diff --git a/README.md b/README.md
index cf3851ef2..286915e03 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@
     </a>
 </p>
 
-**DeepEval** is a simple-to-use, open-source evaluation framework for LLM applications. It is similar to Pytest but specialized for unit testing LLM applications. DeepEval evaluates performance based on metrics such as hallucination, answer relevancy, RAGAS, etc., using LLMs and various other NLP models **locally on your machine**.
+**DeepEval** is a simple-to-use, open-source LLM evaluation framework for LLM applications. It is similar to Pytest but specialized for unit testing LLM applications. DeepEval evaluates performance based on metrics such as hallucination, answer relevancy, RAGAS, etc., using LLMs and various other NLP models that runs **locally on your machine**.
 
 Whether your application is implemented via RAG or fine-tuning, LangChain or LlamaIndex, DeepEval has you covered. With it, you can easily determine the optimal hyperparameters to improve your RAG pipeline, prevent prompt drifting, or even transition from OpenAI to hosting your own Llama2 with confidence.
 
@@ -26,7 +26,7 @@ Whether your application is implemented via RAG or fine-tuning, LangChain or Lla
 
 # Features
 
-- Large variety of ready-to-use evaluation metrics powered by LLMs (all with explanations), statistical methods, or NLP models that runs **locally on your machine**:
+- Large variety of ready-to-use LLM evaluation metrics powered by LLMs (all with explanations), statistical methods, or NLP models that runs **locally on your machine**:
   - Hallucination
   - Summarization
   - Answer Relevancy
@@ -38,8 +38,8 @@ Whether your application is implemented via RAG or fine-tuning, LangChain or Lla
   - Toxicity
   - Bias
   - etc.
+- Evaluate your entire dataset in bulk in under 20 lines of Python code **in parallel**. Do this via the CLI in a Pytest-like manner, or through our `evaluate()` function.
 - Easily create your own custom metrics that are automatically integrated with DeepEval's ecosystem by inheriting DeepEval's base metric class.
-- Evaluate your entire dataset in bulk in under 20 lines of Python code **in parallel**.
 - [Automatically integrated with Confident AI](https://app.confident-ai.com) for continous evaluation throughout the lifetime of your LLM (app):
   - log evaluation results and analyze metrics pass / fails
   - compare and pick the optimal hyperparameters (eg. prompt templates, chunk size, models used, etc.) based on evaluation results
@@ -115,6 +115,29 @@ deepeval test run test_chatbot.py
 
 <br />
 
+## Evaluating Without Pytest Integration
+
+Alternatively, you can evaluate without Pytest, which is more suited for a notebook environment.
+
+```python
+from deepeval import evalate
+from deepeval.metrics import HallucinationMetric
+from deepeval.test_case import LLMTestCase
+
+input = "What if these shoes don't fit?"
+context = ["All customers are eligible for a 30 day full refund at no extra costs."]
+# 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)
+test_case = LLMTestCase(
+    input=input,
+    actual_output=actual_output,
+    context=context
+)
+evalate([test_case], [hallucination_metric])
+```
+
 ## Evaluting a Dataset / Test Cases in Bulk
 
 In DeepEval, a dataset is simply a collection of test cases. Here is how you can evaluate things in bulk:
@@ -148,7 +171,7 @@ deepeval test run test_<filename>.py -n 4
 
 <br/>
 
-Alternatively, although we recommend using `deepeval test run`, you can evaluate a dataset/test cases without using pytest:
+Alternatively, although we recommend using `deepeval test run`, you can evaluate a dataset/test cases without using our Pytest integration:
 
 ```python
 from deepeval import evaluate
@@ -168,6 +191,7 @@ We offer a [free web platform](https://app.confident-ai.com) for you to:
 3. Compare and pick the optimal hyperparameteres (prompt templates, models, chunk size, etc.).
 4. Create, manage, and centralize your evaluation datasets.
 5. Track events in production and augment your evaluation dataset for continous evaluation in production.
+6. Track events in production and view live evaluation results over time.
 
 Everything on Confident AI, including how to use Confident is available [here](https://docs.confident-ai.com/docs/confident-ai-introduction).
