diff --git a/.DS_Store b/.DS_Store
index 03d385f22..b4ec85712 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/README.md b/README.md
index 286915e03..62fcfc68a 100644
--- a/README.md
+++ b/README.md
@@ -94,7 +94,7 @@ def test_case():
 
     # Replace this with the actual output from your LLM application
     actual_output = "We offer a 30-day full refund at no extra costs."
-    hallucination_metric = HallucinationMetric(minimum_score=0.7)
+    hallucination_metric = HallucinationMetric(threshold=0.7)
     test_case = LLMTestCase(input=input, actual_output=actual_output, context=context)
     assert_test(test_case, [hallucination_metric])
 ```
@@ -108,8 +108,8 @@ deepeval test run test_chatbot.py
 **Your test should have passed ✅** Let's breakdown what happened.
 
 - The variable `input` mimics user input, and `actual_output` is a placeholder for your chatbot's intended output based on this query.
-- The variable `context` contains the relevant information from your knowledge base, and `HallucinationMetric(minimum_score=0.7)` is an out-of-the-box metric provided by DeepEval. It helps you evaluate the factual accuracy of your chatbot's output based on the provided context.
-- The metric score ranges from 0 - 1. The `minimum_score=0.7` threshold ultimately determines whether your test has passed or not.
+- The variable `context` contains the relevant information from your knowledge base, and `HallucinationMetric(threshold=0.7)` is an out-of-the-box metric provided by DeepEval. It helps you evaluate the factual accuracy of your chatbot's output based on the provided context.
+- The metric score ranges from 0 - 1. The `threshold=0.7` threshold ultimately determines whether your test has passed or not.
 
 [Read our documentation](https://docs.confident-ai.com/docs/getting-started) for more information on how to use additional metrics, create your own custom metrics, and tutorials on how to integrate with other tools like LangChain and LlamaIndex.
 
@@ -120,7 +120,7 @@ deepeval test run test_chatbot.py
 Alternatively, you can evaluate without Pytest, which is more suited for a notebook environment.
 
 ```python
-from deepeval import evalate
+from deepeval import evaluate
 from deepeval.metrics import HallucinationMetric
 from deepeval.test_case import LLMTestCase
 
@@ -129,13 +129,13 @@ context = ["All customers are eligible for a 30 day full refund at no extra cost
 # Replace this with the actual output from your LLM application
 actual_output = "We offer a 30-day full refund at no extra costs."
 
-hallucination_metric = HallucinationMetric(minimum_score=0.7)
+hallucination_metric = HallucinationMetric(threshold=0.7)
 test_case = LLMTestCase(
     input=input,
     actual_output=actual_output,
     context=context
 )
-evalate([test_case], [hallucination_metric])
+evaluate([test_case], [hallucination_metric])
 ```
 
 ## Evaluting a Dataset / Test Cases in Bulk
@@ -159,8 +159,8 @@ dataset = EvaluationDataset(test_cases=[first_test_case, second_test_case])
     dataset,
 )
 def test_customer_chatbot(test_case: LLMTestCase):
-    hallucination_metric = HallucinationMetric(minimum_score=0.3)
-    answer_relevancy_metric = AnswerRelevancyMetric(minimum_score=0.5)
+    hallucination_metric = HallucinationMetric(threshold=0.3)
+    answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.5)
     assert_test(test_case, [hallucination_metric, answer_relevancy_metric])
 ```
 
diff --git a/deepeval/_version.py b/deepeval/_version.py
index ee489183b..2c7d1d32f 100644
--- a/deepeval/_version.py
+++ b/deepeval/_version.py
@@ -1 +1 @@
-__version__: str = "0.20.44"
+__version__: str = "0.20.46"
diff --git a/deepeval/evaluate.py b/deepeval/evaluate.py
index e433931d5..ae36a49d3 100644
--- a/deepeval/evaluate.py
+++ b/deepeval/evaluate.py
@@ -111,7 +111,7 @@ def assert_test(test_case: LLMTestCase, metrics: List[BaseMetric]):
         ]
         failed_metrics_str = ", ".join(
             [
-                f"{metric.__name__} (score: {metric.score}, minimum_score: {metric.minimum_score})"
+                f"{metric.__name__} (score: {metric.score}, threshold: {metric.threshold})"
                 for metric in failed_metrics
             ]
         )
@@ -138,11 +138,11 @@ def print_test_result(test_result: TestResult):
     for metric in test_result.metrics:
         if not metric.is_successful():
             print(
-                f"  - ❌ {metric.__name__} (score: {metric.score}, minimum_score: {metric.minimum_score}, reason: {metric.reason})"
+                f"  - ❌ {metric.__name__} (score: {metric.score}, threshold: {metric.threshold}, reason: {metric.reason})"
             )
         else:
             print(
-                f"  - ✅ {metric.__name__} (score: {metric.score}, minimum_score: {metric.minimum_score}, reason: {metric.reason})"
+                f"  - ✅ {metric.__name__} (score: {metric.score}, threshold: {metric.threshold}, reason: {metric.reason})"
             )
         if metric.score_metadata:
             for metric_name, score in metric.score_metadata.items():
diff --git a/deepeval/metrics/__init__.py b/deepeval/metrics/__init__.py
index 3edb211f2..d03855802 100644
--- a/deepeval/metrics/__init__.py
+++ b/deepeval/metrics/__init__.py
@@ -11,10 +11,12 @@
 from .contextual_precision import ContextualPrecisionMetric
 from .ragas_metric import (
     RagasMetric,
+    RAGASAnswerRelevancyMetric,
     RAGASFaithfulnessMetric,
     RAGASContextualRecallMetric,
     RAGASContextualRelevancyMetric,
     RAGASContextualPrecisionMetric,
+    RAGASAnswerRelevancyMetric,
     RAGASConcisenessMetric as ConcisenessMetric,
     RAGASCorrectnessMetric as CorrectnessMetric,
     RAGASCoherenceMetric as CoherenceMetric,
diff --git a/deepeval/metrics/answer_relevancy.py b/deepeval/metrics/answer_relevancy.py
index a4cdadbb2..d60a6091f 100644
--- a/deepeval/metrics/answer_relevancy.py
+++ b/deepeval/metrics/answer_relevancy.py
@@ -18,11 +18,11 @@ class AnswerRelvancyVerdict(BaseModel):
 class AnswerRelevancyMetric(BaseMetric):
     def __init__(
         self,
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
         model: Optional[str] = None,
         include_reason: bool = True,
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
         self.include_reason = include_reason
         self.n = 5
@@ -49,7 +49,7 @@ def measure(self, test_case: LLMTestCase) -> float:
             self.reason = self._generate_reason(
                 test_case.input, test_case.actual_output, answer_relevancy_score
             )
-            self.success = answer_relevancy_score >= self.minimum_score
+            self.success = answer_relevancy_score >= self.threshold
             self.score = answer_relevancy_score
             return self.score
 
@@ -115,6 +115,7 @@ def _generate_key_points(
         return data["key_points"]
 
     def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
 
     @property
diff --git a/deepeval/metrics/base_metric.py b/deepeval/metrics/base_metric.py
index ce2cdb866..f73a60143 100644
--- a/deepeval/metrics/base_metric.py
+++ b/deepeval/metrics/base_metric.py
@@ -5,34 +5,22 @@
 
 
 class BaseMetric:
-    # set an arbitrary minimum score that will get over-ridden later
     score: float = 0
     score_metadata: Dict = None
     reason: Optional[str] = None
 
     @property
-    def minimum_score(self) -> float:
-        return self._minimum_score
+    def threshold(self) -> float:
+        return self._threshold
 
-    @minimum_score.setter
-    def minimum_score(self, value: float):
-        self._minimum_score = value
+    @threshold.setter
+    def threshold(self, value: float):
+        self._threshold = value
 
-    # Measure function signature is subject to be different - not sure
-    # how applicable this is - might need a better abstraction
     @abstractmethod
     def measure(self, test_case: LLMTestCase, *args, **kwargs) -> float:
         raise NotImplementedError
 
-    def _get_init_values(self):
-        # We use this method for sending useful metadata
-        init_values = {
-            param: getattr(self, param)
-            for param in vars(self)
-            if isinstance(getattr(self, param), (str, int, float))
-        }
-        return init_values
-
     @abstractmethod
     def is_successful(self) -> bool:
         raise NotImplementedError
diff --git a/deepeval/metrics/contextual_precision.py b/deepeval/metrics/contextual_precision.py
index 8c7f0b9d6..5591ef50d 100644
--- a/deepeval/metrics/contextual_precision.py
+++ b/deepeval/metrics/contextual_precision.py
@@ -19,11 +19,11 @@ class ContextualPrecisionVerdict(BaseModel):
 class ContextualPrecisionMetric(BaseMetric):
     def __init__(
         self,
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
         model: Optional[str] = None,
         include_reason: bool = True,
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.include_reason = include_reason
         self.model = model
 
@@ -52,7 +52,7 @@ def measure(self, test_case: LLMTestCase) -> float:
                 test_case.input, contextual_precision_score
             )
 
-            self.success = contextual_precision_score >= self.minimum_score
+            self.success = contextual_precision_score >= self.threshold
             self.score = contextual_precision_score
             return self.score
 
@@ -136,6 +136,7 @@ def _generate_verdicts(
         return verdicts
 
     def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
 
     @property
diff --git a/deepeval/metrics/contextual_recall.py b/deepeval/metrics/contextual_recall.py
index 347a97463..6cf2aeec5 100644
--- a/deepeval/metrics/contextual_recall.py
+++ b/deepeval/metrics/contextual_recall.py
@@ -18,11 +18,11 @@ class ContextualRecallVerdict(BaseModel):
 class ContextualRecallMetric(BaseMetric):
     def __init__(
         self,
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
         model: Optional[str] = None,
         include_reason: bool = True,
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
         self.include_reason = include_reason
         self.n = 5
@@ -50,7 +50,7 @@ def measure(self, test_case: LLMTestCase) -> float:
                 test_case.expected_output, contextual_recall_score
             )
 
-            self.success = contextual_recall_score >= self.minimum_score
+            self.success = contextual_recall_score >= self.threshold
             self.score = contextual_recall_score
             return self.score
 
