msfabric-devops 1.0.1

Python package to interact with Microsoft Fabric items.

msfabric-devops

A Python package to interact with Microsoft Fabric objects, providing functionality to manage workspaces, semantic models, and items through the Fabric REST API.

Table of Contents

• Installation
• Requirements
• Quick Start
• FabricClient
  • Authentication
  • Workspaces
    • get_workspaces
    • get_workspace_by_id
    • get_workspaces_by_name
    • create_workspace
    • delete_workspace
  • Items
    • get_items
    • get_item_by_id
    • get_items_by_name
    • get_item_definition_by_id
    • import_item
    • delete_item_by_id
• Semantic Models
  • set_semantic_model_parameters
• Error Handling
• Complete Example
• License
• Author

---

Installation

pip install msfabric-devops

Requirements

• Python >= 3.10
• Azure AD service principal or any credential supported by DefaultAzureCredential (managed identity, Azure CLI, etc.)

---

Quick Start

from msfabric_devops import FabricClient

# Authenticate with a service principal
client = FabricClient(
    tenant_id="your-tenant-id",
    client_id="your-client-id",
    client_secret="your-client-secret",
)

# List workspaces
for ws in client.get_workspaces():
    print(ws["displayName"])

# Import a semantic model
result = client.import_item(
    workspace_id="your-workspace-id",
    path=r"C:\exports\MyModel.SemanticModel",
)
print(f"Imported: {result['displayName']} ({result['id']})")

---

FabricClient

FabricClient is the recommended way to interact with the Fabric REST API. It manages credentials and refreshes the bearer token automatically when it is about to expire, so you never need to handle tokens yourself.

Authentication

from msfabric_devops import FabricClient

# Option 1 — service principal (explicit credentials)
client = FabricClient(
    tenant_id="your-tenant-id",
    client_id="your-client-id",
    client_secret="your-client-secret",
)

# Option 2 — DefaultAzureCredential (managed identity, Azure CLI, environment variables, …)
client = FabricClient()

Parameters

Parameter	Type	Description
tenant_id	str, optional	Azure AD Tenant ID
client_id	str, optional	Azure AD Application (client) ID
client_secret	str, optional	Azure AD Client Secret

When all three are omitted, DefaultAzureCredential is used (suitable for managed identities, az login, and environment-variable-based auth).

---

Workspaces

get_workspaces

Returns all Fabric workspaces accessible to the authenticated principal.

workspaces = client.get_workspaces()
for ws in workspaces:
    print(f"{ws['displayName']}  ({ws['id']})")

Returns: list[dict] — each dict contains at minimum id and displayName.

---

get_workspace_by_id

Returns a single workspace by its GUID.

ws = client.get_workspace_by_id("workspace-id")
print(ws["displayName"])

Parameters

Parameter	Type	Description
workspace_id	str	The GUID of the target workspace

Returns: dict — workspace object, or empty dict if not found.

---

get_workspaces_by_name

Returns all workspaces whose displayName matches the given name (exact, case-sensitive). Multiple results are possible because Fabric permits duplicate names.

matches = client.get_workspaces_by_name("My Workspace")

Parameters

Parameter	Type	Description
workspace_name	str	Exact display name to search for

Returns: list[dict]

---

create_workspace

Creates a new workspace. Returns None with a warning if a workspace with that name already exists.

ws = client.create_workspace("My New Workspace")
if ws:
    print(f"Created: {ws['id']}")

Parameters

Parameter	Type	Description
workspace_name	str	Display name for the new workspace

Returns: dict on success, None if the name already exists.

---

delete_workspace

Deletes a workspace by its GUID.

client.delete_workspace("workspace-id")

Parameters

Parameter	Type	Description
workspace_id	str	The GUID of the workspace to delete

---

Items

get_items

Returns all items in a workspace.

items = client.get_items("workspace-id")
for item in items:
    print(f"{item['displayName']}  [{item['type']}]")

Parameters

Parameter	Type	Description
workspace_id	str	The GUID of the workspace

Returns: list[dict]

---

get_item_by_id

Returns a single item by its GUID.

item = client.get_item_by_id("workspace-id", "item-id")
print(item["displayName"])

Parameters

Parameter	Type	Description
workspace_id	str	The GUID of the workspace
item_id	str	The GUID of the item

Returns: dict

---

get_items_by_name

Returns all items whose displayName matches the given name (exact, case-sensitive).

items = client.get_items_by_name("workspace-id", "My Model")

Parameters

Parameter	Type	Description
workspace_id	str	The GUID of the workspace
item_name	str	Exact display name to search for

Returns: list[dict]

---

get_item_definition_by_id

Exports an item definition from a workspace. Optionally writes the decoded files to a local directory.

# Get the raw definition dict
definition = client.get_item_definition_by_id("workspace-id", "item-id")

# Export files to disk
definition = client.get_item_definition_by_id(
    workspace_id="workspace-id",
    item_id="item-id",
    output_dir=r"C:\exports\MyModel",
    format="TMDL",
)

Parameters

Parameter	Type	Description
workspace_id	str	The GUID of the workspace
item_id	str	The GUID of the item
output_dir	str, optional	Local directory where decoded files are written
format	str, optional	Export format, e.g. "TMDL" or "PBIP"

Returns: dict — raw API response containing the definition with a parts array. Each part includes path, payload (Base64-encoded), and payloadType.

