added beta banner in docs

Sinaptik-AI · Jan 17, 2025 · aa7ee42 · aa7ee42
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diff --git a/README.md b/README.md
@@ -27,14 +27,14 @@ import pandasai as pai
 
 pai.api_key.set("your-pai-api-key")
 
-df = pai.read_csv("./filepath.csv")
+file = pai.read_csv("./filepath.csv")
 
-df = pai.create(path="your-organization/dataset-name",
-    df=df,
+dataset = pai.create(path="your-organization/dataset-name",
+    df=file,
     name="dataset-name",
     description="dataset-description")
 
-df.push()
+dataset.push()
 ```
 Your team can now access and query this data using natural language through the platform.
 

diff --git a/docs/v3/agent.mdx b/docs/v3/agent.mdx
@@ -3,6 +3,10 @@ title: 'Agent'
 description: 'Add few-shot learning to your PandaAI agent'
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 You can train PandaAI to understand your data better and to improve its performance. Training is as easy as calling the `train` method on the `Agent`.
 
 

diff --git a/docs/v3/ai-dashboards.mdx b/docs/v3/ai-dashboards.mdx
@@ -3,6 +3,10 @@ title: 'AI Dashboards'
 description: 'Turn your dataframes into collaborative AI dashboards'
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 PandaAI provides a [data platform](https://app.pandabi.ai) that maximizes the power of your [semantic dataframes](/v3/dataframes). 
 With a single line of code, you can turn your dataframes into auto-updating AI dashboards - no UI development needed.
 Each dashboard comes with a pre-generated set of insights and a conversational agent that helps you and your team explore the data through natural language.

diff --git a/docs/v3/chat-and-cache.mdx b/docs/v3/chat-and-cache.mdx
@@ -2,6 +2,12 @@
 title: "Chat and cache"
 description: "Learn how to use PandaAI's powerful chat functionality for natural language data analysis and understand how caching improves performance"
 ---
+
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
+
 ## Chat
 
 The `.chat()` method is PandaAI's core feature that enables natural language interaction with your data. It allows you to:
@@ -18,7 +24,7 @@ import pandasai as pai
 
 df_customers = pai.load("company/customers")
 
-response = df.chat("Which are our top 5 customers?")
+response = df_customers.chat("Which are our top 5 customers?")
 ```
 
 ### Chat with multiple DataFrames

diff --git a/docs/v3/conversational-agent.mdx b/docs/v3/conversational-agent.mdx
@@ -3,6 +3,10 @@ title: "Conversational Agent"
 description: "Learn how to customize and improve PandaAI's conversational capabilities"
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 ## Custom Head
 
 In some cases, you might want to provide custom data samples to the conversational agent to improve its understanding and responses. For example, you might want to:

diff --git a/docs/v3/data-ingestion.mdx b/docs/v3/data-ingestion.mdx
@@ -3,6 +3,11 @@ title: 'Add Data Sources'
 description: 'Learn how to ingest data from various sources in PandaAI'
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
+
 ## What type of data does PandaAI support?
 PandaAI mission is to make data analysis and manipulation more efficient and accessible to everyone. You can work with data in various ways:
 
@@ -21,13 +26,13 @@ Loading data from CSV files is straightforward with PandaAI:
 import pandasai as pai
 
 # Basic CSV loading
-df = pai.read_csv("data.csv")
+file = pai.read_csv("data.csv")
 
 # Use the semantic layer on CSV
 df = pai.create(
     path="company/sales-data",
     name="sales_data",
-    df = df,
+    df = file,
     description="Sales data from our retail stores",
     columns={
         "transaction_id": {"type": "string", "description": "Unique identifier for each sale"},

diff --git a/docs/v3/data-layer.mdx b/docs/v3/data-layer.mdx
@@ -3,6 +3,11 @@ title: 'Data Layer'
 description: 'Understanding the core data management components of PandaAI'
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
+
 The Data Layer is built around a powerful [Semantic Layer](/v3/semantic-layer) that handles data processing and representation, enhancing the comprehension of tabular data from various [data sources](/v3/data-ingestion):
 - CSV and Excel files
 - SQL databases (PostgreSQL, MySQL)

diff --git a/docs/v3/dataframes.mdx b/docs/v3/dataframes.mdx
@@ -15,12 +15,15 @@ When working with local files (CSV, Parquet) or datasets based on such files, th
 - Ideal for local file processing or cross-source analysis
 
 ```python
-import pandas as pd
-from pandasai import SmartDataframe
+import pandasai as pai
 
 # Load local files as materialized dataframes
-df = pd.read_csv("local_file.csv")
-smart_df = SmartDataframe(df)
+file= pai.read_csv("local_file.csv")
+
+df = pai.create(path="organization/dataset-name",
+    name="dataset-name",
+    df = file,
+    description="describe your dataset")
 ```
 
