How Embeddings Power Semantic Search, RAG and Recommendations

Consider a support request that reads, “I can’t get into my account.” Although those words barely overlap with a support article titled “Recover access after a failed login,” a text embedding model can place the two items near each other in a vector space, reflecting the relationship between their meanings.

Once both items have numerical representations, an application can measure their proximity. For example, a search system might compare the request with thousands of article embeddings and return the closest matches. Embeddings support semantic search, retrieval-augmented generation (RAG), recommendations, clustering and many other AI applications.

Each stored vector may be compact, but creating and maintaining embeddings across a large, frequently changing collection introduces a substantial inference workload. Existing records require an initial processing pass, revised content needs fresh embeddings and every incoming search produces a query vector. As semantic retrieval supports more RAG, agent and multimodal applications, practitioners need to evaluate two concerns together: whether the embeddings retrieve the right material and whether the surrounding architecture can generate, refresh and serve them at the required scale.

An embedding is a vector — a fixed-length sequence of numbers — that represents an object such as a word, passage, image or product. An embedding model generates the sequence so the object can be compared mathematically with other items processed by the same model.

Each number corresponds to one dimension of the vector. Current text embedding models often produce hundreds or thousands of dimensions, although diagrams usually reduce the space to two or three so it can be shown on a page. Taken together, the dimensions locate the object at a particular position within the model’s vector space.

The value of any one dimension usually has little meaning on its own. A practitioner generally can’t inspect the 217th number and conclude that it represents account access, for example. The useful information is distributed across the complete vector and emerges through its relationship to other vectors.

During training, the model learns to position related items near one another. A model built for document retrieval might place passages together when they address similar questions, while a recommendation model could group products that appeal to people with similar purchasing patterns. Proximity reflects the kind of relationship the model was trained to recognize.

This representation gives applications a common numerical basis for search, recommendation, clustering and other tasks. The original sentence, image or product record remains available, but the embedding supplies an additional form that the system can compare efficiently.

Embeddings can serve as learned features, replacing or complementing manually engineered variables in classification, clustering, recommendation and other machine learning workflows.

Learn how to scale embeddings with GPUs from Snowflake Notebooks on Container Runtime:

The values in an embedding reflect patterns established during model training. Later, during inference, the trained model applies those patterns to new inputs.

Learning the embedding space

During training, embedding models typically use pairs or groups of examples to learn what proximity should mean for the task. As training proceeds, the model adjusts its parameters to organize the representation space according to patterns and relationships in the training data.

Many embedding models use contrastive learning to organize those examples. The training process reduces the distance between related inputs while increasing the separation between unrelated ones. SimCSE, for instance, applies a contrastive objective to sentence embeddings, using positive and negative examples to produce a space in which semantically related sentences occupy nearby positions.

The resulting geometry reflects the task and data used for training. A model trained on product interactions, for example, will typically emphasize behavioral relationships, including associations that aren’t apparent from the product descriptions alone.

Coverage affects the quality of those representations as well. Specialized abbreviations, languages or document formats that appear infrequently in the training data may receive less useful embeddings, which helps explain why a model with strong general benchmark results can still perform poorly on an organization’s internal content.

Generating an embedding

After training, the model can generate a vector for a new input. The input-processing stage depends on the modality: text models process tokens, image models process pixel-based patches or features, and audio or video models may divide content into frames or time segments.

For text, the process usually begins with tokenization: the model first divides the content into tokens and processes them in context through the neural network. It then combines information from those token-level representations into a fixed-length vector for the selected unit of content.

That unit might be a sentence, paragraph or complete document. Regardless of input length, the model produces the same number of dimensions, allowing a short query and a longer passage to be compared within the same vector space.

Choosing the unit of representation is part of the retrieval design. Paragraph embeddings allow a search system to return a focused section, while one vector for an entire document blends information from all of its parts. Smaller units can preserve local detail, while larger units retain more surrounding context. Practitioners typically evaluate that trade-off against the material users need to retrieve.

