Understanding Embeddings and Vector Databases

Our Fine-Tuning vs. Prompting vs. RAG article explains that RAG works by retrieving relevant information before the model answers, usually backed by a vector database. This article goes one level deeper: what embeddings and vector databases actually are, and how they let an AI agent search by meaning instead of exact words.

What Is an Embedding?

An embedding is a way of converting a piece of text (or an image, or audio) into a long list of numbers that captures its meaning, rather than just its exact wording. Pieces of content with similar meaning end up numerically close to each other, even if they don't share any of the same words.

A commonly used example: the words "king" and "queen" end up close together in this numerical space, because they're used in similar contexts and carry related meaning. "Dog" and "cat" end up closer to each other than either does to "table," since they're both animals. This is what makes it possible to search by meaning: a search for "how to cut costs" can find a document about "budget optimization" even though the two share no words in common.

What Is a Vector Database?

A vector database is a database built specifically to store embeddings and quickly find the ones most similar to a given query. This is fundamentally different from how a traditional database works. A traditional database looks for exact matches, like finding every row where a customer ID equals a specific number. A vector database instead ranks results by how close they are in meaning, using a distance calculation between the numerical vectors (commonly a measure called cosine similarity).

To do this quickly, even across millions of entries, vector databases rely on approximate nearest-neighbor search rather than checking every single entry one by one. This tradeoff, near-instant results in exchange for results that are extremely likely, but not mathematically guaranteed, to be the single closest match, is what makes vector search practical at scale.

How This Powers RAG

Embeddings and vector databases are the actual mechanics behind the retrieval step described in our RAG article. The full pipeline typically looks like this:

1. Your documents get broken into chunks. A support wiki, a set of PDFs, or any other knowledge source gets split into smaller pieces, usually a paragraph or a few sentences at a time.
2. Each chunk gets converted into an embedding. An embedding model processes each chunk and produces its numerical vector.
3. Those embeddings get stored in a vector database. This becomes the searchable knowledge base the AI agent will pull from later.
4. Your question gets embedded too. When you ask something, the same embedding model converts your question into a vector, using the same numerical space as the stored chunks.
5. The vector database finds the closest matches. It compares your question's embedding against every stored chunk and returns the most similar ones.
6. Those chunks get handed to the model. The retrieved text is inserted into the model's context alongside your question, so it can answer using your actual content instead of guessing from training data alone.

Where This Shows Up in Bluehost's AI Apps

Hermes Agent's knowledge-retrieval skill is a direct, real-world example of this pipeline. It combines vector-based semantic search (exactly what's described above) with traditional keyword search and a reranking step, all running locally, so it can pull relevant chunks from your own documents into its context before responding, rather than relying purely on what it learned during training.

The Connection to Accuracy

As covered in our hallucinations article, a model answering from actual retrieved source material is far more reliable than one answering purely from what it memorized during training. This is exactly why RAG, powered by embeddings and vector search underneath, is one of the most effective ways to reduce hallucinations for a self-hosted AI setup.

Resource Considerations for Self-Hosting

Running your own vector database adds another moving piece alongside the language model itself. The embedding model needs its own compute to convert documents and queries into vectors, and the vector database needs storage and memory to hold and search through them, on top of whatever resources the model you're actually chatting with is using. As covered in our RAM and CPU usage article, every additional moving part in an AI setup adds to the total load on your server, so it's worth accounting for this when sizing a Self-Managed VPS or VDS plan for a RAG-based setup rather than just the language model on its own.

Summary

An embedding is a numerical representation of meaning, letting an AI system compare content by what it's actually about rather than by exact wording. A vector database stores these embeddings and quickly finds the ones most similar to a given query, which is the actual mechanism behind RAG's retrieval step. This combination is what allows tools like Hermes Agent to search your own documents by meaning and ground their answers in real content, though it does add its own resource cost on top of running the language model itself.