 ## Virtualized Dataframes
@@ -31,7 +34,7 @@ When loading remote datasets, dataframes are virtualized by default, providing:
 - Optimal for remote data sources
 
 ```python
-from pandasai import load
+import pandasai as pai
 
 # Load remote datasets (virtualized by default)
-df = load("organization/dataset-name")
+df = pai.load("organization/dataset-name")
diff --git a/docs/v3/getting-started.mdx b/docs/v3/getting-started.mdx
@@ -34,10 +34,10 @@ pai.api_key.set("YOUR_PANDABI_API_KEY")
 import pandasai as pai
 
 # read csv - replace "filepath" with your file path
-df = pai.read_csv("filepath")
+file = pai.read_csv("filepath")
 
 # ask questions
-df.chat('Which are the top 5 countries by sales?')
+file.chat('Which are the top 5 countries by sales?')
 ```
 
 When you ask a question, PandaAI will use the LLM to generate the answer and output a response.
@@ -58,11 +58,11 @@ This allows you to avoid reading the data every time.
 import pandasai as pai
 
 # read csv - replace "filepath" with your file path
-df = pai.read_csv("filepath")
+file = pai.read_csv("filepath")
 
 df = pai.create(path="organization/dataset-name",
     name="dataset-name",
-    df = df,
+    df = file,
     description="describe your dataset")
 ```
 
@@ -107,5 +107,4 @@ df_customers = pai.load("company/customers")
 df_orders = pai.load("company/orders")
 df_products = pai.load("company/products")
 
-response = pai.chat('Who are our top 5 customers and what products do they buy most frequently?', df_customers, df_orders, df_products)
-```
+response = pai.chat('Who are our top 5 customers and what products do they buy most frequently?', df_customers, df_orders, df_products)
diff --git a/docs/v3/introduction.mdx b/docs/v3/introduction.mdx
@@ -3,6 +3,10 @@ title: 'Introduction'
 description: 'PandaAI is a Python library designed for end-to-end conversational data analysis.'
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 ## What is PandaAI?
 
 PandaAI is a Python library that makes it easy to turn tabular datasets into conversational agents. It consists of a [Data Layer](/v3/data-layer) that handles data processing, transformation and semantic enhancement; and a [Natural Language Layer](/v3/overview-nl) that converts user queries into executable code, including charts generation.

diff --git a/docs/v3/large-language-models.mdx b/docs/v3/large-language-models.mdx
@@ -3,6 +3,10 @@ title: "Set up LLM"
 description: "Set up Large Language Model in PandaAI"
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 PandaAI supports multiple LLMs.
 To make the library lightweight, the default LLM is BambooLLM, developed by PandaAI team themselves.
 To use other LLMs, you need to install the corresponding llm extension. Once a LLM extension is installed, you can configure it simply using `pai.config.set()`.

diff --git a/docs/v3/output-formats.mdx b/docs/v3/output-formats.mdx
@@ -28,7 +28,6 @@ The response format is automatically determined based on the type of analysis pe
 
 Example:
 ```python
-import pandas as pd
 import pandasai as pai
 
 df = pai.load("my-org/users")

diff --git a/docs/v3/overview-nl.mdx b/docs/v3/overview-nl.mdx
@@ -3,6 +3,10 @@ title: 'NL Layer'
 description: 'Understanding the AI and natural language processing capabilities of PandaAI'
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 ## How does PandaAI NL Layer work?
 
