Feat/unify env file resolution

app/ai-service/api/routes.py

@@ -76,6 +76,7 @@ async def process_ocr(
         metrics.logger.info(f"OCR Inference completed in {inference_latency:.4f}s")
 
         processing_time_ms = int((time.time() - start_time) * 1000)
+        model_version = settings.groq_model if settings.get_active_provider() == "groq" else settings.openai_model
 
         return OCRResponse(
             success=True,
@@ -88,17 +89,21 @@ async def process_ocr(
                 processing_time_ms=processing_time_ms,
             ),
             processing_time_ms=processing_time_ms,
+            model_version=model_version,
         )
 
     except HTTPException:
         raise
     except Exception as e:
         processing_time_ms = int((time.time() - start_time) * 1000)
+        model_version = settings.groq_model if settings.get_active_provider() == "groq" else settings.openai_model
         return OCRResponse(
             success=False,
             error={
                 "code": "processing_error",
                 "message": str(e),
             },
             processing_time_ms=processing_time_ms,
+            model_version=model_version,
         )
+

app/ai-service/api/v1/anonymize.py

@@ -22,7 +22,11 @@ async def anonymize_text(request: AnonymizeRequest):
 
     try:
         result = _main.pii_scrubber_service.anonymize(request.text)
-        return AnonymizeResponse(success=True, **result)
+        from config import settings
+        active_p = settings.get_active_provider()
+        model_version = settings.groq_model if active_p == "groq" else settings.openai_model
+        return AnonymizeResponse(success=True, model_version=model_version, **result)
     except Exception as e:
         logger.error(f"Anonymization failed: {str(e)}", exc_info=True)
         raise HTTPException(status_code=500, detail="Failed to anonymize text")
+

app/ai-service/api/v1/humanitarian.py

@@ -44,7 +44,22 @@ async def verify_humanitarian_claim(request: HumanitarianVerificationRequest):
                 )
             else:
                 raise exc
-        return HumanitarianVerificationResponse(success=True, **result)
+        from config import settings
+        provider = result.get("provider")
+        if provider == "openai":
+            model_version = settings.openai_model
+        elif provider == "groq":
+            model_version = settings.groq_model
+        else:
+            # Default fallback to active provider model or settings.openai_model
+            active_p = settings.get_active_provider()
+            model_version = settings.groq_model if active_p == "groq" else settings.openai_model
+
+        return HumanitarianVerificationResponse(success=True, model_version=model_version, **result)
     except Exception as e:
         logger.error("Humanitarian verification failed: %s", str(e), exc_info=True)
-        return HumanitarianVerificationResponse(success=False, error=str(e))
+        from config import settings
+        active_p = settings.get_active_provider()
+        model_version = settings.groq_model if active_p == "groq" else settings.openai_model
+        return HumanitarianVerificationResponse(success=False, error=str(e), model_version=model_version)
+

app/ai-service/api/v1/ocr.py

@@ -87,6 +87,7 @@ async def process_ocr(
         metrics.logger.info(f"OCR Inference completed in {inference_latency:.4f}s")
 
         processing_time_ms = int((time.time() - start_time) * 1000)
+        model_version = settings.groq_model if settings.get_active_provider() == "groq" else settings.openai_model
 
         return OCRResponse(
             success=True,
@@ -99,17 +100,21 @@ async def process_ocr(
                 processing_time_ms=processing_time_ms,
             ),
             processing_time_ms=processing_time_ms,
+            model_version=model_version,
         )
 
     except HTTPException:
         raise
     except Exception as e:
         processing_time_ms = int((time.time() - start_time) * 1000)
+        model_version = settings.groq_model if settings.get_active_provider() == "groq" else settings.openai_model
         return OCRResponse(
             success=False,
             error={
                 "code": "processing_error",
                 "message": str(e),
             },
             processing_time_ms=processing_time_ms,
+            model_version=model_version,
         )
+

app/ai-service/schemas/anonymization.py

@@ -1,4 +1,4 @@
-from typing import Dict
+from typing import Dict, Optional
 
 from pydantic import BaseModel, Field
 
@@ -20,3 +20,4 @@ class AnonymizeResponse(BaseModel):
     original_length: int
     pii_summary: PIISummary
     token_counts: Dict[str, int] = Field(default_factory=dict)
+    model_version: Optional[str] = None

app/ai-service/schemas/humanitarian.py

@@ -1,19 +1,20 @@
-from typing import Any, Dict, List, Literal, Optional
-from pydantic import BaseModel, Field
-
-
-class HumanitarianVerificationRequest(BaseModel):
-    aid_claim: str = Field(min_length=10, description="Aid claim to verify")
-    supporting_evidence: List[str] = Field(default_factory=list)
-    context_factors: Dict[str, Any] = Field(default_factory=dict)
-    provider_preference: Literal["auto", "test", "openai", "groq"] = "auto"
-    timeout: Optional[float] = Field(default=None, description="Request-level timeout in seconds for provider call")
-
-
-class HumanitarianVerificationResponse(BaseModel):
-    success: bool
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    prompt_variant: Optional[str] = None
-    verification: Optional[Dict[str, Any]] = None
-    error: Optional[str] = None
+from typing import Any, Dict, List, Literal, Optional
+from pydantic import BaseModel, Field
+
+
+class HumanitarianVerificationRequest(BaseModel):
+    aid_claim: str = Field(min_length=10, description="Aid claim to verify")
+    supporting_evidence: List[str] = Field(default_factory=list)
+    context_factors: Dict[str, Any] = Field(default_factory=dict)
+    provider_preference: Literal["auto", "test", "openai", "groq"] = "auto"
+    timeout: Optional[float] = Field(default=None, description="Request-level timeout in seconds for provider call")
+
+
+class HumanitarianVerificationResponse(BaseModel):
+    success: bool
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    prompt_variant: Optional[str] = None
+    verification: Optional[Dict[str, Any]] = None
+    error: Optional[str] = None
+    model_version: Optional[str] = None

app/ai-service/schemas/humanitarian_verification_v2.py

@@ -0,0 +1,24 @@
+from typing import Any, Dict, List, Literal, Optional
+from pydantic import BaseModel, Field
+
+
+class HumanitarianVerificationDetailsV2(BaseModel):
+    verdict: Literal["credible", "partially_credible", "inconclusive", "not_credible"] = Field(description="The verification verdict")
+    confidence: float = Field(ge=0.0, le=1.0, description="Confidence score between 0.0 and 1.0")
+    summary: str = Field(description="Neutral summary of the claim verification")
+    criteria_assessment: Optional[List[Dict[str, Any]]] = Field(default=None, description="Detailed assessment mapping to Sphere criteria")
+    risk_flags: Optional[List[str]] = Field(default=None, description="Identified risk flags")
+    missing_information: Optional[List[str]] = Field(default=None, description="List of missing information needed")
+    recommended_next_steps: Optional[List[str]] = Field(default=None, description="Recommended next steps")
+
+
+class HumanitarianVerificationResponseV2(BaseModel):
+    success: bool
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    prompt_variant: Optional[str] = None
+    verification: Optional[HumanitarianVerificationDetailsV2] = None
+    error: Optional[str] = None
+    model_version: Optional[str] = None
+    stamp: Optional[Dict[str, str]] = None
+

app/ai-service/schemas/ocr.py

@@ -1,3 +1,4 @@
+from typing import Optional
 from pydantic import BaseModel, Field
 
 
@@ -17,3 +18,5 @@ class OCRResponse(BaseModel):
     data: OCRData | None = None
     error: dict[str, str] | None = None
     processing_time_ms: int
+    model_version: Optional[str] = None
+