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The Science Behind Vector Search: How it Transforms Information Retrieval

The exponential growth of data in today’s data centers and online repositories has ushered in a new era of information management challenges for organizations. Beyond the sheer storage capacity, the efficient retrieval of this vast pool of Big Data has become a paramount concern. Vector Search algorithms have emerged as a transformative solution, enabling organizations to navigate this data deluge effectively. This article delves into the game-changing impact of vector search, revolutionizing the way we access and harness data across the web.

How does vector search work?

Now that we have an idea of what big data and vector search is, let us see how it exactly works.

Vector search engines — known as vector database, semantic, or cosine search — find the nearest neighbors to a given (vectorized) query.

There are basically three methods to the vector search algorithm, let us discuss each of them one by one.

Vector Embedding

Wouldn’t it be simple to store data in simply one form? Thinking about it, a database having data points in one fixed form will make it so much easier and more efficient to carry out operations and computations on the database. In vector search, vector embedding is how one can do so. Vector embeddings are the numeric representation of data and related context, stored in high dimensional (dense) vectors.

Similarity Score

Another method under vector search that simplifies comparing two datasets is the similarity score. The idea of similarity score is that if two data points are similar their vector representation will be similar as well. By indexing both queries and documents with vector embeddings, you find similar documents as the nearest neighbors of your query.

ANN Algorithm

The ANN algorithm is yet another method to account for the similarity between two datasets. The reason why the ANN algorithm is efficient is because it sacrifices perfect accuracy in exchange for executing efficiently in high dimensional embedding spaces, at scale. This proves to be effective relative to the traditional nearest neighbor algorithms like the k-nearest neighbor algorithm (kNN) which leads to excessive execution times and zaps computational resources. 

Vector Search v/s Traditional Search

Looking at a detailed differentiating analysis of Vector Search and Traditional Search will provide a way to have a better understanding of how Vector Search has revolutionalized searching algorithms and information retrieval.

Aspect Vector Search Traditional Search
Query Approach Semantic understanding of context and meaning Keyword-based with exact matching
Matching Technique Similarity matching between vectors String matching based on keywords
Context Awareness High, understands context and intent Limited, relies on specific keywords
Handling Ambiguity Handles polysemy and word ambiguity Vulnerable to keyword ambiguity
Data Types Versatile, works with various data types Primarily text-based search
Efficiency Efficient, suitable for large datasets May become less effective as data scales
Examples Content recommendation, image search Standard web search, database queries

How are vector representations for data items created?

It’s all well and good that vector search algorithms are the new and faster way to retrieve information on the web but how exactly is a data item represented as a vector in the database? Vector Space Models are what make it possible for data engineers to store data items as vectors in a multi-dimensional space. 

The selection of an appropriate Vector Space Model is crucial as a wrong choice could lead to inaccuracy and inefficiency in the data. 

The process of vector transformation for data items varies depending on their data type. Here’s a brief explanation of how various data items are transformed as vectors.

Text Data

  • To begin transforming text data into a vector, the text must be tokenized, meaning, the text has to be broken down into smaller units such as words or phrases.
  • Next comes some text preprocessing steps such as stemming and lemmatization.
  • In the next step, these tokens are converted into numerical vectors.

Image Data

  • In order to map images as vectors, image features need to be extracted. Convolutional Neural Networks (CNNs) are some well-known deep learning models that are used to extract high-definition image features. 
  • These features are necessarily the edges, textures, and shapes in an image.
  • These features can then easily be converted into numerical counterparts as vectors. 

Structured Data

  • Another variation of data is structured data which is usually stored in the form of rows and columns. 
  •  Extracting features from this format is done by choosing the most informative columns from the dataset.
  • The numerical values that are retrieved need to be squeezed into a viable range and for that normalization is applied to the numerical data before mapping it into a vector. 

Future Trends in Vector Search

With the consistent developments in the field of AI and Machine Learning, this whole science of Vector Search and Machine learning algorithms is only going to expand more. Managing huge chunks of data also known as Big Data is the real challenge for most organizations in today’s date. The field of Vector Search and corresponding search algorithms are going to take care of all of these concerns in the near future. 

Some of the new and advanced concepts that we might get to see in the near future trends of Vector Search are:

  1. MultiModal Search
  2. Cross-Modal Search
  3. Hybrid Models
  4. Few-Shot Learning
  5. Explainable AI
  6. Federated Learning
  7. Enhanced Personalization
  8. Integration with Knowledge Graphs
  9. Semantic Search for Code
  10. Voice and Conversational Search
  11. Ethical AI and Fairness

Ethical Considerations with AI

Pay attention to the last point mentioned in the future trends for Vector Search. While AI can be really helpful to achieve efficiency and accuracy, a proper probe is required to keep ethical activities in check. Recently, the CEO of OpenAI, Sam Altman suggested that it’s the right time now to appoint a committee that will be responsible for checking whether the AI practices being carried out are ethical are not. Ethical implications related to vector search involve privacy concerns and bias in results. Only when these ethical aspects are taken into consideration can we really say that AI is actually “intelligent”. In order to do so, Best practices for addressing these ethical issues have to be presented and implemented. 

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