What is a Vector Database

Vector Database Vectors are like lists of numbers that capture important features of data, such as images, text, or sounds. A vector database is a special type of database that stores and searches information represented as vectors.

  • An image can be represented as a vector based on its colors, shapes, or other details.
  • Text can be turned into a vector using language models that help understand the meaning behind the words.
  • Sounds, like music or speech, can also be converted into vectors that represent their tone or frequency.

These vectors are useful because they allow computers to understand and compare different pieces of information, which is essential for building applications like recommendation systems, search engines, and other AI technologies.

How do Vector Databases Work

Traditional databases work well when you are looking for specific information in structured data (such as spreadsheets), but vector databases work with unstructured data and are optimized for similarity searches and approximate nearest neighbor searches, which are key operations while working with vectors. Its work process can be explained by the points below:

  • Vector Storage: Each piece of data called entity contains a vector. It often contains additional pieces of information such as labels or descriptions.
  • Indexing: To quickly search through large amounts of data, vector databases use special methods called indexing. Memora uses a variety of Approximate Nearest Neighbor (ANN) algorithms for indexing such as Hierarchical Navigable Small World (HNSW) and Score-aware quantization loss.
  • Similarity Metrics: When comparing vectors, the database uses similarity measures—like figuring out how close or far apart two vectors are. Common ways to measure this include comparing angles (cosine similarity) or distances (Euclidean distance).

Use Cases for Vector Databases

Vector databases can be used to improve the efficiency of many applications of modern artificial intelligence and machine learning. Here are some common examples:

  1. Recommendation Systems
    Think of how online stores suggest products you might like or how streaming platforms recommend movies and music. These systems use vector databases to find items similar to your past choices by comparing your preferences (stored as vectors) with the vectors of other products.

  2. Image and Video Search
    Imagine searching for images using another picture instead of typing words. Vector databases store images as vectors capturing their details, and when you search, they find images that are visually similar by comparing the vectors.

  3. Natural Language Processing (NLP)
    This technology helps computers understand and respond to human language. For example, when you use a chatbot, the text you type is turned into a vector so the system can find similar responses based on the meaning of your words, not just the exact keywords.

  4. Retrieval-Augmented Generation (RAG)
    This is a technique used with AI systems to provide more accurate answers. When you ask a question, the vector database finds the most relevant information by doing a similarity search between your question as a vector and the information in the database held as vectors, which the AI then uses to generate a response. This helps make answers more precise and relevant.

  5. Anomaly Detection
    In areas like finance or cybersecurity, vector databases can detect unusual patterns. For example, they might identify unusual credit card transactions by comparing current behavior with past patterns stored as vectors.