Microsoft Azure

AzureAILanguageClient

Basic wrapper around the Azure TextAnalyticsClient.

https://learn.microsoft.com/en-us/python/api/overview/azure/ai-textanalytics-readme?view=azure-python

init

__init__(api_key=None, azure_endpoint=None)

Initialize client.

Parameters:

Name	Type	Description	Default
`api_key`	`str`	Credential for using the service. Defaults to environment variable `AZURE_AI_LANGUAGE_KEY`	`None`
`azure_endpoint`	`str`	URL AI Language resource, for example https://your-resource.cognitiveservices.azure.com/. Defaults to enVIronment variable `AZURE_AI_LANGUAGE_KEY`	`None`

AzureBlobStorageClient

Wrapper around the Azure BlobServiceClient that implements additional specialist methods.

https://learn.microsoft.com/en-us/python/api/overview/azure/storage-blob-readme?view=azure-python

Attributes:

Name	Type	Description
`organization`	`str`	Organization containing the workspace session to connect to. Must match an Azure Blob Storage container name.
`workspace`	`str`	The workspace containing the session.
`session_id`	`str`	The id of the session.
`directory`	`str`	Default directory where new blobs will be created. Concatenation of workspace and session_id.
`connection_string`	`str`	Credential for connecting to the Azure Blob Storage resource.
`url_prefix`	`str`	The root URL to the Azure Blob Storage resource.
`client`	`BlobServiceClient`	Blob service client. Use to access all sub methods.

init

__init__(organization=None, workspace=None, session_id=None, connection_string=os.environ['BLOB_CONNECTION_STRING'], url_prefix=os.environ['BLOB_URL_PREFIX'])

Initialize client.

Parameters:

Name	Type	Description	Default
`organization`	`str`	Organization containing the workspace session to connect to. Must match an Azure Blob Storage container name.	`None`
`workspace`	`str`	The workspace containing the session. Must match a folder in the parent container.	`None`
`session_id`	`str`	The id of the session. Must match a folder in the parent workspace folder in the organization container.	`None`
`connection_string`	`str`	Credential for connecting to the Azure Blob Storage resource. Defaults to environment variable `BLOB_CONNECTION_STRING`.	`environ['BLOB_CONNECTION_STRING']`
`url_prefix`	`str`	The root URL to the Azure Blob Storage resource. Defaults to environment variable `BLOB_URL_PREFIX`.	`environ['BLOB_URL_PREFIX']`

download_blob_json

download_blob_json(blob_name)

Used to download and access JSON files.

Parameters:

Name	Type	Description	Default
`blob_name`	`str`	The path to the blob in the container.	required

Returns:

Type	Description
`Any`	The JSON content of the blob, likely a list or dictionary.

get_blob_client

get_blob_client(blob_name)

Return a client for interacting with blob objects.

Parameters:

Name	Type	Description	Default
`blob_name`	`str`	Name of the blob.	required

Returns:

Type	Description
`BlobClient`	Blob client.

get_signed_url

get_signed_url(blob_name, minutes=720)

Generate a signed URL for an Azure Blob Storage object, valid for specified duration.

Parameters:

Name	Type	Description	Default
`blob_name`	`str`	Name of the blob.	required
`minutes`	`int`	Time duration for validity of URL.	`720`

Returns:

Type	Description
`str`	The signed URL.

list_blobs_in_directory

list_blobs_in_directory(ignore_files=[])

Lists all blobs in the client directory within an Azure Blob container.

Parameters:

Name	Type	Description	Default
`ignore_files`	`List[str]`	Blob names containing any string in this list will not be included in the outputted list.	`[]`

Returns:

Type	Description
`List[str]`	A list of blob names in the client directory.

list_blobs_with_prefix

list_blobs_with_prefix(prefix, ignore_files=[])

Lists all blobs in the Azure Blob container with the supplied prefix.

Parameters:

Name	Type	Description	Default
`prefix`	`str`	The prefix to search for	required
`ignore_files`	`List[str]`	Blob names containing any string in this list will not be included in the outputted list	`[]`

Returns:

Type	Description
`List[str]`	A list of blob names with the matching prefix.

upload_blob_json

upload_blob_json(blob_name, blob_content)

Used to upload JSON files to a blob in the container.

