Vector Search

In today's data-driven, AI-first world, the ability to efficiently search and retrieve relevant information from vast amounts of unstructured data has become a critical competence.  Traditional keyword-based search methods often fall short when dealing with complex, high-dimensional data such as text, images, and audio. This is where vector search emerges as a game-changing technology, revolutionizing the way we interact with and extract insights from unstructured data.

This guide explores the fundamental concepts, terminologies, and techniques behind vector search, its role in augmenting the capabilities of Retrieval Augmented Generation (RAG), Large Language Models (LLM), and Natural Language Processing (NLP) frameworks, and some of its real-world applications. 

Read more on our NLP Guide: Discover NLP

Vector search, or vector similarity search, is a technique used in information retrieval and natural language processing that uses numerical representations, or vectors, to categorize data as high-dimensional vector embeddings. Traditional search models are based on comparing and matching keywords or phrases. Vector search, however, takes into account the semantic meaning and context of the search query in order to extract data representations that are the most semantically similar and contextually relevant to the query. Another key distinction is that vector embeddings can be used to represent any type of data, including text, images, audio, products, users, etc.

There are several compelling reasons that make vector search a critical technique in the age of AI-powered applications. 

The power of semantic context

Vector Search enhances search relevance by going beyond mere keywords to capture the semantic relationships between words and concepts. Vector search builds a deeper understanding of data based on underlying contextual relationships, thereby eliminating the need for users to define precise synonyms in order to get the most relevant results. This key capability opens up several novel opportunities both in terms of processing extremely complex queries and in a broader range of advanced knowledge retrieval applications. 

Multimodal, multi-lingual, & versatile

The concept of vector embeddings can be extended to a diverse range of data types other than text, including time series data, images, audio, video, three-dimensional (3D) models and even multi-omics sequence data. This capability makes it possible to build multimodal search frameworks, operating across multiple modalities, with a richer understanding of each data point based on cross-modal data. Vector search is also language-agnostic, making it ideal for multilingual and cross-lingual datasets and applications.    

Scalability & flexibility

Vector search techniques can effortlessly scale to handle large and high-dimensional datasets, making them suitable for big data applications and high-throughput queries. This technique can also be used with a variety of distance metrics, such as Euclidean distance, cosine similarity, dot product similarity, or Jaccard similarity, thereby providing the flexibility to choose the vector similarity/dissimilarity measure that is most appropriate for a specific dataset and/or application. 

Domain-specific

Vector embeddings can be fine-tuned or retrained on domain-specific data, allowing vector search systems to adapt to different domains or use cases more effectively. Vector search can also help improve the output of LLMs, with domain-specific vector embeddings providing the additional context for Retrieval Augmented Generation (RAG) frameworks designed to enable more timely, accurate, and contextually relevant responses.

A vector search engine typically includes three key processes:

Generating vector embeddings

In a vector search system, datasets are converted (encoded) into vector representations that capture contextual and semantic relationships within the data. The process of vector generation is defined by the type of data involved, for instance, embeddings for text data can be created using Word2Vec, GloVe, or BERT, image data with convolutional neural networks (CNNs) and protein sequences with ProteinBERT, ProtTrans etc.

Vector indexing

The key purpose of vector indexing is to enable the efficient storage and quick retrieval of high-dimensional vector data based on similarity or distance relationships. Common vector indexing techniques include Flat indexing, Locality Sensitive Hashing (LSH) indexes, Inverted file (IVF) indexes, and Hierarchical Navigable Small Worlds (HNSW) indexes.

Querying

Every search query is also converted into a vector embedding, typically with the same techniques used for vector generation. The system then calculates the similarity between the query vector and the vectors of the indexed data using a distance metric, like cosine similarity or Euclidean distance. The most similar vectors are then returned as the search results.

Vector embeddings are high-dimensional vector representations of words, phrases, or larger pieces of text that capture their semantic and contextual meaning.

Embeddings are a way of representing any kind of data as points in n-dimensional space so that semantically similar data points are clustered together. They are numerical machine-learning representations of complex, high-dimensional data that enable more efficient data processing and analysis.

There are different techniques for generating vector embeddings, including Word2Vec, GloVe (Global Vectors for Word Representation), FastText for word embeddings, InferSent, Skip-Thought, Doc2Vec, and Universal Sentence Encoder for longer text sequences, and convolutional neural networks (CNNs) for image embeddings.

There are three key characteristics of vector embeddings that make them particularly suitable for vector search:
 

1. Semantic Similarity:

Representing data as vector embeddings that are closer together or further apart depending on similarity enables the more accurate and relevant retrieval of information than keyword-based models.

2. Compositionality:

Vector embeddings can be combined and used compositionally to generate more complex units without losing semantic context.

3. Dimensionality Reduction:

Embeddings map text data from a sparse, high-dimensional space (e.g., one-hot encoding) to a dense, lower-dimensional space, making them more computationally efficient for similarity calculations and indexing.