 
diff --git a/deepeval/__init__.py b/deepeval/__init__.py
index 099bbdfb8..6d6c39dc6 100644
--- a/deepeval/__init__.py
+++ b/deepeval/__init__.py
@@ -2,13 +2,13 @@
 import re
 
 # Optionally add telemtry
-from .telemetry import *
 from ._version import __version__
 
 from .decorators.hyperparameters import set_hyperparameters
 from deepeval.event import track
 from deepeval.evaluate import evaluate, run_test, assert_test
 from deepeval.test_run import on_test_run_end
+from deepeval.telemetry import *
 
 __all__ = [
     "set_hyperparameters",
diff --git a/deepeval/_version.py b/deepeval/_version.py
index f81ac3edf..ee489183b 100644
--- a/deepeval/_version.py
+++ b/deepeval/_version.py
@@ -1 +1 @@
-__version__: str = "0.20.43"
+__version__: str = "0.20.44"
diff --git a/deepeval/cli/test.py b/deepeval/cli/test.py
index 0187476e8..7ea86e1bb 100644
--- a/deepeval/cli/test.py
+++ b/deepeval/cli/test.py
@@ -6,6 +6,7 @@
 from deepeval.test_run import test_run_manager, TEMP_FILE_NAME
 from deepeval.utils import delete_file_if_exists
 from deepeval.test_run import invoke_test_run_end_hook
+from deepeval.telemetry import capture_evaluation_count
 
 app = typer.Typer(name="test")
 
@@ -74,6 +75,7 @@ def run(
     pytest_args.extend(["-p", "plugins"])
 
     retcode = pytest.main(pytest_args)
+    capture_evaluation_count()
     test_run_manager.wrap_up_test_run()
     invoke_test_run_end_hook()
 
diff --git a/deepeval/evaluate.py b/deepeval/evaluate.py
index 53ddf5a36..e433931d5 100644
--- a/deepeval/evaluate.py
+++ b/deepeval/evaluate.py
@@ -6,6 +6,7 @@
 from dataclasses import dataclass
 import copy
 
+from deepeval.telemetry import capture_evaluation_count
 from deepeval.progress_context import progress_context
 from deepeval.metrics import BaseMetric
 from deepeval.test_case import LLMTestCase
@@ -90,6 +91,7 @@ def run_test(
     test_run_manager.reset()
     with progress_context("Executing run_test()..."):
         test_result = execute_test([test_case], metrics, False)[0]
+        capture_evaluation_count()
         print_test_result(test_result)
         print("")
         print("-" * 70)
@@ -120,6 +122,7 @@ def evaluate(test_cases: List[LLMTestCase], metrics: List[BaseMetric]):
     test_run_manager.reset()
     with progress_context("Evaluating testcases..."):
         test_results = execute_test(test_cases, metrics, True)
+        capture_evaluation_count()
         for test_result in test_results:
             print_test_result(test_result)
         print("")
diff --git a/deepeval/metrics/answer_relevancy.py b/deepeval/metrics/answer_relevancy.py
index 8380976ff..a4cdadbb2 100644
--- a/deepeval/metrics/answer_relevancy.py
+++ b/deepeval/metrics/answer_relevancy.py
@@ -7,6 +7,7 @@
 from deepeval.metrics import BaseMetric
 from deepeval.models import GPTModel
 from deepeval.templates import AnswerRelevancyTemplate
+from deepeval.progress_context import metrics_progress_context
 
 
 class AnswerRelvancyVerdict(BaseModel):
@@ -35,24 +36,22 @@ def measure(self, test_case: LLMTestCase) -> float:
             raise ValueError(
                 "Input, actual output, or retrieval context cannot be None"
             )
-        print(
-            "✨ 🍰 ✨ You're using DeepEval's latest Answer Relevancy Metric! This may take a minute..."
-        )
-        self.key_points: List[str] = self._generate_key_points(
-            test_case.actual_output, "\n".join(test_case.retrieval_context)
-        )
-        self.verdicts: List[AnswerRelvancyVerdict] = self._generate_verdicts(
-            test_case.input
-        )
+        with metrics_progress_context(self.__name__):
+            self.key_points: List[str] = self._generate_key_points(
+                test_case.actual_output, "\n".join(test_case.retrieval_context)
+            )
+            self.verdicts: List[
+                AnswerRelvancyVerdict
+            ] = self._generate_verdicts(test_case.input)
 
-        answer_relevancy_score = self._generate_score()
+            answer_relevancy_score = self._generate_score()
 
-        self.reason = self._generate_reason(
-            test_case.input, test_case.actual_output, answer_relevancy_score
-        )
-        self.success = answer_relevancy_score >= self.minimum_score
-        self.score = answer_relevancy_score
-        return self.score
+            self.reason = self._generate_reason(
+                test_case.input, test_case.actual_output, answer_relevancy_score
+            )
+            self.success = answer_relevancy_score >= self.minimum_score
+            self.score = answer_relevancy_score
+            return self.score
 
     def _generate_score(self):
         relevant_count = 0
diff --git a/deepeval/metrics/contextual_precision.py b/deepeval/metrics/contextual_precision.py
index 622c6c1b1..8c7f0b9d6 100644
--- a/deepeval/metrics/contextual_precision.py
+++ b/deepeval/metrics/contextual_precision.py
@@ -7,6 +7,7 @@
 from deepeval.metrics import BaseMetric
 from deepeval.models import GPTModel
 from deepeval.templates import ContextualPrecisionTemplate
+from deepeval.progress_context import metrics_progress_context
 
 
 class ContextualPrecisionVerdict(BaseModel):
@@ -36,25 +37,24 @@ def measure(self, test_case: LLMTestCase) -> float:
             raise ValueError(
                 "Input, actual output, expected output, or retrieval context cannot be None"
             )
-        print(
-            "✨ 🍰 ✨ You're using DeepEval's latest Contextual Precision Metric! This may take a minute..."
