diff --git a/deepeval/_version.py b/deepeval/_version.py
index c48754365..1d495ccb5 100644
--- a/deepeval/_version.py
+++ b/deepeval/_version.py
@@ -1 +1 @@
-__version__: str = "0.20.30"
+__version__: str = "0.20.33"
diff --git a/deepeval/evaluate.py b/deepeval/evaluate.py
index 5f798edc7..33540568e 100644
--- a/deepeval/evaluate.py
+++ b/deepeval/evaluate.py
@@ -22,6 +22,7 @@ class TestResult:
     actual_output: str
     expected_output: str
     context: List[str]
+    retrieval_context: List[str]
 
 
 def create_test_result(
@@ -37,6 +38,7 @@ def create_test_result(
             actual_output=test_case.actual_output,
             expected_output=test_case.expected_output,
             context=test_case.context,
+            retrieval_context=test_case.retrieval_context,
         )
     else:
         raise ValueError("TestCase not supported yet.")
@@ -139,3 +141,4 @@ def print_test_result(test_result: TestResult):
     print(f"  - actual output: {test_result.actual_output}")
     print(f"  - expected output: {test_result.expected_output}")
     print(f"  - context: {test_result.context}")
+    print(f"  - retrieval context: {test_result.retrieval_context}")
diff --git a/deepeval/test_run.py b/deepeval/test_run.py
index 85d3454b7..2e35285c5 100644
--- a/deepeval/test_run.py
+++ b/deepeval/test_run.py
@@ -72,6 +72,7 @@ class APITestCase(BaseModel):
     run_duration: float = Field(..., alias="runDuration")
     traceStack: Optional[dict] = Field(None)
     context: Optional[list] = Field(None)
+    retrieval_context: Optional[list] = Field(None, alias="retrievalContext")
     id: Optional[str] = None
 
 
@@ -133,6 +134,7 @@ def add_llm_test_case(
                 metricsMetadata=[metrics_metadata],
                 runDuration=run_duration,
                 context=test_case.context,
+                retrievalContext=test_case.retrieval_context,
                 traceStack=get_trace_stack(),
                 id=test_case.id,
             )
diff --git a/docs/docs/evaluation-metrics.mdx b/docs/docs/evaluation-metrics.mdx
deleted file mode 100644
index a77f58343..000000000
--- a/docs/docs/evaluation-metrics.mdx
+++ /dev/null
@@ -1,352 +0,0 @@
----
-id: evaluation-metrics
-title: Metrics
-sidebar_label: Metrics
----
-
-## Quick Summary
-
-In `deepeval`, a metric serves as a standard of measurement for evaluating the performance of an LLM output based on a specific criteria of interest. Essentially, while the metric acts as the ruler, the test case represents what you're assessing. `deepeval` offers a range of default metrics for you to quickly get started with, which includes:
-
-- Hallucination
-- Answer Relevancy
-- RAGAS
-- Toxicity
-- Bias
-
-`deepeval` also offers you a straightforward way to develop your own custom LLM-based evaluation metrics. This is noteworthy because all default metrics in `deepeval` are derived from traditional NLP models, not LLMs. All metrics are measured on a test case. Visit the [test cases section](evaluation-test-cases) to learn how to apply any metric on test cases for evaluation.
-
-## Types of Metrics
-
-A **_custom_** metric is a type of metric you can easily create by implementing abstract methods and properties of base classes provided by `deepeval`. They are extremely versitle and seamlessly integrate with Confident AI without requiring any additional setup. As you'll see later, a custom metric can either be an **_LLM evaluated_** or **_classic_** metric. A classic metric is a type of metric whose criteria isn't evaluated using an LLM.
-
-`deepeval` also offer **_default_** metrics. All default metrics offered by `deepeval` are classic metrics. This means all default metrics in `deepeval` does not use LLMs for evaluation. This is delibrate for two main reasons:
-
-- LLM evaluated metrics are versitle in nature and it's better if you create one using `deepeval`'s build-ins
-- Classic metrics are much harder to compute and requires extensive research
-
-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`.
-
-:::note
-Our suggestion is to begin with custom LLM evaluated metrics (which frequently surpass and offer more versatility than leading NLP models), and gradually transition to `deepeval`'s default metrics when feasible. We recommend using default metrics as an optimization to your evaluation workflow because they are more cost-effective.
-:::
-
-## Hallucination
-
-Hallucination determines whether your LLM application outputs factually correct information by comparing the `actual_output` to the provided `context`. You'll have to supply `context` when creating an `LLMTestCase` to evaluate hallucination.
-
-```python
-import pytest
-from deepeval import evaluate
-from deepeval.metrics import HallucinationMetric
-from deepeval.test_case import LLMTestCase
-
-# Replace this with the actual documents that you are passing as input to your LLM.
-context=["A man with blond-hair, and a brown shirt drinking out of a public water fountain."]
-
-# Replace this with the actual output from your LLM application
-actual_output="A blond drinking water in public.",
-
-test_case = LLMTestCase(input="placeholder", actual_output=actual_output, context=context)
-metric = HallucinationMetric(minimum_score=0.5)
-
-metric.measure(test_case)
-print(metric.score)
-
-# or
-# evaluate([test_case], [metric])
-```
-
-:::info
-This metric uses vectara's hallucination evaluation model.