Here’s a quick primer on some of the key terminologies associated with vector search:

Vector

In mathematics and computer science, a vector is an entity that has both magnitude and direction. In the context of similarity search, a vector is data that has been transliterated into a mathematical representation that is semantically meaningful to machines.  

Embedding

An embedding is a dense, low-dimensional vector representation of complex, high-dimensional data, including semantics and contexts. 

Index

In vector search, an index is a specialized data structure designed to organize vector data based on similarity and optimize the search and retrieval of vector representations. 

Ground Truth

Ground truth is a curated dataset used to evaluate the accuracy and relevance of vector search results. 

Recall

Recall is a metric that measures the percentage of relevant items, out of the total number of relevant items, that have been successfully retrieved by a vector search system. 

Precision

Precision measures true positives in vector search results, i.e. the proportion of retrieved items, out of all retrieved items, that are actually relevant to the query. 

Approximate Nearest Neighbor Search (ANN)

ANN is a technique used to efficiently find the nearest, but not necessarily the absolute closest, vector embeddings to a given query vector, without having to calculate the exact distances to all vectors in the index. This is essential for scalability in large datasets.

Cosine Similarity

Cosine similarity is a widely used mathematical metric in vector search to measure the similarity between two vectors. It is a measure of the orientation, how different data points are related, rather than the magnitude, and the specific details of each data point, that enables the quick comparison of high-dimensional sparse vectors.

Euclidean Distance

Euclidean distance, also known as the L2 norm,  is another commonly used similarity metric in vector search that measures the straight-line distance between two vectors in high-dimensional space. It is sensitive to both the orientation and magnitude of the vectors.

Pruning

Pruning is the process of reducing the search space by eliminating nodes that are not likely to contain nearest neighbors. 

Restrict

Restrict is a technique for limiting vector matching searches based on specific criteria, for example, restricting a search to items within a specific distance threshold from a query item.

Integrating vector search capabilities into Retrieval-Augmented Generation (RAG) models, Large Language Models (LLMs), and Natural Language Processing (NLP) frameworks offers several advantages that enhance the capabilities of each individual technology. 

RAG frameworks already play an important role in the post-training grounding of LLMs for critical enterprise applications. RAG-enhanced LLMs have access to targeted, up-to-date, contextually relevant knowledge bases that help address many of the limitations associated with standalone language models. Vector embeddings are now emerging as a key component of RAG-LLM frameworks. Vectorizing and indexing the knowledge bases that RAG frameworks access integrates the scale, speed, and semantic precision of vector search to the information retrieval pipeline and enhances LLM knowledge grounding:

There are several advantages to this blended RAG-LLM-Vector embedding model. 

Semantic vector embeddings can significantly enhance the accuracy and relevance of information retrieved and augment RAG-LLM performance. Domain-specific embedding can help retrain and fine-tune vector search systems on domain-specific knowledge to further optimize accuracy and performance on specialized tasks.

Vector search provides a scalable solution for integrating and querying large knowledge bases, enabling LLMs and NLP systems to access and leverage a vast amount of information effectively. The multimodal information retrieval capabilities of vector systems can engender exponentially higher utility in RAG, LLM, and NLP frameworks by seamlessly incorporating information from diverse sources to enrich overall modeling performance and enable more comprehensive and complex applications.

Overall, the integration of vector search into RAG, LLM, and NLP frameworks can significantly enhance capabilities in terms of scalability, adaptability, and versatility.

Vector search is currently being widely used across a range of applications, including question-answering systems, content analysis, recommendation systems, document similarity analysis, anomaly detection, image, and video search, to list just a few.

Vector embeddings and search are playing an increasingly important role in enhancing the capabilities of RAG, LLM, and NLP models.  In RAG models, for instance, vector search has become instrumental in retrieving factual evidence and citations from trusted sources that validate or debunk generated outputs. This enhances the credibility and accuracy of generated content. The contextual information retrieval capabilities of vector search help RAG models can generate more coherent and contextually relevant content. This approach enhances the quality and diversity of generated text. The integration of LLM-generated knowledge graphs with RAG approaches that use vector similarity search techniques have demonstrated substantial improvements in performance for document analysis of complex information. 

The advanced information retrieval capabilities of vector search can play a critical role in augmenting various aspects of the drug discovery process. 

Vector representations of large volumes of non-traditional unstructured pharmaceutical industry data can potentially help uncover contextually relevant studies, compounds, or  mechanisms that may have been overlooked by traditional keyword-based search methods.

The ability to conduct large-scale similarity searches across large chemical databases and compound libraries enables drug discovery researchers to identify matches based on pharmacology, structure, or property. This capability can help accelerate the drug discovery process, reducing the time and cost associated with traditional screening methods. Representing diverse datasets, such as gene expression data, protein sequences and structures, pathways, and literature, as vector embeddings, can help connect information from various sources and new relationships.

Vector search can transform drug discovery by supporting data integration and analysis across diverse data sources, enabling more efficient and contextual retrieval of relevant information, and expediting the discovery of novel therapeutics.