Pseudo Code for Importing Data, Chatting with the Agent, and Training the Agent

Libraries and Data Preparation

1. Import Required Libraries:

   • Import the os library.
   • Import the pandas library as pd.

2. Data Initialization:

   • Create a dictionary employees_data with the keys:
     • "EmployeeID" -> List of employee IDs.
     • "Name" -> List of employee names.
     • "Department" -> List of departments.
   • Create a dictionary salaries_data with the keys:
     • "EmployeeID" -> List of employee IDs.
     • "Salary" -> List of salaries.

3. Convert Dictionaries to DataFrames:

   • Convert employees_data to a DataFrame called employees_df.
   • Convert salaries_data to a DataFrame called salaries_df.

Agent Setup and Chat Interaction

4. Set API Key:

   • Set the environment variable PANDASAI_API_KEY to "your-api-key".

5. Create Agent:

   • Initialize an Agent with the DataFrames [employees_df, salaries_df].
   • Set the agent's memory size to 10.

6. Query the Agent:

   • Chat with the agent by asking, "Who gets paid the most?" Save the response.
   • Print the response.

Clarification Questions and Explanation

7. Get Clarification Questions:

   • Request clarification questions from the agent for the query "Who gets paid the most?".
   • Print each question.

8. Explain Response:

   • Ask the agent to explain how the chat response was generated.
   • Print the explanation.

Agent Training

9. Prepare Training Data:

   • Create a list queries with questions related to data visualization and distribution:
     • "Display the distribution of ages in the population."
     • "Visualize the distribution of product ratings."
     • "Show the distribution of household incomes in a region."
   • Create a list codes with corresponding function calls:
     • "display_age_distribution()"
     • "visualize_product_ratings_distribution()"
     • "show_household_incomes_distribution_in_region()"

10. Train the Agent:

    • Train the agent using the queries and codes.

11. Indicate Completion:

    • Print "Done".

Complete Pseudo Code

# Import Required Libraries
import os
import pandas as pd

# Initialize Data
employees_data = {
    "EmployeeID": [1, 2, 3, 4, 5],
    "Name": ["John", "Emma", "Liam", "Olivia", "William"],
    "Department": ["HR", "Sales", "IT", "Marketing", "Finance"],
}

salaries_data = {
    "EmployeeID": [1, 2, 3, 4, 5],
    "Salary": [5000, 6000, 4500, 7000, 5500],
}

# Convert to DataFrames
employees_df = pd.DataFrame(employees_data)
salaries_df = pd.DataFrame(salaries_data)

# Set API Key
os.environ["PANDASAI_API_KEY"] = "your-api-key"

# Create Agent
agent = Agent([employees_df, salaries_df], memory_size=10)

# Chat with the Agent
response = agent.chat("Who gets paid the most?")
print(response)

# Get Clarification Questions
questions = agent.clarification_questions("Who gets paid the most?")
for question in questions:
    print(question)

# Explain Chat Response
response = agent.explain()
print(response)

# Prepare Training Data
queries = [
    "Display the distribution of ages in the population.",
    "Visualize the distribution of product ratings.",
    "Show the distribution of household incomes in a region.",
]

codes = [
    "display_age_distribution()",
    "visualize_product_ratings_distribution()",
    "show_household_incomes_distribution_in_region()",
]

# Train the Agent
agent.train(queries, codes)

# Indicate Completion
print("Done")