feat: added topic scoping to the SDK

CLAUDE.md

@@ -42,15 +42,17 @@ make check-safety
 
 The SDK has a minimal structure centered on the `Client` class:
 
-- **`qualifire/client.py`**: Main `Client` class with two public methods:
+- **`qualifire/client.py`**: Main `Client` class with three public methods:
   - `evaluate()` - Run ad-hoc evaluations with various checks (hallucinations, grounding, PII, content moderation, etc.)
   - `invoke_evaluation()` - Run pre-configured evaluations from the Qualifire dashboard
+  - `compile_prompt()` - Compile a prompt with topic scoping and policy targets
 
 - **`qualifire/types.py`**: All data classes and enums:
   - `EvaluationRequest`/`EvaluationResponse` - API request/response structures
   - `LLMMessage`, `LLMToolCall`, `LLMToolDefinition` - Message and tool types for conversation-based evaluation
   - `ModelMode` (SPEED/BALANCED/QUALITY) - Quality vs speed trade-off for checks
   - `SyntaxCheckArgs` - Configuration for syntax validation
+  - `PolicyTarget`, `CompilePromptResponse` - Types for prompt compilation with topic scoping
 
 - **`qualifire/utils.py`**: Helper functions for API key and base URL resolution from environment variables
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "qualifire"
-version = "0.13.0"
+version = "0.13.1"
 description = "Qualifire Python SDK"
 authors = [{ name = "qualifire-dev", email = "dror@qualifire.ai" }]
 requires-python = ">=3.8,<4"

qualifire/client.py

@@ -63,6 +63,10 @@ def evaluate(
         grounding_multi_turn_mode: bool = False,
         policy_multi_turn_mode: bool = False,
         policy_target: PolicyTarget = PolicyTarget.BOTH,
+        topic_scoping_mode: Optional[ModelMode] = None,
+        topic_scoping_multi_turn_mode: bool = False,
+        topic_scoping_target: PolicyTarget = PolicyTarget.BOTH,
+        allowed_topics: Optional[List[str]] = None,
     ) -> Union[EvaluationResponse, None]:
         """
         Evaluates the given input and output pairs.
@@ -105,6 +109,10 @@ def evaluate(
         :param grounding_multi_turn_mode: Enable multi-turn mode for grounding check.
         :param policy_multi_turn_mode: Enable multi-turn mode for policy check.
         :param policy_target: Target for policy checks (input/output/both).
+        :param topic_scoping_mode: Model mode for topic scoping check (speed/balanced/quality).
+        :param topic_scoping_multi_turn_mode: Enable multi-turn mode for topic scoping check.
+        :param topic_scoping_target: Target topic for topic scoping check.
+        :param allowed_topics: List of allowed topics for topic scoping check.
 
         :return: An EvaluationResponse object containing the evaluation results.
         :raises Exception: If an error occurs during the evaluation.
@@ -224,6 +232,10 @@ def evaluate(
             grounding_multi_turn_mode=grounding_multi_turn_mode,
             policy_multi_turn_mode=policy_multi_turn_mode,
             policy_target=policy_target,
+            topic_scoping_mode=topic_scoping_mode,
+            topic_scoping_multi_turn_mode=topic_scoping_multi_turn_mode,
+            topic_scoping_target=topic_scoping_target,
+            allowed_topics=allowed_topics,
         )
 
         response = requests.post(

qualifire/types.py

@@ -66,6 +66,10 @@ class EvaluationRequest(BaseModel):
     grounding_multi_turn_mode: bool = False
     policy_multi_turn_mode: bool = False
     policy_target: PolicyTarget = PolicyTarget.BOTH
+    topic_scoping_mode: Optional[ModelMode] = None
+    topic_scoping_multi_turn_mode: bool = False
+    topic_scoping_target: Optional[str] = None
+    allowed_topics: Optional[List[str]] = None
 
     @model_validator(mode="after")
     def validate_model(self) -> "EvaluationRequest":