Semantic search vs Vector search — Unfolding the complexity of modern information retrieval

In the era of data explosion and advanced AI technologies, the way we search for and retrieve information has evolved dramatically. Two key players in this evolution are semantic search and vector search. While often mentioned in the same breath, these technologies have distinct characteristics and applications. This article aims to provide a comprehensive, in-depth look at semantic search and vector search, exploring their similarities, differences, and real-world applications.

Understanding the basics

Before diving into the intricacies of semantic and vector search, let’s establish a foundational understanding of these concepts.

What is semantic search?

Semantic search is an advanced information retrieval method that focuses on understanding the intent and contextual meaning of a search query, rather than just matching keywords. It employs natural language processing (NLP) and machine learning techniques to interpret the nuances, synonyms, and relationships inherent in language.

Check this article if you are interested to learn more.

What is vector search?

Vector search, on the other hand, is a technique that transforms words, sentences, or entire documents into vectors—numerical representations in a multidimensional space. These vectors capture the essence of the content, allowing for similarity comparisons based on their positions in this vector space.

The technology behind semantic search

Semantic search leverages several advanced technologies to understand and process natural language:

1. Natural Language Processing (NLP): NLP techniques help in parsing and understanding the structure and meaning of text.
2. Machine Learning (ML): ML algorithms learn from vast amounts of data to improve their understanding of language and context over time.
3. Knowledge graphs: These represent relationships between concepts, entities, and data points, allowing for more contextual understanding.
4. Inference engines: These systems use logical rules to derive new information from existing knowledge.

How semantic search works

1. Query analysis: The search engine interprets the user’s query, considering context, intent, and potential ambiguities.
2. Concept extraction: Key concepts and entities are identified from the query.
3. Knowledge graph consultation: The system consults its knowledge graph to understand relationships between concepts.
4. Context-aware matching: Instead of exact keyword matching, the system looks for content that matches the query’s intent and context.
5. Result ranking: Results are ranked based on relevance to the query’s semantic meaning.

More about vector search

Vector search is at the heart of many modern search systems, including those that power semantic search. Let’s explore its inner workings:

Vector embeddings

At the core of vector search are vector embeddings. These are numerical representations of words, phrases, or documents in a high-dimensional space. For example, a simple vector might look like this:

word_vector = [0.2, -0.5, 0.8, 0.1, ...]

In practice, these vectors can have hundreds or thousands of dimensions.

Creating vector embeddings

Vector embeddings are typically created using machine learning models trained on large corpora of text. Popular models include:

• Word2Vec
• GloVe
• BERT
• GPT

These models learn to represent words or pieces of text in a way that captures semantic relationships. For instance, in a well-trained model, the vectors for “king” and “queen” might be close to each other, reflecting their semantic similarity.

For example:

Similarity measures

Once we have vector representations, we need ways to measure how similar they are. Common similarity measures include:

1. Cosine similarity: Measures the cosine of the angle between two vectors.
   • Formula: cos(θ) = (A · B) / (||A|| ||B||)
2. Euclidean distance: Measures the straight-line distance between two points in space.
   • Formula: d(p,q) = √(Σ(qi – pi)²)
3. Dot product: A simple multiplication of corresponding elements.
   • Formula: A · B = Σ(Ai * Bi)

Vector search process

1. Indexing: Documents or items are converted into vector embeddings and stored in a database.
2. Query vectorization: The search query is converted into a vector using the same embedding model.
3. Similarity calculation: The system calculates the similarity between the query vector and all (or a subset of) document vectors.
4. Ranking: Results are ranked based on their similarity scores.

Semantic search vs Vector search: Key differences

While semantic search and vector search are related and often used together, they have some key differences:

1. Approach to understanding:
   • Semantic search: Focuses on understanding the meaning and intent behind queries and content.
   • Vector search: Relies on mathematical representations and similarity calculations.
2. Contextual understanding:
   • Semantic search: Can handle complex queries and understand context beyond individual words.
   • Vector search: Primarily relies on learned representations, which may or may not capture all contextual nuances.
3. Knowledge representation:
   • Semantic search: Often uses knowledge graphs and ontologies to represent relationships.
   • Vector search: Represents knowledge as points in a high-dimensional space.
4. Query processing:
   • Semantic search: May involve complex NLP pipelines to understand query intent.
   • Vector search: Typically involves converting the query to a vector and performing similarity calculations.
5. Scalability:
   • Semantic search: Can be computationally intensive, especially with large knowledge graphs.
   • Vector search: Often more scalable, with efficient indexing techniques for large datasets.

