Standard 7 stops to get here

Vector Database

A database optimized for storing and searching high-dimensional vectors (embeddings), enabling semantic search and RAG.

Your route here

7 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Feature ✓ understood

    A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.

  3. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  4. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  5. Deep Learning ✓ understood

    A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

  6. Representation Learning ✓ understood

    Learning useful features or representations of data automatically, rather than hand-crafting them.

  7. Embedding ✓ understood

    A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together.

  8. Vector Database · you are here ✓ understood

Where it sits

Before this

Embedding
Vector Database

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Language & LLMs Retrieval-Augmented Generation Augmenting LLM generation with retrieved relevant documents, improving factuality and enabling knowledge updates without retraining. Language & LLMs Semantic Similarity Measuring how similar two pieces of text are in meaning, often using embedding-based distance metrics. Foundations Knowledge Graph A structured representation of knowledge as entities and their relationships, used for reasoning and information retrieval. Shipping AI Feature Store A centralized platform for managing, storing, and serving features for ML models.

In the research

All papers →

A paper that builds on Vector Database .