What an embedding model produces
An embedding model converts an input such as text, code, an image, audio, video, or a document into a numerical vector. A vector is simply an ordered list of values, commonly floating-point numbers. An API may return it in a field named embedding or values.
The vector is not normally intended for people to read. It is an intermediate representation that software can compare mathematically. For example, a search system may convert a collection of documents and a user query into vectors, then look for documents whose vectors are closest to the query vector.
A useful analogy is a map. The map does not provide a written explanation of every location, but it places related locations near one another. Similarly, an embedding space attempts to place semantically related items near one another, although the individual coordinates usually do not have simple human-readable meanings.
What “project-global” usually means
Project-global embeddings is not a broadly standardized name for a special embedding format or model family. The phrase most plausibly describes the access scope of vectors within an application.
- Project-scoped: vectors and their metadata are available only within one project, workspace, repository, tenant, or application.
- Organization-wide: vectors can be shared across approved teams or projects within an organization.
- Global: vectors may be searchable across several projects or applications, subject to permissions and governance.
This scope affects indexing, permissions, privacy, deletion, and tenancy. It does not inherently change the vector’s mathematical structure or prove that a different embedding model was used. If a platform uses “global” as an internal product term, its documentation should define the exact boundary.
Input and output are different capabilities
An embedding model may accept one or more modalities, but that does not mean it generates those modalities. A text embedding model takes text and returns a vector. A multimodal embedding model may accept text, images, video, audio, or documents and map them into a shared vector space, while still returning vectors rather than media.
For example, a cross-modal search system might embed a written query and product images into the same space. The system can then find images related to the query. The embedding model has enabled image retrieval, but it has not generated a new image.
This distinction separates embeddings from generative and transformation capabilities:
- Text generation returns readable language.
- Image, video, or audio generation returns media or a media stream.
- Speech recognition converts audio into text.
- Classification returns labels or probabilities.
- Reranking scores an existing set of search results.
- Embeddings return vectors for comparison and downstream computation.
How embeddings support search and retrieval
In a typical semantic-search workflow, documents are divided into useful sections, sometimes called chunks. An embedding model converts each chunk into a vector, and the application stores those vectors in a vector database or approximate-nearest-neighbor index.
When a user submits a query, the application embeds the query with the same or a compatible model. It then compares the query vector with stored vectors using a measure such as cosine similarity, dot product, or Euclidean distance. The closest matches are returned as candidate results.
In a retrieval-augmented generation system, the retrieved documents are usually passed to a separate language model as context. The language model may write the final answer, but it is not necessarily the component that created the embeddings or performed the vector search.
This architecture matters when evaluating claims about native capability. A chat application may offer “memory,” “semantic search,” or “document Q&A” through a combination of an embedding service, a database, permission controls, retrieval code, and a generative model. Those product features should not automatically be treated as native embedding output from the conversational model itself.
Practical uses
Semantic search
Embeddings can match meaning rather than only exact words. A query such as “ways to reduce cloud spending” may retrieve a document titled “controlling infrastructure costs,” even though the wording differs. Exact identifiers, numbers, and rare names may still require keyword or hybrid search.
Retrieval-augmented generation
An organization can embed internal documents, retrieve relevant passages for a question, and provide those passages to a language model. The vectors help locate potentially relevant material; they do not guarantee that the final answer is correct or that the retrieved source is authoritative.
Cross-modal search
Multimodal embeddings can connect different types of content. A retailer might embed product descriptions and product photos so a text query can find visually associated items. A media archive might search images, video segments, transcripts, or audio using a shared representation when the selected model supports it.
Recommendations and similarity
Products, articles, songs, users, or support tickets can be represented as vectors. An application can then find items with similar representations and use those relationships to suggest related content or detect near-duplicates.
Clustering and classification
Vectors can help organize a large collection into groups or serve as features for a separate classifier. For example, support requests may be grouped by topic before an operations team defines categories and routing rules.
What matters when comparing embedding models
Embedding models should be compared on the retrieval or similarity task they need to support, not simply on the size of the underlying language model. Important criteria include:
- Task quality: performance on representative queries and labeled relevant results.
- Modality support: whether the model handles text, code, images, audio, video, documents, or cross-modal comparisons.
- Language and domain coverage: multilingual performance and suitability for fields such as law, medicine, science, or software.
- Vector dimensions: larger vectors may preserve useful information but increase storage, indexing, and comparison costs.
- Input limits: maximum text or document length, truncation behavior, and whether chunking is required.
- Task instructions: some models distinguish query and document embeddings or expect task-specific instructions and prefixes.
- Similarity compatibility: the metric, normalization method, and index configuration recommended by the provider.
- Latency and throughput: especially important when indexing millions of records or serving interactive search.
- Pricing: costs may depend on tokens, characters, requests, batch processing, or hosting infrastructure.
- Output controls: configurable dimensions, batching, normalization, and precision options.
- Version stability: changing models can make existing vectors incompatible or reduce retrieval quality, often requiring a full re-embedding process.
- Privacy and scope: data isolation, access controls, retention, deletion, and regional processing.
The most useful evaluation uses real queries and measures results with metrics such as recall, precision, mean reciprocal rank, or nDCG. Test synonyms, paraphrases, multilingual input, long documents, exact names, numbers, permission boundaries, and difficult or ambiguous queries.
Limitations and trade-offs
Embeddings represent relationships learned by a model; they do not preserve every fact or detail in a source. Similarity is also task-dependent. Two items may be close because they share a topic or writing style without being factually equivalent.
Long documents commonly need to be split into chunks. Poor chunk boundaries can separate important context, while excessively large chunks can reduce retrieval precision. Metadata filters, hybrid keyword search, and reranking may be needed to handle exact terms, dates, identifiers, or access restrictions that vector similarity alone misses.
Vectors are difficult to interpret directly. If a result seems wrong, the cause may be the embedding model, chunking strategy, query formulation, similarity metric, index, metadata filter, or reranker. A vector database also does not automatically solve these integration problems; it generally stores and searches vectors produced elsewhere, although some platforms bundle the steps together.
Global sharing introduces additional governance risks. A vector may reveal useful information about a document even when the original document is not returned, and weak tenant or permission controls can expose results across projects. Applications should apply authorization checks during retrieval, define deletion behavior, and decide whether shared indexes are appropriate for sensitive data.
When do you need an embedding model?
You probably need embeddings when an application must find, compare, group, or recommend items based on meaning or learned similarity. Typical signals include a document search system that must understand paraphrases, a RAG pipeline over a private knowledge base, a recommendation workflow, or a multimodal archive that needs text-to-image or text-to-video retrieval.
You may not need embeddings for a simple exact-match lookup, a fixed set of categories, or a task where a conventional database query is more precise and easier to audit. In many production systems, the best approach combines embeddings with keyword search, structured filters, reranking, and explicit permission checks.
The practical definition
For this model-output category, the safest definition is: an embedding is a numerical vector used by software to measure relationships between inputs. “Project-global” should generally be treated as a scope qualifier describing where those vectors can be used or shared, unless a particular platform documents a more specific meaning.
The vector is rarely the final user-facing result. Its value comes from the system built around it: the embedding model, indexing strategy, similarity metric, retrieval logic, access controls, and any later reranking or answer-generation step.