Semantic Search vs Vector Search: Key Differences

The rise in the usage of LLMs and AI technologies has transformed the way organizations retrieve information. Traditional keyword-based searches often fall short of understanding the context, intent, and relevance behind queries. To address this limitation, several industries are utilizing vector and semantic search.

Semantic and vector search are often mentioned together because both approaches sometimes work in tandem. However, both search types serve distinct roles, which is important to understand before adopting them into your workflows. This blog will delve into the semantic search vs vector search differences to help you holistically learn about these information retrieval processes.

What is Semantic Search?

Semantic search is an advanced search technique that uses natural language processing (NLP) and machine learning to understand the intent behind a query. Here, the search engine analyzes the relationship between words instead of looking for a literal match, which is done in traditional keyword-based search. Semantic search is most beneficial for ambiguous or complex queries where multiple interpretations are possible.

How Semantic Search Works?

It may seem that semantic search delivers meaningful and accurate results almost instantaneously. However, the search engine processes multiple steps internally to implement semantic search. Let’s understand the steps briefly:

• Understanding Query: The search engine utilizes NLP techniques, such as tokenization and part-of-speech tagging, to identify the relationship between words. The words are often transformed into word embeddings, where words with similar meanings get grouped together.‍
• Entity Recognition: The system detects key entities, like people, places, or concepts within the query to enhance contextual understanding.‍
• Content Matching: Next, the search engine will compare the query with the indexed content through semantic relationships. Instead of simply relying on word matching, the engine will implement semantic search by analyzing the overall topic, sentiment, and meaning of the query.‍
• Contextual Analysis: The semantic search engine also factors in the user’s location, search history, and known preferences to refine the output.‍
• Ranking and Retrieval: Finally, by leveraging knowledge graphs, the system will rank results based on relevance and user intent. The most context-aware and accurate result is then presented as the output.

Pros

• Enhanced User Experience: Implementing semantic search helps obtain faster and more relevant answers, making interactions more intuitive and engaging. The engine also tailors search results based on user history and preferences, allowing you to boost customer satisfaction rates.‍
• Accurate Information Retrieval: Semantic search engines make use of knowledge graphs that store vast information about data entities, concepts, and relationships. It allows them to deeply understand the intent and context, making results more meaningful and accurate.‍
• Adaptability: The NLP techniques used in the semantic search engine can learn new languages and terminology quickly. By implementing semantic search, you can keep up with changing language trends and adapt your services to a new market.

Cons

• Privacy Concerns: The semantic search engine uses a user’s location and browsing history. It can raise privacy concerns, especially when user consent is not obtained, or their information is not governed by data protection laws like GDPR.‍
• Algorithm Bias: Machine learning models that contribute to a semantic search engine’s efficiency may have been trained with biased data. This inherited bias can contribute to skewed and unfair search results.‍
• Performance Limitations: The semantic search engine will require significant processing power and memory if the knowledge graph does not have enough information for a complex user query.

What is Vector Search?

Vector search allows you to understand the meaning and context of unstructured data, such as images and text, through numerical representations called vector embeddings. It utilizes machine learning algorithms, like approximate nearest neighbor (ANN), to identify similar data efficiently.

The vector embeddings capture contextual relationships between data points, making them essential for AI-driven applications. Compared to traditional keyword searches, implementing a vector search will enable you to deliver relevant results in a short span of time.

How Vector Search Works?

For vector search to succeed, the vector search engine at the core first generates vector embeddings. These embeddings capture each data item’s attributes, ensuring that the search engine is focused on the meaning of the item and not the exact keywords.

The search engine uses optimization techniques, such as data partitioning and indexing, to narrow the search space. Different machine learning algorithms work together to group similar vector attributes. Traditionally, vector search engines leveraged methods like k-nearest neighbor (k-NN). However, ANN algorithms have proven to be faster and more accurate for large-scale data.

Pros

• Multilingual Capabilities: Advanced vector search engines utilize several large language models (LLMs) to understand linguistic nuances. It helps them retrieve nearly accurate information across multiple languages, allowing you to gain cross-lingual knowledge.‍
• Context-Awareness: Vector search engines do not rely on matching keywords. Instead, they analyze the meaning and relationship of embeddings to provide you with contextually relevant results.‍
• Scalability: To implement vector search, the search engines leverage optimized algorithms and data structures. It is most suited when you need to process and analyze large volumes of unstructured data that include documents, videos, and images.

Cons

• Data Maintenance: To obtain relevant results at all times, the search engine must constantly keep your vector indexes up-to-date and have efficient methods to remove obsolete data. This is a challenge that several gen-AI companies are still trying to solve.‍
• Challenges with Specialized Data: In highly specialized industries, such as legal practitioning or healthcare, the vector search engine may struggle to fully comprehend jargon and terminology. Implementing vector search here may lead to suboptimal results that can hamper your objective.‍
• Higher Cost of Operating: As the number of embeddings and dimensions increase, the computational cost of your vector search engine will rise, leading to unanticipated expenses.