-:::
-
-## LLM Evaluated Metrics
-
-A LLM evalated metric, is a custom metric whose score is calculated by LLMs. To create a custom metric that uses LLMs for evaluation, simply instantiate an `LLMEvalMetric` class and define an evaluation criteria in natural language:
-
-```python
-from deepeval.metrics import LLMEvalMetric
-from deepeval.test_case import LLMTestCase, LLMTestCaseParams
-
-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,
-    model="gpt-4"
-)
-```
-
-There are three mandatory and two optional parameters required when instantiating an `LLMEvalMetric` class:
-
-- `name`: name of metric
-- `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] `minimum_score`: 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] `deployment_id`: the deployment name you chose when you deployed Azure OpenAI. Only required if you're using Azure OpenAI.
-
-All instances of `LLMEvalMetric` returns a score ranging from 0 - 1. A metric is only successful if the evaluation score is equal to or greater than `minimum_score`.
-
-:::danger
-For accurate and valid results, only the parameters that are mentioned in `criteria` should be included as a member of `evaluation_params`.
-:::
-
-## Answer Relevancy
-
-Answer Relevancy measures how relevant the `actual_output` of your LLM application is compared to the provided `input`. You don't have to supply `context` or `expected_output` when creating an `LLMTestCase` if you're just evaluating answer relevancy.
-
-```python
-import pytest
-from deepeval import evaluate
-from deepeval.metrics import AnswerRelevancyMetric
-from deepeval.test_case import LLMTestCase
-
-
-input = "What if these shoes don't fit?"
-
-# Replace this with the actual output from your LLM application
-actual_output = "We offer a 30-day full refund at no extra cost."
-
-answer_relevancy_metric = AnswerRelevancyMetric(minimum_score=0.7)
-test_case = LLMTestCase(input=input, actual_output=actual_output)
-
-metric.measure(test_case)
-print(metric.score)
-
-# or
-# evaluate([test_case], [metric])
-```
-
-## RAGAS
-
-`deepeval` offers the RAGAS metric, which is useful for evaluating RAG pipelines (ie. LLM applications built with RAG). The RAGAS score is calculated by taking an unweighted harmonic mean of five distinct metrics.
-
-1. **Faithfulness Metric**: measures hallucination to ensure output align with context. Calculated using `actual_output` and `retrieval_context`.
-
-2. **Contextual Precision Metric**: determines whether more relevant retrieved contexts are ranked higher than less relevant ones. Calculated using `input` and `retrieval_context`.
-
-3. **Answer Relevancy Metric**: measures how relevant an answer is relative to the question. Penalizes redundancy or incompleteness. Derived from the `input` and `actual_output`.
-
-4. **Contextual Relevancy Metric**: assesses the relevance of retrieved contexts to input. Penalizes redundant information. Based on the `input` and `retrieval_context`.
-
-5. **Context Recall Metric**: gauges the recall of the retrieved context using the annotated answer as a reference. Based on the `expected_output` and `retrieval_context`.
-
-The Faithfulness and Answer Relevancy metric assess the quality of the generator in your RAG pipeline, while the Contextual Relevancy, Precision, and Recall metric evaluate the performance of your retriever.
-
-Create an `LLMTestCase` and supply all parameters to calculate the RAGAS score:
-
-```python
-from deepeval import evaluate
-from deepeval.metrics import RagasMetric
-from deepeval.test_case import LLMTestCase
-
-input = "What if these shoes don't fit?"
-expected_output = "You're eligible for a 30 day 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 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."]
-
-ragas_metric = RagasMetric()
-test_case = LLMTestCase(
-    input=input,
-    actual_output=actual_output,
-    expected_output=expected_output,
-    retrieval_context=retrieval_context,
-)
-
-ragas_metric.measure(test_case)
-print(ragas_metric.score)
-
-# You can also print out the 5 scores that make up the RAGAS score.
-print(ragas_metric.score_metadata)
-
-# or
-# evaluate([test_case], [ragas_metric])
-```
-
-:::info
-Since the RAGAS score is the harmonic mean of 5 different scores, a zero value for either one of the scores will yield a final score of 0 for the RAGAS metric.
-:::
-
-As mentioned earlier, the RAGAS score is the harmonic mean of five different metrics. You can however import these metrics individually and utilize them in exactly the same way as all other metrics offered by `deepeval`.
-
-```python
-from deepeval.metrics import ContextualPrecisionMetric
-from deepeval.metrics import ContextualRelevancyMetric
-from deepeval.metrics import AnswerRelevancyMetric
-from deepeval.metrics import FaithfulnessMetric
-from deepeval.metrics import ContextRecallMetric
-from deepeval.metrics import ConcisenessMetric
-from deepeval.metrics import CorrectnessMetric
-from deepeval.metrics import CoherenceMetric
-from deepeval.metrics import MaliciousnessMetric
-```
-
-## Toxicity
-
-Unlike other default metrics, Toxicity is a **referenceless** metric, meaning it doesn't require comparison to a "source of truth" for evaluation. First, install detoxify.
-
-```console
-pip install detoxify
-```
-
-Being a referenceless metric means `NonToxicMetric` requires an extra parameter named `evaluation_params`. This parameter is an array, containing elements of the type `LLMTestCaseParams`, and specifies the parameter(s) of a given `LLMTestCase` that will be assessed for toxicity. The `NonToxicMetric` will then compute a score based on the average toxicity levels of each individual component being evaluated.