-        )
-        self.verdicts: List[
-            ContextualPrecisionVerdict
-        ] = self._generate_verdicts(
-            test_case.input,
-            test_case.expected_output,
-            test_case.retrieval_context,
-        )
-        contextual_precision_score = self._generate_score()
 
-        self.reason = self._generate_reason(
-            test_case.input, contextual_precision_score
-        )
+        with metrics_progress_context(self.__name__):
+            self.verdicts: List[
+                ContextualPrecisionVerdict
+            ] = self._generate_verdicts(
+                test_case.input,
+                test_case.expected_output,
+                test_case.retrieval_context,
+            )
+            contextual_precision_score = self._generate_score()
+
+            self.reason = self._generate_reason(
+                test_case.input, contextual_precision_score
+            )
 
-        self.success = contextual_precision_score >= self.minimum_score
-        self.score = contextual_precision_score
-        return self.score
+            self.success = contextual_precision_score >= self.minimum_score
+            self.score = contextual_precision_score
+            return self.score
 
     def _generate_reason(self, input: str, score: float):
         if self.include_reason is False:
diff --git a/deepeval/metrics/contextual_recall.py b/deepeval/metrics/contextual_recall.py
index bdf65878a..347a97463 100644
--- a/deepeval/metrics/contextual_recall.py
+++ b/deepeval/metrics/contextual_recall.py
@@ -7,6 +7,7 @@
 from deepeval.metrics import BaseMetric
 from deepeval.models import GPTModel
 from deepeval.templates import ContextualRecallTemplate
+from deepeval.progress_context import metrics_progress_context
 
 
 class ContextualRecallVerdict(BaseModel):
@@ -36,22 +37,22 @@ def measure(self, test_case: LLMTestCase) -> float:
             raise ValueError(
                 "Input, actual output, expected output, or retrieval context cannot be None"
             )
-        print(
-            "✨ 🍰 ✨ You're using DeepEval's latest Contextual Recall Metric! This may take a minute..."
-        )
-        self.verdicts: List[ContextualRecallVerdict] = self._generate_verdicts(
-            test_case.expected_output, test_case.retrieval_context
-        )
+        with metrics_progress_context(self.__name__):
+            self.verdicts: List[
+                ContextualRecallVerdict
+            ] = self._generate_verdicts(
+                test_case.expected_output, test_case.retrieval_context
+            )
 
-        contextual_recall_score = self._generate_score()
+            contextual_recall_score = self._generate_score()
 
-        self.reason = self._generate_reason(
-            test_case.expected_output, contextual_recall_score
-        )
+            self.reason = self._generate_reason(
+                test_case.expected_output, contextual_recall_score
+            )
 
-        self.success = contextual_recall_score >= self.minimum_score
-        self.score = contextual_recall_score
-        return self.score
+            self.success = contextual_recall_score >= self.minimum_score
+            self.score = contextual_recall_score
+            return self.score
 
     def _generate_reason(self, expected_output: str, score: float):
         if self.include_reason is False:
diff --git a/deepeval/metrics/contextual_relevancy.py b/deepeval/metrics/contextual_relevancy.py
index 73ccbfc43..556d0fe5b 100644
--- a/deepeval/metrics/contextual_relevancy.py
+++ b/deepeval/metrics/contextual_relevancy.py
@@ -8,6 +8,7 @@
 from deepeval.metrics import BaseMetric
 from deepeval.models import GPTModel
 from deepeval.templates import ContextualRelevancyTemplate
+from deepeval.progress_context import metrics_progress_context
 
 
 class ContextualRelevancyVerdict(BaseModel):
@@ -35,24 +36,22 @@ def measure(self, test_case: LLMTestCase) -> float:
             raise ValueError(
                 "Input, actual output, or retrieval context cannot be None"
             )
-        print(
-            "✨ 🍰 ✨ You're using DeepEval's latest Contextual Relevancy Metric! This may take a minute..."
-        )
-        self.verdicts_list: List[
-            List[ContextualRelevancyVerdict]
-        ] = self._generate_verdicts_list(
-            test_case.input, test_case.retrieval_context
-        )
-        contextual_recall_score = self._generate_score()
+        with metrics_progress_context(self.__name__):
+            self.verdicts_list: List[
+                List[ContextualRelevancyVerdict]
+            ] = self._generate_verdicts_list(
+                test_case.input, test_case.retrieval_context
+            )
+            contextual_recall_score = self._generate_score()
 
-        self.reason = self._generate_reason(
-            test_case.input, contextual_recall_score
-        )
+            self.reason = self._generate_reason(
+                test_case.input, contextual_recall_score
+            )
 
-        self.success = contextual_recall_score >= self.minimum_score
-        self.score = contextual_recall_score
+            self.success = contextual_recall_score >= self.minimum_score
+            self.score = contextual_recall_score
 
-        return self.score
+            return self.score
 
     def _generate_reason(self, input: str, score: float):
         if self.include_reason is False:
diff --git a/deepeval/metrics/faithfulness.py b/deepeval/metrics/faithfulness.py
index 643ef2c40..74a5ddc45 100644
--- a/deepeval/metrics/faithfulness.py
+++ b/deepeval/metrics/faithfulness.py
@@ -8,6 +8,7 @@
 from deepeval.utils import trimToJson
 from deepeval.models import GPTModel
 from deepeval.templates import FaithfulnessTemplate
+from deepeval.progress_context import metrics_progress_context
 
 
 class FaithfulnessVerdict(BaseModel):
@@ -37,22 +38,20 @@ def measure(self, test_case: LLMTestCase):
             raise ValueError(
                 "Input, actual output, or retrieval context cannot be None"
             )
-        print(
-            "✨ 🍰 ✨ You're using DeepEval's latest Faithfulness Metric! This may take a minute..."
