Affected module
Search / Discovery
Describe the bug
The OpenMetadata configuration schema prevents the use of Azure OpenAI (or custom enterprise gateways) when both LLM Chat and Semantic Search Embeddings are enabled. The top-level llmConfiguration.openai block requires a single deploymentName, but Azure OpenAI exposes chat models (e.g., gpt-4o-mini) and embedding models (e.g., text-embedding-3-small) on completely separate, distinct deployment endpoints. Because the llmConfiguration.embeddings.openai sub-block lacks its own unique deploymentName variable, the system is forced to reuse the chat deployment name for embedding requests, causing all embedding generations to fail with an API routing error.
To Reproduce
- In Azure OpenAI Studio, deploy a chat model named
om-chat-prod and an embedding model named om-embedding-prod.
- Configure the
openmetadata.yaml file (or equivalent environment variables) with the following parameters:
LLM_PROVIDER: openai
LLM_OPENAI_DEPLOYMENT: om-chat-prod (Points to your chat deployment)
EMBEDDING_PROVIDER: openai
OPENAI_EMBEDDING_MODEL_ID: text-embedding-3-small
- Start the OpenMetadata server and trigger an operation that uses LLM chat or embedding. The connector won't be able to determine which deployment from Azure OpenAI to use
Expected behavior
The openmetadata.yaml file should provide an independent configuration mapping for the embedding deployment (e.g., llmConfiguration.embeddings.openai.deploymentName).When an embedding action is triggered, the OpenMetadata backend should route the API payload to the specific Azure OpenAI deployment endpoint dedicated to embeddings. This would allow the system to simultaneously generate conversational text completions via om-chat-prod and vector calculations via an embedding deployment without throwing errors.
OS
No response
Python version
No response
OpenMetadata version
2.0.0
OpenMetadata Ingestion package version
No response
Additional context
No response
Pre-submission checklist
Affected module
Search / Discovery
Describe the bug
The OpenMetadata configuration schema prevents the use of Azure OpenAI (or custom enterprise gateways) when both LLM Chat and Semantic Search Embeddings are enabled. The top-level
llmConfiguration.openaiblock requires a singledeploymentName, but Azure OpenAI exposes chat models (e.g.,gpt-4o-mini) and embedding models (e.g.,text-embedding-3-small) on completely separate, distinct deployment endpoints. Because thellmConfiguration.embeddings.openaisub-block lacks its own uniquedeploymentNamevariable, the system is forced to reuse the chat deployment name for embedding requests, causing all embedding generations to fail with an API routing error.To Reproduce
om-chat-prodand an embedding model namedom-embedding-prod.openmetadata.yamlfile (or equivalent environment variables) with the following parameters:LLM_PROVIDER: openai
LLM_OPENAI_DEPLOYMENT: om-chat-prod (Points to your chat deployment)
EMBEDDING_PROVIDER: openai
OPENAI_EMBEDDING_MODEL_ID: text-embedding-3-small
Expected behavior
The
openmetadata.yamlfile should provide an independent configuration mapping for the embedding deployment (e.g.,llmConfiguration.embeddings.openai.deploymentName).When an embedding action is triggered, the OpenMetadata backend should route the API payload to the specific Azure OpenAI deployment endpoint dedicated to embeddings. This would allow the system to simultaneously generate conversational text completions via om-chat-prod and vector calculations via an embedding deployment without throwing errors.OS
No response
Python version
No response
OpenMetadata version
2.0.0
OpenMetadata Ingestion package version
No response
Additional context
No response
Pre-submission checklist