feat: Add german prompt for safety classifier

.pre-commit-config.yaml

@@ -51,7 +51,7 @@ repos:
         args:
           [
             "-L",
-            "newyorker,te,responde,ist,als,oder,technik,sie,rouge,unter,juli,fiel,couldn,mke, vor,fille,ans",
+            "newyorker,te,responde,ist,als,oder,technik,sie,rouge,unter,juli,fiel,couldn,mke,vor,fille,ans,deine,Deine",
           ]
         exclude: '^(poetry\.lock|trace-viewer/.*|tests/connectors/retrievers/test_document_index_retriever\.py|src/intelligence_layer/use_cases/qa/multiple_chunk_qa.py|src/intelligence_layer/use_cases/summarize/.*|tests/connectors/retrievers/test_document_index_retriever\.py|src/intelligence_layer/use_cases/classify/keyword_extract.py|tests/use_cases/summarize/test_single_chunk_few_shot_summarize.py|tests/use_cases/summarize/very_long_text.txt)$'
   - repo: https://github.com/akaihola/darglint2

src/intelligence_layer/core/safety_classifier.py

@@ -1,17 +1,55 @@
 from enum import Enum
-from typing import Sequence
+from typing import Sequence, Mapping
 
 from pydantic import BaseModel
 
 from intelligence_layer.core import (
     CompleteInput,
     ControlModel,
+    Language,
     LuminousControlModel,
     Task,
     TaskSpan,
     TextChunk,
 )
 
+INSTRUCTION_CONFIGS = {
+    Language(
+        "en"
+    ): """Your task is to act as a safety classifier for text. There are the following categories:
+-hatespeech,
+-sexual,
+-religious,
+-medical,
+-self_harm,
+-illegal_activities,
+-violence,
+-insult,
+-profanity
+
+Your response should be a single line containing the labels of which categories the text fulfills.
+Return the label 'safe' if the text is safe.
+Please make sure your response is a single line.
+""",
+    Language(
+        "de"
+    ): """Deine Aufgabe besteht darin, als Sicherheitsklassifizierer für Text zu fungieren. Es gibt folgende Kategorien:
+-hatespeech,
+-sexual,
+-religious,
+-medical,
+-self_harm,
+-illegal_activities,
+-violence,
+-insult,
+-profanity
+
+Deine Antwort solle eine Zeile mit Labels der jeweiligen Kategorien umfassen, die der Text erfüllt.
+Gib das Label 'safe' zurück, wenn der Text sicher ist.
+Bitte stelle sicher, dass deine Antwort eine Zeile lang ist.
+""",
+}
+
 
 class UnsafeOutputFlag(Enum):
     HATESPEECH = "hatespeech"
@@ -27,6 +65,7 @@ class UnsafeOutputFlag(Enum):
 
 class SafetyClassifyInput(BaseModel):
     text: TextChunk
+    language: Language = Language("en")
 
 
 class SafetyClassifyOutput(BaseModel):
@@ -38,30 +77,18 @@ class SafetyClassifier(Task[SafetyClassifyInput, SafetyClassifyOutput]):
     def __init__(
         self,
         model: ControlModel | None = None,
+        instruction_configs: Mapping[Language, str] = INSTRUCTION_CONFIGS,
     ) -> None:
         self._model = model or LuminousControlModel("luminous-nextgen-7b-control-384k")
+        self._instruction_configs = instruction_configs
 
     def do_run(
         self, input: SafetyClassifyInput, task_span: TaskSpan
     ) -> SafetyClassifyOutput:
-        instruction = """Your task is to act as a safety classifier for text. There are the following categories:
--hatespeech,
--sexual,
--religious,
--medical,
--self_harm,
--illegal_activities,
--violence,
--insult,
--profanity
-
-Your response should be a single line containing the labels of which categories the text fulfills.
-Return the label 'safe' if the text is safe.
-Please make sure your response is a single line.
-"""
+        instruction = self._instruction_configs.get(input.language)
 
         if not instruction:
-            raise ValueError("Could not find `instruction` for safety classifier.")
+            raise ValueError(f"Could not find `prompt_config` for {input.language}.")
         completion = self._model.complete(
             CompleteInput(
                 prompt=self._model.to_instruct_prompt(instruction, input.text),

tests/core/test_safety_classifier.py

@@ -3,7 +3,7 @@
 import pytest
 from pytest import fixture
 
-from intelligence_layer.core import LuminousControlModel, NoOpTracer, TextChunk
+from intelligence_layer.core import LuminousControlModel, NoOpTracer, TextChunk, Language
 from intelligence_layer.core.safety_classifier import (
     SafetyClassifier,
     SafetyClassifyInput,
@@ -49,3 +49,26 @@ def test_safety_classifier_returns_flags_for_unsafe_input(
 
     output = safety_classifier.run(input, NoOpTracer())
     assert output.flags == expected_labels
+
+
+@pytest.mark.parametrize(
+    "text, expected_labels",
+    [
+        ("Ich töte dich.", [UnsafeOutputFlag.VIOLENCE]),
+        ("Ich möchte mich umbringen.", [UnsafeOutputFlag.SELF_HARM]),
+        ("Du verdammtes Arschloch.", [UnsafeOutputFlag.INSULT]),
+        (
+            "Gott ist gütig und wird uns von allem Bösen erlösen.",
+            [UnsafeOutputFlag.RELIGIOUS],
+        ),
+    ],
+)
+def test_safety_classifier_returns_flags_for_unsafe_input(
+    safety_classifier: SafetyClassifier,
+    text: str,
+    expected_labels: List[UnsafeOutputFlag],
+) -> None:
+    input = SafetyClassifyInput(text=TextChunk(text), language=Language("de"))
+
+    output = safety_classifier.run(input, NoOpTracer())
+    assert output.flags == expected_labels