-
-```python
-from deepeval import run_test
-from deepeval.metrics import NonToxicMetric
-from deepeval.test_case import LLMTestCase, LLMTestCaseParams
-
-
-input = "What if these shoes don't fit?"
-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 cost."
-
-non_toxic_metric = NonToxicMetric(
-    evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],
-    minimum_score=0.7
-)
-
-test_case = LLMTestCase(
-    input=input,
-    actual_output=actual_output
-)
-
-non_toxic_metric.measure(test_case)
-print(non_toxic_metric.score)
-
-# or
-# evaluate([test_case], [metric])
-```
-
-Notice that `expected_output` or `context` are not required as `NonToxicMetric` is a referenceless metric.
-
-:::note
-In `deepeval`, a higher score is always better (which remember, ranges from 0-1). This is why the metric is called `NonToxicMetric` instead of `ToxicMetric`.
-:::
-
-## Bias
-
-`deepeval` offers an `UnBiasedMetric` to tackle bias that can occur after finetuning from any RLHF or optimizations (gender, racial, and political, just to name a few).
-
-```console
-pip install Dbias
-```
-
-`UnBiasedMetric` is similar to `NonToxicMetric` because it is also a referenceless metric.
-
-```python
-from deepeval import run_test
-from deepeval.metrics import UnBiasedMetric
-from deepeval.test_case import LLMTestCase, LLMTestCaseParams
-
-
-input = "What if these shoes don't fit?"
-
-# Replace this with the actual output from your LLM application
-actual_output = "We offer a 30-day full refund at no extra cost."
-
-unbias_metric = UnBiasedMetric(
-    evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
-    minimum_score=0.7
-)
-
-test_case = LLMTestCase(
-    input=input,
-    actual_output=actual_output
-)
-
-unbias_metric.measure(test_case)
-print(unbias_metric.score)
-
-# or
-# evaluate([test_case], [unbias_metric])
-```
-
-## Custom Metrics
-
-You can implement your own evaluator (for example, your own GPT evaluator) by creating a custom metric. All custom metrics are automatically integrated with Confident AI.
-
-```python
-from deepeval.metrics import BaseMetric
-
-# 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
-
-    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
-        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 = "..."
-        return self.score
-
-    def is_successful(self):
-        return self.success
-
-    @property
-    def name(self):
-        return "Length"
-```
-
-Noticed that we accessed `test_case.actual_output` in `measure`. you will have to supply the optional `context` or `expected_output` arguments in the `LLMTestCase` depending on your `measure` implementation.
-
-In this example, you would instantiate `LengthMetric` as follows:
-
-```python
-length_metric = LengthMetric(max_length=20)
-```
-
-:::info
-You must implement `measure`, `is_successful`, and `name` yourself, as these are abstract methods and properties inherited from `Metric`.
-:::
-
-## JudgementalGPT
-
-`JudgementalGPT` is an LLM agent developed in-house by [Confident AI](https://confident-ai.com) that's dedicated to evaluation and is superior to `LLMEvalMetric`. While it operates similarly to `LLMEvalMetric` by utilizing LLMs for scoring, it:
-
-- offers enhanced accuracy and reliability.
-- is capable of generating justifications for its scores
-- has the ability to conditionally execute code that helps detect logical fallacies during evaluations
-
-To use `JudgementalGPT`, start by logging into Confident AI:
-
-```console
-deepeval login
-```
-
-Then paste in the following code to define a metric powered by `JudgementalGPT`:
-
-```python
-from deepeval.metrics import JudgementalGPT
-from deepeval.test_case import LLMTestCase, LLMTestCaseParams
-
-code_correctness_metric = JudgementalGPT(
-    name="Code Correctness",
-    criteria="Code Correctness - determine whether the python code in the 'actual output' produces a valid JSON.",
-    evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
-    minimum_score=0.5,
-)
-```
-
-Under the hood, `JudgementalGPT(...)` sends a request to Confident AI's servers that hosts `JudgementalGPT`. `JudgementalGPT` accepts four arguments:
-
-- `name`: name of metric
-- `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] `minimum_score`: the passing threshold, defaulted to 0.5.
diff --git a/docs/docs/evaluation-test-cases.mdx b/docs/docs/evaluation-test-cases.mdx
index a54f8f928..0c544bd34 100644
--- a/docs/docs/evaluation-test-cases.mdx
+++ b/docs/docs/evaluation-test-cases.mdx
@@ -28,7 +28,7 @@ test_case = LLMTestCase(
 
 **Note that only `input` and `actual_output` is mandatory.**
 
-However, depending on the specific metric you're evaluating your test cases on, you may or may not require a `retrieval_context`, `expected_output` and/or `context` as additional parameters. For example, you won't need `expected_output` and `context` if you're just measuring answer relevancy, but if you're evaluating factual consistency you'll have to supply `context` in order for `deepeval` to know what the **ground truth** is.
+However, depending on the specific metric you're evaluating your test cases on, you may or may not require a `retrieval_context`, `expected_output` and/or `context` as additional parameters. For example, you won't need `expected_output` and `context` if you're just measuring answer relevancy, but if you're evaluating factual consistency you'll have to provide `context` in order for `deepeval` to know what the **ground truth** is.
 
 Let's go through the purpsoe of each parameter.