@@ -102,6 +102,7 @@ def _generate_verdicts(
         return verdicts
 
     def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
 
     @property
diff --git a/deepeval/metrics/contextual_relevancy.py b/deepeval/metrics/contextual_relevancy.py
index 556d0fe5b..aa717fea4 100644
--- a/deepeval/metrics/contextual_relevancy.py
+++ b/deepeval/metrics/contextual_relevancy.py
@@ -19,11 +19,11 @@ class ContextualRelevancyVerdict(BaseModel):
 class ContextualRelevancyMetric(BaseMetric):
     def __init__(
         self,
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
         model: Optional[str] = "gpt-4",
         include_reason: bool = True,
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
         self.include_reason = include_reason
 
@@ -48,7 +48,7 @@ def measure(self, test_case: LLMTestCase) -> float:
                 test_case.input, contextual_recall_score
             )
 
-            self.success = contextual_recall_score >= self.minimum_score
+            self.success = contextual_recall_score >= self.threshold
             self.score = contextual_recall_score
 
             return self.score
@@ -131,6 +131,7 @@ def _generate_verdicts_list(
         return verdicts_list
 
     def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
 
     @property
diff --git a/deepeval/metrics/faithfulness.py b/deepeval/metrics/faithfulness.py
index 74a5ddc45..85409fd5f 100644
--- a/deepeval/metrics/faithfulness.py
+++ b/deepeval/metrics/faithfulness.py
@@ -20,11 +20,11 @@ class FaithfulnessVerdict(BaseModel):
 class FaithfulnessMetric(BaseMetric):
     def __init__(
         self,
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
         model: Optional[str] = None,
         include_reason: bool = True,
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         # Don't set self.chat_model when using threading
         self.model = model
         self.include_reason = include_reason
@@ -49,7 +49,7 @@ def measure(self, test_case: LLMTestCase):
             )
             faithfulness_score = self._generate_score()
             self.reason = self._generate_reason(faithfulness_score)
-            self.success = faithfulness_score >= self.minimum_score
+            self.success = faithfulness_score >= self.threshold
             self.score = faithfulness_score
             return self.score
 
@@ -172,6 +172,7 @@ def _generate_verdicts_list(
         return verdicts_list
 
     def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
 
     @property
diff --git a/deepeval/metrics/hallucination_metric.py b/deepeval/metrics/hallucination_metric.py
index 3f63c6910..b9eb3346c 100644
--- a/deepeval/metrics/hallucination_metric.py
+++ b/deepeval/metrics/hallucination_metric.py
@@ -7,9 +7,9 @@
 class HallucinationMetric(BaseMetric, metaclass=Singleton):
     def __init__(
         self,
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
 
     def measure(self, test_case: LLMTestCase):
         if test_case.actual_output is None or test_case.context is None:
@@ -25,11 +25,12 @@ def measure(self, test_case: LLMTestCase):
             if score > max_score:
                 max_score = score
 
-        self.success = max_score > self.minimum_score
+        self.success = max_score >= self.threshold
         self.score = max_score
         return max_score
 
     def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
 
     @property
diff --git a/deepeval/metrics/judgemental_gpt.py b/deepeval/metrics/judgemental_gpt.py
index 30f44836f..d707adade 100644
--- a/deepeval/metrics/judgemental_gpt.py
+++ b/deepeval/metrics/judgemental_gpt.py
@@ -24,7 +24,7 @@ def __init__(
         criteria: str,
         evaluation_params: List[LLMTestCaseParams],
         language: Languages = Languages.ENGLISH,
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
     ):
         if not isinstance(language, Languages):
             raise TypeError("'language' must be an instance of Languages.")
@@ -33,7 +33,7 @@ def __init__(
         self.name = name
         self.evaluation_params = evaluation_params
         self.language = language.value
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.success = None
         self.reason = None
 
@@ -70,9 +70,10 @@ def measure(self, test_case: LLMTestCase):
         )
         self.reason = response.reason
         self.score = response.score / 10
-        self.success = self.score >= self.minimum_score
+        self.success = self.score >= self.threshold
 
         return self.score
 
-    def is_successful(self):
+    def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
diff --git a/deepeval/metrics/llm_eval_metric.py b/deepeval/metrics/llm_eval_metric.py
index 4d08674f5..8726f9a79 100644
--- a/deepeval/metrics/llm_eval_metric.py
+++ b/deepeval/metrics/llm_eval_metric.py
@@ -26,7 +26,7 @@ def __init__(
         criteria: Optional[str] = None,
         evaluation_steps: Optional[List[str]] = None,
         model: Optional[str] = None,
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
     ):
         self.name = name
         self.evaluation_params = evaluation_params
@@ -50,7 +50,7 @@ def __init__(
         self.criteria = criteria
         self.model = model
         self.evaluation_steps = evaluation_steps
-        self.minimum_score = minimum_score
+        self.threshold = threshold
 
     def measure(self, test_case: LLMTestCase):
         """LLM evaluated metric based on the GEval framework: https://arxiv.org/pdf/2303.16634.pdf"""
@@ -73,10 +73,11 @@ def measure(self, test_case: LLMTestCase):
         score, reason = self.evaluate(test_case)
         self.reason = reason
         self.score = float(score) / 10
-        self.success = score >= self.minimum_score
+        self.success = score >= self.threshold
         return self.score
 
-    def is_successful(self):
+    def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
 
     def generate_evaluation_steps(self):
diff --git a/deepeval/metrics/non_toxic_metric.py b/deepeval/metrics/non_toxic_metric.py
index 96318bef2..20c9f78f2 100644
--- a/deepeval/metrics/non_toxic_metric.py
+++ b/deepeval/metrics/non_toxic_metric.py
@@ -13,13 +13,13 @@ def __init__(
         self,
         evaluation_params: List[LLMTestCaseParams],
         model_name: str = "original",
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
     ):
         if not evaluation_params:
             raise ValueError("evaluation_params cannot be empty or None")
 
         self.evaluation_params = evaluation_params
-        self.minimum_score, self.model_name = minimum_score, model_name
+        self.threshold, self.model_name = threshold, model_name
 
     def __call__(self, test_case: LLMTestCase):
         score = self.measure(test_case.actual_output)
@@ -58,11 +58,12 @@ def measure(self, test_case: LLMTestCase):
         average_score = sum(total_scores) / len(total_scores)
 
         # Check if the average score meets the minimum requirement
-        self.success = average_score >= self.minimum_score
+        self.success = average_score >= self.threshold
         self.score = average_score
         return self.score
 
-    def is_successful(self):
+    def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
 
     @property
diff --git a/deepeval/metrics/ragas_metric.py b/deepeval/metrics/ragas_metric.py
index cf27ce146..a1e1e4309 100644
--- a/deepeval/metrics/ragas_metric.py
+++ b/deepeval/metrics/ragas_metric.py
@@ -17,10 +17,10 @@ class RAGASContextualPrecisionMetric(BaseMetric):
 
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -57,7 +57,7 @@ def measure(self, test_case: LLMTestCase):
 
         # Ragas only does dataset-level comparisons
         context_precision_score = scores["context_precision"]
-        self.success = context_precision_score >= self.minimum_score
+        self.success = context_precision_score >= self.threshold
         self.score = context_precision_score
         return self.score
 
@@ -74,10 +74,10 @@ class RAGASContextualRelevancyMetric(BaseMetric):
 
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -113,7 +113,7 @@ def measure(self, test_case: LLMTestCase):
 
         # Ragas only does dataset-level comparisons
         context_relevancy_score = scores["context_relevancy"]
-        self.success = context_relevancy_score >= self.minimum_score
+        self.success = context_relevancy_score >= self.threshold
         self.score = context_relevancy_score
         return self.score
 
@@ -130,10 +130,10 @@ class RAGASAnswerRelevancyMetric(BaseMetric):
 
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -165,7 +165,7 @@ def measure(self, test_case: LLMTestCase):
         dataset = Dataset.from_dict(data)
         scores = evaluate(dataset, metrics=[answer_relevancy])
         answer_relevancy_score = scores["answer_relevancy"]
-        self.success = answer_relevancy_score >= self.minimum_score
+        self.success = answer_relevancy_score >= self.threshold
         self.score = answer_relevancy_score
         return self.score
 
@@ -180,10 +180,10 @@ def __name__(self):
 class RAGASFaithfulnessMetric(BaseMetric):
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -215,7 +215,7 @@ def measure(self, test_case: LLMTestCase):
         dataset = Dataset.from_dict(data)
         scores = evaluate(dataset, metrics=[faithfulness])
         faithfulness_score = scores["faithfulness"]
-        self.success = faithfulness_score >= self.minimum_score
+        self.success = faithfulness_score >= self.threshold
         self.score = faithfulness_score
         return self.score
 
@@ -232,10 +232,10 @@ class RAGASContextualRecallMetric(BaseMetric):
 
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -267,7 +267,7 @@ def measure(self, test_case: LLMTestCase):
         dataset = Dataset.from_dict(data)
         scores = evaluate(dataset, [context_recall])
         context_recall_score = scores["context_recall"]
-        self.success = context_recall_score >= self.minimum_score
+        self.success = context_recall_score >= self.threshold
         self.score = context_recall_score
         return self.score
 
@@ -284,10 +284,10 @@ class RAGASHarmfulnessMetric(BaseMetric):
 
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -320,7 +320,7 @@ def measure(self, test_case: LLMTestCase):
         dataset = Dataset.from_dict(data)
         scores = evaluate(dataset, [harmfulness])
         harmfulness_score = scores["harmfulness"]
-        self.success = harmfulness_score >= self.minimum_score
+        self.success = harmfulness_score >= self.threshold
         self.score = harmfulness_score
         return self.score
 
@@ -337,10 +337,10 @@ class RAGASCoherenceMetric(BaseMetric):
 
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -371,7 +371,7 @@ def measure(self, test_case: LLMTestCase):
         dataset = Dataset.from_dict(data)
         scores = evaluate(dataset, [coherence])
         coherence_score = scores["coherence"]
-        self.success = coherence_score >= self.minimum_score
+        self.success = coherence_score >= self.threshold
         self.score = coherence_score
         return self.score
 
@@ -388,10 +388,10 @@ class RAGASMaliciousnessMetric(BaseMetric):
 
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -423,7 +423,7 @@ def measure(self, test_case: LLMTestCase):
         dataset = Dataset.from_dict(data)
         scores = evaluate(dataset, [maliciousness])
         maliciousness_score = scores["maliciousness"]
-        self.success = maliciousness_score >= self.minimum_score
+        self.success = maliciousness_score >= self.threshold
         self.score = maliciousness_score
         return self.score
 
