What is vector search?

Vector search is a method used to find items in a dataset using a mathematical vector representation. It involves transforming data into multi-dimensional vectors so each item is mapped in a vector space. This process allows efficient comparison and retrieval based on proximity or similarity.

Vectors represent data points as positions in a mathematical space, permitting the algorithm to measure distances between them, indicating similarity or relevance. This search method is beneficial in scenarios where contextual or nuanced relationships between data points must be considered.

Unlike traditional keyword searches, vector search leverages the entire data context, offering precision. This makes it useful for applications such as recommendation systems, image recognition, and natural language processing.

What is semantic search?

Semantic search seeks to improve search accuracy by understanding the searcher’s intent and the contextual meaning of terms. It moves beyond keyword matching, aiming to understand the semantics of both queries and indexed data. This approach uses natural language processing techniques to interpret the relationships between words, phrases, and the searcher’s intent.

This method is particularly effective in environments where understanding user intent or nuanced content meanings is essential. By focusing on context and entity recognition, semantic search can improve user experience by tailoring search results more precisely to the user’s needs.

This is part of a series of articles about vector databases.

How vector search works

Vector search operates by converting data items into numerical representations, known as vectors, within a multi-dimensional vector space. This transformation is typically achieved using embedding models, such as those based on machine learning or deep learning.

For example, text data might be processed using neural network models like word2vec, GloVe, or transformers, which map words or sentences to dense numerical vectors. Similarly, images can be embedded using convolutional neural networks (CNNs).

Once the data is represented as vectors, the search process involves computing distances between these vectors to measure similarity. Common distance metrics include:

  • Euclidean distance: Measures straight-line distance in the vector space.
  • Cosine similarity: Calculates the cosine of the angle between two vectors, focusing on their direction rather than magnitude.
  • Dot product: Evaluates the correlation or alignment between vectors.

When a query is submitted, it is also converted into a vector using the same embedding model. The system then identifies the closest vectors in the dataset, typically using approximate nearest neighbor (ANN) algorithms to expedite searches within large datasets.

Modern vector search implementations often leverage specialized libraries, such as FAISS or Annoy, and integrate with vector databases like Pinecone or Weaviate. These tools optimize storage, retrieval, and scalability, enabling high-performance operations with millions of vectors.

How semantic search works

Semantic search begins with preprocessing both the query and the database content. This involves parsing and embedding textual data using natural language processing (NLP) models. Common techniques include tokenization, stemming, and the use of transformer-based models like BERT or GPT to generate context-aware embeddings.

These embeddings capture the semantic relationships between words and phrases, accounting for polysemy (words with multiple meanings) and synonyms. For example, “bank,” as a financial institution, and “bank,” as a river’s edge, are disambiguated through contextual understanding.

The search engine compares the query embedding with embeddings of database items, calculating similarity using techniques such as cosine similarity or dot product. Results are ranked based on relevance scores derived from these comparisons.

Semantic search systems often incorporate additional components, such as:

  • Entity recognition: Identifying and categorizing entities like people, locations, or organizations.
  • Context expansion: Enriching the query by recognizing related terms or broader concepts.
  • Feedback loops: Using user behavior to refine future searches.

By combining these techniques, semantic search achieves a deeper understanding of both the query and the indexed content, delivering results that align closely with the user’s intent.

Tips from the expert

Ritam Das

Ritam Das

Solution Architect

Ritam Das is a trusted advisor with a proven track record in translating complex business problems into practical technology solutions, specializing in cloud computing and big data analytics.

In my experience, here are tips that can help you better implement and leverage vector search and semantic search effectively:

  1. Hybrid search strategies yield the best results: Combine vector search and semantic search for better accuracy. Use vector search for initial candidate retrieval and semantic search to refine results, aligning them with user intent and contextual relevance.
  2. Optimize embeddings for domain-specific needs: While general-purpose models like BERT or CLIP are powerful, training or fine-tuning embeddings on domain-specific data improves relevance and accuracy for niche applications.
  3. Utilize clustering for vector dataset management: Use clustering algorithms like k-means or hierarchical clustering to organize data into meaningful groups. This structure enhances retrieval speed and interpretability in large datasets.
  4. Balance precision and recall with ANN techniques: Approximate Nearest Neighbor (ANN) algorithms, like HNSW or KD-trees, can be adjusted to find the right balance between speed (recall) and accuracy (precision), depending on the specific application requirements.
  5. Integrate real-time feedback loops: Implement feedback mechanisms to dynamically refine embeddings or scoring models based on user interactions, improving search relevance over time.

Vector search vs. semantic search: Key differences

Here’s an overview of the main differences between these two search approaches.

