Vector Embeddings Explained: Semantic Search & LLM Integration Guide

In modern search systems and large language models, semantic similarity embeddings play a key role, enabling computers to work not only with words but also with content. Thanks to text and word embeddings, text, documents, and queries are transformed into semantic vectors, in which the proximity between vectors reflects semantic similarity rather than the formal coincidence of words.

This idea is built on vector search - a mechanism that underlies semantic search, recommender systems, and data integration with LLM. Instead of searching for keywords, the system compares vectors to find the most relevant results, even when the query formulation differs significantly from the source text.

How vector embeddings represent meaning and how we compare them

Vector embeddings are numerical representations of data (usually text) in which the content is encoded as multidimensional vectors. Text embeddings and word embeddings transform words, sentences, or documents into semantic vectors, where each coordinate has no direct human meaning, but the entire set of coordinates reflects the semantics of the object.

Semantically close objects have vectors that are located close to each other in vector space. This is why the queries "cat" and "pet" may be closer to each other than "cat" and "car," even though the words are not the same.

Comparison of semantic vectors is usually performed using mathematical metrics. The most common of these is cosine similarity, which measures the angle between the vectors rather than their absolute length.

Object (Text)	Purpose / Meaning
cat	Word embeddings for a single word; captures basic semantic meaning
dog	Semantically close to cat; shows embeddings semantic similarity
car	Semantically distant; illustrates how unrelated concepts are represented
The cat sleeps on the sofa	Text embeddings for a full sentence; captures context and overall meaning
User query	Used in vector search to find closest semantic vectors

How the comparison works

The text or query is converted into a set of text embeddings.
The system calculates the similarity between the query vector and the database vectors.
Using a metric (e.g., cosine similarity), the level of semantic similarity is determined.
Vector search returns objects with the closest semantic vectors.

Models that create embeddings: pretrained, fine-tuned, and custom

Models for generating vector embeddings are trained to transform text into semantic vectors that preserve the semantic relationships between words and documents. Depending on the task and domain, pretrained, fine-tuned, or custom models are used.

Word embeddings typically operate at the level of individual words, while modern text embeddings model the context and meaning of entire text fragments, making them much more useful for search and integration with LLMs.

Model Type	Description	Advantages	Limitations	Typical Use Cases
Pretrained	Models trained on large general text corpora and ready to use	Quick to start, high-quality semantic vectors, no additional training needed	May not capture domain-specific nuances	General vector search, semantic search, basic RAG systems
Fine-tuned	Pretrained models additionally trained on domain-specific data	Better embeddings semantic similarity in a narrow context, higher relevance	Requires data and resources for fine-tuning	Enterprise search, technical documentation, industry-specific knowledge
Custom	Models trained from scratch for a specific task	Maximum adaptation to domain and task	High cost, complex training, requires large datasets	Unique products, non-standard data types, research systems

Vector embeddings across modalities

The principle of semantic similarity embeddings works for different modalities: images, audio, video, and structured data. In each case, the data is transformed into semantic vectors that reflect the object's content rather than its format or surface characteristics.

For text, text embeddings and word embeddings are used, which encode the meaning of words, sentences, and documents. For images, embeddings represent visual concepts — objects, scenes, and styles. Audio embeddings can convey language features, intonation, or acoustic patterns. Regardless of modality, objects with similar content will have similar semantic vectors.

Core applications: semantic search, recommendation systems, and beyond

Core Application	Description	Example Use Cases	How Embeddings Are Used
Semantic search	Retrieving information based on meaning rather than exact words	Document search, knowledge bases, FAQs	Text embeddings convert queries and documents into semantic vectors; vector search finds the closest matches by meaning
Recommendation systems	Suggesting content based on similarity between items	Movies, products, music	Embeddings semantic similarity between text embeddings or word embeddings identifies relevant items
Clustering & grouping	Automatically grouping similar items	User segmentation, document categorization	Semantic vectors are used to measure proximity and form clusters
Multimodal applications	Comparing data across different types (text, images, audio)	Image search from text, integrating multimodal data	Vector search in a shared vector space of semantic vectors across modalities
LLM integration/ RAG	Enhancing LLM accuracy and relevance of answers	Q&A systems, intelligent assistants	Text embeddings and semantic vectors are used to retrieve relevant content blocks before generating a response

Infrastructure basics: vector databases and approximate nearest neighbor search

To work effectively with vector embeddings, especially in semantic search tasks and multimodal systems, the right infrastructure is critical. The main components are vector databases and approximate nearest neighbor (ANN) search algorithms.

Vector databases

Vector databases are specialized repositories for semantic vectors. They are optimized for fast storage, indexing, and retrieval of text, word, and other vector embeddings. The key goal is to quickly find the closest vectors by content, i.e., those with high semantic similarity to the query. Features:

Scaling to millions and billions of vectors.
Support for vector search with different similarity metrics (cosine similarity, Euclidean distance).
Integration with LLM for RAG architectures and semantic search.

Approximate nearest neighbor (ANN) search

Searching for the exact nearest vectors in a large space is very computationally expensive. ANN algorithms allow finding semantic vectors close to the query with high speed and low error.

Popular ANN methods:

HNSW (Hierarchical Navigable Small World graphs) — graph structures for fast search.
IVF (Inverted File Index) — partitioning space into clusters to speed up search.
PQ (Product Quantization) — vector compression to save memory and speed up comparisons.

How it works together

Data is converted into text embeddings or word embeddings, forming semantic vectors.
A vector database stores these vectors and creates indexes for vector search.
When a query is received, its vector is compared against the database using an ANN, enabling fast, efficient retrieval of the most relevant semantic vectors.
The results can be used for semantic search, recommendations, or content preparation for LLM.

FAQ

What are vector embeddings?

Vector embeddings are numerical representations of data, such as text, images, or audio, in which semantic meaning is encoded as vectors. They allow machines to measure the semantic similarity between objects.

How do text embeddings differ from word embeddings?

Word embeddings represent individual words, while text embeddings capture the meaning of entire sentences or documents. Both produce semantic vectors for comparison in vector search.

What is semantic similarity in embeddings?

Embeddings' semantic similarity measures how close two semantic vectors are in meaning. It is used to find related words, sentences, or documents beyond exact word matches.

Why are vector databases important?

Vector databases efficiently store and index semantic vectors, enabling fast vector search across millions of embeddings. They are critical for scalable semantic search and LLM integration.

What is the approximate nearest neighbor (ANN) search?

ANN search finds vectors close to a query vector quickly without computing exact distances for all entries. It allows efficient retrieval of relevant semantic vectors for vector search applications.

What types of embedding models exist?

There are pretrained, fine-tuned, and custom models. Pretrained models work out of the box, fine-tuned models adapt to specific domains, and custom models are built from scratch for unique tasks.

How are embeddings used in recommendation systems?

Embeddings' semantic similarity enables systems to find items that match user preferences. Both text embeddings and word embeddings can be used to compare content for recommendations.

What are the main applications of vector embeddings?

They are used in semantic search, recommendation systems, clustering, multimodal applications, and LLM augmentation. Embeddings' semantic similarity enables these applications by representing data in a comparable vector space.