Large Language Models

The large language model (LLM) catalog uses a service-and-model hierarchy:

LLM Service
└── LLM Model

An LLM Service represents a provider or deployment boundary. Common types include OpenAI, Anthropic, and Amazon Bedrock. Other types cover Vertex AI, Ollama, and custom providers. An LLM Model captures identity, specifications, and evaluation results. It also tracks deployment, cost, and governance status. An LLM service name is globally unique. An LLM model uses the fully qualified name service.model, such as openai_production.gpt_5.

LLM Service Endpoints

Method	Endpoint	Description
`GET`	`/v1/services/llmServices`	List LLM services
`GET`	`/v1/services/llmServices/{id}`	Retrieve a service by ID
`GET`	`/v1/services/llmServices/name/{name}`	Retrieve a service by name
`POST`	`/v1/services/llmServices`	Create a service
`PUT`	`/v1/services/llmServices`	Create or update a service
`PATCH`	`/v1/services/llmServices/{id}`	Patch a service by ID
`PATCH`	`/v1/services/llmServices/name/{fqn}`	Patch a service by name
`PUT`	`/v1/services/llmServices/{id}/testConnectionResult`	Store a connection test result
`PUT`	`/v1/services/llmServices/{id}/followers`	Add the authenticated user as a follower
`DELETE`	`/v1/services/llmServices/{id}/followers/{userId}`	Remove a follower
`GET`	`/v1/services/llmServices/{id}/versions`	List versions
`GET`	`/v1/services/llmServices/{id}/versions/{version}`	Retrieve a version
`DELETE`	`/v1/services/llmServices/{id}`	Soft-delete or hard-delete by ID
`DELETE`	`/v1/services/llmServices/async/{id}`	Delete asynchronously
`DELETE`	`/v1/services/llmServices/name/{name}`	Soft-delete or hard-delete by name
`PUT`	`/v1/services/llmServices/restore`	Restore a soft-deleted service

The required create fields are name and serviceType. Valid service types are OpenAI, Anthropic, AzureOpenAI, Bedrock, VertexAI, Ollama, HuggingFace, and CustomLLM.

LLM Model Endpoints

Method	Endpoint	Description
`GET`	`/v1/llmModels`	List models, optionally filtered by `service`
`GET`	`/v1/llmModels/{id}`	Retrieve a model by ID
`GET`	`/v1/llmModels/name/{fqn}`	Retrieve a model by fully qualified name
`POST`	`/v1/llmModels`	Create a model
`PUT`	`/v1/llmModels`	Create or update a model
`PATCH`	`/v1/llmModels/{id}`	Patch a model by ID
`PATCH`	`/v1/llmModels/name/{fqn}`	Patch a model by fully qualified name
`PUT`	`/v1/llmModels/{id}/followers`	Add the authenticated user as a follower
`DELETE`	`/v1/llmModels/{id}/followers/{userId}`	Remove a follower
`GET`	`/v1/llmModels/{id}/versions`	List versions
`GET`	`/v1/llmModels/{id}/versions/{version}`	Retrieve a version
`DELETE`	`/v1/llmModels/{id}`	Soft-delete or hard-delete by ID
`DELETE`	`/v1/llmModels/async/{id}`	Delete asynchronously
`DELETE`	`/v1/llmModels/name/{fqn}`	Soft-delete or hard-delete by fully qualified name
`PUT`	`/v1/llmModels/restore`	Restore a soft-deleted model

The required create fields are name, baseModel, and the parent service’s fully qualified name in service. Optional metadata covers modelVersion, modelProvider, and modelSpecifications. Evaluation, cost, and deployment fields track operations. Regulatory compliance, ownership, domains, and data products add governance context.

Create a Service and Model

The Java example assumes a configured OpenMetadataClient named client.

Java SDK

import org.openmetadata.schema.api.ai.CreateLLMModel;
import org.openmetadata.schema.api.services.CreateLLMService;

var service = client.llmServices().create(
    new CreateLLMService()
        .withName("openai_production")
        .withDisplayName("OpenAI Production")
        .withServiceType(CreateLLMService.LlmServiceType.fromValue("OpenAI"))
);

var model = client.llmModels().create(
    new CreateLLMModel()
        .withName("gpt_5")
        .withDisplayName("GPT-5")
        .withBaseModel("gpt-5")
        .withModelProvider("OpenAI")
        .withService(service.getFullyQualifiedName())
);

System.out.println(model.getFullyQualifiedName());

curl -X POST "{base_url}/api/v1/services/llmServices" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "openai_production",
    "displayName": "OpenAI Production",
    "serviceType": "OpenAI"
  }'

curl -X POST "{base_url}/api/v1/llmModels" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "gpt_5",
    "displayName": "GPT-5",
    "baseModel": "gpt-5",
    "modelProvider": "OpenAI",
    "service": "openai_production",
    "governanceStatus": "Approved"
  }'

The v2.0 Java SDK exposes both resources through the direct client. The v2.0 Python core SDK has no typed routes for LLM services or models, so use the REST API.

Update and Delete

Send an RFC 6902 JSON Patch document with Content-Type: application/json-patch+json to a PATCH endpoint. Set hardDelete=true on a DELETE request for permanent deletion. Restore a soft-deleted entity by sending its id to the corresponding /restore endpoint.

API Reference

Data Assets

AI Assets

AI SDK

SDK

Discovery

Lineage

Data Contracts

Teams And Users

Data Governance

Data Quality

Main Concepts

Large Language Models

Large Language Models

LLM Service Endpoints

LLM Model Endpoints

Create a Service and Model

Update and Delete

​Large Language Models

​LLM Service Endpoints

​LLM Model Endpoints

​Create a Service and Model

​Update and Delete

Large Language Models

LLM Service Endpoints

LLM Model Endpoints

Create a Service and Model

Update and Delete