What is VectorSearch?
VectorSearch is a way for computers to find items that are “similar” to a given piece of data by comparing their numeric representations, called vectors. Instead of looking for exact word matches, it looks for closeness in a multi-dimensional space.
Let's break it down
- Vector: a list of numbers that captures the meaning or features of something (like a sentence, image, or product).
- Search: the act of looking through a collection to find something that matches a query.
- Similarity: how close two vectors are to each other, measured with math formulas (e.g., cosine similarity).
- Multi-dimensional space: imagine a graph with many axes; each axis holds one number from the vector, and the whole point’s position shows its characteristics.
Why does it matter?
Because many modern data types (text, images, audio) can’t be searched effectively with simple keyword matching. VectorSearch lets us retrieve relevant results even when the exact words differ, making AI-driven applications more useful and intuitive.
Where is it used?
- Recommendation engines (suggesting movies, products, or music you’ll like).
- Semantic text search in knowledge bases or customer support chatbots.
- Image and video similarity search (finding pictures that look alike).
- Fraud detection by spotting unusual patterns in transaction vectors.
Good things about it
- Finds relevant items even with vague or misspelled queries.
- Works across many data types (text, images, audio, etc.).
- Scales to large datasets with specialized indexing structures.
- Enables personalized and context-aware experiences.
Not-so-good things
- Requires a lot of computational power to create and store high-dimensional vectors.
- Quality depends heavily on the underlying embedding model; poor models give poor results.
- May return “close” items that are not truly relevant, leading to false positives.
- Indexing and updating large vector databases can be complex to manage.