Feature extraction - wrapper around schema.apply_udf

langkit/__init__.py

@@ -1,6 +1,6 @@
 from dataclasses import dataclass, field
 from typing import Dict, List
-
+from .extract import extract
 import importlib.resources as resources
 
 
@@ -67,4 +67,4 @@ def package_version(package: str = __package__) -> str:
 
 __version__ = package_version()
 
-__ALL__ = [__version__, LangKitConfig]
+__ALL__ = [__version__, LangKitConfig, extract]

langkit/extract.py

@@ -0,0 +1,20 @@
+import pandas as pd
+from typing import Any, Dict, Optional, Union
+from whylogs.experimental.core.udf_schema import udf_schema, UdfSchema
+
+
+def extract(
+    data: Union[pd.DataFrame, Dict[str, Any]],
+    schema: Optional[UdfSchema] = None,
+):
+    if schema is None:
+        schema = udf_schema()
+    if isinstance(data, pd.DataFrame):
+        df_enhanced, _ = schema.apply_udfs(pandas=data)
+        return df_enhanced
+    elif isinstance(data, dict):
+        _, row_enhanced = schema.apply_udfs(row=data)
+        return row_enhanced
+    raise ValueError(
+        f"Extract: data of type {type(data)} is invalid: supported input types are pandas dataframe or dictionary"
+    )

langkit/response_hallucination.py

@@ -255,7 +255,12 @@ def init(llm: LLMInvocationParams, num_samples=1):
 def response_hallucination(text):
     series_result = []
     for prompt, response in zip(text[_prompt], text[_response]):
-        result: ConsistencyResult = checker.consistency_check(prompt, response)
+        if checker is not None:
+            result: ConsistencyResult = checker.consistency_check(prompt, response)
+        else:
+            raise Exception(
+                "Response Hallucination: you need to call init() before using this function"
+            )
         series_result.append(result.final_score)
     return series_result
 

langkit/tests/test_extract.py

@@ -0,0 +1,48 @@
+import langkit
+import pandas as pd
+from whylogs.experimental.core.udf_schema import UdfSchema, UdfSpec
+
+
+def test_extract_pandas():
+    from langkit import textstat
+
+    textstat.init()
+    df = pd.DataFrame({"prompt": ["I love you", "I hate you"]})
+    enhanced_df = langkit.extract(data=df)
+    assert "prompt.flesch_reading_ease" in enhanced_df.columns
+
+
+def test_extract_row():
+    from langkit import regexes
+
+    regexes.init()
+    row = {"prompt": "I love you", "response": "address: 123 Main St."}
+    enhanced_row = langkit.extract(data=row)
+    assert enhanced_row.get("response.has_patterns") == "mailing address"
+    assert not enhanced_row.get("prompt.has_patterns")
+
+
+def test_extract_light_metrics():
+    from langkit import light_metrics
+
+    light_metrics.init()
+
+    row = {"prompt": "I love you", "response": "address: 123 Main St."}
+    enhanced_row = langkit.extract(row)
+    assert enhanced_row.get("response.has_patterns") == "mailing address"
+    assert not enhanced_row.get("prompt.has_patterns")
+    assert "prompt.flesch_reading_ease" in enhanced_row.keys()
+
+
+def test_extract_with_custom_schema():
+    schema = UdfSchema(
+        udf_specs=[
+            UdfSpec(
+                column_names=["prompt"],
+                udfs={"prompt.customfeature": lambda x: x["prompt"]},
+            )
+        ],
+    )
+    row = {"prompt": "I love you", "response": "address: 123 Main St."}
+    enhanced_row = langkit.extract(row, schema=schema)
+    assert enhanced_row.get("prompt.customfeature") == "I love you"