Metadata-Version: 2.4
Name: inmydata
Version: 0.0.11
Summary: This package provides simple API access to the inmydata platform
Project-URL: Homepage, https://inmydata.ai
Project-URL: Documentation, https://developer.inmydata.com
Project-URL: Reference, https://docs.inmydata.ai/agents/
Project-URL: Issues, https://support.inmydata.com
Author-email: inmydata <info@inmydata.com>
License-Expression: MIT
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.9
Requires-Dist: aiohttp>=3.11.18
Requires-Dist: jsonpickle>=4.0.5
Requires-Dist: pandas>=2.3.0
Requires-Dist: requests>=2.32.4
Requires-Dist: signalrcore>=0.9.5
Description-Content-Type: text/markdown


![Logo](https://inmydata.ai/hs-fs/hubfs/Horizontal-1.png?width=200&height=59&name=Horizontal-1.png)




# 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

```bash
  pip install inmydata
```
    
## Documentation

See [https://developer.inmydata.com](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

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

driver = StructuredDataDriver(os.environ['INMYDATA_TENANT'])

# -- 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
    ) 
df = driver.get_data_simple(
    "Inmystore Sales", # Name of the subject we want to extract data from
    ["Financial Year","Sales Value"], # List of fields we want to extract
    filter, # Filters to apply
    False) # Whether filters are case sensitive

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 sensitiv
    )
)
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 sensitiv
    )
)
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

print(df)
```

Example of retrieving conversational data

```python
import os

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

load_dotenv()

driver = ConversationalDataDriver(os.environ['INMYDATA_TENANT'])

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

# Callback handler for AI question updates
driver.on("ai_question_update", on_ai_question_update) 

# Ask the question
# Note: This is an asynchronous operation, so we need to run it in an event loop
async def ask_question(question):
    response = await driver.get_answer(question)
    return response

# Example question
Answer = asyncio.run(ask_question("Give me the top 10 stores this year"))
print("=================================================================")
print(Answer.answer)
```

Example of retrieving calendar periods

```python
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))
```



