Category: Dataverse

Python + Dataverse Series – Post #03: Create, Update, Delete records via Web API

Hi Folks,

This is continuation in this Python with Dataverse Series, in this blog post, we will perform a full CRUD(Create, Retrieve, Update, Delete) in Dataverse using Web API.

Please use the below code for the same…to make any calls using WEB API to Dataverse.

import pyodbc
import msal
import requests
import json
import re
# Azure AD details
client_id = 'XXXX'
client_secret = 'XXXX'
tenant_id = 'XXXX'
authority = f'https://login.microsoftonline.com/{tenant_id}'
resource = 'https://XXXX.crm8.dynamics.com'
# SQL endpoint
sql_server = 'XXXX.crm8.dynamics.com'
database = 'XXXX'
# Get token with error handling
try:
print(f"Attempting to authenticate with tenant: {tenant_id}")
print(f"Authority URL: {authority}")
app = msal.ConfidentialClientApplication(client_id, authority=authority, client_credential=client_secret)
print("Acquiring token…")
token_response = app.acquire_token_for_client(scopes=[f'{resource}/.default'])
if 'error' in token_response:
print(f"Token acquisition failed: {token_response['error']}")
print(f"Error description: {token_response.get('error_description', 'No description available')}")
else:
access_token = token_response['access_token']
print("Token acquired successfully and your token is!"+access_token)
print(f"Token length: {len(access_token)} characters")
except ValueError as e:
print(f"Configuration Error: {e}")
print("\nPossible solutions:")
print("1. Verify your tenant ID is correct")
print("2. Check if the tenant exists and is active")
print("3. Ensure you're using the right Azure cloud (commercial, government, etc.)")
except Exception as e:
print(f"Unexpected error: {e}")
#Get 5 contacts from Dataverse using Web API
import requests
import json
try:
#Full CRUD Operations – Create, Read, Update, Delete a contact in Dataverse
print("Making Web API request to perform CRUD operations on contacts…")
# Dataverse Web API endpoint for contacts
web_api_url = f"{resource}/api/data/v9.2/contacts"
# Base headers with authorization token
headers = {
'Authorization': f'Bearer {access_token}',
'OData-MaxVersion': '4.0',
'OData-Version': '4.0',
'Accept': 'application/json',
'Content-Type': 'application/json'
}
# Create a new contact
new_contact = { "firstname": "John", "lastname": "Doe" }
print("Creating a new contact…")
# Request the server to return the created representation. If not supported or omitted,
# Dataverse often returns 204 No Content and provides the entity id in a response header.
create_headers = headers.copy()
create_headers['Prefer'] = 'return=representation'
response = requests.post(web_api_url, headers=create_headers, json=new_contact)
created_contact = {}
contact_id = None
# If the API returned the representation, parse the JSON
if response.status_code in (200, 201):
try:
created_contact = response.json()
except ValueError:
created_contact = {}
contact_id = created_contact.get('contactid') or created_contact.get('contactid@odata.bind')
print("New contact created successfully (body returned).")
print(f"Created Contact ID: {contact_id}")
# If the API returned 204 No Content, Dataverse includes the entity URL in 'OData-EntityId' or 'Location'
elif response.status_code == 204:
entity_url = response.headers.get('OData-EntityId') or response.headers.get('Location')
if entity_url:
# Extract GUID using regex (GUID format)
m = re.search(r"([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12})", entity_url)
if m:
contact_id = m.group(1)
created_contact = {'contactid': contact_id}
print("New contact created successfully (no body). Extracted Contact ID from headers:")
print(f"Created Contact ID: {contact_id}")
else:
print("Created but couldn't parse entity id from response headers:")
print(f"Headers: {response.headers}")
else:
print("Created but no entity location header found. Headers:")
print(response.headers)
else:
print(f"Failed to create contact. Status code: {response.status_code}")
print(f"Error details: {response.text}")
# Read the created contact
if not contact_id:
# Defensive: stop further CRUD if we don't have an id
print("No contact id available; aborting read/update/delete steps.")
else:
print("Reading the created contact…")
response = requests.get(f"{web_api_url}({contact_id})", headers=headers)
if response.status_code == 200:
print("Contact retrieved successfully!")
contact_data = response.json()
print(json.dumps(contact_data, indent=4))
else:
print(f"Failed to retrieve contact. Status code: {response.status_code}")
print(f"Error details: {response.text}")
# Update the contact's email
updated_data = { "emailaddress1": "john.doe@example.com" }
response = requests.patch(f"{web_api_url}({contact_id})", headers=headers, json=updated_data)
if response.status_code == 204:
print("Contact updated successfully!")
else:
print(f"Failed to update contact. Status code: {response.status_code}")
print(f"Error details: {response.text}")
# Delete the contact
response = requests.delete(f"{web_api_url}({contact_id})", headers=headers)
if response.status_code == 204:
print("Contact deleted successfully!")
else:
print(f"Failed to delete contact. Status code: {response.status_code}")
print(f"Error details: {response.text}")
except requests.exceptions.RequestException as e:
print(f"Request error: {e}")
except KeyError as e:
print(f"Token not available: {e}")
except Exception as e:
print(f"Unexpected error: {e}")
view raw FullCRUDWebAPI hosted with ❤ by GitHub