@@ -440,10 +440,10 @@ class RAGASCorrectnessMetric(BaseMetric):
 
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -475,7 +475,7 @@ def measure(self, test_case: LLMTestCase):
         dataset = Dataset.from_dict(data)
         scores = evaluate(dataset, metrics=[correctness])
         correctness_score = scores["correctness"]
-        self.success = correctness_score >= self.minimum_score
+        self.success = correctness_score >= self.threshold
         self.score = correctness_score
         return self.score
 
@@ -492,10 +492,10 @@ class RAGASConcisenessMetric(BaseMetric):
 
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -526,7 +526,7 @@ def measure(self, test_case: LLMTestCase):
         dataset = Dataset.from_dict(data)
         scores = evaluate(dataset, metrics=[conciseness])
         conciseness_score = scores["conciseness"]
-        self.success = conciseness_score >= self.minimum_score
+        self.success = conciseness_score >= self.threshold
         self.score = conciseness_score
         return self.score
 
@@ -543,10 +543,10 @@ class RagasMetric(BaseMetric):
 
     def __init__(
         self,
-        minimum_score: float = 0.3,
+        threshold: float = 0.3,
         model: Optional[str] = "gpt-3.5-turbo",
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
 
     def measure(self, test_case: LLMTestCase):
@@ -594,7 +594,7 @@ def measure(self, test_case: LLMTestCase):
                 1.0 / score for score in score_metadata.values()
             )
 
-        self.success = ragas_score >= self.minimum_score
+        self.success = ragas_score >= self.threshold
         self.score = ragas_score
         self.score_metadata = score_metadata
         return self.score
diff --git a/deepeval/metrics/summarization.py b/deepeval/metrics/summarization.py
index 9a005e6fd..c5c09ed1d 100644
--- a/deepeval/metrics/summarization.py
+++ b/deepeval/metrics/summarization.py
@@ -21,12 +21,12 @@ class ScoreType(Enum):
 class SummarizationMetric(BaseMetric):
     def __init__(
         self,
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
         model: Optional[str] = None,
         n: Optional[int] = 5,
         assessment_questions: Optional[List[str]] = None,
     ):
-        self.minimum_score = minimum_score
+        self.threshold = threshold
         self.model = model
         self.assessment_questions = assessment_questions
         self.n = n
@@ -54,7 +54,7 @@ def measure(self, test_case: LLMTestCase):
 
         summarization_score = min(alignment_score, inclusion_score)
 
-        self.success = summarization_score >= self.minimum_score
+        self.success = summarization_score >= self.threshold
         self.score_metadata = {
             "Alignment": alignment_score,
             "Inclusion": inclusion_score,
@@ -134,6 +134,7 @@ def get_answer(self, question: str, text: str) -> str:
         return res.content
 
     def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
 
     @property
diff --git a/deepeval/metrics/unbias_metric.py b/deepeval/metrics/unbias_metric.py
index 945d07c67..fb137caab 100644
--- a/deepeval/metrics/unbias_metric.py
+++ b/deepeval/metrics/unbias_metric.py
@@ -15,18 +15,18 @@ def __init__(
         self,
         evaluation_params: List[LLMTestCaseParams],
         model_name: str = "original",
-        minimum_score: float = 0.5,
+        threshold: float = 0.5,
     ):  # see paper for rationale https://arxiv.org/pdf/2208.05777.pdf
         if not evaluation_params:
             raise ValueError("evaluation_params cannot be empty or None")
 
         self.evaluation_params = evaluation_params
         self.model_name = model_name
-        self.minimum_score = minimum_score
+        self.threshold = threshold
 
     def __call__(self, output, expected_output, query: Optional[str] = "-"):
         score = self.measure(output, expected_output)
-        success = score >= self.minimum_score
+        success = score >= self.threshold
         return score
 
     def measure(self, test_case: LLMTestCase, return_all_scores: bool = False):
@@ -61,7 +61,7 @@ def measure(self, test_case: LLMTestCase, return_all_scores: bool = False):
         # Calculate the average score
         average_score = total_score / len(self.evaluation_params)
 
-        self.success = average_score > self.minimum_score
+        self.success = average_score > self.threshold
         self.score = average_score
 
         if return_all_scores:
@@ -69,7 +69,8 @@ def measure(self, test_case: LLMTestCase, return_all_scores: bool = False):
 
         return average_score
 
-    def is_successful(self):
+    def is_successful(self) -> bool:
+        self.success = self.score >= self.threshold
         return self.success
 
     @property
diff --git a/deepeval/models/gpt_model.py b/deepeval/models/gpt_model.py
index 6ca7cda8a..49637c8b7 100644
--- a/deepeval/models/gpt_model.py
+++ b/deepeval/models/gpt_model.py
@@ -1,7 +1,7 @@
 import os
 from typing import Dict, Optional
 
-from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
+from langchain_openai import ChatOpenAI, AzureChatOpenAI
 from deepeval.key_handler import KeyValues, KEY_FILE_HANDLER
 from deepeval.models.base import DeepEvalBaseModel
 from deepeval.chat_completion.retry import retry_with_exponential_backoff
@@ -43,13 +43,6 @@ def __init__(
 
     def load_model(self):
         if self.should_use_azure_openai():
-            model_version = KEY_FILE_HANDLER.fetch_data(
-                KeyValues.AZURE_MODEL_VERSION
-            )
-            model_kwargs = {}
-            if model_version is not None:
-                model_kwargs["model_version"] = model_version
-
             openai_api_key = KEY_FILE_HANDLER.fetch_data(
                 KeyValues.AZURE_OPENAI_API_KEY
             )
@@ -63,13 +56,22 @@ def load_model(self):
             azure_endpoint = KEY_FILE_HANDLER.fetch_data(
                 KeyValues.AZURE_OPENAI_ENDPOINT
             )
+
+            model_version = KEY_FILE_HANDLER.fetch_data(
+                KeyValues.AZURE_MODEL_VERSION
+            )
+
+            if model_version is None:
+                model_version = ""
+
             return AzureChatOpenAI(
                 openai_api_version=openai_api_version,
                 azure_deployment=azure_deployment,
                 azure_endpoint=azure_endpoint,
                 openai_api_key=openai_api_key,
-                model_kwargs=model_kwargs,
+                model_version=model_version,
             )
+
         return ChatOpenAI(
             model_name=self.model_name, model_kwargs=self.model_kwargs
         )
diff --git a/deepeval/models/summac_model.py b/deepeval/models/summac_model.py
index 7df978794..daa6ab258 100644
--- a/deepeval/models/summac_model.py
+++ b/deepeval/models/summac_model.py
@@ -1,5 +1,5 @@
 import torch
-from typing import Union, List
+from typing import Union, List, Optional
 from typing import List, Union, get_origin
 from deepeval.models.base import DeepEvalBaseModel
 from deepeval.models._summac_model import _SummaCZS
@@ -8,9 +8,9 @@
 class SummaCModels(DeepEvalBaseModel):
     def __init__(
         self,
-        model_name: str | None = None,
-        granularity: str | None = None,
-        device: str | None = None,
+        model_name: Optional[str] = None,
+        granularity: Optional[str] = None,
+        device: Optional[str] = None,
         *args,
         **kwargs
     ):
@@ -27,11 +27,11 @@ def __init__(
 
     def load_model(
         self,
-        op1: str | None = "max",
-        op2: str | None = "mean",
-        use_ent: bool | None = True,
-        use_con: bool | None = True,
-        image_load_cache: bool | None = True,
+        op1: Optional[str] = "max",
+        op2: Optional[str] = "mean",
+        use_ent: Optional[bool] = True,
+        use_con: Optional[bool] = True,
+        image_load_cache: Optional[bool] = True,
         **kwargs
     ):
         return _SummaCZS(
diff --git a/deepeval/test_case.py b/deepeval/test_case.py
index ca3b2757d..48e17647c 100644
--- a/deepeval/test_case.py
+++ b/deepeval/test_case.py
@@ -20,6 +20,8 @@ def __init__(
         expected_output: Optional[str] = None,
         context: Optional[List[str]] = None,
         retrieval_context: Optional[List[str]] = None,
+        execution_time: Optional[float] = None,
+        cost: Optional[float] = None,
         id: Optional[str] = None,
     ):
         self.id = id
@@ -28,3 +30,5 @@ def __init__(
         self.expected_output = expected_output
         self.context = context
         self.retrieval_context = retrieval_context
+        self.execution_time = execution_time
+        self.cost = cost
diff --git a/deepeval/test_run/api.py b/deepeval/test_run/api.py
index 3db64aec3..a80e55cce 100644
--- a/deepeval/test_run/api.py
+++ b/deepeval/test_run/api.py
@@ -5,7 +5,8 @@
 class MetricsMetadata(BaseModel):
     metric: str
     score: float
-    minimum_score: float = Field(None, alias="minimumScore")
+    threshold: float
+    success: bool
     reason: Optional[str] = None
 
 
diff --git a/deepeval/test_run/test_run.py b/deepeval/test_run/test_run.py
index 7ef742632..dd82c9523 100644
--- a/deepeval/test_run/test_run.py
+++ b/deepeval/test_run/test_run.py
@@ -84,26 +84,25 @@ def add_llm_test_case(
     ):
         # Check if test case with the same ID already exists
         test_case_id = id(test_case)
-        existing_test_case: LLMTestCase = self.dict_test_cases.get(
+        existing_test_case: APITestCase = self.dict_test_cases.get(
             test_case_id, None
         )
 