-        )
-        self.truths_list: List[List[str]] = self._generate_truths_list(
-            test_case.retrieval_context
-        )
-        self.verdicts_list: List[
-            List[FaithfulnessVerdict]
-        ] = self._generate_verdicts_list(
-            self.truths_list, test_case.actual_output
-        )
-        faithfulness_score = self._generate_score()
-        self.reason = self._generate_reason(faithfulness_score)
-        self.success = faithfulness_score >= self.minimum_score
-        self.score = faithfulness_score
-        return self.score
+        with metrics_progress_context(self.__name__):
+            self.truths_list: List[List[str]] = self._generate_truths_list(
+                test_case.retrieval_context
+            )
+            self.verdicts_list: List[
+                List[FaithfulnessVerdict]
+            ] = self._generate_verdicts_list(
+                self.truths_list, test_case.actual_output
+            )
+            faithfulness_score = self._generate_score()
+            self.reason = self._generate_reason(faithfulness_score)
+            self.success = faithfulness_score >= self.minimum_score
+            self.score = faithfulness_score
+            return self.score
 
     def _generate_score(self):
         total_verdicts = 0
diff --git a/deepeval/progress_context.py b/deepeval/progress_context.py
index ed1b3c523..f7986a602 100644
--- a/deepeval/progress_context.py
+++ b/deepeval/progress_context.py
@@ -17,3 +17,19 @@ def progress_context(
     ) as progress:
         progress.add_task(description=description, total=total)
         yield
+
+
+@contextmanager
+def metrics_progress_context(
+    metric_name: str, total: int = 9999, transient: bool = True
+):
+    description = f"✨ 🍰 ✨ You're using DeepEval's latest {metric_name} Metric! This may take a minute..."
+    console = Console(file=sys.stderr)  # Direct output to standard error
+    with Progress(
+        SpinnerColumn(),
+        TextColumn("[progress.description]{task.description}"),
+        console=console,  # Use the custom console
+        transient=transient,
+    ) as progress:
+        progress.add_task(description=description, total=total)
+        yield
diff --git a/deepeval/telemetry.py b/deepeval/telemetry.py
index c0c2e5364..d6915bf11 100644
--- a/deepeval/telemetry.py
+++ b/deepeval/telemetry.py
@@ -1,9 +1,10 @@
 import os
 import socket
 import sys
+import sentry_sdk
 
 
-def check_firewall():
+def blocked_by_firewall():
     try:
         socket.create_connection(("www.google.com", 80))
         return False
@@ -11,29 +12,24 @@ def check_firewall():
         return True
 
 
-if os.getenv("ERROR_REPORTING") == "YES" and not check_firewall():
-    try:
-        import sentry_sdk
-
-        sentry_sdk.init(
-            dsn="https://5ef587d58109ee45d6544f3657efdd1f@o4506098477236224.ingest.sentry.io/4506098479136768",
-            # Set traces_sample_rate to 1.0 to capture 100%
-            # of transactions for performance monitoring.
-            traces_sample_rate=1.0,
-            # Set profiles_sample_rate to 1.0 to profile 100%
-            # of sampled transactions.
-            # We recommend adjusting this value in production.
-            profiles_sample_rate=1.0,
-        )
-
-        # Add a global error handler
-        def handle_exception(exc_type, exc_value, exc_traceback):
-            print({"exc_type": exc_type, "exc_value": exc_value})
-            sentry_sdk.capture_exception(exc_value)
-            sys.__excepthook__(exc_type, exc_value, exc_traceback)
-
-        sys.excepthook = handle_exception
-
-    except ModuleNotFoundError:
-        # sentry_sdk not installed
-        pass
+def capture_evaluation_count():
+    sentry_sdk.capture_message("evaluation ran!")
+
+
+sentry_sdk.init(
+    dsn="https://5ef587d58109ee45d6544f3657efdd1f@o4506098477236224.ingest.sentry.io/4506098479136768",
+    profiles_sample_rate=1.0,
+    traces_sample_rate=1.0,  # For performance monitoring
+    send_default_pii=False,  # Don't send personally identifiable information
+    attach_stacktrace=False,  # Don't attach stack traces to messages
+    default_integrations=False,  # Disable Sentry's default integrations
+)
+
+if os.getenv("ERROR_REPORTING") == "YES" and not blocked_by_firewall():
+
+    def handle_exception(exc_type, exc_value, exc_traceback):
+        print({"exc_type": exc_type, "exc_value": exc_value})
+        sentry_sdk.capture_exception(exc_value)
+        sys.__excepthook__(exc_type, exc_value, exc_traceback)
+
+    sys.excepthook = handle_exception
diff --git a/deepeval/templates.py b/deepeval/templates.py
index b4ae5d584..f32f4b10a 100644
--- a/deepeval/templates.py
+++ b/deepeval/templates.py
@@ -97,6 +97,7 @@ def generate_verdicts(truths, text):
 
 You should NOT incorporate any prior knowledge you have and take each context at face value. Since you are going to generate a verdict for each context, the number of 'verdicts' SHOULD BE STRICTLY EQUAL to that of contexts.
