Backwards Compatibility

from pandasai import SmartDataframe
import pandasai as pai
import pandas as pd
from pandasai_litellm.litellm import LiteLLM

# Configure LLM globally (required)
llm = LiteLLM(model="gpt-4o-mini", api_key="your-api-key")
pai.config.set({"llm": llm})

# v2 style still works
df = pd.DataFrame({
    "country": ["US", "UK", "France"],
    "sales": [5000, 3200, 2900]
})

smart_df = SmartDataframe(df)
response = smart_df.chat("What are the top countries by sales?")

import pandasai as pai
import pandas as pd

# Configure LLM globally
pai.config.set({"llm": llm})

# Simple approach
df = pd.DataFrame({
    "country": ["US", "UK", "France"],
    "sales": [5000, 3200, 2900]
})
df = pai.DataFrame(df)
response = df.chat("What are the top countries by sales?")

from pandasai import SmartDatalake
import pandasai as pai
import pandas as pd
from pandasai_litellm.litellm import LiteLLM

# Configure LLM globally (required)
llm = LiteLLM(model="gpt-4o-mini", api_key="your-api-key")
pai.config.set({"llm": llm})

# v2 style still works
employees_df = pd.DataFrame({
    "name": ["John", "Jane", "Bob"],
    "department": ["Sales", "Engineering", "Sales"]
})

salaries_df = pd.DataFrame({
    "name": ["John", "Jane", "Bob"],
    "salary": [60000, 80000, 55000]
})

lake = SmartDatalake([
    employees_df,
    salaries_df
])

response = lake.chat("Who gets paid the most?")

import pandasai as pai

# Configure LLM globally
pai.config.set({"llm": llm})

# Create dataframes
employees = pai.DataFrame(employees_df)
salaries = pai.DataFrame(salaries_df)

# Query across multiple dataframes directly
response = pai.chat("Who gets paid the most?", employees, salaries)

from pandasai import Agent
import pandasai as pai
from pandasai_litellm.litellm import LiteLLM

# Configure LLM globally (required in v3)
llm = LiteLLM(model="gpt-4o-mini", api_key="your-api-key")
pai.config.set({"llm": llm})

# Agent works as before
df1 = pai.DataFrame(sales_data)
df2 = pai.DataFrame(costs_data)

agent = Agent([df1, df2])
response = agent.chat("Analyze the data and provide insights")

agent = Agent([df1, df2])

# Start conversation
response = agent.chat('What is the total revenue?')

# Follow up without losing context
follow_up = agent.follow_up('What about last quarter?')