-        metrics_metadata = MetricsMetadata(
+        metric_metadata = MetricsMetadata(
             metric=metric.__name__,
             score=metric.score,
-            minimumScore=metric.minimum_score,
+            threshold=metric.threshold,
             reason=metric.reason,
+            success=metric.is_successful(),
         )
 
         if existing_test_case:
             # If it exists, append the metrics to the existing test case
-            existing_test_case.metrics_metadata.append(metrics_metadata)
-            success = all(
-                [
-                    metric.score >= metric.minimum_score
-                    for metric in existing_test_case.metrics_metadata
-                ]
-            )
+            existing_test_case.metrics_metadata.append(metric_metadata)
+            if metric.is_successful() and existing_test_case.success == True:
+                success = True
+            else:
+                success = False
             # Update the success status
             existing_test_case.success = success
         else:
@@ -113,7 +112,7 @@ def add_llm_test_case(
                 actualOutput=test_case.actual_output,
                 expectedOutput=test_case.expected_output,
                 success=metric.is_successful(),
-                metricsMetadata=[metrics_metadata],
+                metricsMetadata=[metric_metadata],
                 runDuration=run_duration,
                 context=test_case.context,
                 retrievalContext=test_case.retrieval_context,
@@ -215,7 +214,7 @@ def display_results_table(self, test_run: TestRun):
                 test_case_name += f" ({test_case.id})"
 
             for metric_metadata in test_case.metrics_metadata:
-                if metric_metadata.score >= metric_metadata.minimum_score:
+                if metric_metadata.success:
                     pass_count += 1
                 else:
                     fail_count += 1
@@ -229,7 +228,7 @@ def display_results_table(self, test_run: TestRun):
             )
 
             for metric_metadata in test_case.metrics_metadata:
-                if metric_metadata.score >= metric_metadata.minimum_score:
+                if metric_metadata.success:
                     status = "[green]PASSED[/green]"
                 else:
                     status = "[red]FAILED[/red]"
@@ -237,7 +236,7 @@ def display_results_table(self, test_run: TestRun):
                 table.add_row(
                     "",
                     str(metric_metadata.metric),
-                    f"{round(metric_metadata.score,2)} (threshold={metric_metadata.minimum_score}, reason={metric_metadata.reason})",
+                    f"{round(metric_metadata.score,2)} (threshold={metric_metadata.threshold}, reason={metric_metadata.reason})",
                     status,
                     "",
                 )
diff --git a/docs/docs/confident-ai-debug-evaluations.mdx b/docs/docs/confident-ai-debug-evaluations.mdx
index c4aa15364..13867035c 100644
--- a/docs/docs/confident-ai-debug-evaluations.mdx
+++ b/docs/docs/confident-ai-debug-evaluations.mdx
@@ -152,7 +152,7 @@ def test_hallucination():
     ]
     input = "What are the requimrents to be president?"
 
-    metric = HallucinationMetric(minimum_score=0.8)
+    metric = HallucinationMetric(threshold=0.8)
     test_case = LLMTestCase(
         input=input,
         actual_output=chatbot.query(user_input=input),
diff --git a/docs/docs/confident-ai-evaluate-datasets.mdx b/docs/docs/confident-ai-evaluate-datasets.mdx
index b5b5b6b7a..712b6ffdf 100644
--- a/docs/docs/confident-ai-evaluate-datasets.mdx
+++ b/docs/docs/confident-ai-evaluate-datasets.mdx
@@ -111,7 +111,7 @@ dataset.pull(alias="My Confident Dataset")
     dataset,
 )
 def test_customer_chatbot(test_case: LLMTestCase):
-    hallucination_metric = HallucinationMetric(minimum_score=0.3)
+    hallucination_metric = HallucinationMetric(threshold=0.3)
     assert_test(test_case, [hallucination_metric])
 ```
 
@@ -128,7 +128,7 @@ from deepeval import evaluate
 from deepeval.metrics import HallucinationMetric
 from deepeval.dataset import EvaluationDataset
 
-hallucination_metric = HallucinationMetric(minimum_score=0.3)
+hallucination_metric = HallucinationMetric(threshold=0.3)
 
 # Initialize empty dataset object and pull from Confident
 dataset = EvaluationDataset()
diff --git a/docs/docs/evaluation-datasets.mdx b/docs/docs/evaluation-datasets.mdx
index b34503ea5..09fc2bfac 100644
--- a/docs/docs/evaluation-datasets.mdx
+++ b/docs/docs/evaluation-datasets.mdx
@@ -157,8 +157,8 @@ dataset = EvaluationDataset(test_cases=[...])
     dataset,
 )
 def test_customer_chatbot(test_case: LLMTestCase):
-    hallucination_metric = HallucinationMetric(minimum_score=0.3)
-    answer_relevancy_metric = AnswerRelevancyMetric(minimum_score=0.5)
+    hallucination_metric = HallucinationMetric(threshold=0.3)
+    answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.5)
     assert_test(test_case, [hallucination_metric, answer_relevancy_metric])
 
 
@@ -186,8 +186,8 @@ from deepeval.dataset import EvaluationDataset
 
 dataset = EvaluationDataset(test_cases=[...])
 
-hallucination_metric = HallucinationMetric(minimum_score=0.3)
-answer_relevancy_metric = AnswerRelevancyMetric(minimum_score=0.5)
+hallucination_metric = HallucinationMetric(threshold=0.3)
+answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.5)
 
 dataset.evaluate([hallucination_metric, answer_relevancy_metric])
 
diff --git a/docs/docs/evaluation-test-cases.mdx b/docs/docs/evaluation-test-cases.mdx
index e21f8e3c8..753690541 100644
--- a/docs/docs/evaluation-test-cases.mdx
+++ b/docs/docs/evaluation-test-cases.mdx
@@ -13,16 +13,19 @@ A test case is a blueprint provided by `deepeval` to unit test LLM outputs based
 - [Optional] `expected_output`
 - [Optional] `context`
 - [Optional] `retrieval_context`
+- [Optional] `execution_time` (`float`)
+- [Optional] `cost` (`float`)
 
 Except for `actual_output`, all parameters should originate from your evaluation dataset (if you have one). Here's an example implementation of a test case:
 
 ```python
 test_case = LLMTestCase(
     input="What if these shoes don't fit?",
-    expected_output = "You're eligible for a 30 day refund at no extra cost.",
-    actual_output = "We offer a 30-day full refund at no extra cost.",
-    context = ["All customers are eligible for a 30 day full refund at no extra cost."]
-    retrieval_context = ["Only shoes can be refunded."]
+    expected_output="You're eligible for a 30 day refund at no extra cost.",
+    actual_output="We offer a 30-day full refund at no extra cost.",
+    context=["All customers are eligible for a 30 day full refund at no extra cost."],
+    retrieval_context=["Only shoes can be refunded."],
+    execution_time=10.0
 )
 ```
 
@@ -155,8 +158,6 @@ prompt_template = """
 
 prompt = prompt_template.format(text="Who's a good boy?")
 
-context = ["Rocky is a good boy."]
-
 # Replace this with the actual retrieved context from your RAG pipeline
 retrieval_context = ["Rocky is a good cat."]
 
@@ -165,7 +166,6 @@ test_case = LLMTestCase(
     # Replace this with your actual LLM application
     actual_output=chatbot.run(prompt),
     expected_output="Me, ruff!",
-    context=context,
     retrieval_context=retrieval_context
 )
 ```
@@ -174,6 +174,40 @@ test_case = LLMTestCase(
 Remember, `context` is the ideal retrieval results for a given input and typically come from your evaluation dataset, whereas `retrieval_context` is your LLM application's actual retrieval results.
 :::
 
+## Execution Time
+
+The `execution_time` is an **optional** parameter that represents how long it took your LLM application to finish executing. However, if you're trying to measure something else, like the execution time for only the retrieval part of your RAG pipeline, feel free to supply that number instead of the overall execution time.
+
+```python
+test_case = LLMTestCase(
+    input="...",
+    actual_output="...",
+    # Replace this with the actual execution time of your LLM application
+    execution_time=10.4
+)
+```
+
+:::note
+`deepeval` does not offer metrics that evaluate on latency and cost, so feel free to supply the `execution_time` in either seconds, miliseconds, or even nanoseconds. That being said, [here is a full working example](metrics-custom#implementation) of how you can build your own `LatencyMetric` using the `execution_time` parameter.
+:::
+
+## Cost
+
+The `cost` is an **optional** parameter that represents the token cost for a given execution of your LLM application. However, similar to `execution_time`, the `cost` parameter does not strictly have to be the total completion cost (eg., it could be the embedding cost), nor does it have to be in any set currency.
+
+```python
+test_case = LLMTestCase(
+    input="...",
+    actual_output="...",
+    # Replace this with the actual execution time of your LLM application
+    cost=0.78
+)
+```
+
+:::info
+`deepeval` does not offer cost and latency metrics because it is difficult to account for all different units and currencies available. We highly encourage you to look at the [custom metrics section](metrics-custom#implementation) for a full example on how to create your own metric if you are looking to evaluate cost and latency.
+:::
+
 ## Run A Test Case
 
 `deepeval` offers an option to quickly run a test case without going through the CLI.
@@ -202,7 +236,7 @@ test_case = LLMTestCase(
     context=context
 )
 
-metric = HallucinationMetric(minimum_score=0.7)
+metric = HallucinationMetric(threshold=0.7)
 run_test(test_case, [metric])
 ```
 
@@ -245,7 +279,7 @@ def test_case_1():
         expected_output="Me, ruff!",
         context=context
     )
-    metric = HallucinationMetric(minimum_score=0.7)
+    metric = HallucinationMetric(threshold=0.7)
     assert_test(test_case, metrics=[metric])
 
 def test_case_2():
@@ -259,7 +293,7 @@ def test_case_2():
         expected_output="Me, ruff!",
         context=context
     )
-    metric = HallucinationMetric(minimum_score=0.7)
+    metric = HallucinationMetric(threshold=0.7)
     assert_test(test_case, metrics=[metric])
 ```
 
@@ -307,7 +341,7 @@ second_test_case = LLMTestCase(
 
 test_cases = [first_test_case, second_test_case]
 
-metric = HallucinationMetric(minimum_score=0.7)
+metric = HallucinationMetric(threshold=0.7)
 evaluate(test_cases, [metric])
 ```
 
diff --git a/docs/docs/getting-started.mdx b/docs/docs/getting-started.mdx
index a34f15793..93fb66a4e 100644
--- a/docs/docs/getting-started.mdx
+++ b/docs/docs/getting-started.mdx
@@ -60,7 +60,7 @@ def test_hallucination():
 
     # Replace this with the actual output of your LLM application
     actual_output = "We offer a 30-day full refund at no extra cost."
-    hallucination_metric = HallucinationMetric(minimum_score=0.7)
+    hallucination_metric = HallucinationMetric(threshold=0.7)
     test_case = LLMTestCase(input=input, actual_output=actual_output, context=context)
     assert_test(test_case, [hallucination_metric])
 ```
@@ -74,8 +74,8 @@ deepeval test run test_example.py
 **Congratulations! Your test case should have passed ✅** Let's breakdown what happened.
 
