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Merge branch 'm-kovalsky/rti-nltkql'
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‎src/sempy_labs/eventhouse/_items.py‎

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@@ -94,6 +94,10 @@ def list_eventhouses(workspace: Optional[str | UUID] = None) -> pd.DataFrame:
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"Eventhouse Name": "string",
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"Eventhouse Id": "string",
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"Description": "string",
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"Query Service URI": "string",
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"Ingestion Service URI": "string",
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"Database Item Ids": "list",
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"Minimum Consumption Units": "float",
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}
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df = _create_dataframe(columns=columns)
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@@ -108,11 +112,16 @@ def list_eventhouses(workspace: Optional[str | UUID] = None) -> pd.DataFrame:
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rows = []
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for r in responses:
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for v in r.get("value", []):
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p = v.get("properties", {})
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rows.append(
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{
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"Eventhouse Name": v.get("displayName"),
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"Eventhouse Id": v.get("id"),
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"Description": v.get("description"),
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"Query Service URI": p.get("queryServiceUri"),
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"Ingestion Service URI": p.get("ingestionServiceUri"),
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"Database Item Ids": p.get("databasesItemIds"),
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"Minimum Consumption Units": p.get("minimumConsumptionUnits"),
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}
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)
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‎src/sempy_labs/notebook/_items.py‎

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@@ -74,6 +74,8 @@ def get_notebook_definition(
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This is a wrapper function for the following API: `Items - Get Notebook Definition <https://learn.microsoft.com/rest/api/fabric/notebook/items/get-notebook-definition>`_.
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Service Principal Authentication is supported (see `here <https://github.com/microsoft/semantic-link-labs/blob/main/notebooks/Service%20Principal.ipynb>`_ for examples).
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Parameters
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----------
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notebook_name : str
@@ -193,6 +195,10 @@ def create_notebook(
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"""
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Creates a new notebook with a definition within a workspace.
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This is a wrapper function for the following API: `Items - Create Notebook Definition <https://learn.microsoft.com/rest/api/fabric/notebook/items/create-notebook>`_.
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Service Principal Authentication is supported (see `here <https://github.com/microsoft/semantic-link-labs/blob/main/notebooks/Service%20Principal.ipynb>`_ for examples).
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Parameters
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----------
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name : str
@@ -253,6 +259,10 @@ def update_notebook_definition(
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"""
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Updates an existing notebook with a new definition.
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This is a wrapper function for the following API: `Items - Update Notebook Definition <https://learn.microsoft.com/rest/api/fabric/notebook/items/update-notebook-definition>`_.
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Service Principal Authentication is supported (see `here <https://github.com/microsoft/semantic-link-labs/blob/main/notebooks/Service%20Principal.ipynb>`_ for examples).
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Parameters
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----------
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name : str
@@ -308,6 +318,10 @@ def list_notebooks(workspace: Optional[str | UUID] = None) -> pd.DataFrame:
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"""
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Shows the notebooks within a workspace.
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This is a wrapper function for the following API: `Items - List Notebooks <https://learn.microsoft.com/rest/api/fabric/notebook/items/list-notebooks>`_.
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Service Principal Authentication is supported (see `here <https://github.com/microsoft/semantic-link-labs/blob/main/notebooks/Service%20Principal.ipynb>`_ for examples).
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Parameters
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----------
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workspace : str | uuid.UUID, default=None

‎src/sempy_labs/rti/__init__.py‎

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@@ -0,0 +1,7 @@
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from ._copilot import (
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nl_to_kql,
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)
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__all__ = [
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"nl_to_kql",
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]

