sinaptik-ai · gventuri · Jan 13, 2025 · Jan 10, 2025 · Jan 11, 2025 · Jan 12, 2025
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -1,4 +1,4 @@
-# 🐼 Contributing to PandasAI
+# 🐼 Contributing to PandaAI
 
 Hi there! We're thrilled that you'd like to contribute to this project. Your help is essential for keeping it great.
 

diff --git a/LICENSE b/LICENSE
@@ -3,7 +3,7 @@ Copyright (c) 2023 Sinaptik GmbH
 Portions of this software are licensed as follows:
 
 - All content that resides under any "pandasai/ee/" directory of this repository, if such directories exists, are licensed under the license defined in "pandasai/ee/LICENSE".
-- All third party components incorporated into the PandasAI Software are licensed under the original license provided by the owner of the applicable component.
+- All third party components incorporated into the PandaAI Software are licensed under the original license provided by the owner of the applicable component.
 - Content outside of the above mentioned directories or restrictions above is available under the "MIT Expat" license as defined below.
 
 Permission is hereby granted, free of charge, to any person obtaining a copy

diff --git a/README.md b/README.md
@@ -1,4 +1,4 @@
-# ![PandasAI](assets/logo.png)
+# ![PandaAI](assets/logo.png)
 
 [![Release](https://img.shields.io/pypi/v/pandasai?label=Release&style=flat-square)](https://pypi.org/project/pandasai/)
 [![CI](https://github.com/sinaptik-ai/pandas-ai/actions/workflows/ci.yml/badge.svg)](https://github.com/sinaptik-ai/pandas-ai/actions/workflows/ci.yml/badge.svg)
@@ -8,27 +8,27 @@
 [![Downloads](https://static.pepy.tech/badge/pandasai)](https://pepy.tech/project/pandasai) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ZnO-njhL7TBOYPZaqvMvGtsjckZKrv2E?usp=sharing)
 
-PandasAI is a Python platform that makes it easy to ask questions to your data in natural language. It helps non-technical users to interact with their data in a more natural way, and it helps technical users to save time, and effort when working with data.
+PandaAI is a Python platform that makes it easy to ask questions to your data in natural language. It helps non-technical users to interact with their data in a more natural way, and it helps technical users to save time, and effort when working with data.
 
-# 🚀 Deploying PandasAI
+# 🚀 Deploying PandaAI
 
-PandasAI can be used in a variety of ways. You can easily use it in your Jupyter notebooks or Streamlit apps, or you can deploy it as a REST API such as with FastAPI or Flask.
+PandaAI can be used in a variety of ways. You can easily use it in your Jupyter notebooks or Streamlit apps, or you can deploy it as a REST API such as with FastAPI or Flask.
 
-If you are interested in the managed PandasAI Cloud or our self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
+If you are interested in the managed PandaAI Cloud or our self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 
 # 🔧 Getting started
 
-You can find the full documentation for PandasAI [here](https://pandas-ai.readthedocs.io/en/latest/).
+You can find the full documentation for PandaAI [here](https://pandas-ai.readthedocs.io/en/latest/).
 
-You can either decide to use PandasAI in your Jupyter notebooks, Streamlit apps, or use the client and server architecture from the repo.
+You can either decide to use PandaAI in your Jupyter notebooks, Streamlit apps, or use the client and server architecture from the repo.
 
 ## ☁️ Using the platform
 
-[![PandasAI platform](assets/demo.gif?raw=true)](https://www.youtube.com/watch?v=kh61wEy9GYM)
+[![PandaAI platform](assets/demo.gif?raw=true)](https://www.youtube.com/watch?v=kh61wEy9GYM)
 
