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The model catalog lists the models available on SeekrFlow, along with each model’s identifier, type, capabilities, and country of origin. You select a model from the catalog when you configure an agent, start a fine-tuning job, or create a vector database.

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Model types

The catalog contains two classes of models, distinguished by what they produce.

Generative models

Produce text as output. These models reason, follow instructions, and generate responses. They serve as an agent’s cognitive engine and are the models you fine-tune and deploy for inference.

Embedding models

Produce a fixed-length vector of numbers as output, and generate no language. Embedding models turn documents and queries into comparable vectors, which is what makes semantic search in a vector database possible.
Both classes include models that accept images alongside text. A generative vision model describes or reasons about an image, while a multimodal embedding model represents it as a vector. The two classes are not interchangeable. A generative model cannot build a vector database, and an embedding model cannot answer a prompt.

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Model attributes

Every model in the catalog reports the following attributes.

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Country of origin

Country of origin identifies where a model originated. Deployments with model provenance requirements, including government and other highly regulated environments, use it to determine whether a model is permitted for a given workload. The catalog spans several countries of origin. Review this attribute before selecting a model if your deployment restricts model provenance. Some models have no single country of origin and are listed as Mixed. Mixed origins apply when a model’s corporate parent and its development lab are based in different countries, or when a model was fine-tuned in one country from a base model developed in another.
Confirm provenance directly with Seekr before using a model with mixed origins in a deployment that restricts model provenance.

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Retrieve the catalog

Retrieve the full catalog, including every attribute, from the models endpoint. Filter by model_type to return a single class of models. See List models.

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Select a model

Choosing a generative model and choosing an embedding model involve different trade-offs, and the two decisions carry different consequences. For generative models, weigh task complexity, response speed, domain specificity, and cost. You can change an agent’s model at any time by updating its configuration. See Generative models. For embedding models, weigh vector dimension, maximum input length, and language coverage. This choice is durable. The embedding model is fixed when you create a vector database and cannot be changed afterward without re-embedding your documents. See Embedding models. If your deployment restricts model provenance, filter by country of origin before comparing anything else.
Last modified on August 21, 2026