inmydata-openedge 0.0.1

This package provides simple API data access to PAS and Classic AppServers via the inmydata platform

OpenEdge Agent SDK

The inmydata OpenEdge agent SDK enables you to build AI agents that can rapidly access data from the PAS and Classic AppServer instances.

Features

• 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-openedge

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
# get_schema retrieves metadata about available subjects (datasets), including:
# - Field names and types (dimensions and metrics)
# - AI descriptions for fields
# - Number of available dimensions and metrics per subject
# The optional 'source' parameter helps track where schema requests originate from
schema = driver.get_schema("Readme Documentation")
print(schema)

# Example output:
# {
#   "schemaVersion": 1,
#   "generatedAt": "2025-11-18T11:24:16Z",
#   "source": "Readme Documentation",
#   "subjectsCount": 1,
#   "subjects": [
#     {
#       "name": "Inmystore Sales",
#       "aiDescription": "This subject (dataset) contains transactional data for a retail organisation...",
#       "factFieldTypes": {
#         "Customer": {"name": "Customer", "type": "System.String", "aiDescription": null},
#         "Date": {"name": "Date", "type": "System.DateTime", "aiDescription": null},
#         "Financial Year": {"name": "Financial Year", "type": "System.Int32", 
#                           "aiDescription": "This dimension contains a Year value..."}
#         # ... more dimension fields
#       },
#       "metricFieldTypes": {
#         "Cost of Sale": {"name": "Cost of Sale", "type": "System.Decimal", 
#                         "dimensionsUsed": null, "aiDescription": ""},
#         "Sales Value": {"name": "Sales Value", "type": "System.Decimal", 
#                        "dimensionsUsed": null, "aiDescription": ""}
#         # ... more metric fields
#       },
#       "numDimensions": 26,
#       "numMetrics": 14
#     }
#   ]
# }

# -- 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
    SummaryRequest, # True if the request is one that should summarize data, false if it should retrieve unsummarized records
    System) # The name of the system the subject is in that should be used to get the data 

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)

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"))