diff --git a/docs/howtos/customizations/metrics/_write_your_own_metric.md b/docs/howtos/customizations/metrics/_write_your_own_metric.md
index b5386ceff..4913309b1 100644
--- a/docs/howtos/customizations/metrics/_write_your_own_metric.md
+++ b/docs/howtos/customizations/metrics/_write_your_own_metric.md
@@ -34,7 +34,7 @@ choose_evaluator_llm.md
 ```python
 from ragas.llms import llm_factory
 
-evaluator_llm = llm_factory('gpt-4o')
+evaluator_llm = llm_factory("gpt-4o")
 ```
 
 ## Aspect Critic - Simple Criteria Scoring
@@ -56,7 +56,7 @@ from ragas.metrics import AspectCritic
 hallucinations_binary = AspectCritic(
     name="hallucinations_binary",
     definition="Did the model hallucinate or add any information that was not present in the retrieved context?",
-    llm=evaluator_llm
+    llm=evaluator_llm,
 )
 
 await hallucinations_binary.single_turn_ascore(eval_dataset[0])
@@ -93,9 +93,7 @@ Now lets init the metric with the rubric and evaluator llm and evaluate the data
 from ragas.metrics import RubricsScoreWithoutReference
 
 hallucinations_rubric = RubricsScoreWithoutReference(
-    name="hallucinations_rubric",
-    llm=evaluator_llm,
-    rubrics=rubric
+    name="hallucinations_rubric", llm=evaluator_llm, rubrics=rubric
 )
 
 await hallucinations_rubric.single_turn_ascore(eval_dataset[0])
@@ -125,7 +123,6 @@ For our example, we need to to use LLMs to evaluate our metric so we will subcla
 
 As for the implementation, we will use the [Faithfulness][ragas.metrics.Faithfulness] metric to evaluate our metric to measure the hallucinations with the formula 
 
-
 $$
 \text{Hallucinations} = 1 - \text{Faithfulness}
 $$
@@ -144,19 +141,28 @@ import typing as t
 from ragas.callbacks import Callbacks
 from ragas.dataset_schema import SingleTurnSample
 
+
 @dataclass
 class HallucinationsMetric(MetricWithLLM, SingleTurnMetric):
     # name of the metric
     name: str = "hallucinations_metric"
     # we need to define the required columns for the metric
-    _required_columns: t.Dict[MetricType, t.Set[str]] = field(default_factory=lambda: {MetricType.SINGLE_TURN: {"user_input", "response", "retrieved_contexts"}})
+    _required_columns: t.Dict[MetricType, t.Set[str]] = field(
+        default_factory=lambda: {
+            MetricType.SINGLE_TURN: {"user_input", "response", "retrieved_contexts"}
+        }
+    )
 
     def __post_init__(self):
         # init the faithfulness metric
         self.faithfulness_metric = Faithfulness(llm=self.llm)
 
-    async def _single_turn_ascore(self, sample: SingleTurnSample, callbacks: Callbacks) -> float:
-        faithfulness_score = await self.faithfulness_metric.single_turn_ascore(sample, callbacks)
+    async def _single_turn_ascore(
+        self, sample: SingleTurnSample, callbacks: Callbacks
+    ) -> float:
+        faithfulness_score = await self.faithfulness_metric.single_turn_ascore(
+            sample, callbacks
+        )
         return 1 - faithfulness_score
 ```
 
@@ -181,12 +187,8 @@ Now let's evaluate the entire dataset with the metrics we have created.
 from ragas import evaluate
 
 results = evaluate(
-    eval_dataset, 
-    metrics=[
-        hallucinations_metric,
-        hallucinations_rubric,
-        hallucinations_binary
-    ], 
+    eval_dataset,
+    metrics=[hallucinations_metric, hallucinations_rubric, hallucinations_binary],
 )
 ```
 
