How Embeddings Work: The Foundation of Semantic Search

If you’ve read anything about RAG, vector search, or semantic search, you’ve almost certainly run into the word embeddings. It’s one of those concepts that tends to get used without explanation, as if its meaning should be obvious. It isn’t. But it’s also not complicated once someone walks you through it.

This is that walkthrough.

The Problem With Text and Computers

Computers are excellent at working with numbers. They can compare them, sort them, measure distance between them, and perform complex mathematical operations on them instantly.

Text, on the other hand, is awkward for computers. The word “contract” and the word “agreement” are completely different strings — a computer doing a simple text comparison would say they have nothing in common. But any human reading them in context knows they’re close synonyms.

Keyword search has always suffered from this problem. If you search for “agreement” and the relevant document uses the word “contract,” a traditional search engine might miss it entirely.

Embeddings solve this by giving computers a way to represent meaning, not just characters.

What an Embedding Is

An embedding is a list of numbers — a vector — that represents a piece of text. The key property is that texts with similar meanings are mapped to vectors that are mathematically close to each other in a high-dimensional space.

So “contract” and “agreement” would have vectors that are near each other. “Invoice” would be in the neighborhood. “Banana” would be far away.

These vectors are produced by neural networks — specifically, models trained to develop representations where similar meanings cluster together. The resulting space isn’t one you can visualize (vectors often have hundreds or thousands of dimensions), but it has a consistent mathematical structure you can work with.

How the Math Works (Simply)

When you have two embedding vectors, you can measure how “similar” they are using a metric called cosine similarity — essentially, the angle between the two vectors in their high-dimensional space.

Vectors pointing in the same direction (angle close to zero) have high similarity. Vectors pointing in opposite directions have low similarity. This gives you a number between -1 and 1 that tells you how semantically related two pieces of text are.

This is what makes semantic search possible. Instead of looking for exact keyword matches, you convert the query to an embedding, compare it against embeddings of all your documents, and return the ones with the highest similarity score.

How Embedding Models Are Built

Embedding models are trained on massive amounts of text with specific objectives designed to push similar content together in the vector space.

One common training approach — contrastive learning — shows the model pairs of sentences that are paraphrases of each other and pairs that are unrelated. The model learns to push the paraphrase pairs close together and the unrelated pairs apart.

The result is a model that has, through millions of training examples, learned a rich representation of language where proximity in vector space correlates with proximity in meaning.

What Can Be Embedded

Words are the obvious starting point, but modern embedding models can handle much more:

• Sentences and paragraphs — the most common unit for search and retrieval
• Full documents — useful for document-level comparison and clustering
• Code — embedding models trained on code can find semantically similar functions or snippets
• Images — multimodal models can produce embeddings for images, enabling search across media types
• Cross-modal pairs — some models embed images and text into the same space, enabling text-to-image search

For most business applications — document search, knowledge retrieval, FAQ matching, support ticket routing — sentence-level embeddings are the workhorse.

From Embeddings to Vector Databases

Once you’ve converted your documents into embeddings, you need a place to store them and search them efficiently. That’s what a vector database does.

A vector database is optimized for a specific kind of query: given an input vector, find the k nearest vectors in the stored collection. This is called approximate nearest neighbor (ANN) search, and specialized databases (Pinecone, Weaviate, Qdrant, pgvector, and others) implement it efficiently even over millions of documents.

This is the foundation of every RAG system: your documents become embeddings, those embeddings live in a vector database, and at query time you search that database to find the most relevant content to feed to your LLM.

Practical Considerations

A few things matter a lot when you’re actually building with embeddings:

Chunking strategy. Embedding models have input limits. Long documents need to be split into chunks before embedding. How you chunk — by sentence, paragraph, fixed token count, or semantic unit — significantly affects retrieval quality. Too small and you lose context; too large and you dilute the signal.

Model choice. Different embedding models perform differently on different tasks. A model trained primarily on general web text may not capture the nuances of, say, pharmaceutical regulatory documents. Domain-specific embedding models often outperform general-purpose ones for specialized retrieval tasks.

Embedding freshness. If your documents change, you need to re-embed the changed content. This is generally fast and cheap, but it needs to be part of your pipeline.

Reranking. Vector similarity is a good first-pass filter, but it’s not perfect. Many production systems add a reranking step — using a more computationally intensive model to re-score the top candidates returned by the vector search before passing them to the LLM.

Why This Matters for AI-Powered Workflows

Embeddings are the infrastructure layer that makes language-aware automation possible. They’re what allows a system to find the right document when a user asks a question in their own words. They’re what enables agents to route tasks intelligently, match incoming requests to relevant templates, and surface the right information at the right time.

At Komposer, embedding-based retrieval is built into how our agents discover and work with your data. You connect your sources; the platform handles the embedding, indexing, and search — so your agents always have access to the context they need to act accurately.

Once you understand embeddings, a lot of what feels like “AI magic” becomes a concrete engineering problem you can reason about, measure, and improve.