 
diff --git a/docs/docs/metrics-answer-relevancy.mdx b/docs/docs/metrics-answer-relevancy.mdx
new file mode 100644
index 000000000..96a8863eb
--- /dev/null
+++ b/docs/docs/metrics-answer-relevancy.mdx
@@ -0,0 +1,37 @@
+---
+id: metrics-answer-relevancy
+title: Answer Relevancy
+sidebar_label: Answer Relevancy
+---
+
+Answer Relevancy measures how relevant the `actual_output` of your LLM application is compared to the provided `input`. You don't have to supply `context` or `expected_output` when creating an `LLMTestCase` if you're just evaluating answer relevancy.
+
+## Required Parameters
+
+To use the `AnswerRelevancyMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+
+## Example
+
+```python
+from deepeval import evaluate
+from deepeval.metrics import AnswerRelevancyMetric
+from deepeval.test_case import LLMTestCase
+
+# Replace this with the actual output from your LLM application
+actual_output = "We offer a 30-day full refund at no extra cost."
+
+metric = AnswerRelevancyMetric(minimum_score=0.7)
+test_case = LLMTestCase(
+    input="What if these shoes don't fit?",
+    actual_output=actual_output
+)
+
+metric.measure(test_case)
+print(metric.score)
+
+# or evaluate test cases in bulk
+evaluate([test_case], [metric])
+```
diff --git a/docs/docs/metrics-bias.mdx b/docs/docs/metrics-bias.mdx
new file mode 100644
index 000000000..4d8714f8b
--- /dev/null
+++ b/docs/docs/metrics-bias.mdx
@@ -0,0 +1,54 @@
+---
+id: metrics-bias
+title: Bias
+sidebar_label: Bias
+---
+
+Bias determines whether your LLM has gender, racial, or political bias in whatever parameters you want to evaluate it on. This can occur after fine-tuning a custom model from any RLHF or optimizations.
+
+:::info
+Bias in `deepeval` is a **referenceless** metric. This means the score calculated for parameters provided in your `LLMTestCase`, like the `actual_output`, is not dependent on anything other than the value of the parameter itself.
+:::
+
+## Installation
+
+Bias in `deepeval` requires an additional installation:
+
+```
+pip install Dbias
+```
+
+## Required Parameters
+
+To use the `UnBiasedMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+
+## Example
+
+Unlike other metrics you've encountered to far, the `UnBiasedMetric` requires an extra parameter named evaluation_params. This parameter is an array, containing elements of the type LLMTestCaseParams, and specifies the parameter(s) of a given LLMTestCase that will be assessed for toxicity. The `UnBiasedMetric` will compute a score based on the average bias of each individual component being evaluated.
+
+```python
+from deepeval import evaluate
+from deepeval.metrics import UnBiasedMetric
+from deepeval.test_case import LLMTestCase, LLMTestCaseParams
+
+# Replace this with the actual output from your LLM application
+actual_output = "We offer a 30-day full refund at no extra cost."
+
+metric = UnBiasedMetric(
+    evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
+    minimum_score=0.5
+)
+test_case = LLMTestCase(
+    input="What if these shoes don't fit?",
+    actual_output=actual_output,
+)
+
+metric.measure(test_case)
+print(metric.score)
+
+# or evaluate test cases in bulk
+evaluate([test_case], [metric])
+```
diff --git a/docs/docs/metrics-contextual-precision.mdx b/docs/docs/metrics-contextual-precision.mdx
new file mode 100644
index 000000000..a6a5370f4
--- /dev/null
+++ b/docs/docs/metrics-contextual-precision.mdx
@@ -0,0 +1,46 @@
+---
+id: metrics-contextual-precision
+title: Contextual Precision
+sidebar_label: Contextual Precision
+---
+
+Contextual Precision determines whether more relevant retrieved contexts in your RAG pipeline are ranked higher than less relevant ones. It assesses your RAG pipeline's retriever and is calculated using `input` and `retrieval_context`.
+
+## Required Parameters
+
+To use the `ContextualPrecisionMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+- `retrieval_context`
+
+:::note
+Remember, `input` and `actual_output` are **mandatory** arguments to an `LLMTestCase` and so are always required even if not used for evaluation.
+:::
+
+## Example
+
+```python
+from deepeval import evaluate
+from deepeval.metrics import ContextualPrecisionMetric
+from deepeval.test_case import LLMTestCase
+
+# Replace this with the actual output from your LLM application
+actual_output = "We offer 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 = ContextualPrecisionMetric(minimum_score=0.7)
+test_case = LLMTestCase(
+    input="What if these shoes don't fit?",
+    actual_output=actual_output,
+    retrieval_context=retrieval_context
+)
+
+metric.measure(test_case)
+print(metric.score)
+
+# or evaluate test cases in bulk
+evaluate([test_case], [metric])
+```
diff --git a/docs/docs/metrics-contextual-recall.mdx b/docs/docs/metrics-contextual-recall.mdx
new file mode 100644
index 000000000..1c3949155
--- /dev/null
+++ b/docs/docs/metrics-contextual-recall.mdx
@@ -0,0 +1,42 @@
+---
+id: metrics-contextual-recall
+title: Contextual Recall
+sidebar_label: Contextual Recall
+---
+
+Contextual Recall determines the recall of the retrieved context using the annotated answer as a reference to evaluate the performance of your RAG pipeline's retriever. Calculated using `expected_output` and `retrieval_context`.
