Embedding

What it is

In the context of Artificial Intelligence, an Embedding is a way of converting information that humans understand (such as words, phrases, or paragraphs) into a list of numbers (a vector) that a computer can process. Unlike simple word counts, an embedding captures context and meaning.

Imagine you have a catalog of 5,000 pieces of furniture. If you search for "comfortable seat", a traditional Keyword Search might fail if the product description only uses the words "armchair" or "recliner". An embedding solves this because it assigns similar numerical values to concepts that are close in the real world. For the mathematical model, "armchair" and "seat" occupy neighboring coordinates in a multidimensional space.

How it works

When we pass text through an embedding model (such as those from OpenAI, Cohere, or open-source models), the system analyzes statistical relationships between billions of words to generate a unique mathematical coordinate.

1. Transformation: Your text (e.g., "supplier invoice") is converted into a vector, which can have hundreds or thousands of dimensions (numbers).
2. Vector Space: These vectors are placed on an invisible "map". If two texts deal with the same topic, their coordinates will be close together.
3. Similarity Calculation: When a user asks a question, the question is also converted into an embedding. The system calculates the distance (usually via cosine similarity) between the question vector and your document vectors. The closest result is, theoretically, the most relevant answer.

It is the invisible engine behind RAG (Retrieval-Augmented Generation), allowing an LLM to access your company's specific knowledge without needing to be trained from scratch.

When to use

SMEs should focus on using embeddings whenever traditional keyword search becomes insufficient or when the volume of unstructured data is high:

• Semantic Search: Implementing a search engine on the website or internal database that understands user intent, even if they use synonyms or incorrect technical terms.
• Product Recommendation: Suggesting items based on the actual similarity of attributes and descriptions, rather than just fixed categories.
• Automated Classification: Automatically grouping support emails, tickets, or invoices by theme (e.g., complaints vs. quote requests) without rigid manual rules.
• Sentiment Analysis: Identifying the tone of customer comments or reviews based on the deep context of the sentence.

Common pitfalls

• Ignoring token limits: Trying to create an embedding for a 50-page document all at once. The result will be diluted and imprecise. Text should be divided into logical units (chunking).
• Using generic models for extreme niches: If your company works in a very specific technical niche (e.g., high-precision industrial molds), a generic embedding model may not capture the grammatical nuances of your industry. It might be necessary to choose a more advanced or optimized model.
• Confusing Embedding with Database: An embedding is just the numerical representation. To store and search these numbers efficiently, you need a dedicated Vector Database.
• Underestimating infrastructure costs: Converting millions of documents into embeddings requires processing. Although costs have dropped drastically, it is necessary to plan a strategy for updating these vectors.

Practical example for an SME

A climatization and HVAC company receives hundreds of technical assistance requests a day via email and WhatsApp. Many customers do not know the technical terms: some write "the air conditioning is leaking water," others write "dripping in the indoor unit," or "excessive condensation."

With traditional search, the technician would have to search for each term individually. With Embeddings, the company creates a system where all technical manuals and intervention history are converted into vectors. When the administrative staff searches for "drainage problems," the system instantly identifies that descriptions of "dripping" or "water coming out" are semantically the same thing, presenting the correct technical solution in seconds. This reduces response time and avoids errors in the initial diagnosis.

Frequently Asked Questions

Q: What happens if I update my document? A: If a document's content changes, its previous embedding becomes obsolete. You will need to generate a new vector for that text section and update your vector database.

Q: Can I convert images into embeddings? A: Yes. There are multi-modal models that convert images into vectors. This allows, for example, searching for similar product photos using only a text description.

Q: Are embeddings the same as keywords (SEO)? A: No. Keywords are literal. An embedding is conceptual. Google uses embeddings (like BERT) to understand what the user means, rather than just looking for exact words on the page.

Q: Where are these numbers stored? A: They are generally stored in Vector Databases, which are optimized to calculate distances between thousands of lists of numbers almost instantaneously.

• 01Converting a 100-page technical manual into vectors to allow questions and answers about the content.
• 02Automatically grouping thousands of customer reviews by semantic topics like 'price' or 'durability'.
• 03Improving an e-commerce search bar to suggest running shoes when the user types 'sports footwear'.
• 04Detecting duplicate or very similar documents within a company file server.

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