Manage Vector Embeddings with Airbyte

To avoid performance slowdowns and update the stored vector embeddings, you can use a vector database. A vector database is a data management system where you can store and process vectors. Many popular vector databases offer several features to help you index and retrieve vector embeddings for efficient search operations.

To move your data into a vector database, you can try using a robust data movement platform. Airbyte is an AI-powered data integration and replication tool that offers an expansive library of 550+ no-code connectors. In this library, you can find dedicated connectors to eight popular vector databases, including Chroma, Milvus, Pinecone, and Weaviate.

Airbyte allows you to extract semi-structured and unstructured data from multiple sources and move it to a destination of your choice. You even have the option to build custom connectors through its Connector Builder or low-code CDK features.

To improve the outcome of your vector embeddings, you can integrate Airbyte with popular LLM frameworks, such as LangChain or LlamaIndex. By performing RAG transformations, you can greatly improve the outcome of your LLM-generated content, which, in turn, improves the results of your vector search engine.

You can also generate vector embeddings in Airbyte by leveraging the platform’s compatibility with pre-built LLM providers. These encompass OpenAI, Cohere, Anthropic, and many more popular ones. Through Airbyte’s automatic chunking and indexing options, your team can transform the captured raw data and directly move it into the vector database for further analysis.

Semantic Search vs Vector Search: Key Differences

Here’s a tabular comparison of semantic search vs vector search:

Let’s understand the vector search vs semantic search differences in more detail:

Architectural Differences

In the semantic search vs vector search architectural difference, it is important to note that both search engines leverage machine learning algorithms at the backend. Semantic search focuses on understanding the sentiment and intent of a query through tokenization and other NLP techniques.

On the other hand, vector search prioritizes transforming the data into mathematical embeddings and draws comparisons based on proximity in the vector space. To further understand the detailed differences between both internal processes, you can read this article on tokenization vs embeddings.

Performance

When comparing vector search vs semantic search in terms of performance, you must understand that both search engines are continuously developing effective data retrieval strategies. However, vector searches are known to outperform semantic searches, especially when you input vast datasets.

Vector search uses ANN algorithms that efficiently find close enough vectors through heuristics. The algorithm has a slight trade-off in accuracy by greatly reducing query result times. In contrast, if you desire precision over the speed of query retrieval, you must go with semantic search. Semantic search may have a slightly slower processing time because it understands the nuanced meaning behind your query.

Scalability

Vector search engines are known to be more scalable than semantic search engines. Vector search employs robust indexing techniques to manage large amounts of multidimensional vectors. These vector embeddings also encapsulate the semantic meaning of every entity, allowing the search engine to find related information beyond the query’s topic. This feature becomes particularly useful when your organization is dealing with massive workloads of varying content.

Use case

Semantic search is useful for developing customer support chatbots and recommendation systems. Conversely, vector search finds applications in image and video searching as well as anomaly detection in financial applications.

Semantic Search vs Vector Search: Real-World Examples

Semantic and vector searches find applications across several industries. Take a look at how different organizations implement vector search and semantic search in routine workflows:

Semantic Search

• Healthcare: In the healthcare industry, medical practitioners can use semantic search engines to conduct patient similarity analysis. By inputting the current patient’s symptoms and medical history, they can identify previous patients with similar profiles. Semantic search results prompt doctors to explore effective treatments and predict potential risk factors, especially in complex diagnoses.
• ‍Music Streaming Platforms: Music streaming services can implement semantic search engines to enhance track recommendations. By analyzing a user’s song preferences and listening habits, the platform can suggest tracks in similar genres, melodies, or of a particular artist.

Vector Search

• Fraud Detection: Banks can use vector search engines for fraud detection and cybersecurity purposes. By comparing data points with established transaction patterns, teams can identify anomalies based on their distance from typical vector embeddings. This approach enables early threat detection, allowing banks to protect data from breaches and fraud.‍
• Autonomous Vehicles: Autonomous vehicles rely on vector search engines and databases to process sensor data from LiDAR, radar, and cameras. By converting real-world inputs into vector embeddings, they can identify pedestrians, traffic conditions, and obstacles. It allows them to quickly adapt to environmental surroundings, contributing to safe driving conditions.

Conclusion

It is important to conduct a vector search vs semantic search comparison to comprehend which search method is more relevant to your organization’s purpose. Both approaches have distinct functioning processes and advantages.

If you are looking to achieve highly relevant and context-aware results, you should go with a semantic search. On the flip side, vector searches will help you conduct high-dimensional similarity analysis for vast unstructured datasets.