+
+## Required Parameters
+
+To use the `ContextRecallMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+- `expected_output`
+
+## Example
+
+```python
+from deepeval import evaluate
+from deepeval.metrics import ContextRecallMetric
+from deepeval.test_case import LLMTestCase
+
+# Replace this with the actual output from your LLM application
+actual_output = "We offer a 30-day full refund at no extra cost."
+
+# Replace this with the expected output from your RAG generator
+expected_output = "You are eligible for a 30 day full refund at no extra cost."
+
+metric = ContextRecallMetric(minimum_score=0.7)
+test_case = LLMTestCase(
+    input="What if these shoes don't fit?",
+    actual_output=actual_output,
+    expected_output=expected_output
+)
+
+metric.measure(test_case)
+print(metric.score)
+
+# or evaluate test cases in bulk
+evaluate([test_case], [metric])
+```
diff --git a/docs/docs/metrics-contextual-relevancy.mdx b/docs/docs/metrics-contextual-relevancy.mdx
new file mode 100644
index 000000000..e9a22cacb
--- /dev/null
+++ b/docs/docs/metrics-contextual-relevancy.mdx
@@ -0,0 +1,46 @@
+---
+id: metrics-contextual-relevancy
+title: Contextual Relevancy
+sidebar_label: Contextual Relevancy
+---
+
+Contextual Relevancy assesses the relevance of the retrieved contexts to input, and penalizes redundant information. It evaluates the performance of your RAG pipeline's retriever and is calculated using `input` and `retrieval_context`.
+
+## Required Parameters
+
+To use the `ContextualRelevancyMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+- `retrieval_context`
+
+:::note
+Similar to `ContextualPrecisionMetric`, the `ContextualRelevancyMetric` uses `retrieval_context` from your RAG pipeline for evaluation.
+:::
+
+## Example
+
+```python
+from deepeval import evaluate
+from deepeval.metrics import ContextualRelevancyMetric
+from deepeval.test_case import LLMTestCase
+
+# Replace this with the actual output from your LLM application
+actual_output = "We offer 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 = ContextualRelevancyMetric(minimum_score=0.7)
+test_case = LLMTestCase(
+    input="What if these shoes don't fit?",
+    actual_output=actual_output,
+    retrieval_context=retrieval_context
+)
+
+metric.measure(test_case)
+print(metric.score)
+
+# or evaluate test cases in bulk
+evaluate([test_case], [metric])
+```
diff --git a/docs/docs/metrics-custom.mdx b/docs/docs/metrics-custom.mdx
new file mode 100644
index 000000000..7e24304c9
--- /dev/null
+++ b/docs/docs/metrics-custom.mdx
@@ -0,0 +1,63 @@
+---
+id: metrics-custom
+title: Custom Metrics
+sidebar_label: Custom Metrics
+---
+
+`deepeval` allows you to implement your own evaluator (for example, your own GPT evaluator) by creating a custom metric. All custom metrics are automatically integrated with the deepeval ecosystem, which includes Confident AI.
+
+## Required Parameters
+
+To use a custom metric, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+
+You'll also need to supply any additional arguments such as `expected_output` and `context` if your custom metric's `measure()` method is dependent on these parameters.
+
+## Implementation
+
+To create a custom metric, you'll need to inherite `deepeval`'s `BaseMetric` class, and implement abstract methods and properties such as `measure()`, `is_successful()`, and `name()`. Here's an example:
+
+```python
+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
+
+    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
+        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 = "..."
+        return self.score
+
+    def is_successful(self):
+        return self.success
+
+    @property
+    def name(self):
+        return "Length"
+```
+
+Notice that a few things has happened:
+
+- `self.minimum_score` was set in `__init__()`
+- `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
+
+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.
diff --git a/docs/docs/metrics-faithfulness.mdx b/docs/docs/metrics-faithfulness.mdx
new file mode 100644
index 000000000..6d36d1029
--- /dev/null
+++ b/docs/docs/metrics-faithfulness.mdx
@@ -0,0 +1,46 @@
+---
+id: metrics-faithfulness
+title: Faithfulness
+sidebar_label: Faithfulness
+---
+
+Faithfulness measures hallucination in a RAG pipeline to ensure output aligns with the retrieved context. It evaluates the quality of your RAG pipeline's generator and is calculated using `actual_output` and `retrieval_context`.
+
+:::info
+Although similar to the `HallucinationMetric`, the faithfulness metric in `deepeval` is more concerned with hallucination in RAG pipelines, rather than the actual LLM itself.
+:::
+
+## Required Parameters
+
+To use the `FaithfulnessMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+- `retrieval_context`
+
+## Example
+
+```python
+from deepeval import evaluate
+from deepeval.metrics import FaithfulnessMetric
+from deepeval.test_case import LLMTestCase
+
+# Replace this with the actual output from your LLM application
+actual_output = "We offer 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 = FaithfulnessMetric(minimum_score=0.5)
+test_case = LLMTestCase(
+    input="What if these shoes don't fit?",
+    actual_output=actual_output,
+    retrieval_context=retrieval_context
+)
+
+metric.measure(test_case)
+print(metric.score)
+
+# or evaluate test cases in bulk
+evaluate([test_case], [metric])
+```
diff --git a/docs/docs/metrics-hallucination.mdx b/docs/docs/metrics-hallucination.mdx
new file mode 100644
index 000000000..f1c10eb2a
--- /dev/null
+++ b/docs/docs/metrics-hallucination.mdx
@@ -0,0 +1,46 @@
+---
+id: metrics-hallucination
+title: Hallucination
+sidebar_label: Hallucination
+---
+
+Hallucination determines whether your LLM generates factually correct information by comparing the `actual_output` to the provided `context`.
