What is vector search?

Vector search is a smart way of finding things that are similar in meaning not just in words. It works by turning data like text, images or videos into numbers called vectors. These vectors represent the meaning of the content. So instead of just matching exact words vector search finds things that are related or similar in context.

Vector search vs. Traditional search

• Traditional search works by matching exact keywords. It looks for the same words or phrases in documents and ranks results based on how often those words appear which means it might miss relevant information that uses different wording.
• In contrast, vector search understands the meaning behind the words. It turns both the query and the data into numerical representations called vectors and finds results that are similar in meaning even if the exact words don’t match.
• While traditional search is fast and simple, vector search is smarter and more accurate for finding relevant results especially in complex or large datasets.

Distance measurement algorithms

Distance measurement algorithms are used to calculate how far or near vectors should be based on their similarity and dissimilarity. Words or data with same or similar context have closer or similar vector helping in searching. Blow are various Distance measurement algorithms:

1. Euclidean Distance

• Euclidean distance measures the straight line distance between two points in space.
• It extends to more dimensions by summing the squared differences of all coordinates and taking the square root. Its formula is:

d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}

2. Cosine Similarity

• Cosine similarity measures how similar two vectors are by computing the cosine of the angle between them. It ranges from -1 (opposite direction) to 1 (same direction).
• Unlike Euclidean distance it focuses on the direction of the vectors not their magnitude useful when comparing text embeddings or normalised data.

S_{C}(x, y) = \frac{x \cdot y}{\|x\| \|y\|}

3. Approximate Nearest Neighbour (ANN)

• ANN search quickly finds vectors that are close enough to a query vector without scanning the entire dataset.
• It reduces some accuracy for speed making it ideal for large scale similarity search. A common ANN method is HNSW (Hierarchical Navigable Small World) which organizes vectors in a graph and navigates through layers to efficiently find the nearest neighbours.

How Vector Search Works

Step 1: Data Vectorization

• The first step in vector search is to convert all the data items in the database whether they are texts, images or videos into numerical vectors called embeddings.
• These embeddings are generated using machine learning models designed to capture the semantic meaning or key features of the data.
• This transformation allows complex data to be represented as points in a high dimensional space where similar items are close together.

Step 2: Query Vectorization

• When a user submits a search query the system converts this query into a vector as well using the same or a compatible embedding model as the one used for the data.
• This ensures that the query and the dataset vectors share the same representation space enabling meaningful comparisons.

Step 3: Similarity Calculation

• With both the query vector and the data vectors ready the search system calculates how similar each data vector is to the query vector.
• This is done using mathematical similarity or distance measures such as cosine similarity which evaluates the angle between vectors or Euclidean distance which measures the straight line distance between them.
• Items with vectors that are closer to the query vector are considered more semantically relevant.

Step 4: Ranking Results

• After computing similarity scores between the query vector and each data vector the system ranks the data items from most to least similar based on these scores.
• This ranking helps the search engine decide which results to show at the top of the list prioritizing items that best match the intent and meaning of the query rather than just exact word matches.

Step 5: Returning Results

• Finally the system presents the top ranked items to the user as search results. These results are chosen because their vectors are most similar to the query vector meaning they are semantically or contextually related even if they don’t contain the exact words from the query.
• This makes vector search especially powerful for finding relevant information in large or complex datasets.

Applications

1. Semantic Search: Vector search plays a important role in semantic search where the goal is to retrieve content based on meaning rather than exact keyword matching.
2. Recommendation Systems: In recommendation systems vector search is used to identify items that are most similar to a user's preferences or to another item. Both users and products can be represented as vectors based on their interaction history, content features or behavioural patterns.
3. Image and Video Search: It enables content based image and video retrieval by comparing visual features rather than metadata or filenames.
4. Chatbots and Virtual Assistants: Modern chatbots and virtual assistants rely on vector search to retrieve the most relevant answers from a knowledge base or dialogue history. When a user asks a question the system converts it into a vector and searches for similar queries or passages in its database.