An embedding model converts an item into a numeric vector. Items with related learned characteristics tend to occupy nearby regions of the vector space. Applications use that property to retrieve text that is semantically related to a query.
The embedding is not a summary you can read. It is an input to a similarity calculation.
- Encode
Use the same embedding model and version for the query and compared items. The example query asks how to return a damaged item.
- Compare coordinates
A vector such as [0.18, -0.42, 0.07, ...] supplies coordinates for a distance or similarity calculation.
- Rank candidates
A returns policy or damaged-item procedure may rank ahead of office catering. This is a model-dependent comparison.
- Check evidence
Evaluate the retrieved content for correctness, authorization, and sufficiency before using it.
Nearby vectors suggest similarity under the same embedding model. They do not establish truth or permission.
How to read this model
A comparison instrument with reference samples and a movable probe. The object is a teaching analogy. Inspecting a part highlights its role; it does not execute or simulate the software. Cabinet trays are records, workpieces are data, and latches are checks. Unless an explanation states a quantity, compartments and sample workpieces do not denote data counts. Part dimensions are not a quantitative scale.
Generative and embedding models do different jobs
A generative model predicts output tokens. An embedding model produces a fixed-size representation for comparison or downstream learning. Some model families support both kinds of operation, but the outputs and evaluation criteria remain different.
An embedding pipeline normally uses the same embedding model and representation settings for stored documents and incoming queries. Changing the model can require rebuilding the index because vectors from unrelated spaces are not directly comparable.
Cosine similarity compares direction
Cosine similarity measures the angle between vectors:
cosine(a, b) = (a · b) / (||a|| ||b||)
For nonzero vectors, the result ranges from -1 to 1. A larger value means the vectors point in more similar directions. Some indexes store normalized vectors, which makes cosine similarity equivalent to a dot product for ranking.
A high similarity score means “near according to this model and representation.” It does not mean “factually supports the answer.”
Vector search narrows candidates
Exact nearest-neighbor search compares the query with every stored vector. Approximate nearest-neighbor indexes trade a small amount of recall for much lower search cost. Common index families partition the space, build navigable graphs, or quantize vectors.
The right index depends on collection size, update rate, memory, latency, and acceptable recall. Measure it with your documents and queries.
Metadata enforces scope before ranking
Store fields such as tenant, document type, product, language, and access group beside each vector. Apply authorization filters before returning candidates to the model.
Do not retrieve globally and ask the model to ignore unauthorized results. Once sensitive text enters the prompt, the confidentiality boundary has already failed.
Hybrid search combines signals
Dense retrieval is useful for semantic similarity. Keyword methods are often better for exact identifiers, error codes, names, and rare terms. Hybrid search combines both result sets or scores, then may send candidates to a reranker.
The combination is an engineering choice, not a universal upgrade. Evaluate recall, precision, latency, and access-control behavior separately.