Separate

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.0.91
+current_version = 0.0.92
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
 serialize = 

langkit/core/workflow.py

@@ -194,6 +194,11 @@ def run(self, data: Union[pd.DataFrame, Row, Dict[str, str]]) -> EvaluationResul
         metric_times: List[Tuple[str, float]] = []
 
         for metric in self.metrics.metrics:
+            # check that the dataframe has the metric.input_name present, or else skip
+            if metric.input_name not in df.columns:
+                logger.debug(f"Skipping metric {metric} because {metric.input_name} is not present in the input dataframe")
+                continue
+
             metric_start = time.perf_counter()
             if isinstance(metric, SingleMetric):
                 result = metric.evaluate(df)

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langkit"
-version = "0.0.91"
+version = "0.0.92"
 description = "A language toolkit for monitoring LLM interactions"
 authors = ["WhyLabs.ai <[email protected]>"]
 homepage = "https://docs.whylabs.ai/docs/large-language-model-monitoring"

tests/langkit/metrics/test_workflow.py

@@ -0,0 +1,50 @@
+from typing import List
+
+from langkit.core.workflow import EvaluationWorkflow
+from langkit.metrics.library import lib
+
+
+def test_just_prompt():
+    wf = EvaluationWorkflow(metrics=[lib.presets.recommended()])
+    result = wf.run({"prompt": "hi"})
+    metrics = result.metrics
+
+    metric_names: List[str] = metrics.columns.tolist()  # pyright: ignore[reportUnknownMemberType]
+
+    assert metric_names == [
+        "prompt.pii.phone_number",
+        "prompt.pii.email_address",
+        "prompt.pii.credit_card",
+        "prompt.pii.us_ssn",
+        "prompt.pii.us_bank_number",
+        "prompt.pii.redacted",
+        "prompt.stats.token_count",
+        "prompt.stats.char_count",
+        "prompt.similarity.injection",
+        "prompt.similarity.jailbreak",
+        "id",
+    ]
+
+
+def test_just_response():
+    wf = EvaluationWorkflow(metrics=[lib.presets.recommended()])
+    result = wf.run({"response": "I'm doing great!"})
+    metrics = result.metrics
+
+    metric_names: List[str] = metrics.columns.tolist()  # pyright: ignore[reportUnknownMemberType]
+
+    assert metric_names == [
+        "response.pii.phone_number",
+        "response.pii.email_address",
+        "response.pii.credit_card",
+        "response.pii.us_ssn",
+        "response.pii.us_bank_number",
+        "response.pii.redacted",
+        "response.stats.token_count",
+        "response.stats.char_count",
+        "response.stats.flesch_reading_ease",
+        "response.sentiment.sentiment_score",
+        "response.toxicity.toxicity_score",
+        "response.similarity.refusal",
+        "id",
+    ]