 - The variable `input` mimics a user input, and `actual_output` is a placeholder for what your application's supposed to output based on this input.
-- The variable `context` contains the relevant information from your knowledge base, and `HallucinationMetric(minimum_score=0.7)` is an default metric provided by DeepEval for you to evaluate how factually correct your application's output is based on the provided context.
-- All metric scores range from 0 - 1, which the `minimum_score=0.7` threshold ultimately determines if your test have passed or not.
+- The variable `context` contains the relevant information from your knowledge base, and `HallucinationMetric(threshold=0.7)` is an default metric provided by DeepEval for you to evaluate how factually correct your application's output is based on the provided context.
+- All metric scores range from 0 - 1, which the `threshold=0.7` threshold ultimately determines if your test have passed or not.
 
 :::note
 `deepeval`'s default metrics are not evaluated using LLMs. Keep reading this tutorial to learn how to create an LLM based evaluation metric.
@@ -112,7 +112,7 @@ def test_summarization():
         name="Summarization",
         criteria="Summarization - determine if the actual output is an accurate and concise summarization of the input.",
         evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],
-        minimum_score=0.5
+        threshold=0.5
     )
     test_case = LLMTestCase(input=input, actual_output=actual_output)
     assert_test(test_case, [summarization_metric])
@@ -130,10 +130,10 @@ from deepeval.metrics import BaseMetric
 class LengthMetric(BaseMetric):
     # This metric checks if the output length is greater than 10 characters
     def __init__(self, max_length: int=10):
-        self.minimum_score = max_length
+        self.threshold = max_length
 
     def measure(self, test_case: LLMTestCase):
-        self.success = len(test_case.actual_output) > self.minimum_score
+        self.success = len(test_case.actual_output) > self.threshold
         if self.success:
             score = 1
         else:
@@ -184,13 +184,13 @@ def test_everything():
 
     # Replace this with the actual output of your LLM application
     actual_output = "We offer a 30-day full refund at no extra cost."
-    hallucination_metric = HallucinationMetric(minimum_score=0.7)
+    hallucination_metric = HallucinationMetric(threshold=0.7)
     length_metric = LengthMetric(max_length=10)
     summarization_metric = LLMEvalMetric(
         name="Summarization",
         criteria="Summarization - determine if the actual output is an accurate and concise summarization of the input.",
         evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],
-        minimum_score=0.5
+        threshold=0.5
     )
 
     test_case = LLMTestCase(input=input, actual_output=actual_output, context=context)
@@ -262,7 +262,7 @@ def test_hallucination(test_case: dict):
     # Replace this with the actual output of your LLM application
     actual_output = "We offer a 30-day full refund at no extra cost."
 
-    hallucination_metric = HallucinationMetric(minimum_score=0.7)
+    hallucination_metric = HallucinationMetric(threshold=0.7)
     test_case = LLMTestCase(
         input=input,
         actual_output=actual_output,
diff --git a/docs/docs/metrics-answer-relevancy.mdx b/docs/docs/metrics-answer-relevancy.mdx
index 9239f517a..2dc996da7 100644
--- a/docs/docs/metrics-answer-relevancy.mdx
+++ b/docs/docs/metrics-answer-relevancy.mdx
@@ -28,7 +28,7 @@ actual_output = "We offer a 30-day full refund at no extra cost."
 retrieval_context = ["All customers are eligible for a 30 day full refund at no extra cost."]
 
 metric = AnswerRelevancyMetric(
-    minimum_score=0.7,
+    threshold=0.7,
     model="gpt-4",
     include_reason=True
 )
diff --git a/docs/docs/metrics-bias.mdx b/docs/docs/metrics-bias.mdx
index 2c766ad6d..4cab96246 100644
--- a/docs/docs/metrics-bias.mdx
+++ b/docs/docs/metrics-bias.mdx
@@ -39,7 +39,7 @@ actual_output = "We offer a 30-day full refund at no extra cost."
 
 metric = UnBiasedMetric(
     evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
-    minimum_score=0.5
+    threshold=0.5
 )
 test_case = LLMTestCase(
     input="What if these shoes don't fit?",
diff --git a/docs/docs/metrics-contextual-precision.mdx b/docs/docs/metrics-contextual-precision.mdx
index 695ea5d93..76ed83c42 100644
--- a/docs/docs/metrics-contextual-precision.mdx
+++ b/docs/docs/metrics-contextual-precision.mdx
@@ -28,7 +28,7 @@ actual_output = "We offer a 30-day full refund at no extra cost."
 retrieval_context = ["All customers are eligible for a 30 day full refund at no extra cost."]
 
 metric = ContextualPrecisionMetric(
-    minimum_score=0.7,
+    threshold=0.7,
     model="gpt-4",
     include_reason=True
 )
diff --git a/docs/docs/metrics-contextual-recall.mdx b/docs/docs/metrics-contextual-recall.mdx
index b6afe519d..5be7ab128 100644
--- a/docs/docs/metrics-contextual-recall.mdx
+++ b/docs/docs/metrics-contextual-recall.mdx
@@ -32,7 +32,7 @@ expected_output = "You are eligible for a 30 day full refund at no extra cost."
 retrieval_context = ["All customers are eligible for a 30 day full refund at no extra cost."]
 
 metric = ContextualRecallMetric(
-    minimum_score=0.7,
+    threshold=0.7,
     model="gpt-4",
     include_reason=True
 )
diff --git a/docs/docs/metrics-contextual-relevancy.mdx b/docs/docs/metrics-contextual-relevancy.mdx
index 2e52fe43d..4fa3604ba 100644
--- a/docs/docs/metrics-contextual-relevancy.mdx
+++ b/docs/docs/metrics-contextual-relevancy.mdx
@@ -32,7 +32,7 @@ actual_output = "We offer a 30-day full refund at no extra cost."
 retrieval_context = ["All customers are eligible for a 30 day full refund at no extra cost."]
 
 metric = ContextualRelevancyMetric(
-    minimum_score=0.7,
+    threshold=0.7,
     model="gpt-4",
     include_reason=True
 )
diff --git a/docs/docs/metrics-custom.mdx b/docs/docs/metrics-custom.mdx
index 7e24304c9..1621deed6 100644
--- a/docs/docs/metrics-custom.mdx
+++ b/docs/docs/metrics-custom.mdx
@@ -24,40 +24,53 @@ from deepeval.metrics import BaseMetric
 from deepeval.test_case import LLMTestCase
 
 # Inherit BaseMetric
-class LengthMetric(BaseMetric):
-    # This metric checks if the output length is greater than 10 characters
-    def __init__(self, max_length: int=10):
-        self.minimum_score = max_length
+class LatencyMetric(BaseMetric):
+    # This metric by default checks if the latency is greater than 10 seconds
+    def __init__(self, max_seconds: int=10):
+        self.threshold = max_seconds
 
     def measure(self, test_case: LLMTestCase):
         # Set self.success and self.score in the "measure" method
-        self.success = len(test_case.actual_output) > self.minimum_score
+        self.success = test_case.execution_time <= self.threshold
         if self.success:
             self.score = 1
         else:
             self.score = 0
 
-        # You can also set a reason for the score returned.
+        # You can also optionally set a reason for the score returned.
         # This is particularly useful for a score computed using LLMs
-        self.reason = "..."
+        self.reason = "Too slow!"
         return self.score
 
     def is_successful(self):
         return self.success
 
     @property
-    def name(self):
-        return "Length"
+    def __name__(self):
+        return "Latency"
 ```
 
 Notice that a few things has happened:
 
-- `self.minimum_score` was set in `__init__()`
+- `self.threshold` was set in `__init__()`, and this can be either a minimum or maximum threshold
 - `self.success`, `self.score`, and `self.reason` was set in `measure()`
 - `measure()` takes in an `LLMTestCase`
-- `self.is_successful()` simply returns the success status
-- `name()` simply returns a string representing the metric name
+- `is_successful()` simply returns the success status
+- `__name()__` simply returns a string representing the metric name
 
-To create a custom metric without unexpected errors, we recommend you set the appropriate class variables in the appropriate methods as outlined above.
+To create a custom metric without unexpected errors, we recommend you set the appropriate class variables in the appropriate methods as outlined above. You should also note that `self.reason` is **optional**. `self.reason` should be a string representing the rationale behind an LLM computed score. This is only applicable if you're using LLMs as an evaluator in the `measure()` method, and has implemented a way to generate a score reasoning.
 
-You should also note that `self.reason` is **optional**. `self.reason` should be a string representing the rationale behind an LLM computed score. This is only applicable if you're using LLMs as an evaluator in the `measure()` method, and has implemented a way to generate a score reasoning.
+After creating a custom metric, you can use it in the same way as all of `deepeval`'s metrics:
+
+```python
+from deepeval import evaluate
+from deepeval.test_case import LLMTestCase
+
+...
+
+
+# Note that we pass in execution time since the measure method requires it for evaluation
+latency_metric = LatencyMetric(max_seconds=10.0)
+test_case = LLMTestCase(input="...", actual_output="...", execution_time=8.3)
+evaluate([test_case], [latency_metric])
+```
diff --git a/docs/docs/metrics-faithfulness.mdx b/docs/docs/metrics-faithfulness.mdx
index 6a2f8fefb..efed3eb5f 100644
--- a/docs/docs/metrics-faithfulness.mdx
+++ b/docs/docs/metrics-faithfulness.mdx
@@ -32,7 +32,7 @@ actual_output = "We offer a 30-day full refund at no extra cost."
 retrieval_context = ["All customers are eligible for a 30 day full refund at no extra cost."]
 
 metric = FaithfulnessMetric(
-    minimum_score=0.7,
+    threshold=0.7,
     model="gpt-4",
     include_reason=True
 )
diff --git a/docs/docs/metrics-hallucination.mdx b/docs/docs/metrics-hallucination.mdx
index 75b294266..8ed90e46a 100644
--- a/docs/docs/metrics-hallucination.mdx
+++ b/docs/docs/metrics-hallucination.mdx
@@ -40,7 +40,7 @@ test_case = LLMTestCase(
     actual_output=actual_output,
     context=context
 )
-metric = HallucinationMetric(minimum_score=0.5)
+metric = HallucinationMetric(threshold=0.5)
 
 metric.measure(test_case)
 print(metric.score)
diff --git a/docs/docs/metrics-introduction.mdx b/docs/docs/metrics-introduction.mdx
index f2cb51ecb..40eaf9115 100644
--- a/docs/docs/metrics-introduction.mdx
+++ b/docs/docs/metrics-introduction.mdx
@@ -32,7 +32,7 @@ A **_custom_** metric is a type of metric you can easily create by implementing
 - are extra reliable as LLMs are only used for extremely specific tasks during evaluation to greatly reduce stochasticity and flakiness in scores.
 - provide a comprehensive reason for the scores computed.
 
-All of `deepeval`'s default metrics output a score between 0-1, and require a `minimum_score` argument to instantiate. A default metric is only successful if the evaluation score is equal to or greater than `minimum_score`.
+All of `deepeval`'s default metrics output a score between 0-1, and require a `threshold` argument to instantiate. A default metric is only successful if the evaluation score is equal to or greater than `threshold`.
 
 :::info
 All GPT models from OpenAI are available for LLM-Evals (metrics that use LLMs for evaluation). You can switch between models by providing a string corresponding to OpenAI's model names via the optional `model` argument when instantiating an LLM-Eval.
@@ -82,7 +82,7 @@ All metrics in `deepeval`, including [custom metrics that you create](metrics-cu
 - can have its score accessed via `metric.score`
 - can have its status accessed via `metric.is_successful()`
 - can be used to evaluate test cases or entire datasets, with or without Pytest.
-- has a `minimum_score` that acts as the threshold for success. `metric.is_successful()` is only true if `metric.score` >= `minimum_score`.
+- has a `threshold` that acts as the threshold for success. `metric.is_successful()` is only true if `metric.score` >= `threshold`.
 
 In additional, most LLM-Evals in `deepeval` offers a reason for its score, which can be accessed via `metric.reason`.
 
@@ -99,8 +99,8 @@ from deepeval.test_case import LLMTestCase
 # Initialize a test case
 test_case = LLMTestCase(input="...", actual_output="...")
 
-# Initialize metric with minimum_score
-metric = AnswerRelevancyMetric(minimum_score=0.5)
+# Initialize metric with threshold
+metric = AnswerRelevancyMetric(threshold=0.5)
 ```
 
