Vector search is a powerful technique that allows you to find items based on their meaning and context, rather than just keywords. At its core, vector search transforms data into numerical representations and then finds items with similar representations.
Here's a breakdown of the process:
The Core Concept: Embeddings
The fundamental idea behind vector search is the use of vector embeddings. As stated in the reference, in vector search, items in the database are assigned vector embeddings that describe their attributes and their meaning. These embeddings are high-dimensional numerical vectors that capture the semantic essence of the data.
- What are Embeddings? Think of an embedding as a point in a multi-dimensional space. The position of this point is determined by the data's features and relationships to other data. Data points with similar meanings or attributes will be located closer to each other in this space.
- Creating Embeddings: Embeddings are typically generated using machine learning models, particularly deep learning models like neural networks (e.g., transformer models for text). These models are trained on vast amounts of data to learn how to represent complex information as numerical vectors.
The Search Process
Once items in the database have been embedded, the search process follows these steps:
- Query Embedding: When a user submits a search query (e.g., text, an image, or even audio), the search query is also assigned a vector embedding. The same embedding model used for the database items is typically used for the query to ensure consistency in the vector space.
- Similarity Matching: The database matches those up and returns items that most closely resemble the vector embedding of the search query. This "matching" involves calculating the distance or similarity between the query vector and the vectors of all items in the database. Common similarity metrics include cosine similarity, Euclidean distance, or dot product.
- Ranking Results: Items with the highest similarity scores (or smallest distances, depending on the metric) are considered the most relevant to the query. The search results are then ranked based on these scores and returned to the user.
Why Use Vector Search?
Vector search offers significant advantages over traditional keyword-based search:
- Semantic Understanding: It finds results based on meaning, not just exact word matches. This is crucial for understanding nuances, synonyms, and related concepts.
- Handling Unstructured Data: It's highly effective for searching diverse data types like images, videos, audio, and complex documents where keyword search is ineffective.
- Improved Relevance: By understanding context, vector search often provides more relevant and personalized results.
Practical Applications
Vector search is used in various applications:
- Image Search: Finding images similar to a query image.
- Product Recommendations: Recommending products based on the vector similarity of items a user has viewed or purchased.
- Semantic Text Search: Finding documents or passages that are semantically related to a query, even if they don't contain the exact keywords.
- Anomaly Detection: Identifying data points that are far away in the vector space from typical data points.
How Similarity is Calculated
Different methods exist for calculating how "close" two vectors are.
| Metric | Description | Higher Value Means | Use Case Examples |
|---|---|---|---|
| Cosine Similarity | Measures the cosine of the angle between two vectors. Independent of vector magnitude. | More Similar | Text similarity, image similarity |
| Euclidean Distance | Measures the straight-line distance between two points in space. | Less Similar | Clustering, finding nearest neighbors (often requires normalized vectors) |
| Dot Product | A measure of the magnitude and direction relationship. Related to cosine similarity when vectors are normalized. | More Similar | Often used in recommendation systems |
Specialized databases and indexing techniques (like HNSW - Hierarchical Navigable Small Worlds) are used to perform these similarity calculations efficiently across potentially millions or billions of vectors.
In summary, vector search transforms data into meaningful numerical vectors, enabling the system to find items that are semantically similar to a search query by comparing their corresponding vectors in a multi-dimensional space.