 ### 📦 Installation
 
-PandasAI platform is uses a dockerized client-server architecture. You will need to have Docker installed in your machine.
+PandaAI platform is uses a dockerized client-server architecture. You will need to have Docker installed in your machine.
 
 ```bash
 git clone https://github.com/sinaptik-ai/pandas-ai/
@@ -50,7 +50,7 @@ This will start the client and server, and you can access the client at `http://
 
 ### 📦 Installation
 
-You can install the PandasAI library using pip or poetry.
+You can install the PandaAI library using pip or poetry.
 
 With pip:
 
@@ -66,7 +66,7 @@ poetry add pandasai
 
 ### 🔍 Demo
 
-Try out the PandasAI library yourself in your browser:
+Try out the PandaAI library yourself in your browser:
 
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ZnO-njhL7TBOYPZaqvMvGtsjckZKrv2E?usp=sharing)
 
@@ -111,7 +111,7 @@ The total sales for the top 3 countries by sales is 16500.
 
 #### Visualize charts
 
-You can also ask PandasAI to generate charts for you:
+You can also ask PandaAI to generate charts for you:
 
 ```python
 df.chat(
@@ -123,7 +123,7 @@ df.chat(
 
 #### Multiple DataFrames
 
-You can also pass in multiple dataframes to PandasAI and ask questions relating them.
+You can also pass in multiple dataframes to PandaAI and ask questions relating them.
 
 ```python
 import os
@@ -160,15 +160,15 @@ You can find more examples in the [examples](examples) directory.
 
 ## 📜 License
 
-PandasAI is available under the MIT expat license, except for the `pandasai/ee` directory (which has it's [license here](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE) if applicable.
+PandaAI is available under the MIT expat license, except for the `pandasai/ee` directory (which has it's [license here](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE) if applicable.
 
-If you are interested in managed PandasAI Cloud or self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
+If you are interested in managed PandaAI Cloud or self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 
 ## Resources
 
 - [Docs](https://pandas-ai.readthedocs.io/en/latest/) for comprehensive documentation
 - [Examples](examples) for example notebooks
-- [Discord](https://discord.gg/kF7FqH2FwS) for discussion with the community and PandasAI team
+- [Discord](https://discord.gg/kF7FqH2FwS) for discussion with the community and PandaAI team
 
 ## 🤝 Contributing
 

diff --git a/docs/mint.json b/docs/mint.json
@@ -54,7 +54,7 @@
     "navigation": [
       {
         "group": "Overview",
-        "pages": ["v3/introduction", "v3/getting-started"],
+        "pages": ["v3/introduction", "v3/getting-started", "v3/output-formats"],
         "version": "v3"
       },
       {

diff --git a/docs/v2/advanced-security-agent.mdx b/docs/v2/advanced-security-agent.mdx
@@ -1,11 +1,11 @@
 ---
 title: "Advanced Security Agent"
-description: "Enhance the PandasAI library with the Security Agent to secure applications from malicious code generation"
+description: "Enhance the PandaAI library with the Security Agent to secure applications from malicious code generation"
 ---
 
 ## Introduction to the Advanced Security Agent
 
-The `AdvancedSecurityAgent` (currently in beta) extends the capabilities of the PandasAI library by adding a Security layer to identify if query can generate malicious code.
+The `AdvancedSecurityAgent` (currently in beta) extends the capabilities of the PandaAI library by adding a Security layer to identify if query can generate malicious code.
 
 > **Note:** Usage of the Security Agent may be subject to a license. For more details, refer to the [license documentation](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE).
 

diff --git a/docs/v2/cache.mdx b/docs/v2/cache.mdx
@@ -5,7 +5,7 @@ description: "The cache is a SQLite database that stores the results of previous
 
 # Cache
 
-PandasAI uses a cache to store the results of previous queries. This is useful for two reasons:
+PandaAI uses a cache to store the results of previous queries. This is useful for two reasons:
 
 1. It allows the user to quickly retrieve the results of a query without having to wait for the model to generate a response.
 2. It cuts down on the number of API calls made to the model, reducing the cost of using the model.
@@ -14,7 +14,7 @@ The cache is stored in a file called `cache.db` in the `/cache` directory of the
 
 ## Disabling the cache
 
-The cache can be disabled by setting the `enable_cache` parameter to `False` when creating the `PandasAI` object:
+The cache can be disabled by setting the `enable_cache` parameter to `False` when creating the `PandaAI` object:
 
 ```python
 df = SmartDataframe('data.csv', {"enable_cache": False})
@@ -24,7 +24,7 @@ By default, the cache is enabled.
 
