What is Vector Search?

The Problem with Traditional Search

Traditional keyword search is great at finding exact matches, but it struggles with understanding the *meaning* behind a query. If a user searches for "how to change my login info," a keyword search might miss a document that says "how to update your credentials."

How Vector Search Works

Vector search, also known as semantic search, overcomes this limitation by using machine learning to understand the intent and context of a query. Here's a simplified breakdown of the process:

1. Embedding: Text is converted into a numerical representation called a "vector embedding." These vectors capture the semantic meaning of the text.
2. Indexing: These vectors are stored in a specialized database called a vector database.
3. Searching: When a user enters a query, the query itself is converted into a vector. The vector database then finds the vectors in its index that are most similar to the query vector.

Why Vector Search is Better

• More Relevant Results: By understanding the meaning behind a query, vector search can find conceptually related results, even if they don't contain the exact keywords.
• More Natural Queries: Users can search using natural language, just as they would ask a question to a person.
• Multilingual and Multimodal: Vector search can even be used to search across different languages and data types, such as images and audio.

Where CoderSwap Fits In

While vector search is powerful, it's not a silver bullet. The best results often come from a "hybrid" approach that combines the strengths of both vector and keyword search. CoderSwap makes it easy to build and customize these hybrid search solutions, without requiring any ML expertise.