remove prints, cleanup braintrust in this branch

llama_stack/providers/tests/eval/test_eval.py

@@ -77,7 +77,6 @@ async def test_eval(eval_settings):
 
     eval_response = await eval_impl.job_result(response.job_id)
 
-    print(eval_response)
     assert eval_response is not None
     assert len(eval_response.generations) == 5
     assert "meta-reference::subset_of" in eval_response.scores

llama_stack/providers/tests/scoring/test_scoring.py

@@ -55,6 +55,47 @@ async def test_scoring_functions_list(scoring_settings):
     assert "meta-reference::llm_as_judge_8b_correctness" in function_ids
 
 
+@pytest.mark.asyncio
+async def test_scoring_functions_register(scoring_settings):
+    scoring_impl = scoring_settings["scoring_impl"]
+    scoring_functions_impl = scoring_settings["scoring_functions_impl"]
+    datasets_impl = scoring_settings["datasets_impl"]
+    test_prompt = """Output a number between 0 to 10. Your answer must match the format \n Number: <answer>"""
+    # register the scoring function
+    await scoring_functions_impl.register_scoring_function(
+        ScoringFnDefWithProvider(
+            identifier="meta-reference::llm_as_judge_8b_random",
+            description="Llm As Judge Scoring Function",
+            parameters=[],
+            return_type=NumberType(),
+            context=LLMAsJudgeContext(
+                prompt_template=test_prompt,
+                judge_model="Llama3.1-8B-Instruct",
+                judge_score_regex=[r"Number: (\d+)"],
+            ),
+            provider_id="test-meta",
+        )
+    )
+
+    scoring_functions = await scoring_functions_impl.list_scoring_functions()
+    assert isinstance(scoring_functions, list)
+    assert len(scoring_functions) > 0
+    function_ids = [f.identifier for f in scoring_functions]
+    assert "meta-reference::llm_as_judge_8b_random" in function_ids
+
+    # test score using newly registered scoring function
+    await register_dataset(datasets_impl)
+    response = await datasets_impl.list_datasets()
+    assert len(response) == 1
+    response = await scoring_impl.score_batch(
+        dataset_id=response[0].identifier,
+        scoring_functions=[
+            "meta-reference::llm_as_judge_8b_random",
+        ],
+    )
+    assert "meta-reference::llm_as_judge_8b_random" in response.results
+
+
 @pytest.mark.asyncio
 async def test_scoring_score(scoring_settings):
     scoring_impl = scoring_settings["scoring_impl"]
@@ -73,7 +114,6 @@ async def test_scoring_score(scoring_settings):
         ],
     )
 
-    print(response)
     assert len(response.results) == 3
     assert "meta-reference::equality" in response.results
     assert "meta-reference::subset_of" in response.results