+
+## Required Parameters
+
+To use the `HallucinationMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+- `context`
+
+## Example
+
+```python
+from deepeval import evaluate
+from deepeval.metrics import HallucinationMetric
+from deepeval.test_case import LLMTestCase
+
+# Replace this with the actual documents that you are passing as input to your LLM.
+context=["A man with blond-hair, and a brown shirt drinking out of a public water fountain."]
+
+# Replace this with the actual output from your LLM application
+actual_output="A blond drinking water in public."
+
+test_case = LLMTestCase(
+    input="What was the blond doing?",
+    actual_output=actual_output,
+    context=context
+)
+metric = HallucinationMetric(minimum_score=0.5)
+
+metric.measure(test_case)
+print(metric.score)
+
+# or evaluate test cases in bulk
+evaluate([test_case], [metric])
+```
+
+:::info
+This metric uses vectara's hallucination evaluation model.
+:::
diff --git a/docs/docs/metrics-introduction.mdx b/docs/docs/metrics-introduction.mdx
new file mode 100644
index 000000000..92a85dcdc
--- /dev/null
+++ b/docs/docs/metrics-introduction.mdx
@@ -0,0 +1,87 @@
+---
+id: metrics-introduction
+title: Metrics
+sidebar_label: Introduction
+---
+
+## Quick Summary
+
+In `deepeval`, a metric serves as a standard of measurement for evaluating the performance of an LLM output based on a specific criteria of interest. Essentially, while the metric acts as the ruler, a test case represents the thing you're trying to measure. `deepeval` offers a range of default metrics for you to quickly get started with, which includes:
+
+- Hallucination
+- Answer Relevancy
+- Ragas
+- Toxicity
+- Bias
+
+`deepeval` also offers you a straightforward way to develop your own custom LLM-based evaluation metrics. This is noteworthy because all default metrics in `deepeval` are derived from traditional NLP models, not LLMs. All metrics are measured on a test case. Visit the [test cases section](evaluation-test-cases) to learn how to apply any metric on test cases for evaluation.
+
+## Types of Metrics
+
+A **_custom_** metric is a type of metric you can easily create by implementing abstract methods and properties of base classes provided by `deepeval`. They are extremely versitle and seamlessly integrate with Confident AI without requiring any additional setup. As you'll see later, a custom metric can either be an **_LLM evaluated_** or **_classic_** metric. A classic metric is a type of metric whose criteria isn't evaluated using an LLM.
+
+`deepeval` also offer **_default_** metrics. All default metrics offered by `deepeval` are classic metrics. This means all default metrics in `deepeval` does not use LLMs for evaluation. This is delibrate for two main reasons:
+
+- LLM evaluated metrics are versitle in nature and it's better if you create one using `deepeval`'s build-ins
+- Classic metrics are much harder to compute and requires extensive research
+
+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`.
+
+:::note
+Our suggestion is to begin with custom LLM evaluated metrics (which frequently surpass and offer more versatility than leading NLP models), and gradually transition to `deepeval`'s default metrics when feasible. We recommend using default metrics as an optimization to your evaluation workflow because they are more cost-effective.
+:::
+
+## Measuring a Metric
+
+All metrics in `deepeval`, including [custom metrics that you create](metrics-custom):
+
+- can be executed via the `metric.measure()` method
+- 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`.
+
+Here's a quick example.
+
+```python
+from deepeval.metrics import HallucinationMetric
+from deepeval.test_case import LLMTestCase
+
+# Initialize a test case
+test_case = LLMTestCase(input="...", actual_output="...")
+
+# Initialize metric with minimum_score
+metric = HallucinationMetric(minimum_score=0.5)
+```
+
+Using this metric, you can either evaluate a test case using `deepeval test run`:
+
+```python title="test_file.py"
+from deepeval import evaluate
+...
+
+def test_hallucination():
+    assert_test(test_case, metric)
+```
+
+```console
+deepeval test run test_file.py
+```
+
+The `evaluate` function:
+
+```python
+from deepeval import assert_test
+...
+
+evaluate([test_case], [metric])
+```
+
+Or execute the metric directly and get its score:
+
+```python
+metric.measure(test_case)
+print(metric.score)
+```
+
+For more details on how a metric evaluates a test case, refer to the [test cases section.](evaluation-test-cases#assert-test-cases)
diff --git a/docs/docs/metrics-judgemental.mdx b/docs/docs/metrics-judgemental.mdx
new file mode 100644
index 000000000..e73abcb4c
--- /dev/null
+++ b/docs/docs/metrics-judgemental.mdx
@@ -0,0 +1,49 @@
+---
+id: metrics-judgemental
+title: Judgemental GPT
+sidebar_label: Judgemental GPT
+---
+
+`JudgementalGPT` is an LLM agent developed in-house by [Confident AI](https://confident-ai.com) that's dedicated to evaluation and is superior to `LLMEvalMetric`. While it operates similarly to `LLMEvalMetric` by utilizing LLMs for scoring, it:
+
+- offers enhanced accuracy and reliability.