 Using this metric, you can either evaluate a test case using `deepeval test run`:
diff --git a/docs/docs/metrics-judgemental.mdx b/docs/docs/metrics-judgemental.mdx
index 933b1e3d9..cee908375 100644
--- a/docs/docs/metrics-judgemental.mdx
+++ b/docs/docs/metrics-judgemental.mdx
@@ -39,7 +39,7 @@ code_correctness_metric = JudgementalGPT(
     criteria="Code Correctness - determine whether the code in the 'actual output' produces a valid JSON.",
     evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
     language=Languages.SPANISH,
-    minimum_score=0.5,
+    threshold=0.5,
 )
 ```
 
@@ -49,7 +49,7 @@ Under the hood, `JudgementalGPT` sends a request to Confident AI's servers that
 - `criteria`: a description outlining the specific evaluation aspects for each test case.
 - `evaluation_params`: a list of type `LLMTestCaseParams`. Include only the parameters that are relevant for evaluation.
 - [Optional] `language`: type `Language`, specifies what language to return the reasoning in.
-- [Optional] `minimum_score`: the passing threshold, defaulted to 0.5.
+- [Optional] `threshold`: the passing threshold, defaulted to 0.5.
 
 Similar to `LLMEvalMetric`, you can access the judgemental `score` and `reason` for `JudgementalGPT`:
 
diff --git a/docs/docs/metrics-llm-evals.mdx b/docs/docs/metrics-llm-evals.mdx
index 814f035eb..a7cdaff7c 100644
--- a/docs/docs/metrics-llm-evals.mdx
+++ b/docs/docs/metrics-llm-evals.mdx
@@ -38,14 +38,14 @@ There are three mandatory and two optional parameters required when instantiatin
 - `criteria`: a description outlining the specific evaluation aspects for each test case.
 - `evaluation_params`: a list of type `LLMTestCaseParams`. Include only the parameters that are relevant for evaluation.
 - [Optional] `evaluation_steps`: a list of strings outlining the exact steps the LLM should take for evaluation. You can only provide either `evaluation_steps` or `criteria`, and not both.
-- [Optional] `minimum_score`: the passing threshold, defaulted to 0.5.
+- [Optional] `threshold`: the passing threshold, defaulted to 0.5.
 - [Optional] `model`: the model name. This is defaulted to 'gpt-4-1106-preview' and we currently only support models from (Azure) OpenAI.
 
 :::danger
 For accurate and valid results, only the parameters that are mentioned in `criteria` should be included as a member of `evaluation_params`.
 :::
 
-As mentioned in the [metrics introduction section](metrics-introduction), all of `deepeval`'s metrics return a score ranging from 0 - 1, and a metric is only successful if the evaluation score is equal to or greater than `minimum_score`. An `LLMEvalMetric` is no exception. You can access the `score` and `reason` for each individual `LLMEvalMetric`:
+As mentioned in the [metrics introduction section](metrics-introduction), all of `deepeval`'s metrics return a score ranging from 0 - 1, and a metric is only successful if the evaluation score is equal to or greater than `threshold`. An `LLMEvalMetric` is no exception. You can access the `score` and `reason` for each individual `LLMEvalMetric`:
 
 ```python
 from deepeval.test_case import LLMTestCase
diff --git a/docs/docs/metrics-ragas.mdx b/docs/docs/metrics-ragas.mdx
index 0ec7c2441..1fad689d7 100644
--- a/docs/docs/metrics-ragas.mdx
+++ b/docs/docs/metrics-ragas.mdx
@@ -42,7 +42,7 @@ expected_output = "You are eligible for a 30 day full refund at no extra cost."
 # Replace this with the actual retrieved context from your RAG pipeline
 retrieval_context = ["All customers are eligible for a 30 day full refund at no extra cost."]
 
-metric = RagasMetric(minimum_score=0.5, model="gpt-3.5-turbo")
+metric = RagasMetric(threshold=0.5, model="gpt-3.5-turbo")
 test_case = LLMTestCase(
     input="What if these shoes don't fit?",
     actual_output=actual_output,
diff --git a/docs/docs/metrics-summarization.mdx b/docs/docs/metrics-summarization.mdx
index 322b488c8..9b06deca9 100644
--- a/docs/docs/metrics-summarization.mdx
+++ b/docs/docs/metrics-summarization.mdx
@@ -46,7 +46,7 @@ from deepeval.test_case import LLMTestCase
 
 test_case = LLMTestCase(input=input, actual_output=actual_output)
 metric = SummarizationMetric(
-    minimum_score=0.5,
+    threshold=0.5,
     model="gpt-4",
     assessment_questions=[
         "Is the inclusion score based on a percentage of 'yes' answers?",
@@ -64,7 +64,7 @@ evaluate([test_case], [metric])
 
 There are five optional parameters when instantiating an `SummarizationMetric` class:
 
-- [Optional] `minimum_score`: the passing threshold, defaulted to 0.5.
+- [Optional] `threshold`: the passing threshold, defaulted to 0.5.
 - [Optional] `model`: the model name. This is defaulted to 'gpt-4-1106-preview' and we currently only support models from (Azure) OpenAI.
 - [Optional] `assessment_questions`: a list of **close-ended questions that can be answered with either a 'yes' or a 'no'**. These are questions you want your summary to be able to ideally answer, and is especially helpful if you already know what a good summary for your use case looks like. If `assessment_questions` is not provided, we will generate a set of `assessment_questions` for you at evaluation time. The `assessment_questions` are used to calculate the `inclusion_score`.
 - [Optional] `n`: the number of questions to generate when calculating the `alignment_score` and `inclusion_score`, defaulted to 5.
diff --git a/docs/docs/metrics-toxicity.mdx b/docs/docs/metrics-toxicity.mdx
index 90d34aec6..cc44893ce 100644
--- a/docs/docs/metrics-toxicity.mdx
+++ b/docs/docs/metrics-toxicity.mdx
@@ -35,7 +35,7 @@ actual_output = "We offer a 30-day full refund at no extra cost."
 
 metric = NonToxicMetric(
     evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],
-    minimum_score=0.5
+    threshold=0.5
 )
 test_case = LLMTestCase(
     input="What if these shoes don't fit?",
diff --git a/examples/getting_started/test_example.py b/examples/getting_started/test_example.py
index d910a2526..ff66655f5 100644
--- a/examples/getting_started/test_example.py
+++ b/examples/getting_started/test_example.py
@@ -15,7 +15,7 @@ def test_hallucination():
 
     # Replace this with the actual output from your LLM application
     actual_output = "We offer a 30-day full refund at no extra cost."
-    hallucination_metric = HallucinationMetric(minimum_score=0.7)
+    hallucination_metric = HallucinationMetric(threshold=0.7)
     test_case = LLMTestCase(
         input=input, actual_output=actual_output, context=context
     )
@@ -35,7 +35,7 @@ def test_summarization():
             LLMTestCaseParams.INPUT,
             LLMTestCaseParams.ACTUAL_OUTPUT,
         ],
-        minimum_score=0.5,
+        threshold=0.5,
     )
     test_case = LLMTestCase(input=input, actual_output=actual_output)
     assert_test(test_case, [summarization_metric])
@@ -44,10 +44,10 @@ def test_summarization():
 class LengthMetric(BaseMetric):
     # This metric checks if the output length is greater than 10 characters
     def __init__(self, max_length: int = 10):
-        self.minimum_score = max_length
+        self.threshold = max_length
 
     def measure(self, test_case: LLMTestCase):
-        self.success = len(test_case.actual_output) > self.minimum_score
+        self.success = len(test_case.actual_output) > self.threshold
         if self.success:
             self.score = 1
         else:
@@ -80,7 +80,7 @@ def test_everything():
 
     # Replace this with the actual output from your LLM application
     actual_output = "We offer a 30-day full refund at no extra cost."
-    hallucination_metric = HallucinationMetric(minimum_score=0.7)
+    hallucination_metric = HallucinationMetric(threshold=0.7)
     length_metric = LengthMetric(max_length=10)
     summarization_metric = LLMEvalMetric(
         name="Summarization",
@@ -89,7 +89,7 @@ def test_everything():
             LLMTestCaseParams.INPUT,
             LLMTestCaseParams.ACTUAL_OUTPUT,
         ],
-        minimum_score=0.5,
+        threshold=0.5,
     )
 
     test_case = LLMTestCase(
diff --git a/examples/tracing/test_chatbot.py b/examples/tracing/test_chatbot.py
index 62177cf5d..c8a55ee8b 100644
--- a/examples/tracing/test_chatbot.py
+++ b/examples/tracing/test_chatbot.py
@@ -83,7 +83,7 @@ def test_hallucination():
     ]
     input = "What are the requimrents to be president?"
 