diff --git a/docs/howtos/customizations/testgenerator/_persona_generator.md b/docs/howtos/customizations/testgenerator/_persona_generator.md
new file mode 100644
index 000000000..d4c6d0db0
--- /dev/null
+++ b/docs/howtos/customizations/testgenerator/_persona_generator.md
@@ -0,0 +1,160 @@
+## Persona's in Testset Generation
+
+You can add different persona's to the testset generation process by defining the [Persona][ragas.testset.persona.Persona] class with the name and role description of the different persona's that might be relevant to your use case and you want to generate testset for.
+
+For example, for the [gitlab handbook](https://about.gitlab.com/handbook/) we might want to generate testset for different persona's like a new joinee, a manager, a senior manager, etc. And hence we will define them as follows:
+
+1. New Joinee: Don't know much about the company and is looking for information on how to get started.
+2. Manager: Wants to know about the different teams and how they collaborate with each other.
+3. Senior Manager: Wants to know about the company vision and how it is executed.
+
+Which we can define as follows:
+
+
+```python
+from ragas.testset.persona import Persona
+
+persona_new_joinee = Persona(name="New Joinee", role_description="Don't know much about the company and is looking for information on how to get started.")
+persona_manager = Persona(name="Manager", role_description="Wants to know about the different teams and how they collaborate with each other.")
+persona_senior_manager = Persona(name="Senior Manager", role_description="Wants to know about the company vision and how it is executed.")
+
+personas = [persona_new_joinee, persona_manager, persona_senior_manager]
+personas
+```
+
+
+
+
+    [Persona(name='New Joinee', role_description="Don't know much about the company and is looking for information on how to get started."),
+     Persona(name='Manager', role_description='Wants to know about the different teams and how they collaborate with each other.'),
+     Persona(name='Senior Manager', role_description='Wants to know about the company vision and how it is executed.')]
+
+
+
+And then you can use these persona's in the testset generation process by passing them to the [TestsetGenerator][ragas.testset.generator.TestsetGenerator] class.
+
+
+```python
+from ragas.testset import TestsetGenerator
+from ragas.testset.graph import KnowledgeGraph
+from ragas.llms import llm_factory
+
+# Load the knowledge graph
+kg = KnowledgeGraph.load("../../../../experiments/gitlab_kg.json")
+# Initialize the Generator LLM
+llm = llm_factory("gpt-4o-mini")
+
+# Initialize the Testset Generator
+testset_generator = TestsetGenerator(knowledge_graph=kg, persona_list=personas, llm=llm)
+# Generate the Testset
+testset = testset_generator.generate(testset_size=10)
+testset
+
+```
+
+
+```python
+testset.to_pandas().head()
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>user_input</th>
+      <th>reference_contexts</th>
+      <th>reference</th>
+      <th>synthesizer_name</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>What the Director do in GitLab and how they wo...</td>
+      <td>[09db4f3e-1c10-4863-9024-f869af48d3e0\n\ntitle...</td>
+      <td>The Director at GitLab, such as the Director o...</td>
+      <td>single_hop_specifc_query_synthesizer</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>Wht is the rol of the VP in GitLab?</td>
+      <td>[56c84f1b-3558-4c80-b8a9-348e69a4801b\n\nJob F...</td>
+      <td>The VP, or Vice President, at GitLab is respon...</td>
+      <td>single_hop_specifc_query_synthesizer</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>What GitLab do for career progression?</td>
+      <td>[ead619a5-930f-4e2b-b797-41927a04d2e3\n\nGoals...</td>
+      <td>The Job frameworks at GitLab help team members...</td>
+      <td>single_hop_specifc_query_synthesizer</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Wht is the S-grop and how do they work with ot...</td>
+      <td>[42babb12-b033-493f-b684-914e2b1b1d0f\n\nPeopl...</td>
+      <td>Members of the S-group are expected to demonst...</td>
+      <td>single_hop_specifc_query_synthesizer</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>How does Google execute its company vision?</td>
+      <td>[c3ed463d-1cdc-4ba4-a6ca-2c4ab12da883\n\nof mo...</td>
+      <td>To effectively execute the company vision, man...</td>
+      <td>single_hop_specifc_query_synthesizer</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+## Automatic Persona Generation
+
+If you want to automatically generate persona's from a knowledge graph, you can use the [generate_personas_from_kg][ragas.testset.persona.generate_personas_from_kg] function.
+
+
+
+```python