+- is capable of generating justifications for its scores
+- has the ability to conditionally execute code that helps detect logical fallacies during evaluations
+
+## Required Parameters
+
+To use `JudgementalGPT`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+
+Similar to `LLMEvalMetric`, you'll also need to supply any additional arguments such as `expected_output` and `context` if your evaluation criteria depends on these parameters.
+
+## Example
+
+To use `JudgementalGPT`, start by logging into Confident AI:
+
+```console
+deepeval login
+```
+
+Then paste in the following code to define a metric powered by `JudgementalGPT`:
+
+```python
+from deepeval.metrics import JudgementalGPT
+from deepeval.test_case import LLMTestCase, LLMTestCaseParams
+
+code_correctness_metric = JudgementalGPT(
+    name="Code Correctness",
+    criteria="Code Correctness - determine whether the python code in the 'actual output' produces a valid JSON.",
+    evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
+    minimum_score=0.5,
+)
+```
+
+Under the hood, `JudgementalGPT(...)` sends a request to Confident AI's servers that hosts `JudgementalGPT`. `JudgementalGPT` accepts four arguments:
+
+- `name`: name of metric
+- `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] `minimum_score`: the passing threshold, defaulted to 0.5.
diff --git a/docs/docs/metrics-llm-evals.mdx b/docs/docs/metrics-llm-evals.mdx
new file mode 100644
index 000000000..4d8a28d67
--- /dev/null
+++ b/docs/docs/metrics-llm-evals.mdx
@@ -0,0 +1,48 @@
+---
+id: metrics-llm-evals
+title: LLM Evals
+sidebar_label: LLM Evals
+---
+
+LLM Evals is a custom, LLM evaluated metric. This means its score is calculated using an LLM. An `LLMEvalMetric` is the most verstile type of metric `deepeval` has to offer, and is capable of evaluating almost any use cases.
+
+## Required Parameters
+
+To use the `LLMEvalMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+
+You'll also need to supply any additional arguments such as `expected_output` and `context` if your evaluation criteria depends on these parameters.
+
+## Example
+
+To create a custom metric that uses LLMs for evaluation, simply instantiate an `LLMEvalMetric` class and **define an evaluation criteria in everyday language**:
+
+```python
+from deepeval.metrics import LLMEvalMetric
+from deepeval.test_case import LLMTestCase, LLMTestCaseParams
+
+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,
+    model="gpt-4"
+)
+```
+
+There are three mandatory and two optional parameters required when instantiating an `LLMEvalMetric` class:
+
+- `name`: name of metric
+- `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] `minimum_score`: 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] `deployment_id`: the deployment name you chose when you deployed Azure OpenAI. Only required if you're using Azure OpenAI.
+
+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.
+
+:::danger
+For accurate and valid results, only the parameters that are mentioned in `criteria` should be included as a member of `evaluation_params`.
+:::
diff --git a/docs/docs/metrics-others.mdx b/docs/docs/metrics-others.mdx
new file mode 100644
index 000000000..e022094fb
--- /dev/null
+++ b/docs/docs/metrics-others.mdx
@@ -0,0 +1,14 @@
+---
+id: metrics-others
+title: Others Metrics
+sidebar_label: Others Metrics
+---
+
+`deepeval` offers a few additional metrics for you to plug and use. They follow the same interface as all the previous metrics you've encountered, and using them is as simple as importing them in the `metrics` module.
+
+```python
+from deepeval.metrics import ConcisenessMetric
+from deepeval.metrics import CorrectnessMetric
+from deepeval.metrics import CoherenceMetric
+from deepeval.metrics import MaliciousnessMetric
+```
diff --git a/docs/docs/metrics-ragas.mdx b/docs/docs/metrics-ragas.mdx
new file mode 100644
index 000000000..524663fbc
--- /dev/null
+++ b/docs/docs/metrics-ragas.mdx
@@ -0,0 +1,58 @@
+---
+id: metrics-ragas
+title: RAGAS
+sidebar_label: RAGAS
+---
+
+The RAGAS metric is the harmonic mean of five distinct metrics:
+
+1. `AnswerRelevancyMetric`
+2. `FaithfulnessMetric`
+3. `ContextualPrecisionMetric`
+4. `ContextualRelevancyMetric`
+5. `ContextRecallMetric`
+
+It provides a score to holistically evaluate of your RAG pipeline's generator and retriever.
+
+:::note
+Since RAGAS is the harmonic mean of these five distinct metrics, a value of 0 in either one of these metrics will result in a final 0 value for RAGAS.
+:::
+
+## Required Parameters
+
+To use the `RagasMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+- `expected_output`
+- `retrieval_context`
+
+## Example
+
+```python
+from deepeval import evaluate
+from deepeval.metrics import RagasMetric
+from deepeval.test_case import LLMTestCase
+
+# Replace this with the actual output from your LLM application
+actual_output = "We offer a 30-day full refund at no extra cost."
