Dense, Sparse, and Hybrid Search Techniques

Previously, we have talked about indexing. Indexing places every piece of information exactly where it should be, making it easy to find later. Now, let’s talk about searching and their different techniques in this lesson.

Think of searching as looking for a red shoe in your room. We specify that we are looking for 'red shoes' (the query), and this query is translated into secret codes (vectors) used to search through groups of shoes (indexed data). The system then finds all the shoes in the 'shoe group' that are most similar to our specified 'red shoe' description.

Before we dive into the different search techniques, it’s essential to understand the types of vectors used in these searches. Sparse vectors contain many zero values and are typically used to represent data where only a few features are non-zero. In contrast, dense vectors have most or all elements as non-zero, representing data where most features have some significant value.

Understanding the difference between sparse and dense vectors is crucial as it impacts how searches are conducted. For example, sparse vectors are often used in text analysis where only a few words from a vast vocabulary appear in any given document. On the other hand, dense vectors are commonly used in image or audio data where most features (pixels or sound samples) have relevant values.

Now, let's explore these techniques in detail.

Sparse search 

Sparse search, also known as keyword search, based on matching exact keywords or phrases within a document. This method useful in scenarios where precise term matching is essential, such as finding documents containing specific product names or technical terms. However, it falls short in capturing semantic meaning or understanding contextual relationships between words.

Sparse search is a fundamental method for retrieving information from a large dataset. Tokenization is crucial for breaking down text into individual words or terms. These terms are then used to create an index that maps words to the documents containing them. This index is relatively sparse, meaning most entries are empty or contain few document references. This process is part of vectorization, which can be done using a vectorizer. The search process involves looking up query terms in the index and retrieving documents that match those terms. While efficient for exact keyword matches, sparse search lacks the ability to understand the semantic meaning and context of words.