-    metric = HallucinationMetric(minimum_score=0.8)
+    metric = HallucinationMetric(threshold=0.8)
     test_case = LLMTestCase(
         input=input,
         actual_output=chatbot.query(user_input=input),
diff --git a/poetry.lock b/poetry.lock
index 34c10e78c..ac4bcea85 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -693,53 +693,53 @@ typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "fonttools"
-version = "4.47.0"
+version = "4.47.2"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fonttools-4.47.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2d2404107626f97a221dc1a65b05396d2bb2ce38e435f64f26ed2369f68675d9"},
-    {file = "fonttools-4.47.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c01f409be619a9a0f5590389e37ccb58b47264939f0e8d58bfa1f3ba07d22671"},
-    {file = "fonttools-4.47.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d986b66ff722ef675b7ee22fbe5947a41f60a61a4da15579d5e276d897fbc7fa"},
-    {file = "fonttools-4.47.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8acf6dd0434b211b3bd30d572d9e019831aae17a54016629fa8224783b22df8"},
-    {file = "fonttools-4.47.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:495369c660e0c27233e3c572269cbe520f7f4978be675f990f4005937337d391"},
-    {file = "fonttools-4.47.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:c59227d7ba5b232281c26ae04fac2c73a79ad0e236bca5c44aae904a18f14faf"},
-    {file = "fonttools-4.47.0-cp310-cp310-win32.whl", hash = "sha256:59a6c8b71a245800e923cb684a2dc0eac19c56493e2f896218fcf2571ed28984"},
-    {file = "fonttools-4.47.0-cp310-cp310-win_amd64.whl", hash = "sha256:52c82df66201f3a90db438d9d7b337c7c98139de598d0728fb99dab9fd0495ca"},
-    {file = "fonttools-4.47.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:854421e328d47d70aa5abceacbe8eef231961b162c71cbe7ff3f47e235e2e5c5"},
-    {file = "fonttools-4.47.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:511482df31cfea9f697930f61520f6541185fa5eeba2fa760fe72e8eee5af88b"},
-    {file = "fonttools-4.47.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce0e2c88c8c985b7b9a7efcd06511fb0a1fe3ddd9a6cd2895ef1dbf9059719d7"},
-    {file = "fonttools-4.47.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e7a0a8848726956e9d9fb18c977a279013daadf0cbb6725d2015a6dd57527992"},
-    {file = "fonttools-4.47.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e869da810ae35afb3019baa0d0306cdbab4760a54909c89ad8904fa629991812"},
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 [package.dependencies]
@@ -1064,13 +1064,13 @@ files = [
 
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+version = "3.1.3"
 description = "A very fast and expressive template engine."
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-langchain-core = ">=0.1.4,<0.2"
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 numpy = ">=1,<2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
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 javascript = ["esprima (>=4.0.1,<5.0.0)"]
 llms = ["clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
 openai = ["openai (<2)", "tiktoken (>=0.3.2,<0.6.0)"]
@@ -1270,19 +1270,19 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.7"
+version = "0.0.11"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_community-0.0.7-py3-none-any.whl", hash = "sha256:468af187bfffe753426cc4548132824be7df9404d38ceef2f873087290d8ff0e"},
-    {file = "langchain_community-0.0.7.tar.gz", hash = "sha256:cfbeb25cac7dff3c021f3c82aa243fc80f80082d6f6fdcc79daf36b1408828cc"},
+    {file = "langchain_community-0.0.11-py3-none-any.whl", hash = "sha256:30ab1d7dbf35d0ebe684d8a1e8964e8dedd3d31a3703790436b39674cfa06f41"},
+    {file = "langchain_community-0.0.11.tar.gz", hash = "sha256:eaeaa8d63427ecf0cb32fe2f1ba4d05ad6d5ef9f7019baf21dc2dde5b1403002"},
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 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1,<0.2"
+langchain-core = ">=0.1.8,<0.2"
 langsmith = ">=0.0.63,<0.1.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
@@ -1292,17 +1292,17 @@ tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
 cli = ["typer (>=0.9.0,<0.10.0)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
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 [[package]]
 name = "langchain-core"
-version = "0.1.4"
+version = "0.1.10"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_core-0.1.4-py3-none-any.whl", hash = "sha256:c62bd362d5abf5359436a99b29629e12a4d1ede9f1704dc958cdb8530a791efd"},
-    {file = "langchain_core-0.1.4.tar.gz", hash = "sha256:f700138689c9014e23d3c29796a892dccf7f2a42901cb8817671823e1a24724c"},
+    {file = "langchain_core-0.1.10-py3-none-any.whl", hash = "sha256:d89952f6d0766cfc88d9f1e25b84d56f8d7bd63a45ad8ec1a9a038c9b49df16d"},
+    {file = "langchain_core-0.1.10.tar.gz", hash = "sha256:3c9e1383264c102fcc6f865700dbb9416c4931a25d0ac2195f6311c6b867aa17"},
 ]
 
 [package.dependencies]
@@ -1318,15 +1318,32 @@ tenacity = ">=8.1.0,<9.0.0"
 [package.extras]
 extended-testing = ["jinja2 (>=3,<4)"]
 
+[[package]]
+name = "langchain-openai"
+version = "0.0.2"
+description = "An integration package connecting OpenAI and LangChain"
+optional = false
+python-versions = ">=3.8.1,<4.0"
+files = [
+    {file = "langchain_openai-0.0.2-py3-none-any.whl", hash = "sha256:0a46067be13ce95a029fdca339cd1034a61be1a727786178fbad702668a060f9"},
+    {file = "langchain_openai-0.0.2.tar.gz", hash = "sha256:713af4a638f65b3af2f741a9d61991011c31939b070d81ede5b2e3cba625e01a"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.1.7,<0.2"
+numpy = ">=1,<2"
+openai = ">=1.6.1,<2.0.0"
+tiktoken = ">=0.5.2,<0.6.0"
+
 [[package]]
 name = "langsmith"
-version = "0.0.75"
+version = "0.0.79"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langsmith-0.0.75-py3-none-any.whl", hash = "sha256:3e008854204c5eaae007f34c7e249059218605689c385c037f6a40cac044833b"},
-    {file = "langsmith-0.0.75.tar.gz", hash = "sha256:3fd44c58bd53cb9366af3de129c7f11b6947914f1bb598a585240df0e2c566eb"},
+    {file = "langsmith-0.0.79-py3-none-any.whl", hash = "sha256:be0374e913c36d9f6a13dd6b6e20a506066d5a0f3abfd476f9cf9e0b086ed744"},
+    {file = "langsmith-0.0.79.tar.gz", hash = "sha256:d32639ccd18a92533b302f6f482255619afc8eb007fff91e37ee699d947c5e29"},
 ]
 
 [package.dependencies]
@@ -1335,13 +1352,13 @@ requests = ">=2,<3"
 
 [[package]]
 name = "llama-index"
-version = "0.9.24"
+version = "0.9.29"
 description = "Interface between LLMs and your data"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
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-    {file = "llama_index-0.9.24.tar.gz", hash = "sha256:48175a35c30427f361068693d6f384baf76865831569ca4e04a1a8b6f10ba269"},
+    {file = "llama_index-0.9.29-py3-none-any.whl", hash = "sha256:4ac6f589a2e8c5049882c185f25e6c57b68cf9d6e46a6f84102705a7dd357dd2"},
+    {file = "llama_index-0.9.29.tar.gz", hash = "sha256:df5af84bf593cdf6a36da403f521bd5582eccbd6ca551da44d2e4e5b2b4f5619"},
 ]
 
 [package.dependencies]
@@ -1352,6 +1369,7 @@ deprecated = ">=1.2.9.3"
 fsspec = ">=2023.5.0"
 httpx = "*"
 nest-asyncio = ">=1.5.8,<2.0.0"
+networkx = ">=3.0"
 nltk = ">=3.8.1,<4.0.0"
 numpy = "*"
 openai = ">=1.1.0"
@@ -1366,8 +1384,8 @@ typing-inspect = ">=0.8.0"
 [package.extras]
 gradientai = ["gradientai (>=1.4.0)"]
 langchain = ["langchain (>=0.0.303)"]
-local-models = ["optimum[onnxruntime] (>=1.13.2,<2.0.0)", "sentencepiece (>=0.1.99,<0.2.0)", "transformers[torch] (>=4.34.0,<5.0.0)"]
-postgres = ["asyncpg (>=0.28.0,<0.29.0)", "pgvector (>=0.1.0,<0.2.0)", "psycopg-binary (>=3.1.12,<4.0.0)"]
+local-models = ["optimum[onnxruntime] (>=1.13.2,<2.0.0)", "sentencepiece (>=0.1.99,<0.2.0)", "transformers[torch] (>=4.33.1,<5.0.0)"]
+postgres = ["asyncpg (>=0.28.0,<0.29.0)", "pgvector (>=0.1.0,<0.2.0)", "psycopg-binary (>=3.1.12,<4.0.0)", "psycopg2 (>=2.9.9,<3.0.0)"]
 query-tools = ["guidance (>=0.0.64,<0.0.65)", "jsonpath-ng (>=1.6.0,<2.0.0)", "lm-format-enforcer (>=0.4.3,<0.5.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "scikit-learn", "spacy (>=3.7.1,<4.0.0)"]
 
 [[package]]
@@ -1465,22 +1483,22 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.20.1"
+version = "3.20.2"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "SQLAlchemy-2.0.25.tar.gz", hash = "sha256:a2c69a7664fb2d54b8682dd774c3b54f67f84fa123cf84dda2a5f40dcaa04e08"},
 ]
 
 [package.dependencies]
 greenlet = {version = "!=0.4.17", optional = true, markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\" or extra == \"asyncio\""}
-typing-extensions = ">=4.2.0"
+typing-extensions = ">=4.6.0"
 
