updated v2 and v3 library docs (#1735)

Author: gdcsinaptik

CITATION.cff

@@ -1,8 +1,8 @@
 cff-version: 1.2.0
 date-released: 2023-04-29
 message: "If you use this software, please cite it as below."
-title: "PandaAI: the conversational data analysis framework"
-abstract: "PandaAI is a python library that makes it easy to ask questions to your data in natural language."
+title: "PandasAI: the conversational data analysis framework"
+abstract: "PandasAI is a python library that makes it easy to ask questions to your data in natural language."
 url: "https://github.com/sinaptik-ai/pandas-ai"
 authors:
 - family-names: "Venturi"

CONTRIBUTING.md

@@ -1,4 +1,4 @@
-# 🐼 Contributing to PandaAI
+# 🐼 Contributing to PandasAI
 
 Hi there! We're thrilled that you'd like to contribute to this project. Your help is essential for keeping it great.
 

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 PandaAI Software are licensed under the original license provided by the owner of the applicable component.
+- All third party components incorporated into the PandasAI 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

README.md

@@ -1,4 +1,4 @@
-# ![PandaAI](assets/logo.png)
+# ![PandasAI](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-core.yml/badge.svg)](https://github.com/sinaptik-ai/pandas-ai/actions/workflows/ci-core.yml/badge.svg)
@@ -8,13 +8,13 @@
 [![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)
 
-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.
+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.
 
 # 🔧 Getting started
 
-You can find the full documentation for PandaAI [here](https://pandas-ai.readthedocs.io/en/latest/).
+You can find the full documentation for PandasAI [here](https://pandas-ai.readthedocs.io/en/latest/).
 
-You can either decide to use PandaAI in your Jupyter notebooks, Streamlit apps, or use the client and server architecture from the repo.
+You can either decide to use PandasAI in your Jupyter notebooks, Streamlit apps, or use the client and server architecture from the repo.
 
 ## ☁️ Using the platform
 
@@ -39,7 +39,7 @@ dataset.push()
 
 Your team can now access and query this data using natural language through the platform.
 
-![PandaAI](assets/demo.gif)
+![PandasAI](assets/demo.gif)
 
 ## 📚 Using the library
 
@@ -49,7 +49,7 @@ Python version `3.8+ <3.12`
 
 ### 📦 Installation
 
-You can install the PandaAI library using pip or poetry.
+You can install the PandasAI library using pip or poetry.
 
 With pip:
 
@@ -103,7 +103,7 @@ The total sales for the top 3 countries by sales is 16500.
 
 #### Visualize charts
 
-You can also ask PandaAI to generate charts for you:
+You can also ask PandasAI to generate charts for you:
 
 ```python
 df.chat(
@@ -115,7 +115,7 @@ df.chat(
 
 #### Multiple DataFrames
 
-You can also pass in multiple dataframes to PandaAI and ask questions relating them.
+You can also pass in multiple dataframes to PandasAI and ask questions relating them.
 
 ```python
 import pandasai as pai
@@ -147,7 +147,7 @@ Olivia gets paid the most.
 
 #### Docker Sandbox
 
-You can run PandaAI in a Docker sandbox, providing a secure, isolated environment to execute code safely and mitigate the risk of malicious attacks.
+You can run PandasAI in a Docker sandbox, providing a secure, isolated environment to execute code safely and mitigate the risk of malicious attacks.
 
 ##### Python Requirements
 
@@ -197,9 +197,9 @@ You can find more examples in the [examples](examples) directory.
 
 ## 📜 License
 
-PandaAI is available under the MIT expat license, except for the `pandasai/ee` directory of this repository, which has its [license here](https://github.com/sinaptik-ai/pandas-ai/blob/main/ee/LICENSE).
+PandasAI is available under the MIT expat license, except for the `pandasai/ee` directory of this repository, which has its [license here](https://github.com/sinaptik-ai/pandas-ai/blob/main/ee/LICENSE).
 
-If you are interested in managed PandaAI Cloud or self-hosted Enterprise Offering, [contact us](https://getpanda.ai/pricing).
+If you are interested in managed PandasAI Cloud or self-hosted Enterprise Offering, [contact us](https://getpanda.ai/pricing).
 
