docs: update v3 documentation, readme and examples

README.md

@@ -25,12 +25,16 @@ Load your data, save them as a dataframe, and push them to the platform
 ```python
 import pandasai as pai
 
-df = pai.read_csv("./filepath.csv")
-df.push()
+pai.api_key.set("your-pai-api-key")
+
+file = pai.read_csv("./filepath.csv")
 
-df.save(path="your-organization/dataset-name",
+dataset = pai.create(path="your-organization/dataset-name",
+    df=file,
     name="dataset-name",
     description="dataset-description")
+
+dataset.push()
 ```
 Your team can now access and query this data using natural language through the platform.
 
@@ -113,7 +117,7 @@ df.chat(
 You can also pass in multiple dataframes to PandaAI and ask questions relating them.
 
 ```python
-from pandasai import Agent
+import pandasai as pai
 
 employees_data = {
     'EmployeeID': [1, 2, 3, 4, 5],
@@ -133,8 +137,7 @@ salaries_df = pai.DataFrame(salaries_data)
 # You can get your free API key signing up at https://app.pandabi.ai (you can also configure it in your .env file)
 pai.api_key.set("your-pai-api-key")
 
-agent = Agent([employees_df, salaries_df])
-agent.chat("Who gets paid the most?")
+pai.chat("Who gets paid the most?", employees_df, salaries_df)
 ```
 
 ```
@@ -145,16 +148,20 @@ 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 (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 of this repository, which has its [license here](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE).
 
-If you are interested in managed PandaAI 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://getpanda.ai/pricing).
 
 ## Resources
 
+> **Beta Notice**  
+> Release v3 is currently in beta. The following documentation and examples reflect the features and functionality in progress and may change before the final release.
+
 - [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
 
+
 ## 🤝 Contributing
 
 Contributions are welcome! Please check the outstanding issues and feel free to open a pull request.

docs/mint.json

@@ -64,7 +64,7 @@
       },
       {
         "group": "Natural Language",
-        "pages": ["v3/overview-nl", "v3/large-language-models", "v3/chat-and-cache", "v3/output-formats"],
+        "pages": ["v3/overview-nl", "v3/large-language-models", "v3/chat-and-output"],
         "version": "v3"
       },
       {

docs/v3/agent.mdx

@@ -3,19 +3,22 @@ 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`.
 
 
 ## Prerequisites
 
-Before you start training PandaAI, you need to set your PandaAI API key. You can generate your API key by signing up at [https://app.pandabi.ai](https://app.pandabi.ai).
-
-Then you can set your API key as an environment variable:
+Before you start training PandaAI, you need to set your PandaAI API key. 
+You can generate your API key by signing up at [https://app.pandabi.ai](https://app.pandabi.ai).
 
 ```python
-import os
+import pandasai as pai
 
-os.environ["PANDABI_API_KEY"] = "YOUR_PANDABI_API_KEY"
+pai.api_key.set("your-pai-api-key")
 ```
 
 It is important that you set the API key, or it will fail with the following error: `No vector store provided. Please provide a vector store to train the agent`.
@@ -33,10 +36,10 @@ The training uses by default the `BambooVectorStore` to store the training data,
 As an alternative, if you want to use a local vector store (enterprise only for production use cases), you can use the `ChromaDB`, `Qdrant` or `Pinecone` vector stores (see examples below).
 
 ```python
+import pandasai as pai
 from pandasai import Agent
 
-# Set your PandasAI API key (you can generate one signing up at https://app.pandabi.ai)
-os.environ["PANDABI_API_KEY"] = "YOUR_PANDABI_API_KEY"
+pai.api_key.set("your-pai-api-key")
 
 agent = Agent("data.csv")
 agent.train(docs="The fiscal year starts in April")
@@ -61,19 +64,22 @@ agent = Agent("data.csv")
 
 # Train the model
 query = "What is the total sales for the current fiscal year?"
-response = """
-import pandas as pd
-
-df = dfs[0]
+# The following code is passed as a string to the response variable
+response = '\n'.join([
+    'import pandas as pd',
+    '',
+    'df = dfs[0]',
+    '',
+    '# Calculate the total sales for the current fiscal year',
+    'total_sales = df[df[\'date\'] >= pd.to_datetime(\'today\').replace(month=4, day=1)][\'sales\'].sum()',
+    'result = { "type": "number", "value": total_sales }'
+])
 
