Keyword vs Vector Search: Which One Is Right for Your Search

When designing search systems, the decision to use keyword-based search, vector-based search, or a hybrid approach can significantly impact performance, relevance, and user satisfaction. Each method offers distinct advantages and challenges, making it essential to understand their capabilities and applications. This overview provides more insights into the strengths, limitations, and practical implementation of these approaches, particularly in systems like PostgreSQL.

Below, Trelis Research explains the differences between these two search methods, exploring how they work and where they shine. Whether you’re a developer building a search system, a data enthusiast curious about the tech, or just someone who’s tired of bad search results, this guide will help you understand what’s happening under the hood. Trelis also touch on hybrid solutions that blend the precision of keyword search with the semantic depth of vector search, offering a glimpse into how modern tools like PostgreSQL make it all possible.

What Is Vector Search?

TL;DR Key Takeaways :

Vector search uses numerical embeddings to capture semantic meaning, excelling in multilingual contexts and meaning-based retrieval, but it can be resource-intensive and struggles with long documents.
Keyword search (e.g., BM25) is efficient for long texts, precise queries, and rare terms but lacks semantic depth and is language-dependent unless adapted.
Hybrid search combines the strengths of both methods, using techniques like rank fusion to balance semantic understanding with keyword precision, making it ideal for complex fields like legal or medical research.
PostgreSQL supports both keyword and vector search through extensions like PGVector and VectorCord BM25, allowing efficient indexing and hybrid search implementation.
Optimizing search systems involves using hybrid approaches to balance performance, relevance, and computational efficiency based on specific use cases and datasets.

Vector search is a modern technique for information retrieval that focuses on semantic understanding. Unlike keyword search, which matches exact words, vector search transforms text into numerical vectors that represent the meaning of the content. These vectors are generated using embedding models such as transformers or large language models (LLMs), which position semantically similar concepts closer together in a high-dimensional space.

Advantages of Vector Search:

Semantic Understanding: It excels at identifying meaning-based relationships, even when different words are used to express the same idea. For example, a query like “renewable energy” could retrieve documents discussing “solar power” or “wind energy.”
Multilingual Capability: By focusing on meaning rather than language-specific keywords, vector search is highly effective in multilingual contexts.

Challenges of Vector Search:

Handling Long Documents: Long texts often need to be divided into smaller chunks for effective indexing and retrieval.
Resource Intensity: Calculating distances between vectors in high-dimensional spaces can demand significant computational resources, especially for large datasets.

How Keyword Search Works

Keyword search, often implemented using algorithms like BM25, remains a cornerstone of search technology. It ranks documents based on term frequency (how often a keyword appears in a document) and inverse document frequency (how unique the keyword is across the dataset). This method is particularly effective for exact matches and well-defined queries.

Strengths of Keyword Search:

Efficiency with Long Documents: BM25 is well-suited for indexing and retrieving long texts without requiring additional preprocessing.
Handling Rare Terms: It performs effectively with novel or infrequent terms, as it does not rely on pre-trained models.
Precision with Specific Queries: When users provide detailed and specific queries, keyword search delivers highly relevant results.

Limitations of Keyword Search:

Lack of Semantic Depth: It struggles to interpret context or meaning, making it less effective for ambiguous or meaning-based queries.
Language Dependence: BM25 is language-specific unless explicitly adapted for multilingual datasets.

Keyword vs Vector Search

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Keyword vs. Vector Search: A Comparison

Both keyword and vector search methods have unique strengths, making them suitable for different scenarios. A comparison of their capabilities highlights their complementary nature:

Language Support: Vector search supports multilingual queries, while BM25 is primarily language-specific unless adapted.
Document Length: BM25 efficiently handles long documents, whereas vector search often requires chunking for optimal performance.
Rare Terms: BM25 excels with novel or infrequent terms, while vector models may struggle if not trained on such data.
Query Precision: BM25 thrives on precise queries, which can be further enhanced using query expansion techniques from LLMs.

The Case for Hybrid Search

Hybrid search systems combine the strengths of both keyword and vector search, offering a balanced approach to relevance and precision. By integrating BM25’s efficiency with vector search’s semantic understanding, hybrid systems can deliver more comprehensive results.

How Hybrid Search Works:

Rank Fusion: Techniques like reciprocal rank fusion merge rankings from both methods, making sure neither semantic relevance nor keyword precision is overlooked.
Prioritization: Hybrid systems can prioritize documents with exact keyword matches while also surfacing semantically similar content.

This dual approach is particularly valuable in fields like legal or medical research, where both specificity and contextual understanding are critical for effective information retrieval.

Implementing Search in PostgreSQL

PostgreSQL offers robust tools for implementing both keyword and vector search through extensions like PGVector and VectorCord BM25. These extensions enable efficient indexing and retrieval, making PostgreSQL a versatile platform for hybrid search systems.

Key Features for Implementation:

Indexing Techniques: Vector search often uses hierarchical navigable small world (HNSW) indexing for fast similarity searches, while BM25 relies on block-wise indexing for efficient keyword matching.
Embedding Models: Models like Nomic’s Modern BERT generate high-quality embeddings for vector search, enhancing semantic understanding.
Rank Fusion: Combining results from BM25 and vector search ensures a balanced approach to relevance and precision.

Optimizing Performance

Performance is a critical factor when designing search systems. Vector search, while powerful, can be resource-intensive, requiring significant memory and computational power for large datasets. In contrast, BM25 is faster and more efficient for exact matches but lacks the semantic depth of vector search.

Hybrid Optimization:

BM25 can be used for initial filtering, narrowing down results based on keyword relevance.
Vector search can then refine these results by evaluating semantic similarity, making sure both precision and contextual relevance.

Practical Applications

The choice between keyword, vector, and hybrid search depends on the specific requirements of your use case. Each method offers distinct advantages:

Keyword Search: Best suited for long documents, datasets with novel terms, and precise queries.
Vector Search: Ideal for semantic understanding, multilingual datasets, and meaning-based retrieval.
Hybrid Search: Combines the strengths of both methods to create comprehensive and robust search systems.

By understanding the strengths, limitations, and implementation strategies of these approaches, you can design search systems tailored to your needs. Whether working with PostgreSQL extensions, embedding models, or advanced indexing techniques, the right choice will depend on your data, queries, and performance requirements.

Media Credit: Trelis Research

Filed Under: AI, Top News

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