A single embedding records the position of one item in the learned space. Applications compare embeddings to answer a practical question: which stored items occupy positions closest to a new query, product, image or other input?

The source content remains available for display, filtering and downstream processing. Vector comparison provides the ranking signal.

Similarity and distance metrics

A similarity or distance metric assigns a numerical score to two vectors. Common measures include:

• Cosine similarity, which compares the direction of the vectors and largely disregards their magnitude
• Dot product, which incorporates both direction and magnitude
• Euclidean distance, which measures the straight-line distance between two points

The appropriate metric depends on the model’s training objective and whether the vectors have been normalized. Sentence-BERT, for example, was designed to create semantically meaningful sentence vectors that can be compared efficiently with cosine similarity.

With a similarity metric, higher scores usually indicate a closer relationship. Distance metrics use the opposite convention: smaller values indicate greater proximity.

Those scores are only meaningful within the model and collection that produced them. A cosine similarity of 0.8, for example, has no universal interpretation across models or data sets, and a high score only shows that the model represents two inputs as similar.

Nearest-neighbor search

A similarity metric can score one query vector against one stored vector. In practice, however, a search or recommendation system needs to repeat that comparison across an entire collection, then rank the results. Nearest-neighbor search provides that larger-scale operation, identifying the stored vectors closest to the query.

A basic semantic retrieval flow follows four steps:

1. Divide the source content into retrievable units.
2. Generate and store an embedding for each unit.
3. Generate an embedding when a query arrives.
4. Rank the stored vectors according to their proximity to the query.

For a small collection, the system can calculate a score against every vector. As the index grows, a full scan adds latency and compute. Approximate nearest-neighbor methods organize the vector space so the system can search promising regions first, accepting some possibility that it will miss the mathematically closest candidate.

The index can store metadata with each vector, including the source record, publication date, category, region and access permissions. An application can use those fields to limit the search to eligible records — such as documents a user is authorized to view or products available in a particular region — before ranking the remaining results by vector similarity.

Different embedding types preserve different kinds of relationships because they’re trained on different inputs and objectives. The categories below describe both what each vector represents and what proximity means within that space.

Text embeddings

Text embeddings can represent words, sentences, passages or complete documents. Static word embeddings assign a stored vector to each vocabulary item, while contextual models calculate representations from the surrounding sequence.

For semantic search and clustering, purpose-built sentence or passage models combine contextual information into one fixed-length vector. Sentence-BERT introduced an architecture designed to produce sentence representations that could be compared directly, reducing the computational work required for large-scale semantic similarity search.

Image embeddings

An image embedding model represents visual content through features learned from training images. Depending on its objective, the vector may preserve relationships involving objects, composition, texture, style or broader semantic content. For example, an ecommerce application could compare the embedding of a selected jacket with vectors across its catalog, surfacing visually related products.

Because the model uses learned visual features, similarity doesn’t require identical pixels. Cropping, background changes and alternate viewpoints can still produce nearby vectors as long as the underlying subject remains recognizable to the model.

Multimodal embeddings

Multimodal embedding models align representations across content types. An image encoder and a text encoder, for example, can learn to place an image near the descriptions associated with it.

CLIP demonstrated this approach by jointly training image and text encoders to identify the correct pairings among images and captions. Once the representations are aligned, a text query such as “red hiking backpack beside a tent” can retrieve an image even though the query consists of words and the indexed item consists of pixels.

For long audio and video files, the unit of representation determines how precisely the content can be retrieved. One vector for the complete asset summarizes it broadly, while separate embeddings for scenes, time ranges, audio tracks or transcript segments preserve more local detail. Segment-level indexing allows a search result to point to the relevant moment rather than returning only the full file.

Entity and item embeddings

Entity embeddings represent objects such as products, users, songs or graph nodes using information about their attributes, interactions or connections. The training data determines which of those relationships shape the vector space. In a recommendation system, for example, two products may receive nearby embeddings because similar customers view or purchase them, even when their descriptions differ.

