# Testset Generation for RAG

This simple guide will help you generate a testset for evaluating your RAG pipeline using your own documents.

## Quickstart
Let's walk through a quick example of generating a testset for a RAG pipeline. Following that we will explore the main components of the testset generation pipeline.

### Load Sample Documents

For the sake of this tutorial we will use sample documents from this [repository](https://huggingface.co/datasets/vibrantlabsai/Sample_Docs_Markdown). You can replace this with your own documents.

```bash
git clone https://huggingface.co/datasets/vibrantlabsai/Sample_Docs_Markdown
```

### Load documents

Now we will load the documents from the sample dataset using `DirectoryLoader`, which is one of the document loaders from [langchain_community](https://python.langchain.com/docs/concepts/document_loaders/). You may also use any loaders from [llama_index](https://docs.llamaindex.ai/en/stable/understanding/loading/llamahub/)

```shell
pip install langchain-community
```

```python
from langchain_community.document_loaders import DirectoryLoader

path = "Sample_Docs_Markdown/"
loader = DirectoryLoader(path, glob="**/*.md")
docs = loader.load()
```

### Choose your LLM

You may choose to use any [LLM of your choice](./../howtos/customizations/customize_models.md)
--8<--
choose_generator_llm.md
--8<--

### Generate Testset

Now we will run the test generation using the loaded documents and the LLM setup. If you have used `llama_index` to load documents, please use `generate_with_llama_index_docs` method instead.

```python
from ragas.testset import TestsetGenerator

generator = TestsetGenerator(llm=generator_llm, embedding_model=generator_embeddings)
dataset = generator.generate_with_langchain_docs(docs, testset_size=10)
```

### Analyzing the testset

Once you have generated a testset, you would want to view it and select the queries you see fit to include in your final testset. You can export the testset to a pandas DataFrame and do various analysis on it.

```python
dataset.to_pandas()
```

Output
![testset](./testset_output.png)

!!! note
Generating synthetic test data can be confusing and hard, but if you need we are happy to help you with it. We have built pipelines to generate test data for various use cases. If you need help with it, please talk to us by booking a [slot](https://bit.ly/3EBYq4J) or drop us a line: [founders@vibrantlabs.com](mailto:founders@vibrantlabs.com).

## A Deeper Look

Now that we have a seen how to generate a testset, let's take a closer look at the main components of the testset generation pipeline and how you can quickly customize it.

At the core there are 2 main operations that are performed to generate a testset.

1. **KnowledgeGraph Creation**: We first create a [KnowledgeGraph][ragas.testset.graph.KnowledgeGraph] using the documents you provide and use various [Transformations][ragas.testset.transforms.base.BaseGraphTransformation] to enrich the knowledge graph with additional information that we can use to generate the testset. You can learn more about this from the [core concepts section](../concepts/test_data_generation/rag.md#knowledge-graph-creation).
2. **Testset Generation**: We use the [KnowledgeGraph][ragas.testset.graph.KnowledgeGraph] to generate a set of [scenarios][ragas.testset.synthesizers.base.BaseScenario]. These scenarios are used to generate the [testset][ragas.testset.synthesizers.generate.Testset]. You can learn more about this from the [core concepts section](../concepts/test_data_generation/rag.md#scenario-generation).

Now let's see an example of how these components work together to generate a testset.

### KnowledgeGraph Creation

Let's first create a [KnowledgeGraph][ragas.testset.graph.KnowledgeGraph] using the documents we loaded earlier.

```python
from ragas.testset.graph import KnowledgeGraph

kg = KnowledgeGraph()
```
Output
```
KnowledgeGraph(nodes: 0, relationships: 0)
```

and then add the documents to the knowledge graph.

```python
from ragas.testset.graph import Node, NodeType

for doc in docs:
    kg.nodes.append(
        Node(
            type=NodeType.DOCUMENT,
            properties={"page_content": doc.page_content, "document_metadata": doc.metadata}
        )
    )
```
Output
```
KnowledgeGraph(nodes: 10, relationships: 0)
```

Now we will enrich the knowledge graph with additional information using [Transformations][ragas.testset.transforms.base.BaseGraphTransformation]. Here we will use [default_transforms][ragas.testset.transforms.default_transforms] to create a set of default transformations to apply with an LLM and Embedding Model of your choice.
But you can mix and match transforms or build your own as needed.

```python
from ragas.testset.transforms import default_transforms, apply_transforms


# define your LLM and Embedding Model
# here we are using the same LLM and Embedding Model that we used to generate the testset
transformer_llm = generator_llm
embedding_model = generator_embeddings

trans = default_transforms(documents=docs, llm=transformer_llm, embedding_model=embedding_model)
apply_transforms(kg, trans)
```

Now we have a knowledge graph with additional information. You can save the knowledge graph too.

```python
kg.save("knowledge_graph.json")
loaded_kg = KnowledgeGraph.load("knowledge_graph.json")
loaded_kg
```

Output
```
KnowledgeGraph(nodes: 48, relationships: 605)
```

### Testset Generation

Now we will use the `loaded_kg` to create the [TestsetGenerator][ragas.testset.synthesizers.generate.TestsetGenerator].

```python
from ragas.testset import TestsetGenerator

generator = TestsetGenerator(llm=generator_llm, embedding_model=embedding_model, knowledge_graph=loaded_kg)
```

We can also define the distribution of queries we would like to generate. Here lets use the default distribution.

```python
from ragas.testset.synthesizers import default_query_distribution

query_distribution = default_query_distribution(generator_llm)
```

Output
```
[
    (SingleHopSpecificQuerySynthesizer(llm=llm), 0.5),
    (MultiHopAbstractQuerySynthesizer(llm=llm), 0.25),
    (MultiHopSpecificQuerySynthesizer(llm=llm), 0.25),
]
```

Now we can generate the testset.

```python
testset = generator.generate(testset_size=10, query_distribution=query_distribution)
testset.to_pandas()
```
Output
![testset](./testset_output.png)