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Backwards Compatibility

docs/v3/migration-backwards-compatibility.mdx

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<Note> PandasAI v3 maintains backward compatibility for `SmartDataframe`, `SmartDatalake`, and `Agent`. However, we recommend migrating to the new `pai.DataFrame()` and `pai.chat()` methods for better performance and features. </Note>

SmartDataframe

SmartDataframe continues to work in v3 with the same API. However, you must configure the LLM globally.

Using SmartDataframe in v3 (Legacy)

python
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?")

While SmartDataframe works, we recommend using pai.DataFrame() for better integration with v3 features:

python
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?")

Benefits of pai.DataFrame():

  • Better integration with semantic layer
  • Improved context management
  • Enhanced performance
  • Access to v3-specific features
  • Cleaner API

SmartDatalake

SmartDatalake still works but is no longer necessary. You can query multiple dataframes directly with pai.chat().

Using SmartDatalake in v3 (Legacy)

python
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?")

Query multiple dataframes directly without SmartDatalake:

python
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)

Benefits of pai.chat():

  • No need to instantiate SmartDatalake
  • Cleaner, more intuitive API
  • Better performance
  • Semantic layer support
  • Easier to add/remove dataframes dynamically

Agent

The Agent class works mostly the same way in v3 as it did in v2, but some methods have been removed. The main requirement is to configure the LLM globally.

python
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")

Key Change: Configure LLM globally with pai.config.set() instead of passing it per-agent.

New Agent Methods in v3

PandasAI v3 introduces new Agent methods that enhance conversational capabilities:

  • follow_up(query): Continue conversations without clearing memory (maintains context)
python
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?')

Note: The clarification_questions(), explain() and rephrase_query() methods have been removed in v3.

These methods provide enhanced conversational capabilities not available in v2.

For detailed information about Agent usage, see the Agent documentation. For information about using Skills with Agent, see the Skills documentation.