+from ragas.testset.persona import generate_personas_from_kg
+from ragas.testset.graph import KnowledgeGraph
+from ragas.llms import llm_factory
+
+kg = KnowledgeGraph.load("../../../../experiments/gitlab_kg.json")
+llm = llm_factory("gpt-4o-mini")
+
+personas = generate_personas_from_kg(kg=kg, llm=llm, num_personas=5)
+```
+
+
+```python
+personas
+```
+
+
+
+
+    [Persona(name='Organizational Development Manager', role_description='Responsible for implementing job frameworks and career development strategies to enhance employee growth and clarify roles within the company.'),
+     Persona(name='DevSecOps Product Manager', role_description='Responsible for overseeing the development and strategy of DevSecOps solutions, ensuring alignment with company goals and user needs.'),
+     Persona(name='Product Pricing Analyst', role_description='Responsible for developing and analyzing pricing strategies that align with customer needs and market demands.'),
+     Persona(name='Site Reliability Engineer', role_description='Responsible for maintaining service reliability and performance, focusing on implementing rate limits to prevent outages and enhance system stability.'),
+     Persona(name='Security Operations Engineer', role_description="Works on enhancing security logging processes and ensuring compliance within GitLab's infrastructure.")]
+
+
diff --git a/docs/howtos/customizations/testgenerator/persona_generator.ipynb b/docs/howtos/customizations/testgenerator/persona_generator.ipynb
new file mode 100644
index 000000000..7ed8e7744
--- /dev/null
+++ b/docs/howtos/customizations/testgenerator/persona_generator.ipynb
@@ -0,0 +1,241 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Persona's in Testset Generation\n",
+    "\n",
+    "You can add different persona's to the testset generation process by defining the [Persona][ragas.testset.persona.Persona] class with the name and role description of the different persona's that might be relevant to your use case and you want to generate testset for.\n",
+    "\n",
+    "For example, for the [gitlab handbook](https://about.gitlab.com/handbook/) we might want to generate testset for different persona's like a new joinee, a manager, a senior manager, etc. And hence we will define them as follows:\n",
+    "\n",
+    "1. New Joinee: Don't know much about the company and is looking for information on how to get started.\n",
+    "2. Manager: Wants to know about the different teams and how they collaborate with each other.\n",
+    "3. Senior Manager: Wants to know about the company vision and how it is executed.\n",
+    "\n",
+    "Which we can define as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Persona(name='New Joinee', role_description=\"Don't know much about the company and is looking for information on how to get started.\"),\n",
+       " Persona(name='Manager', role_description='Wants to know about the different teams and how they collaborate with each other.'),\n",
+       " Persona(name='Senior Manager', role_description='Wants to know about the company vision and how it is executed.')]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from ragas.testset.persona import Persona\n",
+    "\n",
+    "persona_new_joinee = Persona(name=\"New Joinee\", role_description=\"Don't know much about the company and is looking for information on how to get started.\")\n",
+    "persona_manager = Persona(name=\"Manager\", role_description=\"Wants to know about the different teams and how they collaborate with each other.\")\n",
+    "persona_senior_manager = Persona(name=\"Senior Manager\", role_description=\"Wants to know about the company vision and how it is executed.\")\n",
+    "\n",
+    "personas = [persona_new_joinee, persona_manager, persona_senior_manager]\n",
+    "personas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then you can use these persona's in the testset generation process by passing them to the [TestsetGenerator][ragas.testset.generator.TestsetGenerator] class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ragas.testset import TestsetGenerator\n",
+    "from ragas.testset.graph import KnowledgeGraph\n",
+    "from ragas.llms import llm_factory\n",
+    "\n",
+    "# Load the knowledge graph\n",
+    "kg = KnowledgeGraph.load(\"../../../../experiments/gitlab_kg.json\")\n",
+    "# Initialize the Generator LLM\n",
+    "llm = llm_factory(\"gpt-4o-mini\")\n",
+    "\n",
+    "# Initialize the Testset Generator\n",
+    "testset_generator = TestsetGenerator(knowledge_graph=kg, persona_list=personas, llm=llm)\n",
+    "# Generate the Testset\n",
+    "testset = testset_generator.generate(testset_size=10)\n",
+    "testset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_input</th>\n",
+       "      <th>reference_contexts</th>\n",
