feat(databricks): support per-model client_id/client_secret in litellm_params

Allow Databricks OAuth M2M credentials (client_id, client_secret) to be
specified per deployment in litellm_params, enabling load balancing across
multiple Databricks workspace instances with different service principals.
Previously these were only read from global env vars (DATABRICKS_CLIENT_ID,
DATABRICKS_CLIENT_SECRET), making multi-instance configs impossible.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

litellm/llms/databricks/chat/transformation.py

@@ -176,6 +176,8 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
             custom_endpoint=False,
             headers=headers,
             custom_user_agent=custom_user_agent,
+            client_id=litellm_params.get("client_id"),
+            client_secret=litellm_params.get("client_secret"),
         )
         # Ensure Content-Type header is set
         headers["Content-Type"] = "application/json"

litellm/llms/databricks/common_utils.py

@@ -314,6 +314,8 @@ class DatabricksBase:
         custom_endpoint: Optional[bool],
         headers: Optional[dict],
         custom_user_agent: Optional[str] = None,
+        client_id: Optional[str] = None,
+        client_secret: Optional[str] = None,
     ) -> Tuple[str, dict]:
         """
         Validate and configure the Databricks environment.
@@ -330,6 +332,8 @@ class DatabricksBase:
             custom_endpoint: Whether using a custom endpoint URL
             headers: Existing headers dict
             custom_user_agent: Optional custom user agent to prefix
+            client_id: OAuth client ID from litellm_params (falls back to env var)
+            client_secret: OAuth client secret from litellm_params (falls back to env var)
 
         Returns:
             Tuple of (api_base, headers) with authentication configured
@@ -337,8 +341,9 @@ class DatabricksBase:
         from litellm._logging import verbose_logger
 
         # Check for OAuth M2M credentials (recommended for production)
-        client_id = os.getenv("DATABRICKS_CLIENT_ID")
-        client_secret = os.getenv("DATABRICKS_CLIENT_SECRET")
+        # Per-model litellm_params take priority over global env vars
+        client_id = client_id or os.getenv("DATABRICKS_CLIENT_ID")
+        client_secret = client_secret or os.getenv("DATABRICKS_CLIENT_SECRET")
 
         # Determine api_base first
         if api_base is None:

litellm/llms/databricks/responses/transformation.py

@@ -58,6 +58,8 @@ class DatabricksResponsesAPIConfig(DatabricksBase, OpenAIResponsesAPIConfig):
             endpoint_type="chat_completions",
             custom_endpoint=False,
             headers=headers,
+            client_id=getattr(litellm_params, "client_id", None),
+            client_secret=getattr(litellm_params, "client_secret", None),
         )
 
         headers["Content-Type"] = "application/json"