> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vectorboard.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# GridSearch Class

# GridSearch Class

The `GridSearch` class is used for performing grid search on a given chain.

## Attributes

* `experiments`: A list of Experiment objects.
* `experiment_results`: A DataFrame containing the results of the experiments.
* `console`: A Rich Console object.
* `vectorstore_list`: A list of vector stores.
* `retrievers_list`: A list of retrievers.
* `chain`: A Chain object.

## Methods

### `__init__(self, chain)`

Initializes a GridSearch object.

#### Parameters

* `chain (Chain)`: A Chain object.

```python
grid_search = GridSearch(chain=RetrievalQA)
```

### `create_experiments(self, param_grid, loader=None, documents=None)`

Creates a list of Experiment objects based on the given parameter grid.

#### Parameters

* `param_grid (dict)`: A dictionary containing the parameter grid.
* `loader (Loader)`: A Loader object.
* `documents (list)`: A list of documents.

#### Example

<CodeGroup>
  ```python With Loader
  param_grid = {
      "chunk_size": [50, 300, 500],
      "vector_store": [FAISS],
      "embeddings": [OpenAIEmbeddings(), HuggingFaceEmbeddings()],
  }

  grid_search.create_experiments(param_grid=param_grid, loader=PyPDFLoader("recycling.pdf"))

  ```

  ```python With Documents
  param_grid = {
      "chunk_size": [50, 300, 500],
      "vector_store": [FAISS],
      "embeddings": [OpenAIEmbeddings(), HuggingFaceEmbeddings()],
  }

  documents = PyPDFLoader("recycling.pdf").load()

  grid_search.create_experiments(param_grid=param_grid, documents=documents)
  ```
</CodeGroup>