 You DON'T have to provide a reason if the answer is 'yes'.
+You should ONLY provide a 'no' answer if IT IS A CONTRADICTION.
 **
 
 Retrieval Contexts:
@@ -113,7 +114,7 @@ def generate_reason(score, contradictions):
         return f"""Below is a list of Contradictions. It is a list of JSON with the `contradiction` and `rank` key.
 The `contradiction` explains why the 'actual output' does not align with a certain node in the 'retrieval context'. Contradictions happen in the 'actual output', NOT the 'retrieval context'.
 The `rank` tells you which node in the 'retrieval context' the actual output contradicted with.
-Given the faithfulness score, which is a 0-1 score indicating how faithful the `actual output` is to the retrieval context (higher the better), concisely summarize the contradictions to justify the score. 
+Given the faithfulness score, which is a 0-1 score indicating how faithful the `actual output` is to the retrieval context (higher the better), CONCISELY summarize the contradictions to justify the score. 
 
 Faithfulness Score:
 {score}
@@ -128,6 +129,7 @@ def generate_reason(score, contradictions):
 IMPORTANT: 
 If there are no contradictions, just say something positive with an upbeat encouraging tone (but don't overdo it otherwise it gets annoying).
 Your reason MUST use information in `contradiction` and the node RANK (eg., first node of the retrieval context) in your reason.
+Be sure in your reason, as if you know what the actual output is from the contradictions.
 **
 
 Reason:
diff --git a/deepeval/test_run/test_run.py b/deepeval/test_run/test_run.py
index 4d1f3d1e0..7ef742632 100644
--- a/deepeval/test_run/test_run.py
+++ b/deepeval/test_run/test_run.py
@@ -91,7 +91,7 @@ def add_llm_test_case(
         metrics_metadata = MetricsMetadata(
             metric=metric.__name__,
             score=metric.score,
-            minimumScore=0.5,
+            minimumScore=metric.minimum_score,
             reason=metric.reason,
         )
 
diff --git a/deepeval/tracing/__init__.py b/deepeval/tracing/__init__.py
index 76ea3893c..ce4c4642a 100644
--- a/deepeval/tracing/__init__.py
+++ b/deepeval/tracing/__init__.py
@@ -1 +1,12 @@
-from .tracing import trace, TraceType, get_trace_stack
+from .tracing import (
+    trace,
+    trace_manager,
+    get_trace_stack,
+    TraceType,
+    TraceStatus,
+    LlmTrace,
+    EmbeddingTrace,
+    GenericTrace,
+    LlmMetadata,
+    EmbeddingMetadata,
+)
diff --git a/deepeval/tracing/integrations/llama_index.py b/deepeval/tracing/integrations/llama_index.py
new file mode 100644
index 000000000..0cd2d1736
--- /dev/null
+++ b/deepeval/tracing/integrations/llama_index.py
@@ -0,0 +1,152 @@
+from typing import Any, Dict, List, Optional, Union
+from time import perf_counter
+
+from llama_index.bridge.pydantic import BaseModel
+from llama_index.callbacks.base_handler import BaseCallbackHandler
+from llama_index.callbacks.schema import CBEventType, EventPayload
+from llama_index.llms import ChatMessage
+
+from deepeval.tracing import (
+    trace_manager,
+    get_trace_stack,
+    LlmTrace,
+    GenericTrace,
+    EmbeddingTrace,
+    TraceStatus,
+    LlmMetadata,
+    EmbeddingMetadata,
+    TraceType,
+)
+from deepeval.utils import dataclass_to_dict
+
+events_to_ignore = [
+    CBEventType.CHUNKING,
+    CBEventType.NODE_PARSING,
+    CBEventType.EMBEDDING,
+    CBEventType.TREE,
+    CBEventType.SUB_QUESTION,
+    CBEventType.FUNCTION_CALL,
+    CBEventType.EXCEPTION,
+    CBEventType.AGENT_STEP,
+]
+
+
+class LlamaIndexCallbackHandler(BaseCallbackHandler):
+    def __init__(self) -> None:
+        self.event_map = {}
+        super().__init__(
+            event_starts_to_ignore=events_to_ignore,
+            event_ends_to_ignore=events_to_ignore,
+        )
+
+    def start_trace(self, trace_id: Optional[str] = None) -> None:
+        self.event_map = {}
+        trace_manager.clear_trace_stack()
+        return
+
+    def end_trace(
+        self,
+        trace_id: Optional[str] = None,
+        trace_map: Optional[Dict[str, List[str]]] = None,
+    ) -> None:
+        return
+
+    def on_event_start(
+        self,
+        event_type: CBEventType,
+        payload: Optional[Dict[str, Any]] = None,
+        event_id: str = "",
+        parent_id: str = "",
+        **kwargs: Any,
+    ) -> str:
+        trace_instance = self.create_trace_instance(event_type)
+        self.event_map[event_id] = trace_instance
+        trace_manager.append_to_trace_stack(trace_instance)
+        return
+
+    def on_event_end(
+        self,
+        event_type: CBEventType,