Parameters:

Name	Type	Description	Default
`blob_name`	`str`	The path to the blob in the container.	required
`blob_content`	`str`	The JSON-formatted content to be uploaded.	required

Returns:

Type	Description
`Dict[str, Any]`	Blob updated property dictionary.

AzureKeyVaultClient

Wrapper around the Azure SecretClient. Uses DefaultAzureCredential for credential, and therefore expects either a managed identity or an identity currently logged into Azure CLI.

https://learn.microsoft.com/en-us/python/api/overview/azure/key-vault?view=azure-python

init

__init__(vault_url=os.environ['KEY_VAULT_NAME'])

Initialize client.

Parameters:

Name	Type	Description	Default
`vault_url`	`str`	URL of the Azure Key Vault resource e.g. https://your-resource.vault.azure.net/. Defaults to environment variable `KEY_VAULT_NAME`	`environ['KEY_VAULT_NAME']`

get_secret

get_secret(secret_name)

Retrieve a secret from the Key Vault.

Parameters:

Name	Type	Description	Default
`secret_name`	`str`	Name of the secret to retrieve	required

Returns:

Type	Description
`str`	Retrieved secret

set_secret

set_secret(secret_name, secret_value)

Set a secret in the Key Vault.

Parameters:

Name	Type	Description	Default
`secret_name`	`str`	Name of the secret to set	required
`secret_value`	`str`	Value of the secret	required

Returns:

Type	Description
`bool`	True if operation successful

AzureOpenAIClient

A wrapper around the AzureOpenAi class.

https://github.com/openai/openai-python?tab=readme-ov-file#microsoft-azure-openai

Attributes:

Name	Type	Description
`api_key`		API key for Azure resource. If not provided will default to environment variable `AZURE_OPENAI_API_KEY`.
`azure_endpoint`		Your Azure endpoint, including the resource, e.g. https://example-resource.azure.openai.com/. if not provided will default to environment variable `AZURE_OPENAI_ENDPOINT`.
`api_version`		API version for Azure resource.
`model`		Model deployment name within the Azure resource. If not provided will default to environment variable `AZURE_OPENAI_DEPLOYMENT`.
`response_format`		The type of response to request from the client. For example for JSON: { "type": "json_object" }.
`client`		The `AzureOpenAI` client. Can be used to access other sub methods.

init

__init__(api_key=None, azure_endpoint=None, api_version=None, model=None, response_format=None)

Initialize the client.

Parameters:

Name	Type	Description	Default
`api_key`	`str`	API key for Azure resource. If not provided will default to environment variable `AZURE_OPENAI_API_KEY`.	`None`
`azure_endpoint`	`str`	Your Azure endpoint, including the resource, e.g. https://example-resource.azure.openai.com/. if not provided will default to environment variable `AZURE_OPENAI_ENDPOINT`.	`None`
`api_version`	`str`	API version for Azure resource.	`None`
`model`	`str`	Model deployment name within the Azure resource. If not provided will default to environment variable `AZURE_OPENAI_DEPLOYMENT`.	`None`
`response_format`	`Dict`	The type of response to request from the client. For example for JSON: { "type": "json_object" }.	`None`

call_chat

call_chat(messages, max_retries=5)

Call the chat completions API.

Parameters:

Name	Type	Description	Default
`messages`	`List[Dict[str, Any]]`	List of dictionary objects specifying the messages to send. Messages must adhere to the prompting standard.	required
`max_retries`	`int`	Number of times to call the API before raising an error	`5`

Returns:

Type	Description
`str`	Response from the chat API.

Raises:

Type	Description
`RuntimeError`	If attempts exceeds `max_retries`

call_embedding

call_embedding(batch, max_retries=5)

Call the embeddings API.

