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docs: persona generator (#1664)
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jjmachan authored Nov 12, 2024
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32 changes: 17 additions & 15 deletions docs/howtos/customizations/metrics/_write_your_own_metric.md
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Expand Up @@ -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
Expand All @@ -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])
Expand Down Expand Up @@ -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])
Expand Down Expand Up @@ -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


$$
\text{Hallucinations} = 1 - \text{Faithfulness}
$$
Expand All @@ -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
```

Expand All @@ -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],
)
```

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160 changes: 160 additions & 0 deletions docs/howtos/customizations/testgenerator/_persona_generator.md
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## 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.")]


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