+        payload: Optional[Dict[str, Any]] = None,
+        event_id: str = "",
+        **kwargs: Any,
+    ) -> None:
+        trace_instance = self.event_map[event_id]
+        trace_instance.executionTime = (
+            perf_counter() - trace_instance.executionTime
+        )
+        input_kwargs = {}
+        if payload is not None:
+            for event in EventPayload:
+                value = payload.get(event.value)
+                if value is not None:
+                    input_kwargs[event.value] = value
+
+        current_trace_stack = trace_manager.get_trace_stack()
+        if len(current_trace_stack) > 1:
+            parent_trace = current_trace_stack[-2]
+            parent_trace.traces.append(trace_instance)
+
+        if len(current_trace_stack) == 1:
+            dict_representation = dataclass_to_dict(current_trace_stack[0])
+            trace_manager.set_dict_trace_stack(dict_representation)
+            trace_manager.clear_trace_stack()
+        else:
+            trace_manager.pop_trace_stack()
+
+        return
+
+    def create_trace_instance(
+        self, event_type: CBEventType
+    ) -> Union[EmbeddingTrace, LlmMetadata, GenericTrace]:
+        current_time = perf_counter()
+        type = self.convert_event_type_to_deepeval_trace_type(event_type)
+        name = event_type.capitalize()
+        trace_instance_input = {"args": None, "kwargs": None}
+        if event_type == CBEventType.LLM:
+            trace_instance = LlmTrace(
+                type=type,
+                executionTime=current_time,
+                name=name,
+                input=trace_instance_input,
+                output=None,
+                status=TraceStatus.SUCCESS,
+                traces=[],
+                llmMetadata=LlmMetadata(model="None"),
+            )
+        elif event_type == CBEventType.EMBEDDING:
+            trace_instance = EmbeddingTrace(
+                type=type,
+                executionTime=current_time,
+                name=name,
+                input=trace_instance_input,
+                output=None,
+                status=TraceStatus.SUCCESS,
+                traces=[],
+                embeddingMetadata=EmbeddingMetadata(model="None"),
+            )
+        else:
+            trace_instance = GenericTrace(
+                type=type,
+                executionTime=current_time,
+                name=name,
+                input=trace_instance_input,
+                output=None,
+                status=TraceStatus.SUCCESS,
+                traces=[],
+            )
+
+        return trace_instance
+
+    def convert_event_type_to_deepeval_trace_type(
+        self, event_type: CBEventType
+    ):
+        # TODO: add more types
+        if event_type == CBEventType.LLM:
+            return TraceType.LLM
+        elif event_type == CBEventType.RETRIEVE:
+            return TraceType.RETRIEVER
+        elif event_type == CBEventType.EMBEDDING:
+            return TraceType.EMBEDDING
+
+        return event_type.value.capitalize()
diff --git a/docs/docs/confident-ai-evals-in-production.mdx b/docs/docs/confident-ai-evals-in-production.mdx
index 7a1fc87b9..98db35258 100644
--- a/docs/docs/confident-ai-evals-in-production.mdx
+++ b/docs/docs/confident-ai-evals-in-production.mdx
@@ -23,7 +23,12 @@ Simply add `deepeval.track(...)` in your application to start tracking events. T
 - [Optional] `token_usage`: type `float`
 - [Optional] `token_cost`: type `float`
 - [Optional] `additional_data`: type `dict`
-- [Optional] `fail_silently`: type `bool`, defaults to True
+- [Optional] `fail_silently`: type `bool`, defaults to True. You should try setting this to `False` if your events are not logging properly.
+- [Optional] `run_on_background_thread`: type `bool`, defaults to True. You should try setting this to `False` if your events are not logging properly.
+
+:::note
+Please do **NOT** provide placeholder values for optional parameters. Leave it blank instead.
+:::
 
 ```python
 import deepeval
@@ -44,6 +49,7 @@ deepeval.track(
     token_cost=0.23,
     additional_data={"example": "example"},
     fail_silently=True
+    run_on_background_thread=True
 )
 
 ```
diff --git a/docs/docs/metrics-toxicity.mdx b/docs/docs/metrics-toxicity.mdx
index 1604d30e7..0af3e030a 100644
--- a/docs/docs/metrics-toxicity.mdx
+++ b/docs/docs/metrics-toxicity.mdx
@@ -6,6 +6,14 @@ sidebar_label: Toxicity
 
 The toxicity metric is another **referenceless** metric that evaluates toxicness in your LLM's outputs. This is particularly useful for a fine-tuning use case.