Parameters:

Name	Type	Description	Default
`batch`	`List[str]`	List of strings to embed	required
`max_retries`	`int`	Number of times to call the API before raising an error	`5`

Returns:

Type	Description
`List[List[float]]`	List of embeddings

Raises:

Type	Description
`RuntimeError`	If attempts exceeds `max_retries`

AzureTableLogHandler

Bases: Handler

Client to write workflow logs to an Azure Table Storage resource, adopting a log key and partition strategy for fast search/filtering across organizations, workspaces and sessions.

https://learn.microsoft.com/en-us/python/api/overview/azure/tables?view=azure-python

Attributes:

Name	Type	Description
`table_name`		Name of the log table.
`host`		The hostname of the host running the process.
`start_time`		When the logger was initialized. Used to set workflow start time.
`parent`		Parent `WorkflowRunner` object. Used to access workflow context.
`service_client`		Client for interacting with Azure Table Storage resource.
`table_client`		Client for interacting with table in the Azure Table Storage resource.

init

__init__(parent, connection_string=os.environ.get('BLOB_CONNECTION_STRING'), table_name='WorkflowLogs', host=socket.gethostname())

Initialize the client.

Parameters:

Name	Type	Description	Default
`parent`	`WorkflowRunner`	Parent `WorkflowRunner` object. Used to access workflow context.	required
`connection_string`	`str`	Credential for connecting to Azure Table Storage resource. Defaults to environment variable `BLOB_CONNECTION_STRING`.	`get('BLOB_CONNECTION_STRING')`
`table_name`	`str`	Name of the log table.	`'WorkflowLogs'`
`host`	`str`	The hostname of the host running the process.	`gethostname()`

emit

emit(record)

Emits log messages to Table Storage, duplicating across partitions and indexing chronologicallys:

messages
- General log messages
{organization}_{workspace}_messages
- General log messages for a workspace
{organization}_{workspace}_{session_id}_messages
- General log messages for a session

emit_metrics

emit_metrics(record)

Emits log messages to Table Storage, duplicating across partitions and indexing chronologicallys:

metrics
- Metrics on document counts, processing time etc
{organization}_metrics
- Metrics for an organization
{organization}_{workspace}_metrics
- Metrics for a workspace

AzureVectorStorageClient

Wrapper around the Azure SearchClient with specialist methods for HARDR classification.

https://learn.microsoft.com/en-us/python/api/overview/azure/search-documents-readme?view=azure-python

init

__init__(endpoint=os.environ['AZURE_SEARCH_ENDPOINT'], index_name=os.environ['AZURE_SEARCH_INDEX_NAME'])

Initialize the client.

Parameters:

Name	Type	Description	Default
`endpoint`	`str`	The URL for the Azure AI Search resource. Defaults to environment variable `AZURE_SEARCH_ENDPOINT`.	`environ['AZURE_SEARCH_ENDPOINT']`
`index_name`	`str`	The vector index to connect to. Defaults to environment variable `AZURE_SEARCH_INDEX_NAME`.	`environ['AZURE_SEARCH_INDEX_NAME']`

neighbours_from_text

neighbours_from_text(text, filter, top=10, vector_fields=['title_embedding', 'parent_embedding', 'description_embedding'], scoring_profile='default')

Retrieve the top 'n' nearest neighbours to an input text query.

Parameters:

Name	Type	Description	Default
`text`	`str`	Text to search for	required
`filter`	`str`	ODATA filter query to limit the scope of the search. For example for a Uniclass index, to scope to the Materials table use 'subsystem eq 'Materials'.	required
`top`	`int`	The number of matches to return.	`10`
`vector_fields`	`List[str]`	The vector fields to include in the search. Must be at least three vector fields. Each vector field is weighted differently in the search results: 1. 2.0 2. 0.5 3. 1.0	`['title_embedding', 'parent_embedding', 'description_embedding']`
`scoring_profile`	`str`	The name of the vector search	`'default'`

Returns:

Type	Description
`Dict[str, Any]`	Dictionary of nearest neighbours. Items have the following fields: code (str): The ID or reference code for the item title (str): Plain-text descriptor for the item examples (str): Extended description of the item similarity (float): Similarity score