 ## Clearing the cache
 
-The cache can be cleared by deleting the `cache.db` file. The file will be recreated automatically when the next query is made. Alternatively, the cache can be cleared by calling the `clear_cache()` method on the `PandasAI` object:
+The cache can be cleared by deleting the `cache.db` file. The file will be recreated automatically when the next query is made. Alternatively, the cache can be cleared by calling the `clear_cache()` method on the `PandaAI` object:
 
 ```python
 import pandas_ai as pai

diff --git a/docs/v2/connectors.mdx b/docs/v2/connectors.mdx
@@ -1,11 +1,11 @@
 ---
 title: "Connectors"
-description: "PandasAI provides connectors to connect to different data sources."
+description: "PandaAI provides connectors to connect to different data sources."
 ---
 
-PandasAI mission is to make data analysis and manipulation more efficient and accessible to everyone. This includes making it easier to connect to data sources and to use them in your data analysis and manipulation workflow.
+PandaAI mission is to make data analysis and manipulation more efficient and accessible to everyone. This includes making it easier to connect to data sources and to use them in your data analysis and manipulation workflow.
 
-PandasAI provides a number of connectors that allow you to connect to different data sources. These connectors are designed to be easy to use, even if you are not familiar with the data source or with PandasAI.
+PandaAI provides a number of connectors that allow you to connect to different data sources. These connectors are designed to be easy to use, even if you are not familiar with the data source or with PandaAI.
 
 To use a connector, you first need to install the required dependencies. You can do this by running the following command:
 
@@ -21,7 +21,7 @@ Have a look at the video of how to use the connectors:
 
 ## SQL connectors
 
-PandasAI provides connectors for the following SQL databases:
+PandaAI provides connectors for the following SQL databases:
 
 - PostgreSQL
 - MySQL
@@ -32,11 +32,11 @@ PandasAI provides connectors for the following SQL databases:
 - Yahoo Finance
 - Airtable
 
-Additionally, PandasAI provides a generic SQL connector that can be used to connect to any SQL database.
+Additionally, PandaAI provides a generic SQL connector that can be used to connect to any SQL database.
 
 ### PostgreSQL connector
 
-The PostgreSQL connector allows you to connect to a PostgreSQL database. It is designed to be easy to use, even if you are not familiar with PostgreSQL or with PandasAI.
+The PostgreSQL connector allows you to connect to a PostgreSQL database. It is designed to be easy to use, even if you are not familiar with PostgreSQL or with PandaAI.
 
 To use the PostgreSQL connector, you only need to import it into your Python code and pass it to a `SmartDataframe` or `SmartDatalake` object:
 
@@ -66,7 +66,7 @@ df.chat('What is the total amount of payments in the last year?')
 
 ### MySQL connector
 
-Similarly to the PostgreSQL connector, the MySQL connector allows you to connect to a MySQL database. It is designed to be easy to use, even if you are not familiar with MySQL or with PandasAI.
+Similarly to the PostgreSQL connector, the MySQL connector allows you to connect to a MySQL database. It is designed to be easy to use, even if you are not familiar with MySQL or with PandaAI.
 
 To use the MySQL connector, you only need to import it into your Python code and pass it to a `SmartDataframe` or `SmartDatalake` object:
 
@@ -96,7 +96,7 @@ df.chat('What is the total amount of loans in the last year?')
 