 ## Resources
 
@@ -208,7 +208,7 @@ If you are interested in managed PandaAI Cloud or self-hosted Enterprise Offerin
 
 - [Docs](https://pandas-ai.readthedocs.io/en/latest/) for comprehensive documentation
 - [Examples](examples) for example notebooks
-- [Discord](https://discord.gg/KYKj9F2FRH) for discussion with the community and PandaAI team
+- [Discord](https://discord.gg/KYKj9F2FRH) for discussion with the community and PandasAI team
 
 ## 🤝 Contributing
 

docs/mint.json

@@ -1,5 +1,5 @@
 {
-    "name": "PandaAI",
+    "name": "PandasAI",
     "logo": {
       "light": "/logo/logo.png",
       "dark": "/logo/logo.png",
@@ -69,7 +69,7 @@
       },
       {
         "group": "Data Platform",
-        "pages": ["v3/ai-dashboards", "v3/share-dataframes", "v3/permission-management"],
+        "pages": ["v3/ai-dashboards", "v3/permission-management"],
         "version": "v3"
       },
       {

docs/v2/advanced-security-agent.mdx

@@ -1,11 +1,11 @@
 ---
 title: "Advanced Security Agent"
-description: "Enhance the PandaAI library with the Security Agent to secure applications from malicious code generation"
+description: "Enhance the PandasAI 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 PandaAI library by adding a Security layer to identify if query can generate malicious code.
+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.
 
 > **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).
 

docs/v2/cache.mdx

@@ -5,7 +5,7 @@ description: "The cache is a SQLite database that stores the results of previous
 
 # Cache
 
-PandaAI uses a cache to store the results of previous queries. This is useful for two reasons:
+PandasAI 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 `PandaAI` object:
+The cache can be disabled by setting the `enable_cache` parameter to `False` when creating the `PandasAI` 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 `PandaAI` 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 `PandasAI` object:
 
 ```python
 import pandas_ai as pai

docs/v2/connectors.mdx

@@ -1,11 +1,11 @@
 ---
 title: "Connectors"
-description: "PandaAI provides connectors to connect to different data sources."
+description: "PandasAI provides connectors to connect to different data sources."
 ---
 
-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 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 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.
+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.
 
 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
 
-PandaAI provides connectors for the following SQL databases:
+PandasAI provides connectors for the following SQL databases:
 
 - PostgreSQL
 - MySQL
@@ -32,11 +32,11 @@ PandaAI provides connectors for the following SQL databases:
 - Yahoo Finance
 - Airtable
 
-Additionally, PandaAI provides a generic SQL connector that can be used to connect to any SQL database.
+Additionally, PandasAI 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 PandaAI.
+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.
 
 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 PandaAI.
+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.
 
 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 PandaAI.
+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.
 
 To use the Sqlite connector, you only need to import it into your Python code and pass it to a `SmartDataframe` or `SmartDatalake` object:
 

docs/v2/contributing.mdx

@@ -1,4 +1,4 @@
-# 🐼 Contributing to PandaAI
+# 🐼 Contributing to PandasAI
 
 Hi there! We're thrilled that you'd like to contribute to this project. Your help is essential for keeping it great.
 

docs/v2/custom-response.mdx

@@ -2,7 +2,7 @@
 title: "Custom Response"
 ---
 
-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.
+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.
 
 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 PandaAI returns the SmartDataFrame
+# By Default PandasAI returns the SmartDataFrame
 class PandasDataFrame(ResponseParser):
 
     def __init__(self, context) -> None:
@@ -42,10 +42,6 @@ salaries_df = pd.DataFrame(
     }
 )
 
-# By default, unless you choose a different LLM, it will use BambooLLM.
-# You can get your free API key signing up at https://pandabi.ai (you can also configure it in your .env file)
-os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"
-
 agent = SmartDatalake(
     [employees_df, salaries_df],
     config={"llm": llm, "verbose": True, "response_parser": PandasDataFrame},
@@ -80,11 +76,6 @@ salaries_df = pd.DataFrame(
     }
 )
 
-
-# By default, unless you choose a different LLM, it will use BambooLLM.
-# You can get your free API key signing up at https://pandabi.ai (you can also configure it in your .env file)
-os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"
-
 agent = SmartDatalake(
     [employees_df, salaries_df],
     config={"verbose": True, "response_parser": StreamlitResponse},