 
+## Installation
+
+Toxicity in `deepeval` requires an additional installation:
+
+```
+pip install detoxify
+```
+
 ## Required Parameters
 
 To use the `NonToxicMetric`, you'll have to provide the following parameters when creating an `LLMTestCase`:
diff --git a/llama_test/chatbot.py b/llama_test/chatbot.py
new file mode 100644
index 000000000..a07724b5e
--- /dev/null
+++ b/llama_test/chatbot.py
@@ -0,0 +1,14 @@
+from llama_index import VectorStoreIndex, SimpleDirectoryReader
+from llama_index import ServiceContext
+import llama_index
+
+llama_index.set_global_handler("deepeval")
+
+service_context = ServiceContext.from_defaults(chunk_size=1000)
+documents = SimpleDirectoryReader("data").load_data()
+index = VectorStoreIndex.from_documents(documents)
+query_engine = index.as_query_engine(similarity_top_k=5)
+
+
+def query(user_input):
+    return query_engine.query(user_input).response
diff --git a/llama_test/data/data.txt b/llama_test/data/data.txt
new file mode 100644
index 000000000..c2bd37022
--- /dev/null
+++ b/llama_test/data/data.txt
@@ -0,0 +1,25 @@
+About MadeUpCompany
+MadeUpCompany is a pioneering technology firm founded in 2010, specializing in cloud computing, data analytics, and machine learning. Our headquarters is based in San Francisco, California, with satellite offices spread across New York, London, and Tokyo. We are committed to offering state-of-the-art solutions that help businesses and individuals achieve their full potential. With a diverse team of experts from various industries, we strive to redefine the boundaries of innovation and efficiency.
+
+Products and Services
+We offer a suite of services ranging from cloud storage solutions, data analytics platforms, to custom machine learning models tailored for specific business needs. Our most popular product is CloudMate, a cloud storage solution designed for businesses of all sizes. It offers seamless data migration, top-tier security protocols, and an easy-to-use interface. Our data analytics service, DataWiz, helps companies turn raw data into actionable insights using advanced algorithms.
+
+Pricing
+We have a variety of pricing options tailored to different needs. Our basic cloud storage package starts at $9.99 per month, with premium plans offering more storage and functionalities. We also provide enterprise solutions on a case-by-case basis, so it’s best to consult with our sales team for customized pricing.
+
+Technical Support
+Our customer support team is available 24/7 to assist with any technical issues. We offer multiple channels for support including live chat, email, and a toll-free number. Most issues are typically resolved within 24 hours. We also have an extensive FAQ section on our website and a community forum for peer support.
+
+Security and Compliance
+MadeUpCompany places the utmost importance on security and compliance. All our products are GDPR compliant and adhere to the highest security standards, including end-to-end encryption and multi-factor authentication.
+
+Account Management
+Customers can easily manage their accounts through our online portal, which allows you to upgrade your service, view billing history, and manage users in your organization. If you encounter any issues or have questions about your account, our account management team is available weekdays from 9 AM to 6 PM.
+
+Refund and Cancellation Policy
+We offer a 30-day money-back guarantee on all our products. If you're not satisfied for any reason, you can request a full refund within the first 30 days of your purchase. After that, you can still cancel your service at any time, but a prorated refund will be issued based on the remaining term of your subscription.
+
+Upcoming Features
+We’re constantly working to improve our services and offer new features. Keep an eye out for updates on machine learning functionalities in DataWiz and more collaborative tools in CloudMate in the upcoming quarters.
+
+Your customer support staff can use these paragraphs to build their responses to customer inquiries, providing both detailed and precise information to address various questions.
\ No newline at end of file
diff --git a/llama_test/main.py b/llama_test/main.py
new file mode 100644
index 000000000..9ddcfa104
--- /dev/null
+++ b/llama_test/main.py
@@ -0,0 +1,6 @@
+from chatbot import query
+
+while True:
+    user_input = input("Enter your question: ")
+    response = query(user_input)
+    print("Bot response:", response)
diff --git a/poetry.lock b/poetry.lock
index 161ba87c4..34c10e78c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -167,21 +167,40 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.1.0"
+version = "23.2.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
-    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
 ]
 
 [package.extras]
 cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]", "pre-commit"]
+dev = ["attrs[tests]", "pre-commit"]
 docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
 tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+
+[[package]]
+name = "beautifulsoup4"
+version = "4.12.2"
+description = "Screen-scraping library"
+optional = false
+python-versions = ">=3.6.0"
+files = [
+    {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
+    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
+]
+
+[package.dependencies]
+soupsieve = ">1.2"
+
+[package.extras]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
 
 [[package]]