Optimization techniques in vector search

As datasets grow larger, efficient search becomes crucial. Several optimization techniques have been developed for vector search:

1. Approximate Nearest Neighbor (ANN) search

ANN algorithms trade off some accuracy for significant speed improvements. Popular ANN algorithms include:

• Locality-Sensitive Hashing (LSH)
• Hierarchical Navigable Small World graphs (HNSW)
• Product Quantization (PQ)

2. Indexing strategies

Various indexing strategies help in organizing and quickly retrieving vectors:

• Inverted File Index (IVF)
• Flat Index (for small datasets)
• Hybrid approaches combining multiple techniques

3. Dimensionality reduction

Techniques like Principal Component Analysis (PCA) can reduce the dimensionality of vectors while preserving most of the important information, leading to faster searches.

Real-world applications

Both semantic and vector search have found numerous applications across industries:

1. E-commerce:
   • Semantic search: Understanding product queries beyond exact matches.
   • Vector search: Finding visually similar products.
2. Content recommendation:
   • Semantic search: Understanding user preferences and content themes.
   • Vector search: Quickly finding similar articles or videos.
3. Healthcare:
   • Semantic search: Interpreting complex medical queries.
   • Vector search: Finding similar patient cases or research papers.
4. Legal research:
   • Semantic search: Understanding the intent behind legal queries.
   • Vector search: Finding relevant case law and precedents.
5. Customer support:
   • Semantic search: Interpreting customer inquiries.
   • Vector search: Quickly retrieving relevant support documentation.

Implementing semantic and vector search

For developers looking to implement these technologies, several tools and platforms are available:

Semantic search tools:

• Apache Lucene/Solr
• Elasticsearch (with semantic search capabilities)
• Google’s Natural Language API

Vector search databases:

• Milvus (Open Source)
• Pinecone
• Weaviate
• Faiss (Facebook AI Similarity Search)

Embedding models:

• OpenAI’s GPT models
• Google’s BERT
• FastText

Code example: Basic vector search implementation

Here’s a simple Python example using numpy to perform vector search:

import numpy as np

# Create some example document vectors
documents = np.array([
    [0.5, 0.2, 0.1],  # Document 1
    [0.1, 0.8, 0.3],  # Document 2
    [0.3, 0.4, 0.7]   # Document 3
])

# Create a query vector
query = np.array([0.4, 0.3, 0.2])

# Calculate cosine similarity
def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# Calculate similarities
similarities = [cosine_similarity(query, doc) for doc in documents]

# Find the most similar document
most_similar = np.argmax(similarities)

print(f"Most similar document: {most_similar}")
print(f"Similarity scores: {similarities}")

This example demonstrates the basic principle of vector search using cosine similarity. In practice, you’d use more sophisticated libraries and larger datasets.

Future trends and challenges

As these technologies continue to evolve, several trends and challenges are emerging:

1. Integration of semantic and vector approaches: Many systems are now combining semantic understanding with vector search for more accurate results.
2. Multimodal search: Incorporating text, images, and even audio in search systems. (check out my semantic search engine Similarix)
3. Privacy concerns: As search becomes more personalized, addressing privacy issues becomes crucial.
4. Scalability: Handling ever-increasing amounts of data while maintaining search speed and accuracy.
5. Explainability: Making search results more interpretable, especially in critical applications like healthcare or finance.
6. Continuous learning: Developing systems that can adapt and improve their understanding over time.

Conclusion

Semantic search and vector search represent significant advancements in information retrieval technology. While they approach the problem from different angles – semantic search focusing on meaning and context, and vector search on mathematical representations – they often work in tandem to provide powerful, accurate search capabilities.

As a developer, understanding these technologies is crucial for building modern, efficient information retrieval systems. Whether you’re working on a small-scale application or a large enterprise system, the principles of semantic and vector search can help you create more intuitive, powerful search experiences for your users.

The field is rapidly evolving, with new models, algorithms, and applications emerging regularly. Staying informed about these developments will be key to leveraging the full potential of semantic and vector search in your projects.