You can use the VS Code as IDE, copy the above code in a python file, next click on Run Python File at the top of the VS Code

Hope this helps someone making Web API Calls using Python.

Cheers,

PMDY

Adding intelligence to Dataverse using Dataverse AI functions

Hi Folks,

While intelligence with the use of AI is being embedded into each and every part of the Microsoft ecosystem, it is good to know the features coming in the Power Platform space.

In this blog post, let’s see how we can use Dataverse AI Functions, their usage, advantages which can greatly ease summarizing, classifying, extracting, translating, assessing sentiment, or drafting a reply for common business scenarios.

To illustrate it better, I used a different AI Function for Canvas App, Model Driven App and Power Automate, hope you can follow the same for others as well.

What are Dataverse AI Functions?

Think of Dataverse AI Functions as prebuilt AI Functions which will add intelligence in your Apps and Flows without need to collect, build and train the models. They can be used in many places such as AI Builder, Power Automate, Power Apps, Low Code Plugins. Following are the AI Functions available…

  1. AIReply – Drafts a reply to the message you provide.
  2. AISentiment – Detects sentiment for the text you provide
  3. AISummarize – Summarizes the text you provide
  4. AIClassify – Classifies the text into one or more categories, you can use this from custom copilot
  5. AIExtract – Extracts specified entities like Names of people, phone numbers, places etc.
  6. AITranslate – Translate text from another language

Let’s see their usage in our favorite Canvas Apps first as illustration is easy and later in the post, I will mention how you can call the Dataverse AI Functions from Model Driven Apps, Power Automate so that you can get the real essence of it….

Utilizing ‘Dataverse AI Functions’ in a Canvas App

Create a new Canvas App and add ‘Environment’ Datasource as shown below.

All the ‘Dataverse AI functions’ can be accessed by ‘Environment‘ as shown below.

Let’s try the AIReply function in Canvas Apps

Add a textbox for storing the prompt or input string and a button control.

On the ‘OnSelect‘ event of the button, use the following formula to store the response in the AIResponse context variable, make sure you name the variables appropriately in your formula as per your naming defined in canvas apps.

UpdateContext({AIReplyResponse:Environment.AIReply({Text:AIInput.Text})})
view raw Dataverse AI Function hosted with ❤ by GitHub

Now create one more text variable to store the response and change the Default value to AIReplyResponse.PreparedResponse.

Try testing the app by providing inputs as below…

You should get a response from AIReply in the response field, you can try out other functions providing the necessary parameters required.

Utilizing ‘Dataverse AI Functions’ in a Power Automate

In Power Automate, all you can do to call Dataverse AI Functions is call the Unbound Action as below.

Passing the relevant input parameters is enough to get the output from these functions.

Let’s try AISentiment

Click on test, you should a response from Power Automate with the sentiment

Utilizing ‘Dataverse AI Functions’ in a Model Driven Apps

Do you want to utilize the similar capabilities of Dataverse AI Functions inside your custom code like in Plugins, Actions etc..

Let’s try AIClassify

var request = new OrganizationRequest("AIClassify")
{
["AllowMultipleCategories"] = false,
["Categories"] = titles,
["Text"] = classifyText
};
var result = service.Execute(request);
view raw DataverseFunctions.cs hosted with ❤ by GitHub

It was pretty much similar in AIBuilder as well…

Please do note that there are quotas to use these AI Functions at tenant level, else you might get similar error like below, while I didn’t get any information regarding this from Microsoft, so I am unsure about this as of writing this post, I will keep this updated if I get to know.

Using Dataverse AI functions needs a bit of Prompt Engineering knowledge, you were looking to learn more about Prompt engineering, then check it out here.

References:

https://learn.microsoft.com/en-us/power-platform/power-fx/reference/function-ai

Cheers,

PMDY