+       "      <th>reference</th>\n",
+       "      <th>synthesizer_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>What the Director do in GitLab and how they wo...</td>\n",
+       "      <td>[09db4f3e-1c10-4863-9024-f869af48d3e0\\n\\ntitle...</td>\n",
+       "      <td>The Director at GitLab, such as the Director o...</td>\n",
+       "      <td>single_hop_specifc_query_synthesizer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Wht is the rol of the VP in GitLab?</td>\n",
+       "      <td>[56c84f1b-3558-4c80-b8a9-348e69a4801b\\n\\nJob F...</td>\n",
+       "      <td>The VP, or Vice President, at GitLab is respon...</td>\n",
+       "      <td>single_hop_specifc_query_synthesizer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>What GitLab do for career progression?</td>\n",
+       "      <td>[ead619a5-930f-4e2b-b797-41927a04d2e3\\n\\nGoals...</td>\n",
+       "      <td>The Job frameworks at GitLab help team members...</td>\n",
+       "      <td>single_hop_specifc_query_synthesizer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Wht is the S-grop and how do they work with ot...</td>\n",
+       "      <td>[42babb12-b033-493f-b684-914e2b1b1d0f\\n\\nPeopl...</td>\n",
+       "      <td>Members of the S-group are expected to demonst...</td>\n",
+       "      <td>single_hop_specifc_query_synthesizer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>How does Google execute its company vision?</td>\n",
+       "      <td>[c3ed463d-1cdc-4ba4-a6ca-2c4ab12da883\\n\\nof mo...</td>\n",
+       "      <td>To effectively execute the company vision, man...</td>\n",
+       "      <td>single_hop_specifc_query_synthesizer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          user_input  ...                      synthesizer_name\n",
+       "0  What the Director do in GitLab and how they wo...  ...  single_hop_specifc_query_synthesizer\n",
+       "1                Wht is the rol of the VP in GitLab?  ...  single_hop_specifc_query_synthesizer\n",
+       "2             What GitLab do for career progression?  ...  single_hop_specifc_query_synthesizer\n",
+       "3  Wht is the S-grop and how do they work with ot...  ...  single_hop_specifc_query_synthesizer\n",
+       "4        How does Google execute its company vision?  ...  single_hop_specifc_query_synthesizer\n",
+       "\n",
+       "[5 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testset.to_pandas().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatic Persona Generation\n",
+    "\n",
+    "If you want to automatically generate persona's from a knowledge graph, you can use the [generate_personas_from_kg][ragas.testset.persona.generate_personas_from_kg] function.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ragas.testset.persona import generate_personas_from_kg\n",
+    "from ragas.testset.graph import KnowledgeGraph\n",
+    "from ragas.llms import llm_factory\n",
+    "\n",
+    "kg = KnowledgeGraph.load(\"../../../../experiments/gitlab_kg.json\")\n",
+    "llm = llm_factory(\"gpt-4o-mini\")\n",
+    "\n",
+    "personas = generate_personas_from_kg(kg=kg, llm=llm, num_personas=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Persona(name='Organizational Development Manager', role_description='Responsible for implementing job frameworks and career development strategies to enhance employee growth and clarify roles within the company.'),\n",
+       " Persona(name='DevSecOps Product Manager', role_description='Responsible for overseeing the development and strategy of DevSecOps solutions, ensuring alignment with company goals and user needs.'),\n",
+       " Persona(name='Product Pricing Analyst', role_description='Responsible for developing and analyzing pricing strategies that align with customer needs and market demands.'),\n",
+       " Persona(name='Site Reliability Engineer', role_description='Responsible for maintaining service reliability and performance, focusing on implementing rate limits to prevent outages and enhance system stability.'),\n",
+       " Persona(name='Security Operations Engineer', role_description=\"Works on enhancing security logging processes and ensuring compliance within GitLab's infrastructure.\")]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "personas"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ragas",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/howtos/integrations/_langgraph_agent_evaluation.md b/docs/howtos/integrations/_langgraph_agent_evaluation.md
index 8ffa47a10..800f0678c 100644
--- a/docs/howtos/integrations/_langgraph_agent_evaluation.md
+++ b/docs/howtos/integrations/_langgraph_agent_evaluation.md
@@ -230,7 +230,7 @@ display(Image(react_graph.get_graph(xray=True).draw_mermaid_png()))
 
