fix(greenpt): enable embeddings and rerank cost tracking

litellm/llms/greenpt/rerank/transformation.py

@@ -1,5 +1,7 @@
 from litellm.llms.hosted_vllm.rerank.transformation import HostedVLLMRerankConfig
 from litellm.secret_managers.main import get_secret_str
+from litellm.types.rerank import RerankBilledUnits
+from litellm.types.utils import ModelInfo
 
 
 class GreenPTRerankConfig(HostedVLLMRerankConfig):
@@ -38,3 +40,20 @@ class GreenPTRerankConfig(HostedVLLMRerankConfig):
         )
         request.setdefault("top_n", len(request["documents"]))
         return request
+
+    def calculate_rerank_cost(
+        self,
+        model: str,
+        custom_llm_provider: str | None = None,
+        billed_units: RerankBilledUnits | None = None,
+        model_info: ModelInfo | None = None,
+    ) -> tuple[float, float]:
+        if model_info is None or billed_units is None:
+            return 0.0, 0.0
+        input_cost_per_token = model_info.get("input_cost_per_token")
+        if input_cost_per_token is None:
+            return 0.0, 0.0
+        total_tokens = billed_units.get("total_tokens")
+        if total_tokens is None:
+            return 0.0, 0.0
+        return input_cost_per_token * total_tokens, 0.0

litellm/llms/openai_like/json_loader.py

@@ -73,6 +73,13 @@ class JSONProviderRegistry:
             return False
         return "/v1/responses" in provider.supported_endpoints
 
+    @classmethod
+    def supports_embeddings_api(cls, slug: str) -> bool:
+        provider = cls._providers.get(slug)
+        if provider is None:
+            return False
+        return "/v1/embeddings" in provider.supported_endpoints
+
     @classmethod
     def list_providers(cls) -> list:
         """List all registered provider slugs"""

litellm/main.py

@@ -6213,6 +6213,7 @@ def embedding(
             custom_llm_provider == "openai_like"
             or custom_llm_provider == "llamafile"
             or custom_llm_provider == "lm_studio"
+            or JSONProviderRegistry.supports_embeddings_api(custom_llm_provider)
         ):
             api_base = api_base or litellm.api_base or get_secret_str("OPENAI_LIKE_API_BASE")
 

tests/test_litellm/llms/greenpt/rerank/test_transformation.py

@@ -64,3 +64,29 @@ def test_transform_rerank_request_preserves_top_n():
         headers={},
     )
     assert request["top_n"] == 1
+
+
+def test_calculate_rerank_cost():
+    prompt_cost, completion_cost = GreenPTRerankConfig().calculate_rerank_cost(
+        model="green-rerank",
+        billed_units={"total_tokens": 1000},
+        model_info={"input_cost_per_token": 1.36524e-07},
+    )
+    assert prompt_cost == pytest.approx(0.000136524)
+    assert completion_cost == 0.0
+
+
+@pytest.mark.parametrize(
+    ("billed_units", "model_info"),
+    [
+        (None, None),
+        ({}, {"input_cost_per_token": 1.36524e-07}),
+        ({"total_tokens": 1000}, {}),
+    ],
+)
+def test_calculate_rerank_cost_missing_usage_or_pricing(billed_units, model_info):
+    assert GreenPTRerankConfig().calculate_rerank_cost(
+        model="green-rerank",
+        billed_units=billed_units,
+        model_info=model_info,
+    ) == (0.0, 0.0)

tests/test_litellm/llms/openai_like/test_greenpt_provider.py

@@ -1,8 +1,12 @@
+import json
 import os
 import sys
+from unittest.mock import Mock, patch
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../..")))
 
+import litellm
+from litellm.llms.custom_httpx.http_handler import HTTPHandler
 from litellm.llms.openai_like.dynamic_config import create_config_class
 from litellm.llms.openai_like.json_loader import JSONProviderRegistry
 from litellm.types.utils import LlmProviders
@@ -39,7 +43,7 @@ def test_greenpt_maps_max_completion_tokens():
     params = config.map_openai_params(
         non_default_params={"max_completion_tokens": 256},
         optional_params={},
-        model="greenpt/gemma4",
+        model="greenpt/glm-5.2",
         drop_params=False,
     )
     assert params == {"max_tokens": 256}
@@ -50,7 +54,7 @@ def test_greenpt_complete_url_appends_endpoint():
     url = config.get_complete_url(
         api_base=GREENPT_BASE_URL,
         api_key="test-key",
-        model="greenpt/gemma4",
+        model="greenpt/glm-5.2",
         optional_params={},
         litellm_params={},
         stream=False,
@@ -62,17 +66,44 @@ def test_greenpt_provider_resolution():
     from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
 
     model, provider, _, api_base = get_llm_provider(
-        model="greenpt/gemma4",
+        model="greenpt/glm-5.2",
         custom_llm_provider=None,
         api_base=None,
         api_key=None,
     )
 
-    assert model == "gemma4"
+    assert model == "glm-5.2"
     assert provider == "greenpt"
     assert api_base == GREENPT_BASE_URL
 
 
+def test_greenpt_embedding_dispatches():
+    response = Mock()
+    response.raise_for_status = Mock()
+    response.json.return_value = {
+        "object": "list",
+        "data": [{"object": "embedding", "embedding": [0.1, 0.2], "index": 0}],
+        "model": "green-embedding",
+        "usage": {"prompt_tokens": 2, "total_tokens": 2},
+    }
+    client = HTTPHandler()
+
+    with patch.object(HTTPHandler, "post", return_value=response) as mock_post:
+        result = litellm.embedding(
+            model="greenpt/green-embedding",
+            input=["renewable inference"],
+            api_key="test-key",
+            client=client,
+        )
+
+    assert result.data[0]["embedding"] == [0.1, 0.2]
+    assert mock_post.call_args.args[0] == f"{GREENPT_BASE_URL}/embeddings"
+    assert json.loads(mock_post.call_args.kwargs["data"]) == {
+        "model": "green-embedding",
+        "input": ["renewable inference"],
+    }
+
+
 def test_greenpt_rerank_config():
     config = ProviderConfigManager.get_provider_rerank_config(
         model="green-rerank",