‎src/sempy_labs/rti/_copilot.py‎

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from typing import Literal, Optional, List
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from sempy_labs._helper_functions import (
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resolve_item_id,
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resolve_workspace_id,
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_base_api,
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)
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from uuid import UUID
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from sempy._utils._log import log
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import sempy_labs._icons as icons
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from sempy_labs.kql_database import resolve_cluster_uri
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@log
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def nl_to_kql(
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kql_database: str | UUID,
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billing_item: str | UUID,
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billing_item_type: Literal["KQLQueryset", "KQLDashboard", "Eventhouse"],
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prompt: str,
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chat_messages: Optional[dict | List[dict]] = None,
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user_shots: Optional[List[dict]] = None,
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workspace: Optional[str | UUID] = None,
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) -> str:
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"""
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Returns a KQL query generated from natural language.
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This is a wrapper function for the following API: `Copilot - NL To KQL <https://learn.microsoft.com/rest/api/fabric/realtimeintelligence/copilot/nl-to-kql(beta)>`_.
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Service Principal Authentication is supported (see `here <https://github.com/microsoft/semantic-link-labs/blob/main/notebooks/Service%20Principal.ipynb>`_ for examples).
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Parameters
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----------
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kql_database : str | uuid.UUID
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The name or UUID of the KQL database.
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billing_item : str | uuid.UUID
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The name or ID of the item for the request. This can be a KQLQueryset, KQLDashboard, or Eventhouse item. This item is used for billing the request.
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billing_item_type : typing.Literal["KQLQueryset", "KQLDashboard", "Eventhouse"]
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The type of the billing item.
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prompt : str
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The natural language to generate the KQL query from.
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chat_messages : Optional[str | List[str]], default=None
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The `chat messages <https://learn.microsoft.com/rest/api/fabric/realtimeintelligence/copilot/nl-to-kql(beta)?tabs=HTTP#chatmessage>`_ for the request. The chat messages provide additional context for generating the KQL query if necessary.
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Example:
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chat_messages = [
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{
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"content": "Content....",
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"role": "User"
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},
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{
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"content": "Content...",
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"role": "Assistant"
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}
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]
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user_shots : Optional[List[str]], default=None
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The `user shots <https://learn.microsoft.com/rest/api/fabric/realtimeintelligence/copilot/nl-to-kql(beta)?tabs=HTTP#usershot>`_ for the request. This consists of user provided pairs of natural language and KQL queries in order to help in generating the current requested KQL query.
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Example:
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user_shots = [
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{
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"kqlQuery": "KQL Query....",
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"naturalLanguage": "Natural language...."
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},
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{
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"kqlQuery": "KQL Query...",
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"naturalLanguage": "Natural language..."
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}
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]
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workspace : str | uuid.UUID, default=None
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The Fabric workspace name or ID.
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Defaults to None which resolves to the workspace of the attached lakehouse
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or if no lakehouse attached, resolves to the workspace of the notebook.
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Returns
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-------
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str
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The generated KQL query.
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"""
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workspace_id = resolve_workspace_id(workspace=workspace)
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db_id = resolve_item_id(
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item=kql_database, type="KQLDatabase", workspace=workspace_id
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)
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cluster_uri = resolve_cluster_uri(kql_database=kql_database, workspace=workspace_id)
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valid_types = ["KQLQueryset", "KQLDashboard", "Eventhouse"]
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if billing_item_type not in valid_types:
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raise ValueError(
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f"{icons.red_dot} The billing_item_type must be either 'KQLQueryset', 'KQLDashboard', or 'Eventhouse'."
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)
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billing_item_id = resolve_item_id(
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item=billing_item, type=billing_item_type, workspace=workspace_id
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)
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payload = {
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"clusterUrl": cluster_uri,
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"databaseName": db_id,
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"itemIdForBilling": billing_item_id,
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"naturalLanguage": prompt,
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}
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if chat_messages:
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if isinstance(chat_messages, str):
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chat_messages = [chat_messages]
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payload["chatMessages"] = chat_messages
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if user_shots:
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if isinstance(user_shots, str):
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user_shots = [user_shots]
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payload["userShots"] = user_shots
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response = _base_api(
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request=f"/v1/workspaces/{workspace_id}/realtimeintelligence/nltokql?beta=True",
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payload=payload,
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method="post",
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client="fabric_sp",
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)
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return response.get("kqlQuery")

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