 ### Sqlite connector
 
-Similarly to the PostgreSQL and MySQL connectors, the Sqlite connector allows you to connect to a local Sqlite database file. It is designed to be easy to use, even if you are not familiar with Sqlite or with PandasAI.
+Similarly to the PostgreSQL and MySQL connectors, the Sqlite connector allows you to connect to a local Sqlite database file. It is designed to be easy to use, even if you are not familiar with Sqlite or with PandaAI.
 
 To use the Sqlite connector, you only need to import it into your Python code and pass it to a `SmartDataframe` or `SmartDatalake` object:
 

diff --git a/docs/v2/contributing.mdx b/docs/v2/contributing.mdx
@@ -1,4 +1,4 @@
-# 🐼 Contributing to PandasAI
+# 🐼 Contributing to PandaAI
 
 Hi there! We're thrilled that you'd like to contribute to this project. Your help is essential for keeping it great.
 

diff --git a/docs/v2/custom-response.mdx b/docs/v2/custom-response.mdx
@@ -2,7 +2,7 @@
 title: "Custom Response"
 ---
 
-PandasAI offers the flexibility to handle chat responses in a customized manner. By default, PandasAI includes a ResponseParser class that can be extended to modify the response output according to your needs.
+PandaAI offers the flexibility to handle chat responses in a customized manner. By default, PandaAI includes a ResponseParser class that can be extended to modify the response output according to your needs.
 
 You have the option to provide a custom parser, such as `StreamlitResponse`, to the configuration object like this:
 
@@ -16,7 +16,7 @@ from pandasai import SmartDatalake
 from pandasai.responses.response_parser import ResponseParser
 
 # This class overrides default behaviour how dataframe is returned
-# By Default PandasAI returns the SmartDataFrame
+# By Default PandaAI returns the SmartDataFrame
 class PandasDataFrame(ResponseParser):
 
     def __init__(self, context) -> None:

diff --git a/docs/v2/custom-whitelisted-dependencies.mdx b/docs/v2/custom-whitelisted-dependencies.mdx
@@ -2,7 +2,7 @@
 title: "Custom whitelisted dependencies"
 ---
 
-By default, PandasAI only allows to run code that uses some whitelisted modules. This is to prevent malicious code from being executed on the server or locally.
+By default, PandaAI only allows to run code that uses some whitelisted modules. This is to prevent malicious code from being executed on the server or locally.
 
 The whitelisted modules are:
 
@@ -18,7 +18,7 @@ These libraries are sandboxed for security reasons, so that malicious code canno
 
 However, it is possible to add custom modules to the whitelist. This can be done by passing a list of modules to the `custom_whitelisted_dependencies` parameter when instantiating the `Agent` class.
 
-**Note**: PandasAI cannot sandbox arbitrary code execution for custom libraries that are whitelisted. If you add a custom library to the whitelist, arbitrary code execution will be possible for that library. Whitelisting a custom library means that the library is "trusted" and can be used without any limitations. **Only whitelist libraries that are under your control or that you trust**.
+**Note**: PandaAI cannot sandbox arbitrary code execution for custom libraries that are whitelisted. If you add a custom library to the whitelist, arbitrary code execution will be possible for that library. Whitelisting a custom library means that the library is "trusted" and can be used without any limitations. **Only whitelist libraries that are under your control or that you trust**.
 
 For example, to add the `scikit-learn` module to the whitelist:
 
@@ -29,6 +29,6 @@ agent = Agent("data.csv", config={
 })
 ```
 
