transformer issues get fixed

Author: Vipul-Pandey-22

libs/langchain/langchain/evaluation/hallucination/detector.py

@@ -1,18 +1,31 @@
-from typing import List, Dict
-from transformers import pipeline
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from transformers import pipeline as PipelineType
+
+# Lazy import for runtime
+try:
+    from transformers import pipeline
+except ImportError:
+    pipeline = None
+
 
 class HallucinationDetector:
-    """
-    Simple Hallucination Detector using NLI models (e.g., facebook/bart-large-mnli).
+    """Simple Hallucination Detector using NLI models (e.g., facebook/bart-large-mnli).
     - Extract claims (basic sentence split)
     - Verify claims against evidence docs using NLI
     - Compute hallucination rate
     """
 
     def __init__(self, model_name: str = "facebook/bart-large-mnli"):
+        if pipeline is None:
+            raise ImportError(
+                "The 'transformers' package is required for HallucinationDetector. "
+                "Install it with `pip install transformers`."
+            )
         self.nli_pipeline = pipeline("text-classification", model=model_name)
 
-    def extract_claims(self, text: str) -> List[str]:
+    def extract_claims(self, text: str) -> list[str]:
         """Naive sentence-based claim extraction"""
         return [c.strip() for c in text.split(".") if c.strip()]
 
@@ -21,14 +34,20 @@ def verify_claim(self, claim: str, evidence: str) -> bool:
         result = self.nli_pipeline(f"{claim} </s></s> {evidence}")
         return result[0]["label"].lower() == "entailment"
 
-    def verify_claim_multi(self, claim: str, evidence_docs: List[str]) -> bool:
+    def verify_claim_multi(self, claim: str, evidence_docs: list[str]) -> bool:
         """A claim is supported if any evidence doc entails it"""
         return any(self.verify_claim(claim, e) for e in evidence_docs)
 
-    def compute_hallucination_rate(self, text: str, evidence_docs: List[str]) -> Dict[str, float]:
+    def compute_hallucination_rate(
+        self, text: str, evidence_docs: list[str]
+    ) -> dict[str, float]:
         claims = self.extract_claims(text)
         if not claims:
-            return {"total_claims": 0, "unsupported_claims": 0, "hallucination_rate": 0.0}
+            return {
+                "total_claims": 0,
+                "unsupported_claims": 0,
+                "hallucination_rate": 0.0,
+            }
 
         unsupported = sum(not self.verify_claim_multi(c, evidence_docs) for c in claims)
         return {

libs/langchain/tests/integration_tests/evaluation/hallucination/test_detector.py

@@ -120,7 +120,7 @@ def test_compute_hallucination_rate_mock(detector_mock: HallucinationDetector) -
     evidence = [
         (
         "Barack Obama served as the 44th President of the United States "
-        "from 2009 to 2017.",
+        "from 2009 to 2017."
         ),
         "Barack Obama was born in Hawaii, not Kenya.",
     ]