1. Approach to understanding

Vector search uses a numeric and spatial approach. It quantifies data relationships by mapping items into a vector space, where proximity indicates similarity. This numeric approach handles high-dimensional data, enabling efficient retrieval through mathematical operations.

Semantic search centers on linguistic and contextual comprehension. By using natural language processing, it deciphers the intent and syntactic relationships within language, improving accuracy in content-based queries. Understanding here transcends direct keyword matching, focusing on deeper meanings to align result relevance better with user queries.

2. Contextual handling

Vector search primarily uses vectors to encapsulate broader data relationships and context. These vectors capture semantic nuances through their multidimensional representation, allowing the search engine to consider the broader context of the query and data relationship.

Semantic search handles context through language comprehension, using syntax and semantics to understand language intent. It identifies contextual nuances in user queries and data, linking entities and concepts to provide precise results.

3. Knowledge representation

Vector search represents knowledge numerically, using numerical vectors to capture data characteristics and relationships. This abstraction transforms diverse data into a consistent format, aiding computational processes.

Semantic search represents knowledge through linguistic relationships and semantic networks, creating connections between words and meanings. This method entails understanding the conceptual and intent-driven aspects of queries and data.

4. Scalability

Vector search achieves scalability through its ability to handle large-scale data efficiently. By converting data into high-dimensional vectors, it capitalizes on advancements in hardware and algorithmic optimizations to scale, even with extensive datasets.

Semantic search scales by leveraging distributed computing and machine learning frameworks. Its scalability stems from the adaptability of natural language processing models that learn and improve with data inflow. Although inherently computationally intensive, semantic search’s architecture allows it to integrate scalability solutions such as parallel processing.

Related content: Read our guide to data architecture principles

Semantic search vs. vector search: How to choose

When deciding between semantic search and vector search, the choice depends on the requirements of the use case. Consider the following factors:

  • Nature of the data: If the dataset is unstructured or contains contextual information like text, images, or multimedia, vector search might be more effective. Its ability to encode complex relationships in multi-dimensional spaces allows it to handle diverse data types. For datasets heavily reliant on language-based understanding, semantic search is preferred. Its focus on natural language processing ensures that linguistic nuances and user intent are accurately interpreted.
  • Search objectives: Choose vector search when the primary goal is to find items based on similarity or proximity within a dataset. Applications like recommendation systems, image retrieval, or content-based filtering benefit from this approach. Opt for semantic search if user intent and contextual relevance are critical. For instance, search engines, customer support systems, or any scenario requiring precise interpretation of user queries align better with semantic search.
  • Complexity and resources: Vector search often requires sophisticated hardware and specialized expertise to manage high-dimensional computations efficiently. However, it offers unmatched performance for large-scale data and real-time applications. Semantic search may involve significant computational resources for training and maintaining language models but generally integrates well with existing text-based search infrastructures, especially for small to medium datasets.
  • Scalability needs: For applications requiring rapid scaling and large-scale indexing, vector search provides high performance due to its reliance on efficient mathematical computations. Semantic search can also scale but requires distributed computing solutions and advanced frameworks to handle computational intensity, particularly for massive language models.
  • Hybrid solutions: In many cases, combining both approaches can yield optimal results. Using vector search to filter candidates based on proximity and semantic search to refine results by language intent allows leveraging the strengths of both methods.

Empowering scalable AI solutions with vector search on the Instaclustr Managed Platform

Instaclustr empowers enterprises to harness the full potential of their data with robust and scalable solutions—and vector search is no exception. With the growing need for advanced search capabilities driven by exponential data growth and unstructured datasets, Instaclustr provides the tools to unlock unparalleled accuracy and efficiency in information retrieval through vector search.

Vector search enables businesses to leverage AI and machine learning to process data not just in terms of keywords, but in nuanced, multidimensional relationships. Instaclustr’s implementation of vector search ensures seamless integration with our proven open source technologies, such as Apache Cassandra®, PostgreSQL®, OpenSearch®, ClickHouse® and other compatible platforms.

Whether you’re building a recommendation engine, personalized customer experiences, or need ultra-fast, AI-driven search capabilities, Instaclustr delivers the infrastructure to support your needs at scale.

Our expert-managed platform ensures reliability, uptime, and performance for your vector search workloads. From initial setup to ongoing optimization, Instaclustr takes care of the heavy lifting, allowing your team to focus on innovation instead of infrastructure management. With our focus on open source solutions, you’re free from vendor lock-in, ensuring flexibility and control over how you harness the power of vector search.

Instaclustr partners with you to create systems that drive meaningful business value. Be it improving search relevance or enabling next-level AI applications, our vector search capabilities position you to stay ahead in an increasingly data-driven world.

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