-# Calculate the total sales for the current fiscal year
-total_sales = df[df['date'] >= pd.to_datetime('today').replace(month=4, day=1)]['sales'].sum()
-result = { "type": "number", "value": total_sales }
-"""
 agent.train(queries=[query], codes=[response])
 
 response = agent.chat("What is the total sales for the last fiscal year?")
 print(response)
+
 # The model will use the information provided in the training to generate a response
 ```
 
@@ -110,15 +116,17 @@ agent = Agent("data.csv", vectorstore=vector_store)
 
 # Train the model
 query = "What is the total sales for the current fiscal year?"
-response = """
-import pandas as pd
-
-df = dfs[0]
+# The following code is passed as a string to the response variable
+response = '\n'.join([
+    'import pandas as pd',
+    '',
+    'df = dfs[0]',
+    '',
+    '# Calculate the total sales for the current fiscal year',
+    'total_sales = df[df[\'date\'] >= pd.to_datetime(\'today\').replace(month=4, day=1)][\'sales\'].sum()',
+    'result = { "type": "number", "value": total_sales }'
+])
 
-# Calculate the total sales for the current fiscal year
-total_sales = df[df['date'] >= pd.to_datetime('today').replace(month=4, day=1)]['sales'].sum()
-result = { "type": "number", "value": total_sales }
-"""
 agent.train(queries=[query], codes=[response])
 
 response = agent.chat("What is the total sales for the last fiscal year?")
@@ -145,3 +153,37 @@ vector_store = BambooVectorStor(api_key="YOUR_PANDABI_API_KEY")
 # Instantiate the agent with the custom vector store
 agent = Agent(connector, config={...} vectorstore=vector_store)
 ```
+## 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:
+- Provide better examples that represent your data patterns
+- Avoid sharing sensitive information
+- Guide the agent with specific data scenarios
+
+You can do this by passing a custom head to the agent:
+
+```python
+import pandas as pd
+import pandasai as pai
+
+# Your original dataframe
+df = pd.DataFrame({
+    'sensitive_id': [1001, 1002, 1003, 1004, 1005],
+    'amount': [150, 200, 300, 400, 500],
+    'category': ['A', 'B', 'A', 'C', 'B']
+})
+
+# Create a custom head with anonymized data
+head_df = pd.DataFrame({
+    'sensitive_id': [1, 2, 3, 4, 5],
+    'amount': [100, 200, 300, 400, 500],
+    'category': ['A', 'B', 'C', 'A', 'B']
+})
+
+# Use the custom head
+smart_df = pai.SmartDataframe(df, config={
+    "custom_head": head_df
+})
+```
+
+The agent will use your custom head instead of the default first 5 rows of the dataframe when analyzing and responding to queries.

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.

docs/v3/output-formats.mdx → docs/v3/chat-and-output.mdx

@@ -1,12 +1,48 @@
 ---
-title: 'Output formats'
-description: 'Understanding the different output formats supported by PandaAI'
+title: "Chat and output formats"
+description: "Learn how to use PandaAI's powerful chat functionality and the output formats for natural language data analysis"
 ---
 
-PandaAI supports multiple output formats for responses, each designed to handle different types of data and analysis results effectively. This document outlines the available output formats and their use cases.
+<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:
+- Query your data using plain English
+- Generate visualizations and statistical analyses
+- Work with multiple DataFrames simultaneously
+
+For a more UI-based data analysis experience, check out our [Data Platform](/v3/ai-dashboards).
+
+### Basic Usage
+
+```python
+import pandasai as pai
+
+df_customers = pai.load("company/customers")
+
+response = df_customers.chat("Which are our top 5 customers?")
+```
+
+### Chat with multiple DataFrames
+
+```python
+import pandasai as pai
+
+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)
+```
 
 ## Available Output Formats
 
+PandaAI supports multiple output formats for responses, each designed to handle different types of data and analysis results effectively. This document outlines the available output formats and their use cases.
+
+
 ### DataFrame Response
 Used when the result is a pandas DataFrame. This format preserves the tabular structure of your data and allows for further data manipulation.
 
@@ -28,7 +64,6 @@ 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")