# Default Data Types

By default, the LLM stack supports the following data types:

### CSV

To use CSV as a source, use the data type (the first argument to the `add_source()` method) as `csv`. Eg:

```python
from genai_stack.model import OpenAIGpt35Model

model = OpenAIGpt35Model.from_kwargs(
 fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("csv", "valid_csv_path_or_url")
```

### PDF

To use pdf as a source, use the data type as `pdf`. Eg:

```python
from genai_stack.model import OpenAIGpt35Model

model = OpenAIGpt35Model.from_kwargs(
 fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("pdf", "valid_pdf_path_or_url")
```

### Web

To use the web as a source, use the data type as `web`. Eg:

```python
from genai_stack.model import OpenAIGpt35Model

model = OpenAIGpt35Model.from_kwargs(
 fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("web", "valid_web_url")
```

### JSON

To use JSON as a source, use the data type as `json`. Eg:

```python
from genai_stack.model import OpenAIGpt35Model

model = OpenAIGpt35Model.from_kwargs(
 fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("json", "valid_json_path_or_url")
```

### Markdown

To use markdown as a source, use the data type as `markdown`. Eg:

```python
from genai_stack.model import OpenAIGpt35Model

model = OpenAIGpt35Model.from_kwargs(
 fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("markdown", "valid_markdown_path_or_url")
```

To make predictions you can execute the below code snippet:

```python
response = model.predict("<Question on top of any of your data>")
print(response)
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://genaistack.aiplanet.com/v0.1.0/getting-started/default-data-types.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.