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In the world of data management, traditional databases organize information into structured tables, with rows and columns. However, with the advent of artificial intelligence (AI) and advanced search, there is a growing need to work with complex data, such as images, audio, and text, more efficiently. This is where vector databases come in, a type of database designed to manage and search high-dimensional data represented as vectors.
Part 1: Vector Database Fundamentals
What is a Vector?
To understand vector databases, it is essential to know what a vector is. A vector is a sequence of numbers that represent characteristics of an object. For example:
An image can be represented by a vector that describes colors, shapes, and textures. For example, an image of a dog could be transformed into a vector like [0.12, 0.85, 0.33, 0.76, 0.44], where each number represents a specific characteristic of the image, such as the predominance of a certain color or the presence of certain edges.
A text can be converted into a vector that represents the meaning of the words.
An audio file can be transformed into a vector with characteristics such as pitch and intensity.
These vectors are often generated by machine learning algorithms, such as neural networks, which transform data into numerical representations useful for search and comparison.
Cosa Sono i Database Vettoriali?
Vector databases are systems optimized for storing and quickly searching high-dimensional vectors. High dimensionality in vector databases refers to the number of features (or attributes) that make up a vector. For example, an image might be represented by a vector with thousands of elements, each corresponding to a specific feature (color, texture, shape, etc.).
As the number of dimensions increases, the search space grows exponentially, making it difficult to find nearby elements with traditional methods. Therefore, vector databases use advanced techniques such as KD trees, locality-sensitive hashing (LSH), and graphs to optimize searches in high-dimensional spaces.
So, unlike relational databases (SQL) that use traditional keys and indexes, vector databases exploit search algorithms based on the distance between vectors, allowing you to find "similar" elements between them.
Practical example:
An image search engine can use a vector database to find images similar to one supplied by the user.
A music recommendation system can suggest songs similar to ones you've previously listened to.
What is Embedding?
Before we look in detail at how a vector database works, we need to understand what embedding is.
Embedding is the process of converting unstructured data, such as words, images, or sounds, into numerical representations in a vector space. This allows AI systems to process and compare data efficiently. For example:
In natural language, algorithms like Word2Vec or BERT transform words into vectors, capturing their semantic meaning.
In images, convolutional neural networks like ResNet generate vectors that represent visual features such as color, shape, and texture.
In audio, techniques like MFCC extract key sonic features and convert them into vectors.
Thanks to embedding, we can create vectors to store in our vector database.
Part 2: How Vector Databases work
1. Creating Vectors
The data is processed and transformed into numerical vectors through embedding algorithms. For example:
Word2Vec e BERT for text.
ResNet e VGG for images.
MFCC for audio.
These models convert information into compact, searchable representations.
2. Database Storage and Structure
A vector database stores vectors in search-efficient structures, such as:
KD Trees (k-dimensional trees): Tree-like structures for partitioning vector space.
LSH (Locality-Sensitive Hashing): A technique that groups similar vectors using hash functions.
HNSW (Hierarchical Navigable Small World): Graph optimized for fast searches.
3. Search and Similarity
Searches in vector databases are not based on exact matches, but on similarity metrics such as:
Euclidean distance: Geometric distance between two points.
Cosine Similarity: Measures the angle between two vectors (useful for text and images).
Manhattan Distance: Sum of the absolute differences between coordinates.
Example: If a user searches for "dog", the vector database will not only search for the exact word, but also for similar concepts such as "puppy", "pet", etc.
Part 3: Practical Applications of Vector Databases
1. Visual Search Engines
Companies like Google and Pinterest use vector databases to enable similar image searches based on visual content rather than text.
2. Personalized Recommendations
Netflix and Spotify use vector databases to suggest movies and songs similar to users' tastes.
3. Facial Recognition
Facebook and Apple use vector databases to compare faces and identify people in photos and videos.
4. Chatbot e NLP
Vector databases help chatbots better understand natural language and provide more accurate responses.
Popular Vector Databases
Here are some of the most used vector databases:
FAISS (Facebook AI Similarity Search): Facebook's open-source library for quick searches.
Annoy (Approximate Nearest Neighbors Oh Yeah): Spotify database optimized for recommendations.
Milvus: Open-source database for scalable vector management.
Pinecone:Cloud solution for vector searches.
Conclusion
Vector databases are a key technology for modern AI, enabling advanced searches on complex data. Thanks to their ability to handle high-dimensional information, they are revolutionizing areas such as image recognition, text search, and content personalization.
If you want to dive deeper, you can experiment with tools like FAISS and Milvus to better understand how vector databases work in practice!
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