inmydata 0.0.17

This package provides simple API access to the inmydata platform

Agent SDK

The inmydata agent SDK enables you to build AI agents that can rapidly access data from the inmydata platform.

Features

• Conversational data interface - retrieve data with natural language queries
• Structured data interface - rapidly build data interfaces for you AI agents
• Calendar assistant - empower your AI agent with detailed knowledge of your financial calendars

Installation

Install the inmydata agent SDK with pip

  pip install inmydata

Documentation

See https://developer.inmydata.com for quickstarts, documentation, and examples.

Usage/Examples

For these examples you will need to set the following environment variables:

• INMYDATA_API_KEY
• INMYDATA_TENANT
• INMYDATA_CALENDAR

Example of retrieving structured data

import os
from dotenv import load_dotenv
from inmydata.StructuredData import (
    StructuredDataDriver, 
    AIDataSimpleFilter, 
    AIDataFilter, 
    LogicalOperator, 
    ConditionOperator, 
    TopNOption, 
    ChartType
)

load_dotenv()

driver = StructuredDataDriver(os.environ['INMYDATA_TENANT'])
driver.user = "demo" # Events to display charts will be available to the user specified here
driver.session_id = "test-session" # Session ID passed in the event to display charts. Can optionally be used to only show charts for the current session

# -- Get a json document that details the available schema
print(driver.get_schema("Readme Documentation"))

# -- Use get_data_simple when your filter is simple (only equality filters, no bracketing, no ORs, etc.)

# Build our simple filter
filter = []
filter.append(
    AIDataSimpleFilter(
        "Store", # Field to filter on
        "Edinburgh") # Value to filter by
    ) 

# Build a TopN filter to only show the Top 10 Sales People based on Sales Value
TopN = TopNOption("Sales Value", 10) # Field to order by and number of records to return (Positive for TopN, negative for BottomN)
TopNOptions = {}
TopNOptions["Sales Person"] = TopN # Apply the Top N option to the Sales Person field

df = driver.get_data_simple(
    "Inmystore Sales", # Name of the subject we want to extract data from
    ["Sales Person","Sales Value"], # List of fields we want to extract
    filter, # Filters to apply
    False, # Whether filters are case sensitive
    TopNOptions) # Apply the Top 10 Sales People based on Sales Value filter

print(df)

# -- Use get_data when your filter more complex (non-equality matches, bracketing, ORs, etc.) --

# Build our filter
filter = [] 
filter.append(
    AIDataFilter(
        "Store",
        ConditionOperator.Equals, # Condition to use in the filter
        LogicalOperator.And, # Logical operator to use in the filter
        "Edinburgh", # Value to filter by
        0, # Number of brackets before this condition
        0, # Number of brackets after this condition
        False # Whether the filter is case sensitive
    )
)
filter.append(
    AIDataFilter(
        "Store",
        ConditionOperator.Equals, # Condition to use in the filter
        LogicalOperator.Or, # Logical operator to use in the filter
        "London", # Value to filter by
        0, # Number of brackets before this condition
        0, # Number of brackets after this condition
        False # Whether the filter is case sensitive
    )
)
df = driver.get_data(
    "Inmystore Sales", # Name of the subject we want to extract data from
    ["Financial Year","Store","Sales Value"], # List of fields we want to extract
    filter, # Filters to apply
    {}) # Apply no TopN options

print(df)

# -- Use get_chart to generate a chart based on the data -- see https://developer.inmydata.com/support/solutions/articles/36000577995-displaying-charts-generated-by-agentic-ai-workflows

# Build our filter
filter = [] 
filter.append(
    AIDataFilter(
        "Store",
        ConditionOperator.Equals, # Condition to use in the filter
        LogicalOperator.And, # Logical operator to use in the filter
        "Edinburgh", # Value to filter by
        0, # Number of brackets before this condition
        0, # Number of brackets after this condition
        False # Whether the filter is case sensitive
    )
)
filter.append(
    AIDataFilter(
        "Financial Year",
        ConditionOperator.Equals, # Condition to use in the filter
        LogicalOperator.And, # Logical operator to use in the filter
        "2025", # Value to filter by
        0, # Number of brackets before this condition
        0, # Number of brackets after this condition
        False # Whether the filter is case sensitive
    )
)

# Build a TopN filter to only show the Top 10 Sales People based on Sales Value
TopN = TopNOption("Sales Value", 10) # Field to order by and number of records to return (Positive for TopN, negative for BottomN)
TopNOptions = {}
TopNOptions["Sales Person"] = TopN # Apply the Top N option to the Sales Person field

chartId = driver.get_chart(
    "Inmystore Sales", # Name of the subject we want to extract data from
    ["Sales Person"], # Chart row fields
    [], # Chart Column Fields
    ["Sales Value"], # Chart value fields
    filter, # Filters to apply
    ChartType.Bar, # Type of chart to generate
    "Top 10 Sales People in Edinburgh for 2025", # Title of the chart
    TopNOptions, # Apply the Top 10 Sales People based on Sales Value filter
)

Example of retrieving conversational data

import os

from dotenv import load_dotenv
from inmydata.ConversationalData import ConversationalDataDriver
import asyncio

load_dotenv()

# get_answer is an async function, so we need to run it in an event loop
async def main():
    driver = ConversationalDataDriver(os.environ['INMYDATA_TENANT'])

    # Register a callback to handle AI question updates
    def on_ai_question_update(caller, message):  
        print(message)

    # Register the callback handler for AI question updates
    driver.on("ai_question_update", on_ai_question_update) 

    question = "Give me the top 10 stores this year"
    answer = await driver.get_answer(question)
    
    print("=================================================================")
    print(f"The answer was: {answer.answer}")
    print(f"The subject used to generate the answer was: {answer.subject}")


asyncio.run(main())

Example of retrieving calendar periods

import os

from datetime import date
from dotenv import load_dotenv
from inmydata.CalendarAssistant import CalendarAssistant

load_dotenv()

# Get today's date
today = date.today()

# Initialize the Calendar Assistant with tenant and calendar name
assistant = CalendarAssistant(os.environ['INMYDATA_TENANT'], os.environ['INMYDATA_CALENDAR'])

# Get the current financial year
print("The current financial year is:  " + str(assistant.get_financial_year(today)))
# Get the current financial quarter
print("The current financial quarter is: " + str(assistant.get_quarter(today)))
# Get the current financial month
print("The current financial month is: " + str(assistant.get_month(today)))
# Get the current financial week
print("The current financial week is: " + str(assistant.get_week_number(today)))
# Get the current financial periods
print("The current periods are:")
print(assistant.get_financial_periods(today))
# Get the date range for the current financial month
response = assistant.get_calendar_period_date_range(assistant.get_financial_year(today), assistant.get_month(today), CalendarPeriodType.month)
if response is not None:
    print("The current financial month date range is: " + response.StartDate.strftime("%A, %B %d, %Y") + " to " + response.EndDate.strftime("%A, %B %d, %Y"))