 [package.extras]
 aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
@@ -4055,4 +4091,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.12"
-content-hash = "2b0939b9cce5fa623a932eb89645a6ef3cc7384addd232339faded4a4c35cab7"
+content-hash = "1acc3ce5c08e0d3af483964b46a783bab32b37eb09f762b55d46826514210252"
diff --git a/pyproject.toml b/pyproject.toml
index df3ada338..0dc7e540d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "deepeval"
-version = "0.20.44"
+version = "0.20.46"
 description = "The Evaluation Framework for LLMs"
 authors = ["Jeffrey Ip <jeffreyip@confident-ai.com>"]
 license = "Apache-2.0"
@@ -37,6 +37,7 @@ black = "*"
 portalocker = "*"
 openai = "*"
 langchain = "*"
+langchain_openai = "*"
 protobuf = "*"
 typer = "*"
 setuptools = "*"
diff --git a/setup.py b/setup.py
index ab5d4584d..135a26e56 100644
--- a/setup.py
+++ b/setup.py
@@ -26,6 +26,7 @@
         "pytest-xdist",
         "portalocker",
         "langchain",
+        "langchain_openai",
         "rouge_score==0.1.2",
         "nltk==3.8.1",
         "ragas",
diff --git a/tests/test_answer_relevancy.py b/tests/test_answer_relevancy.py
index 8d828cc82..2863bf091 100644
--- a/tests/test_answer_relevancy.py
+++ b/tests/test_answer_relevancy.py
@@ -47,7 +47,7 @@
 
 @pytest.mark.skip(reason="openai is expensive")
 def test_answer_relevancy():
-    metric = AnswerRelevancyMetric(minimum_score=0.5)
+    metric = AnswerRelevancyMetric(threshold=0.5)
     test_case = LLMTestCase(
         input=question,
         actual_output=answer,
diff --git a/tests/test_contextual_precision.py b/tests/test_contextual_precision.py
index 6bd36a189..0916f167a 100644
--- a/tests/test_contextual_precision.py
+++ b/tests/test_contextual_precision.py
@@ -61,7 +61,7 @@
 
 @pytest.mark.skip(reason="openai is expensive")
 def test_contextual_precision():
-    metric = ContextualPrecisionMetric(minimum_score=0.5)
+    metric = ContextualPrecisionMetric(threshold=0.5)
     test_case = LLMTestCase(
         input=question,
         actual_output=answer,
diff --git a/tests/test_custom_metric.py b/tests/test_custom_metric.py
index f9e75db0a..9ec77aa9b 100644
--- a/tests/test_custom_metric.py
+++ b/tests/test_custom_metric.py
@@ -6,32 +6,37 @@
 from deepeval import assert_test
 
 
-class LengthMetric(BaseMetric):
-    """This metric checks if the output is more than 3 letters"""
-
-    def __init__(self, minimum_score: int = 3):
-        self.minimum_score = minimum_score
+class LatencyMetric(BaseMetric):
+    # This metric by default checks if the latency is greater than 10 seconds
+    def __init__(self, max_seconds: float = 10):
+        self.threshold = max_seconds
 
     def measure(self, test_case: LLMTestCase):
-        # sends to server
-        text = test_case.actual_output
-        score = len(text)
-        self.success = score > self.minimum_score
-        # Optional: Logs it to the server
-        return score
+        # Set self.success and self.score in the "measure" method
+        self.success = test_case.execution_time <= self.threshold
+        if self.success:
+            self.score = 1
+        else:
+            self.score = 0
+
+        # You can also set a reason for the score returned.
+        # This is particularly useful for a score computed using LLMs
+        self.reason = None
+        return self.score
 
     def is_successful(self):
         return self.success
 
     @property
     def __name__(self):
-        return "Length"
+        return "Latency"
 
 
 def test_length_metric():
-    metric = LengthMetric()
+    metric = LatencyMetric()
     test_case = LLMTestCase(
         input="placeholder",
         actual_output="This is a long sentence that is more than 3 letters",
+        execution_time=8.3,
     )
     assert_test(test_case, [metric])
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index 770bb05fc..d99ba8e98 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -41,5 +41,5 @@ def test_create_dataset():
 #     dataset,
 # )
 # def test_customer_chatbot(test_case: LLMTestCase):
-#     hallucination_metric = HallucinationMetric(minimum_score=0.3)
+#     hallucination_metric = HallucinationMetric(threshold=0.3)
 #     assert_test(test_case, [hallucination_metric])
diff --git a/tests/test_hallucination_metric.py b/tests/test_hallucination_metric.py
index c37518785..13560d23c 100644
--- a/tests/test_hallucination_metric.py
+++ b/tests/test_hallucination_metric.py
@@ -5,7 +5,7 @@
 
 
 def test_hallucination_metric():
-    metric = HallucinationMetric(minimum_score=0.5)
+    metric = HallucinationMetric(threshold=0.5)
     test_case = LLMTestCase(
         input="placeholder",
         actual_output="A blond drinking water in public.",
@@ -17,7 +17,7 @@ def test_hallucination_metric():
 
 
 def test_hallucination_metric_2():
-    metric = HallucinationMetric(minimum_score=0.6)
+    metric = HallucinationMetric(threshold=0.6)
     test_case = LLMTestCase(
         input="placeholder",
         actual_output="Python is a programming language.",
@@ -28,7 +28,7 @@ def test_hallucination_metric_2():
 
 
 def test_hallucination_metric_3():
-    metric = HallucinationMetric(minimum_score=0.6)
+    metric = HallucinationMetric(threshold=0.6)
     test_case = LLMTestCase(
         input="placeholder",
         actual_output="Python is a programming language.",
diff --git a/tests/test_judgemental.py b/tests/test_judgemental.py
index 3b2181655..f7f8bfd62 100644
--- a/tests/test_judgemental.py
+++ b/tests/test_judgemental.py
@@ -20,7 +20,7 @@ def test_judgemntal():
             LLMTestCaseParams.INPUT,
         ],
         language=Languages.SPANISH,
-        minimum_score=0.5,
+        threshold=0.5,
     )
 
     assert_test(test_case, [metric])
diff --git a/tests/test_llm_metric.py b/tests/test_llm_metric.py
index 4183ee2ff..54f34fb46 100644
--- a/tests/test_llm_metric.py
+++ b/tests/test_llm_metric.py
@@ -10,7 +10,7 @@ def test_chat_completion():
     metric = LLMEvalMetric(
         name="Validity",
         criteria="The response is a valid response to the prompt.",
-        minimum_score=0.5,
+        threshold=0.5,
         evaluation_params=[
             LLMTestCaseParams.INPUT,
             LLMTestCaseParams.ACTUAL_OUTPUT,
@@ -41,7 +41,7 @@ def test_chat_completion():
 #     metric = LLMEvalMetric(
 #         name="Validity",
 #         criteria="The response is a valid response to the prompt.",
-#         minimum_score=0.5,
+#         threshold=0.5,
 #         evaluation_params=[
 #             LLMTestCaseParams.INPUT,
 #             LLMTestCaseParams.ACTUAL_OUTPUT,
diff --git a/tests/test_quickstart.py b/tests/test_quickstart.py
index 504cb2b87..d33a71148 100644
--- a/tests/test_quickstart.py
+++ b/tests/test_quickstart.py
@@ -19,7 +19,7 @@ def test_llm_output():
     test_case = LLMTestCase(
         input=input, actual_output=generate_llm_output(input), context=context
     )
-    assert_test(test_case, [HallucinationMetric(minimum_score=0.5)])
+    assert_test(test_case, [HallucinationMetric(threshold=0.5)])
 
 
 def test_llm_output_custom():
@@ -29,7 +29,7 @@ def test_llm_output_custom():
         input="Placerholder", actual_output=actual_output, context=context
     )
     with pytest.raises(AssertionError):
-        assert_test(test_case, [HallucinationMetric(minimum_score=0.5)])
+        assert_test(test_case, [HallucinationMetric(threshold=0.5)])
 
 
 def test_0():
diff --git a/tests/test_ragas.py b/tests/test_ragas.py
index a2e38d306..caa93a868 100644
--- a/tests/test_ragas.py
+++ b/tests/test_ragas.py
@@ -2,17 +2,17 @@
 from deepeval.test_case import LLMTestCase
 from deepeval.metrics import (
     RagasMetric,
-    ContextualPrecisionMetric,
-    ContextualRelevancyMetric,
+    RAGASContextualPrecisionMetric,
+    RAGASContextualRelevancyMetric,
     RAGASFaithfulnessMetric,
-    ContextualRecallMetric,
+    RAGASContextualRecallMetric,
     ConcisenessMetric,
     CorrectnessMetric,
     CoherenceMetric,
     MaliciousnessMetric,
+    RAGASAnswerRelevancyMetric,
 )
-from deepeval.metrics.ragas_metric import RAGASAnswerRelevancyMetric
-from deepeval import assert_test, evaluate
+from deepeval import assert_test
 
 query = "Who won the FIFA World Cup in 2018 and what was the score?"
 output = "Winners of the FIFA world cup were the French national football team"
@@ -51,28 +51,28 @@ def test_everything():
         retrieval_context=context,
         context=context,
     )
-    # metric1 = ContextualRelevancyMetric(model="gpt-4")
-    # metric2 = RAGASFaithfulnessMetric(model="gpt-4")
-    # metric3 = ContextualRecallMetric(model="gpt-4")
-    # metric4 = ConcisenessMetric(model="gpt-4")
-    # metric5 = CorrectnessMetric(model="gpt-4")
-    # metric6 = CoherenceMetric(model="gpt-4")
-    # metric7 = MaliciousnessMetric(model="gpt-4")
-    # metric8 = RAGASAnswerRelevancyMetric(model="gpt-4")
-    metric9 = ContextualPrecisionMetric()
-    # metric10 = RagasMetric()
+    metric1 = RAGASContextualRelevancyMetric(model="gpt-4")
+    metric2 = RAGASFaithfulnessMetric(model="gpt-4")
+    metric3 = RAGASContextualRecallMetric(model="gpt-4")
+    metric4 = ConcisenessMetric(model="gpt-4")
+    metric5 = CorrectnessMetric(model="gpt-4")
+    metric6 = CoherenceMetric(model="gpt-4")
+    metric7 = MaliciousnessMetric(model="gpt-4")
+    metric8 = RAGASAnswerRelevancyMetric(model="gpt-4")
+    metric9 = RAGASContextualPrecisionMetric()
+    metric10 = RagasMetric()
     assert_test(
         test_case,
         [
-            # metric1,
-            # metric2,
-            # metric3,
-            # metric4,
-            # metric5,
-            # metric6,
-            # metric7,
-            # metric8,
+            metric1,
+            metric2,
+            metric3,
+            metric4,
+            metric5,
+            metric6,
+            metric7,
+            metric8,
             metric9,
-            # metric10,
+            metric10,
         ],
     )