This article is an adapted and translated version into Spanish of an original text published on Medium about embeddings and semantic similarity. Here we explain in a practical way what embeddings are, why they matter, and how they are used in real applications.
What embeddings are: embeddings are numerical representations of text in the form of high-dimensional vectors that encode meaning. The central idea is simple: two texts with similar meaning should have similar vectors even if they do not share words. For example, the phrases I love programming in Python and I like coding in a language whose symbol is a snake should produce embeddings that are close at the semantic level.
Practical applications: embeddings are the foundation of semantic searches that find relevant results even when words do not match, of recommendation systems that suggest similar items, of clustering to group documents by topic, and of RAG pipelines to improve language model responses with context extracted from a knowledge base.
How they are generated: large language models learn embeddings as part of their architecture. After tokenization, an embedding layer converts each token into a vector that is refined through transformer layers until generating the output. There are also specialized models for generating high-quality embeddings; for example, OpenAI models called text-embedding-3-small and text-embedding-3-large, which produce vectors of 1536 and 3072 dimensions respectively.
Example of typical output: an embedding can start with values like [0.005132983, 0.017242905, -0.018698474, -0.018558515, -0.047250036] and have a length of 1536. These high dimensions allow capturing complex semantic relationships but require more memory and computation.
How to measure similarity: embeddings are vectors and their similarity is usually measured with cosine similarity, which compares the cosine of the angle between two vectors. In practical terms, cosine similarity is the dot product of the vectors divided by the product of their norms. The resulting value varies between 1 when the vectors are identical, 0 when they are orthogonal, and -1 when they are opposite.
Dot product and norm: the dot product between v and w is the sum of the products of their components. The norm of a vector is the square root of the sum of the squares of its components. For vectors normalized to length 1, cosine similarity coincides with the dot product, and sorting by cosine is equivalent to sorting by dot product or by Euclidean distance under that condition.
Simple numerical example: v = [1, 0], w = [1, 1] has a norm of v equal to 1, a norm of w equal to the square root of 2, approximately 1.414, a dot product of 1, and a cosine similarity of approximately 0.707. In real implementations, it is advisable to use numerical libraries such as NumPy or SciPy to vectorize operations and avoid loops in pure Python.
Normalization and metrics: many models return unit-normalized embeddings, which allows using the dot product directly as a similarity measure and speeds up searches in vector indexes. If the embeddings are not normalized, cosine similarity must be calculated explicitly or they must be normalized beforehand.
Performance considerations: higher-dimensional embeddings capture finer nuances but increase storage costs and search latency. For large-scale systems, optimized vector indexes, dimensionality reduction techniques, and managed services are used to balance precision and cost.
Use in companies: embeddings are key in artificial intelligence solutions applied to business processes, advanced document search, virtual assistants, and AI agents that interact with corporate information. By integrating them into pipelines with aws and azure cloud services, and with business intelligence tools such as power bi, data-driven decision-making is enhanced.
About Q2BSTUDIO: Q2BSTUDIO is a software development company that offers custom applications and custom software, specializing in artificial intelligence, cybersecurity, and aws and azure cloud services. We design comprehensive solutions that include business intelligence services and power bi implementations, AI agents, and AI strategies for companies. Our team implements embedding pipelines, semantic search engines, recommendation systems, and secure architectures to guarantee data confidentiality and integrity.
How we can help you: at Q2BSTUDIO we design and implement custom applications that incorporate embeddings to improve search and user experience, we develop custom software that integrates artificial intelligence models to automate processes and build AI agents, we offer cybersecurity services to protect infrastructures and migrations and management in aws and azure cloud services, and we deploy business intelligence solutions with power bi for strategic visualization and analysis.
Practical recommendations: use embedding models suitable for the domain, store vectors in optimized vector indexes, normalize embeddings if metrics that require it are used, and evaluate trade-offs between dimension and performance. For prototypes, it is advisable to evaluate pre-trained models and then train or fine-tune specialized models if the domain requires it.
Conclusion: embedding similarity allows measuring meaning instead of word matching, enabling semantic searches, recommendations, and deep analysis. If you are looking to implement solutions with artificial intelligence, AI agents, custom software, or improve your business intelligence with power bi, Q2BSTUDIO offers experience and services to take your project to the next level by integrating cybersecurity and aws and azure cloud services.
Contact: if you want to explore use cases, pilots, or custom developments with embeddings and artificial intelligence for companies, contact Q2BSTUDIO and we will help you design the most suitable solution for your needs.