+
+# Replace this with the expected output from your RAG generator
+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)
+test_case = LLMTestCase(
+    input="What if these shoes don't fit?",
+    actual_output=actual_output,
+    retrieval_context=retrieval_context
+)
+
+metric.measure(test_case)
+print(metric.score)
+
+# or evaluate test cases in bulk
+evaluate([test_case], [metric])
+```
diff --git a/docs/docs/metrics-toxicity.mdx b/docs/docs/metrics-toxicity.mdx
new file mode 100644
index 000000000..798fb87c4
--- /dev/null
+++ b/docs/docs/metrics-toxicity.mdx
@@ -0,0 +1,42 @@
+---
+id: metrics-toxicity
+title: Toxicity
+sidebar_label: Toxicity
+---
+
+Toxicity is another **referenceless** metric that evaluates toxicness in your LLM's outputs. This is particularly useful for a fine-tuning use case.
+
+## Required Parameters
+
+To use the `NonToxicMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
+
+- `input`
+- `actual_output`
+
+## Example
+
+Also being a referenceless like `UnBiasedMetric`, the `NonToxicMetric` similarily requires an extra parameter named `evaluation_params`. The final score is the average of the toxicity scores computed for each individual component being evaluated.
+
+```python
+from deepeval import evaluate
+from deepeval.metrics import NonToxicMetric
+from deepeval.test_case import LLMTestCase, LLMTestCaseParams
+
+# Replace this with the actual output from your LLM application
+actual_output = "We offer a 30-day full refund at no extra cost."
+
+metric = UnBiasedMetric(
+    evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],
+    minimum_score=0.5
+)
+test_case = LLMTestCase(
+    input="What if these shoes don't fit?",
+    actual_output=actual_output,
+)
+
+metric.measure(test_case)
+print(metric.score)
+
+# or evaluate test cases in bulk
+evaluate([test_case], [metric])
+```
diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js
index fb2ab075c..e7566e0a1 100644
--- a/docs/docusaurus.config.js
+++ b/docs/docusaurus.config.js
@@ -135,7 +135,7 @@ const config = {
             ],
           },
         ],
-        copyright: `Copyright © ${new Date().getFullYear()} Twilix Inc. Built with ❤️ and confidence.`,
+        copyright: `Copyright © ${new Date().getFullYear()} Confident AI Inc. Built with ❤️ and confidence.`,
       },
       prism: {
         additionalLanguages: ['python'],
diff --git a/docs/sidebars.js b/docs/sidebars.js
index f3acf37de..4015a0c5b 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -14,8 +14,28 @@ module.exports = {
       items: [
         'evaluation-introduction',
         'evaluation-test-cases',
-        'evaluation-metrics',
         'evaluation-datasets',
+        {
+          type: 'category',
+          label: 'Metrics',
+          items: [
+              'metrics-introduction',
+              'metrics-llm-evals',
+              'metrics-hallucination',
+              'metrics-answer-relevancy',
+              'metrics-faithfulness',
+              'metrics-contextual-precision',
+              'metrics-contextual-relevancy',
+              'metrics-contextual-recall',
+              'metrics-ragas',
+              'metrics-bias',
+              'metrics-toxicity',
+              'metrics-judgemental',
+              'metrics-custom',
+              'metrics-others',
+          ],
+          collapsed: true,
+        },
       ],
       collapsed: false,
     },
diff --git a/poetry.lock b/poetry.lock
index 624c4cb0a..16119e11f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -134,24 +134,24 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "3.7.1"
+version = "4.1.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "anyio-3.7.1-py3-none-any.whl", hash = "sha256:91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5"},
-    {file = "anyio-3.7.1.tar.gz", hash = "sha256:44a3c9aba0f5defa43261a8b3efb97891f2bd7d804e0e1f56419befa1adfc780"},
+    {file = "anyio-4.1.0-py3-none-any.whl", hash = "sha256:56a415fbc462291813a94528a779597226619c8e78af7de0507333f700011e5f"},
+    {file = "anyio-4.1.0.tar.gz", hash = "sha256:5a0bec7085176715be77df87fc66d6c9d70626bd752fcc85f57cdbee5b3760da"},
 ]
 
 [package.dependencies]
-exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
 
 [package.extras]
-doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"]
-test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (<0.22)"]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "astunparse"
@@ -220,29 +220,33 @@ transformers = ">=3.0.0"
 
 [[package]]
 name = "black"
-version = "23.11.0"
+version = "23.12.0"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "black-23.11.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dbea0bb8575c6b6303cc65017b46351dc5953eea5c0a59d7b7e3a2d2f433a911"},
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 [package.extras]
@@ -1171,69 +1176,69 @@ test = ["objgraph", "psutil"]
 
 [[package]]
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 [[package]]
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 [package.dependencies]
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 [[package]]
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+version = "4.1.0"
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 [package.dependencies]
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 pymongo = ["pymongo (>=3.1)"]
 pyspark = ["pyspark (>=2.4.4)"]
 quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
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 ]
 
 [package.dependencies]
diff --git a/pyproject.toml b/pyproject.toml
index 9de73be00..8efd21cf5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "deepeval"
-version = "0.20.30"
+version = "0.20.33"
 description = "The Evaluation Framework for LLMs"
 authors = ["Jeffrey Ip <jeffreyip@confident-ai.com>"]
 license = "Apache-2.0"
diff --git a/setup.py b/setup.py
index a915dcc2b..c15405473 100644
--- a/setup.py
+++ b/setup.py
@@ -25,7 +25,7 @@
         "pytest",
         "typer==0.9.0",
         "rich",
-        "protobuf>=4.21.6",
+        "protobuf==4.21.6",
         "pandas",
         "pydantic",  # loosen pydantic requirements as we support multiple
         "sentry-sdk",