A graph model may place two entities near each other because they connect to the same kinds of nodes or occupy similar positions within the network. Proximity therefore has a task-specific interpretation. For text embeddings, it often reflects linguistic or semantic relationships. For product or user embeddings, it typically reflects behavior. For graph embeddings, it may reflect connectivity and structure.

Embeddings are used when an application needs to retrieve, group or compare items according to learned relationships rather than exact matches. Common uses include semantic search, RAG, recommendations, classification, clustering and near-duplicate detection.

Semantic search and RAG

Semantic search retrieves content according to meaning, even when the query and the source use different wording. Production search systems often combine embedding-based retrieval with keyword matching, metadata filters and reranking. Keywords preserve exact matches for names, codes and specialized terminology, while filters restrict results by criteria such as date, region or access permissions. Vector databases and vector-aware search services store the embeddings and support retrieval across large collections.

RAG uses those retrieved passages as context for a language model. The search layer selects the supporting material, and the model uses that material when generating its response. Retrieval quality therefore shapes which evidence reaches the model and, in turn, the quality of the final answer.

Recommendations

Recommendation systems use embeddings to identify products, media or other items associated with similar users, behaviors or attributes, even when their descriptions and categories differ.

The vectors usually supply candidate items rather than the final ranked list. Availability, recency, business priorities and the user’s current context may all influence what appears. A streaming service, for instance, might begin with items near a viewer’s recent activity, then adjust the ranking according to language, region and previously watched content.

Classification and clustering

Embeddings often serve as learned input features for a classifier. A support team might generate embeddings for previously labeled tickets, then train a smaller model to assign new requests to categories such as billing, account access or technical support.

Clustering works without predefined labels. By grouping records with nearby embeddings, practitioners can explore recurring themes in customer feedback, organize document collections or identify common incident patterns. The algorithm supplies the groups, but teams still need to inspect representative records and determine what each cluster means in the business context.

Similarity detection and deduplication

Embedding-based similarity helps identify records that are related without being exact copies, such as a passage that’s been paraphrased or an image that’s been cropped and resized.

These comparisons usually produce candidates for review or further processing. The application may apply a similarity threshold, a secondary model or deterministic checks before merging records or marking them as duplicates. The appropriate threshold depends on the content and the consequences of an incorrect match, so teams typically calibrate it against representative examples.

Snowflake supports embedding workflows within the same environment used to store, process and govern the underlying data. Teams can generate vectors directly, use them in SQL or build a managed retrieval service, depending on how much control the application requires.

The AI_EMBED function creates embeddings from text or images and returns Snowflake’s native VECTOR data type. Vectors can remain alongside the source records and business metadata, then be compared through functions for cosine similarity, inner product and vector distance.

For semantic search and RAG, Cortex Search manages the indexing and serving layer. It combines vector retrieval with keyword search, supporting conceptual matches while preserving precise retrieval for names, codes and specialized terminology. Teams can use managed embeddings or provide vectors created with an open source, commercial or custom-trained model.

Workloads that need specialized dependencies or custom inference logic can run through Snowflake Container Runtime, which supports CPU and GPU environments for experimentation and batch inference. For multimodal retrieval, AI_MULTI_EMBED can generate segment-level representations from text, images, audio and video, subject to current model and regional availability.

Arctic Embed 2.0 models provide an open source option designed for retrieval, including multilingual support and Matryoshka Representation Learning to enable practitioners to test shorter, compressed vectors when storage and search efficiency are priorities.

Together, these capabilities support managed retrieval through Cortex Search, direct vector operations in SQL and custom embedding pipelines for workloads with specialized model or compute requirements.

Embeddings give applications a consistent numerical representation for content and entities that would otherwise be difficult to compare. A model generates the vectors, a similarity measure establishes proximity and a retrieval or analytical process uses those relationships to rank, group or classify the original items.

At production scale, the work extends beyond creating the vector. Teams need to decide what receives an embedding, keep changing content synchronized with the index and serve query vectors within the application’s latency target. Model quality, inference throughput and retrieval design all contribute to the final result.

When those components are evaluated together, embeddings can support search and AI workflows across text, images, media and structured entities without limiting relevance to exact keyword overlap.