-The `custom_whitelisted_dependencies` parameter accepts a list of strings, where each string is the name of a module. The module must be installed in the environment where PandasAI is running.
+The `custom_whitelisted_dependencies` parameter accepts a list of strings, where each string is the name of a module. The module must be installed in the environment where PandaAI is running.
 
-Please, make sure you have installed the module in the environment where PandasAI is running. Otherwise, you will get an error when trying to run the code.
+Please, make sure you have installed the module in the environment where PandaAI is running. Otherwise, you will get an error when trying to run the code.
diff --git a/docs/v2/examples.mdx b/docs/v2/examples.mdx
@@ -2,12 +2,12 @@
 title: "Examples"
 ---
 
-Here are some examples of how to use PandasAI.
+Here are some examples of how to use PandaAI.
 More [examples](https://github.com/Sinaptik-AI/pandas-ai/tree/main/examples) are included in the repository along with samples of data.
 
 ## Working with pandas dataframes
 
-Using PandasAI with a Pandas DataFrame
+Using PandaAI with a Pandas DataFrame
 
 ```python
 import os
@@ -35,7 +35,7 @@ print(response)
 
 ## Working with CSVs
 
-Example of using PandasAI with a CSV file
+Example of using PandaAI with a CSV file
 
 ```python
 import os
@@ -55,13 +55,13 @@ print(response)
 
 ## Working with Excel files
 
-Example of using PandasAI with an Excel file. In order to use Excel files as a data source, you need to install the `pandasai[excel]` extra dependency.
+Example of using PandaAI with an Excel file. In order to use Excel files as a data source, you need to install the `pandasai[excel]` extra dependency.
 
 ```console
 pip install pandasai[excel]
 ```
 
-Then, you can use PandasAI with an Excel file as follows:
+Then, you can use PandaAI with an Excel file as follows:
 
 ```python
 import os
@@ -81,7 +81,7 @@ print(response)
 
 ## Working with Parquet files
 
-Example of using PandasAI with a Parquet file
+Example of using PandaAI with a Parquet file
 
 ```python
 import os
@@ -101,13 +101,13 @@ print(response)
 
 ## Working with Google Sheets
 
-Example of using PandasAI with a Google Sheet. In order to use Google Sheets as a data source, you need to install the `pandasai[google-sheet]` extra dependency.
+Example of using PandaAI with a Google Sheet. In order to use Google Sheets as a data source, you need to install the `pandasai[google-sheet]` extra dependency.
 
 ```console
 pip install pandasai[google-sheet]
 ```
 
-Then, you can use PandasAI with a Google Sheet as follows:
+Then, you can use PandaAI with a Google Sheet as follows:
 
 ```python
 import os
@@ -128,13 +128,13 @@ Remember that at the moment, you need to make sure that the Google Sheet is publ
 
 ## Working with Modin dataframes
 
-Example of using PandasAI with a Modin DataFrame. In order to use Modin dataframes as a data source, you need to install the `pandasai[modin]` extra dependency.
+Example of using PandaAI with a Modin DataFrame. In order to use Modin dataframes as a data source, you need to install the `pandasai[modin]` extra dependency.
 
 ```console
 pip install pandasai[modin]
 ```
 
-Then, you can use PandasAI with a Modin DataFrame as follows:
+Then, you can use PandaAI with a Modin DataFrame as follows:
 
 ```python
 import os
@@ -163,13 +163,13 @@ print(response)
 
 ## Working with Polars dataframes
 
-Example of using PandasAI with a Polars DataFrame (still in beta). In order to use Polars dataframes as a data source, you need to install the `pandasai[polars]` extra dependency.
+Example of using PandaAI with a Polars DataFrame (still in beta). In order to use Polars dataframes as a data source, you need to install the `pandasai[polars]` extra dependency.
 
 ```console
 pip install pandasai[polars]
 ```
 
-Then, you can use PandasAI with a Polars DataFrame as follows:
+Then, you can use PandaAI with a Polars DataFrame as follows:
 
 ```python
 import os
@@ -194,7 +194,7 @@ print(response)
 
 ## Plotting
 
-Example of using PandasAI to plot a chart from a Pandas DataFrame
+Example of using PandaAI to plot a chart from a Pandas DataFrame
 
 ```python
 import os
@@ -241,7 +241,7 @@ print(response)
 
 ## Working with multiple dataframes (using the SmartDatalake)
 
-Example of using PandasAI with multiple dataframes. In order to use multiple dataframes as a data source, you need to use a `SmartDatalake` instead of a `SmartDataframe`. You can instantiate a `SmartDatalake` as follows:
+Example of using PandaAI with multiple dataframes. In order to use multiple dataframes as a data source, you need to use a `SmartDatalake` instead of a `SmartDataframe`. You can instantiate a `SmartDatalake` as follows:
 
 ```python
 import os