI agent call generates training data. Most teams throw it away. Tapes is an open source telemetry proxy that intercepts ...