 name = "bert-score"
@@ -544,13 +563,13 @@ typing-inspect = ">=0.4.0,<1"
 
 [[package]]
 name = "datasets"
-version = "2.16.0"
+version = "2.16.1"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "datasets-2.16.0-py3-none-any.whl", hash = "sha256:301cc39b3d81cd751100b79c85f8ae8626c17b0b113819ba2831c204d90b43f2"},
-    {file = "datasets-2.16.0.tar.gz", hash = "sha256:91b06f7a8f0329179e7d603004102a6cc7a424a2f599315297a061caa1f8fa64"},
+    {file = "datasets-2.16.1-py3-none-any.whl", hash = "sha256:fafa300c78ff92d521473a3d47d60c2d3e0d6046212cc03ceb6caf6550737257"},
+    {file = "datasets-2.16.1.tar.gz", hash = "sha256:ad3215e9b1984d1de4fda2123bc7319ccbdf1e17d0c3d5590d13debff308a080"},
 ]
 
 [package.dependencies]
@@ -586,6 +605,23 @@ tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "elast
 torch = ["torch"]
 vision = ["Pillow (>=6.2.1)"]
 
+[[package]]
+name = "deprecated"
+version = "1.2.14"
+description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "Deprecated-1.2.14-py2.py3-none-any.whl", hash = "sha256:6fac8b097794a90302bdbb17b9b815e732d3c4720583ff1b198499d78470466c"},
+    {file = "Deprecated-1.2.14.tar.gz", hash = "sha256:e5323eb936458dccc2582dc6f9c322c852a775a27065ff2b0c4970b9d53d01b3"},
+]
+
+[package.dependencies]
+wrapt = ">=1.10,<2"
+
+[package.extras]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
+
 [[package]]
 name = "dill"
 version = "0.3.7"
@@ -1194,13 +1230,13 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.0.352"
+version = "0.0.353"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain-0.0.352-py3-none-any.whl", hash = "sha256:43ab580e1223e5d7c3495b3c0cb79e2f3a0ecb52caf8126271fb10d42cede2d0"},
-    {file = "langchain-0.0.352.tar.gz", hash = "sha256:8928d7b63d73af9681fe1b2a2b99b84238efef61ed537de666160fd001f41efd"},
+    {file = "langchain-0.0.353-py3-none-any.whl", hash = "sha256:54cac8b74fbefacddcdf0c443619a7331d6b59fe94fa2a48a4d7da2b59cf1f63"},
+    {file = "langchain-0.0.353.tar.gz", hash = "sha256:a095ea819f13a3606ced699182a8369eb2d77034ec8c913983675d6dd9a98196"},
 ]
 
 [package.dependencies]
@@ -1209,7 +1245,7 @@ async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
 langchain-community = ">=0.0.2,<0.1"
-langchain-core = ">=0.1,<0.2"
+langchain-core = ">=0.1.4,<0.2"
 langsmith = ">=0.0.70,<0.1.0"
 numpy = ">=1,<2"
 pydantic = ">=1,<3"
@@ -1234,13 +1270,13 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.6"
+version = "0.0.7"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_community-0.0.6-py3-none-any.whl", hash = "sha256:13b16da0f89c328df456911ff03069e4d919f647c7dd3bfc5062525cf956ed82"},
-    {file = "langchain_community-0.0.6.tar.gz", hash = "sha256:b7deb63fd8205d54b51cf8b1702de15d1da77987f8465c356b158a65adff378c"},
+    {file = "langchain_community-0.0.7-py3-none-any.whl", hash = "sha256:468af187bfffe753426cc4548132824be7df9404d38ceef2f873087290d8ff0e"},
+    {file = "langchain_community-0.0.7.tar.gz", hash = "sha256:cfbeb25cac7dff3c021f3c82aa243fc80f80082d6f6fdcc79daf36b1408828cc"},
 ]
 
 [package.dependencies]
@@ -1256,17 +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)", "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.dependencies]
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 typing-extensions = ">=4.2.0"
 
 [package.extras]
 aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
 aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
-aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing-extensions (!=3.10.0.1)"]
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 asyncio = ["greenlet (!=0.4.17)"]
 asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
 mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
@@ -3134,7 +3218,7 @@ mssql-pyodbc = ["pyodbc"]
 mypy = ["mypy (>=0.910)"]
 mysql = ["mysqlclient (>=1.4.0)"]
 mysql-connector = ["mysql-connector-python"]
-oracle = ["cx-oracle (>=8)"]
+oracle = ["cx_oracle (>=8)"]
 oracle-oracledb = ["oracledb (>=1.0.1)"]
 postgresql = ["psycopg2 (>=2.7)"]
 postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
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 postgresql-psycopg2cffi = ["psycopg2cffi"]
 postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
-sqlcipher = ["sqlcipher3-binary"]
+sqlcipher = ["sqlcipher3_binary"]
 
 [[package]]
 name = "sympy"
@@ -3630,13 +3714,13 @@ typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "tzdata"
-version = "2023.3"
+version = "2023.4"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
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 ]
 
 [[package]]
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 [package.extras]
 test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
 
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+
 [[package]]
 name = "xxhash"
 version = "3.4.1"
@@ -3892,4 +4055,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.12"
-content-hash = "8b882533d1c305fc650e8609cb93c420c3becbff3abb398cb247f5bbd42a73ec"
+content-hash = "2b0939b9cce5fa623a932eb89645a6ef3cc7384addd232339faded4a4c35cab7"
diff --git a/pyproject.toml b/pyproject.toml
index c12a227af..df3ada338 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "deepeval"
-version = "0.20.43"
+version = "0.20.44"
 description = "The Evaluation Framework for LLMs"
 authors = ["Jeffrey Ip <jeffreyip@confident-ai.com>"]
 license = "Apache-2.0"
@@ -42,7 +42,7 @@ typer = "*"
 setuptools = "*"
 wheel = "*"
 aiohttp = "*"
-
+llama-index = "*"
 
 [tool.black]
 line-length = 80