---

import_item

Imports a .pbism (semantic model) or .pbir (report) from a local PBIP folder into a workspace. Creates a new item if none exists with the same name and type; otherwise updates the existing item.

# Import a semantic model
result = client.import_item(
    workspace_id="workspace-id",
    path=r"C:\exports\MyModel.SemanticModel",
    item_properties={"displayName": "My Model"},
    retain_roles=True,
    retain_all_partitions=True,
)

# Import a semantic model and preserve partitions for specific tables only
result = client.import_item(
    workspace_id="workspace-id",
    path=r"C:\exports\MyModel.SemanticModel",
    item_properties={"displayName": "My Model"},
    retain_partitions_tables=["Sales", "Items"],
)

# Import a report linked to a semantic model
result = client.import_item(
    workspace_id="workspace-id",
    path=r"C:\exports\MyReport.Report",
    item_properties={
        "displayName": "My Report",
        "semanticModelId": "existing-semantic-model-id",
    },
)

print(f"{result['displayName']}  ({result['id']})")

Parameters

Parameter	Type	Default	Description
workspace_id	str	—	The GUID of the destination workspace
path	str	—	Local folder containing the PBIP export (must contain a .pbism or .pbir file)
item_properties	dict, optional	None	Override displayName, type, or semanticModelId
skip_if_exists	bool	False	Return existing item immediately without updating its definition
retain_roles	bool	False	Preserve RLS roles from the published model
retain_all_partitions	bool	False	Preserve partition definitions for all tables
retain_partitions_tables	list[str], optional	None	Preserve partitions for listed table names only (ignored when retain_all_partitions=True)

Note: When importing a report that uses a byPath connection to a semantic model, item_properties.semanticModelId is required — the importer converts the connection to byConnection format automatically.

Returns: dict with id, displayName, and type.

---

delete_item_by_id

Deletes an item by its GUID.

client.delete_item_by_id("workspace-id", "item-id")

Parameters

Parameter	Type	Description
workspace_id	str	The GUID of the workspace
item_id	str	The GUID of the item to delete

---

Semantic Models

Semantic model parameter updates operate on local files and do not require a FabricClient or any credentials.

set_semantic_model_parameters

Updates Power Query parameter values in a local semantic model definition. Supports both TMDL (definition/expressions.tmdl) and TMSL (model.bim) formats, detected automatically.

from msfabric_devops import set_semantic_model_parameters

set_semantic_model_parameters(
    path=r"C:\exports\MyModel.SemanticModel",
    parameters={
        "Param_Server": "prod-server",
        "Param_Database": "sales_db",
    },
)

Parameters

Parameter	Type	Default	Description
path	str	—	Root directory of the semantic model (parent of definition/ or folder containing model.bim)
parameters	dict[str, str]	—	Mapping of parameter name → new value
fail_if_not_found	bool	False	Raise ValueError if a parameter name is not found; when False, logs a warning and continues

Raises: FileNotFoundError if the model definition cannot be found at path.

---

Error Handling

All API errors raise typed exceptions that can be caught individually:

from msfabric_devops import FabricClient, FabricError, FabricThrottlingError

client = FabricClient(...)

try:
    result = client.import_item(workspace_id="...", path="...")
except FabricThrottlingError:
    print("API is throttled — retry later")
except FabricError as e:
    print(f"API error: {e}")

Exception	Raised when
FabricError	Base class for all API errors
FabricAuthError	Authentication or token acquisition fails
FabricNotFoundError	A requested resource does not exist
FabricThrottlingError	The API returns 429 after all automatic retries are exhausted

Note: The library retries 429 responses automatically (up to 3 times, respecting the Retry-After header) before raising FabricThrottlingError.

---

Complete Example

from msfabric_devops import FabricClient, set_semantic_model_parameters

# --- 1. Connect ---
client = FabricClient(
    tenant_id="your-tenant-id",
    client_id="your-client-id",
    client_secret="your-client-secret",
)

# --- 2. Workspaces ---
workspaces = client.get_workspaces()
print(f"Found {len(workspaces)} workspace(s)")

ws = client.create_workspace("My DevOps Workspace")
ws_id = ws["id"]

# --- 3. Update local parameters before publishing ---
set_semantic_model_parameters(
    path=r"C:\exports\MyModel.SemanticModel",
    parameters={"Param_Environment": "prod"},
)

# --- 4. Import the semantic model ---
model = client.import_item(
    workspace_id=ws_id,
    path=r"C:\exports\MyModel.SemanticModel",
    item_properties={"displayName": "My Model"},
    retain_roles=True,
)
print(f"Model imported: {model['displayName']} ({model['id']})")

# --- 5. Import a report connected to the model ---
report = client.import_item(
    workspace_id=ws_id,
    path=r"C:\exports\MyReport.Report",
    item_properties={
        "displayName": "My Report",
        "semanticModelId": model["id"],
    },
)
print(f"Report imported: {report['displayName']} ({report['id']})")

# --- 6. Clean up ---
client.delete_workspace(ws_id)
print("Workspace deleted")

---

License

MIT

Author

Hugo Salaun ([e-mail])