 The Natural Language Layer uses generative AI to transform natural language queries into production-ready code generated by LLMs.

diff --git a/docs/v3/permission-management.mdx b/docs/v3/permission-management.mdx
@@ -3,6 +3,10 @@ title: 'Permission Management'
 description: 'Manage access control and permissions'
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 The [data platform](/v3/ai-dashboards) allows you to control how your AI dashboards and dataframes are shared and accessed. 
 You can choose between four levels of access:
 - Private: for your own use

diff --git a/docs/v3/privacy-and-security.mdx b/docs/v3/privacy-and-security.mdx
@@ -3,4 +3,8 @@ title: "Privacy and Security"
 description: "Learn about PandaAI's privacy and security features"
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 PandaAI provides robust privacy and security features to protect your data and ensure compliance with security requirements.
diff --git a/docs/v3/semantic-layer.mdx b/docs/v3/semantic-layer.mdx
@@ -21,12 +21,12 @@ The simplest way to create a semantic layer for CSV files is using the `create`
 ```python
 import pandasai as pai
 
-df = pai.read_csv("data.csv")
+file = pai.read_csv("data.csv")
 
 df = pai.create(
     path="company/sales-data",         # Format: "organization/dataset"
     name="sales-data",                 # Human-readable name
-    df = df,                           # Input Dataframe
+    df = file,                           # Input Dataframe
     description="Sales data from our retail stores",  # Optional description
     columns=[
         {
@@ -48,7 +48,7 @@ df = pai.create(
 The name field identifies your dataset in the create method.
 
 ```python
-df = pai.read_csv("data.csv")
+file = pai.read_csv("data.csv")
 
 pai.create(
     path="company/sales-data",
@@ -67,7 +67,7 @@ pai.create(
 The path uniquely identifies your dataset in the PandaAI ecosystem using the format "organization/dataset".
 
 ```python
-df = pai.read_csv("data.csv")
+file = pai.read_csv("data.csv")
 
 pai.create(
     path="acme-corp/sales-data",  # Format: "organization/dataset"
@@ -87,11 +87,11 @@ pai.create(
 The input dataframe that contains your data, typically created using `pai.read_csv()`.
 
 ```python
-df = pai.read_csv("data.csv")  # Create the input dataframe
+file = pai.read_csv("data.csv")  # Create the input dataframe
 
 pai.create(
     path="acme-corp/sales-data",
-    df=df,  # Pass your dataframe here
+    df=file,  # Pass your dataframe here
     ...
 )
 ```
@@ -105,12 +105,12 @@ pai.create(
 A clear text description that helps others understand the dataset's contents and purpose.
 
 ```python
-df = pai.read_csv("data.csv")
+file = pai.read_csv("data.csv")
 
 pai.create(
     path="company/sales-data",
     name="sales-data",
-    df = df,
+    df = file,
     description="Daily sales transactions from all retail stores, including transaction IDs, dates, and amounts",
     ...
 )
@@ -129,12 +129,12 @@ Define the structure and metadata of your dataset's columns to help PandaAI unde
 When specified, only the declared columns will be included, allowing you to select specific columns for your semantic layer.
 
 ```python
-df = pai.read_csv("data.csv")
+file = pai.read_csv("data.csv")
 
 pai.create(
     path="company/sales-data",
     name="sales-data",
-    df = df,
+    df = file,
     description="Daily sales transactions from all retail stores",
     columns=[
         {

diff --git a/docs/v3/share-dataframes.mdx b/docs/v3/share-dataframes.mdx
@@ -3,6 +3,10 @@ title: 'Share Dataframes'
 description: 'Learn how to push and pull dataframes to/from the PandaAI Data Platform'
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 ## Pushing Dataframes
 Once you have turned raw data into dataframes using the [semantic layer](/v3/semantic-layer), you can push them to our data platform with one line of code.
 

diff --git a/docs/v3/smart-dataframes.mdx b/docs/v3/smart-dataframes.mdx
@@ -3,6 +3,10 @@ title: 'SmartDataframe'
 description: 'Legacy documentation for SmartDataframe class'
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 ## SmartDataframe (Legacy)
 
 > **Note**: This documentation is for backwards compatibility. For new projects, we recommend using the new [semantic dataframes](/v3/dataframes).

diff --git a/docs/v3/smart-datalakes.mdx b/docs/v3/smart-datalakes.mdx
@@ -3,6 +3,10 @@ title: 'SmartDatalake'
 description: 'Legacy documentation for SmartDatalake class'
 ---
 
+<Note title="Beta Notice">
+Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+</Note>
+
 ## SmartDatalake (Legacy)
 
 > **Note**: This documentation is for backwards compatibility. For new projects, we recommend using the new [semantic dataframes](/v3/dataframes) API with multiple dataframes.