 
     
-![jpeg](../../_static/imgs/_langgraph_agent_evaluation_28_0.jpg)
+![jpeg](_langgraph_agent_evaluation_files/_langgraph_agent_evaluation_23_0.jpg)
     
 
 
diff --git a/docs/howtos/integrations/_langgraph_agent_evaluation_files/_langgraph_agent_evaluation_23_0.jpg b/docs/howtos/integrations/_langgraph_agent_evaluation_files/_langgraph_agent_evaluation_23_0.jpg
new file mode 100644
index 000000000..e580db4d4
Binary files /dev/null and b/docs/howtos/integrations/_langgraph_agent_evaluation_files/_langgraph_agent_evaluation_23_0.jpg differ
diff --git a/docs/howtos/integrations/_llamaindex.md b/docs/howtos/integrations/_llamaindex.md
index abaaa79de..bdff4c78a 100644
--- a/docs/howtos/integrations/_llamaindex.md
+++ b/docs/howtos/integrations/_llamaindex.md
@@ -11,13 +11,15 @@ You will need an testset to evaluate your `QueryEngine` against. You can either
 Let's see how that works with Llamaindex
 
 # load the documents
+
+
 ```python
 from llama_index.core import SimpleDirectoryReader
 
 documents = SimpleDirectoryReader("./nyc_wikipedia").load_data()
 ```
 
-Now lets init the `TestsetGenerator` object with the corresponding generator and critic llms
+Now  lets init the `TestsetGenerator` object with the corresponding generator and critic llms
 
 
 ```python
diff --git a/mkdocs.yml b/mkdocs.yml
index 49954d63d..04a6b81ce 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -84,6 +84,7 @@ nav:
               - Write your own Metrics - (advanced): howtos/customizations/metrics/_write_your_own_metric_advanced.md
           - Testset Generation:
               - howtos/customizations/testgenerator/index.md
+              - Persona Generation: howtos/customizations/testgenerator/_persona_generator.md
       - Applications:
           - howtos/applications/index.md
           - Metrics:
diff --git a/src/ragas/testset/synthesizers/__init__.py b/src/ragas/testset/synthesizers/__init__.py
index 48679a179..7ccde116c 100644
--- a/src/ragas/testset/synthesizers/__init__.py
+++ b/src/ragas/testset/synthesizers/__init__.py
@@ -15,7 +15,6 @@
 
 
 def default_query_distribution(llm: BaseRagasLLM) -> QueryDistribution:
-    """ """
     return [
         (SingleHopSpecificQuerySynthesizer(llm=llm), 0.5),
         (MultiHopAbstractQuerySynthesizer(llm=llm), 0.25),
diff --git a/src/ragas/testset/synthesizers/multi_hop/abstract.py b/src/ragas/testset/synthesizers/multi_hop/abstract.py
index 20162ff4e..65020ddf8 100644
--- a/src/ragas/testset/synthesizers/multi_hop/abstract.py
+++ b/src/ragas/testset/synthesizers/multi_hop/abstract.py
@@ -70,6 +70,10 @@ async def _generate_scenarios(
         logger.info("found %d clusters", len(node_clusters))
         scenarios = []
 
+        if len(node_clusters) == 0:
+            raise ValueError(
+                "No clusters found in the knowledge graph. Use a different Synthesizer."
+            )
         num_sample_per_cluster = int(np.ceil(n / len(node_clusters)))
 
         for cluster in node_clusters: