Mock model providers for client tests

llm-service/app/config.py

@@ -49,6 +49,7 @@
 from enum import Enum
 from typing import cast, Optional, Literal
 
+MODEL_PROVIDER_ENV_VAR_NAME = "MODEL_PROVIDER"
 
 SummaryStorageProviderType = Literal["Local", "S3"]
 ChatStoreProviderType = Literal["Local", "S3"]
@@ -196,7 +197,7 @@ def model_provider(self) -> Optional[ModelSource]:
         """The preferred model provider to use.
         Options: 'AZURE', 'CAII', 'OPENAI', 'BEDROCK'
         If not set, will use the first available provider in priority order."""
-        provider = os.environ.get("MODEL_PROVIDER")
+        provider = os.environ.get(MODEL_PROVIDER_ENV_VAR_NAME)
         try:
             return ModelSource(provider)
         except ValueError:

llm-service/app/main.py

@@ -89,6 +89,15 @@ def _configure_logger() -> None:
 
 _configure_logger()
 
+
+def _enable_http_debug() -> None:
+    urllib3_logger = logging.getLogger("urllib3")
+    urllib3_logger.setLevel(logging.DEBUG)
+    urllib3_logger.propagate = True
+
+
+_enable_http_debug()
+
 if os.environ.get("ENABLE_OPIK") == "True":
     opik.configure(
         use_local=True, url=os.environ.get("OPIK_URL", "http://localhost:5174")

llm-service/app/services/models/providers/_model_provider.py

@@ -36,6 +36,7 @@
 #  DATA.
 #
 import abc
+import itertools
 import os
 
 from llama_index.core.base.embeddings.base import BaseEmbedding
@@ -109,3 +110,12 @@ def get_embedding_model(name: str) -> BaseEmbedding:
     def get_reranking_model(name: str, top_n: int) -> BaseNodePostprocessor:
         """Return reranking model with `name`."""
         raise NotImplementedError
+
+
+def get_all_env_var_names() -> set[str]:
+    """Return the names of all the env vars required by all model providers."""
+    return set(
+        itertools.chain.from_iterable(
+            subcls.get_env_var_names() for subcls in _ModelProvider.__subclasses__()
+        )
+    )

llm-service/app/services/models/reranking.py

@@ -67,22 +67,21 @@ def get_noop() -> BaseNodePostprocessor:
     def list_available() -> list[ModelResponse]:
         return get_provider_class().list_reranking_models()
 
+    _TEST_NODES = [
+        NodeWithScore(node=TextNode(text="test node"), score=0.5),
+        NodeWithScore(node=TextNode(text="another test node"), score=0.4),
+    ]
+
     @classmethod
     def test(cls, model_name: str) -> str:
         models = cls.list_available()
         for model in models:
             if model.model_id == model_name:
-                node = NodeWithScore(node=TextNode(text="test"), score=0.5)
-                another_test_node = NodeWithScore(
-                    node=TextNode(text="another test node"), score=0.4
-                )
                 reranking_model: BaseNodePostprocessor | None = cls.get(
                     model_name=model_name
                 )
                 if reranking_model:
-                    reranking_model.postprocess_nodes(
-                        [node, another_test_node], None, "test"
-                    )
+                    reranking_model.postprocess_nodes(cls._TEST_NODES, None, "test")
                     return "ok"
         raise HTTPException(status_code=404, detail="Model not found")
 

llm-service/app/tests/conftest.py

@@ -35,7 +35,6 @@
 #  BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
 #  DATA.
 # ##############################################################################
-
 import os
 import pathlib
 import uuid
@@ -53,9 +52,7 @@
 from app.ai.vector_stores.qdrant import QdrantVectorStore
 from app.main import app
 from app.services.metadata_apis import data_sources_metadata_api
-from app.services import models
 from app.services.metadata_apis.data_sources_metadata_api import RagDataSource
-from app.services.models.providers import BedrockModelProvider
 
 
 @dataclass
@@ -177,16 +174,16 @@ def get_datasource_metadata(data_source_id: int) -> RagDataSource:
     )
 
 
-@pytest.fixture(autouse=True)
-def embedding_model(monkeypatch: pytest.MonkeyPatch) -> None:
-    model = DummyEmbeddingModel()
-    monkeypatch.setattr(models.Embedding, "get", lambda cls, model_name=None: model)
-
-
-@pytest.fixture(autouse=True)
-def llm(monkeypatch: pytest.MonkeyPatch) -> None:
-    model = models.LLM.get_noop()
-    monkeypatch.setattr(models.LLM, "get", lambda cls, model_name=None: model)
+# @pytest.fixture(autouse=True)
+# def embedding_model(monkeypatch: pytest.MonkeyPatch) -> None:
+#     model = DummyEmbeddingModel()
+#     monkeypatch.setattr(models.Embedding, "get", lambda cls, model_name=None: model)
+#
+#
+# @pytest.fixture(autouse=True)
+# def llm(monkeypatch: pytest.MonkeyPatch) -> None:
+#     model = models.LLM.get_noop()
+#     monkeypatch.setattr(models.LLM, "get", lambda cls, model_name=None: model)
 
 
 @pytest.fixture
@@ -219,10 +216,10 @@ def client() -> Iterator[TestClient]:
         yield test_client
 
 
-@pytest.fixture(autouse=True)
-def _get_model_arn_by_suffix(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(
-        BedrockModelProvider,
-        "_get_model_arns",
-        lambda: [],
-    )
+# @pytest.fixture(autouse=True)
+# def _get_model_arn_by_suffix(monkeypatch: pytest.MonkeyPatch) -> None:
+#     monkeypatch.setattr(
+#         BedrockModelProvider,
+#         "_get_model_arns",
+#         lambda: [],
+#     )

llm-service/app/tests/model_provider_mocks/__init__.py

@@ -0,0 +1,38 @@
+#
+#  CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
+#  (C) Cloudera, Inc. 2025
+#  All rights reserved.
+#
+#  Applicable Open Source License: Apache 2.0
+#
+#  NOTE: Cloudera open source products are modular software products
+#  made up of hundreds of individual components, each of which was
+#  individually copyrighted.  Each Cloudera open source product is a
+#  collective work under U.S. Copyright Law. Your license to use the
+#  collective work is as provided in your written agreement with
+#  Cloudera.  Used apart from the collective work, this file is
+#  licensed for your use pursuant to the open source license
+#  identified above.
+#
+#  This code is provided to you pursuant a written agreement with
+#  (i) Cloudera, Inc. or (ii) a third-party authorized to distribute
+#  this code. If you do not have a written agreement with Cloudera nor
+#  with an authorized and properly licensed third party, you do not
+#  have any rights to access nor to use this code.
+#
+#  Absent a written agreement with Cloudera, Inc. ("Cloudera") to the
+#  contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY
+#  KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED
+#  WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO
+#  IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND
+#  FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,
+#  AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS
+#  ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE
+#  OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR
+#  CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES
+#  RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF
+#  BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
+#  DATA.
+#
+