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

# Installation & Quickstart

> Start building your data preparation layer with PandasAI and chat with your data

## Installation

PandasAI requires Python `3.8+ <=3.11`. We recommend using Poetry for dependency management:

```bash theme={null}
# Using poetry (recommended)
poetry add pandasai

# Alternative: using pip
pip install pandasai
```

## Quick setup

In order to use PandasAI, you need a large language model (LLM). You can use any LLM, but for this guide we'll use OpenAI through the LiteLLM extension.

First, install the required extension:

```bash theme={null}
pip install pandasai-litellm
```

Then, import PandasAI and configure the LLM:

```python theme={null}
import pandasai as pai
from pandasai_litellm.litellm import LiteLLM

# Initialize LiteLLM with your OpenAI model
llm = LiteLLM(model="gpt-4.1-mini", api_key="YOUR_OPENAI_API_KEY")

# Configure PandasAI to use this LLM
pai.config.set({
    "llm": llm
})
```

## Chat with your data

```python theme={null}
import pandasai as pai
from pandasai_litellm.litellm import LiteLLM

# Initialize LiteLLM with your OpenAI model
llm = LiteLLM(model="gpt-4.1-mini", api_key="YOUR_OPENAI_API_KEY")

# Configure PandasAI to use this LLM
pai.config.set({
    "llm": llm
})

# Load your data
df = pai.read_csv("data/companies.csv")

response = df.chat("What is the average revenue by region?")
print(response)
```

When you ask a question, PandasAI will use the LLM to generate the answer and output a response.
Depending on your question, it can return different kind of responses:

* string
* dataframe
* chart
* number

Find it more about output data formats [here](/v3/chat-and-output#available-output-formats).

## Next Steps

* [Config NL Layer](/v3/overview-nl)
* [Set up LLM](/v3/large-language-models)