Semantic Search vs Vector Search

The increasing use of LLMs and AI technologies has revolutionised how organizations retrieve information. Traditional keyword-based searches often struggle to capture the context, intent and relevance behind queries. To overcome this limitation industries are increasingly adopting vector and semantic search techniques. Lets understand about semantic and vector search before moving on the differences.

What is Vector Search?

Vector search is a retrieval technique that represents data (text, images, audio, etc.) as high-dimensional numeric vectors embeddings using machine learning models. When a query is made, it is also converted into a vector and the system retrieves items based on similarity measures like cosine similarity between the query vector and indexed vectors. This approach is fast, scalable and excels at finding items that are statistically similar, making it ideal for large-scale, multi-modal datasets.

Key Concepts in Vector Search:

• Embeddings: In vector search each item like a word, sentence or image is converted into a vector which is a numerical representation of the item. This transformation is often done using embedding models like Word2Vec, GloVe or more advanced models like BERT or Sentence-BERT for text.
• Similarity Metrics: Once the data is represented as vectors, search queries are also converted into vectors. The search engine then compares the query vector to the stored vectors using similarity metrics like cosine similarity, Euclidean distance or dot product. The closer the vectors are in the vector space, the more similar they are.
• Vector Databases: Special databases like FAISS (Facebook AI Similarity Search) or Milvus are designed to store and index high-dimensional vectors efficiently. These databases are optimized to handle the heavy computational demands of vector search.

Working of Vector Search

• Converts data and queries to vectors using embedding models.
• Finds and ranks results by mathematical similarity like cosine distance.
• Excels at fast, large-scale retrieval including images and audio.

Use Cases of Vector Search

• Image, audio and video search
• Recommendation engines
• Anomaly detection in large datasets
• E-commerce product similarity and suggestions

What is Semantic Search?

Semantic search focuses on understanding the meaning, context and intent behind a user's query. It uses natural language processing (NLP) and advanced language models such as transformers like BERT or GPT to interpret relationships between words, understand meanings and deliver results that align with what the user actually wants even if their query is ambiguous or complex.

Key Concepts in Semantic Search:

• Natural Language Processing (NLP): Semantic search uses NLP techniques to analyze and understand the meanings behind words and their relationships. This helps search systems comprehend user queries in a more human-like way.
• Contextual Understanding: It can grasp the context of words used. For example, the word "bass" could refer to a fish or a musical instrument. A semantic search system understands the context and returns the appropriate results.
• Entities and Relationships: It also focuses on recognizing entities like people, places, events and their relationships within a text. Technologies like entity recognition and knowledge graphs help do this.
• Query Expansion and Refinement: To improve search results, semantic search engines may expand the query by adding synonyms or related terms, refining the search to deliver more accurate results.

Working of Semantic Search

• Processes queries with NLP to understand intent, context and relationships.
• Uses deep learning models to match user intent with relevant content.
• Delivers personalized, context-aware results even for complex queries.

Use Cases Semantic Search

• Customer support chatbots
• Knowledge base and FAQ retrieval
• Legal, medical and academic document search
• Personalized content discovery

Differences between semantic vs vector search

Performance Comparison of Semantic search and Vector search

Vector Search:

• Optimized for large datasets, offering faster query retrieval times.
• Utilizes Approximate Nearest Neighbor (ANN) algorithms for efficient matching using heuristic methods.
• Faster performance with a slight trade-off in accuracy, making it ideal for real-time applications and scalable systems.

Semantic Search:

• Focuses on precision and understanding the deeper meaning of the query.
• Slower processing times due to the use of complex Natural Language Processing (NLP) techniques.
• Delivers accurate results by interpreting context, intent and nuances, making it ideal for queries requiring deep semantic understanding.

Benefits and Challenges

Vector Search

• Benefits: Fast, scalable, handles diverse data types, efficient for similarity search.
• Challenges: Limited contextual understanding, may miss nuanced meanings or intent.

Semantic Search

• Benefits: High contextual accuracy, understands user intent, excellent for ambiguous or complex queries.
• Challenges: Computationally expensive, slower for large datasets, requires high-quality training data.

Hybrid Approache

Many modern systems combine both methods known as Hybrid search which uses vector search for speed and semantic search for contextual accuracy, balancing performance and relevance ideal for e-commerce, knowledge retrieval and conversational AI.