Classifiers

Classifiers categorise an input. Some classifiers use generative AI models to do this, while others use quantitative techniques like vector similarity search.

azure_openai_json_classification

azure_openai_json_classification(classifier, description=None, filename=None, content=None, metadata=None, image_url=None, max_characters=200000, system_prompt=None, api_key=None, azure_endpoint=None, api_version=None, model=None, max_retries=5, openai_client=None)

Classify an artefact (e.g. a file or page) into a category from a provided list of classifiers. Uses an LLM provided by the Azure OpenAI service.

Parameters:

Name	Type	Description	Default
classifier	list[dict[str, Any]]	A list of classifiers.	required
filename	str | None	Title of the artefact.	None
description	str | None	Description of the artefact.	None
content	str | None	Main body content of the artefact.	None
metadata	dict | None	Metadata properties about the artefact.	None
image_url	str | None	A URL to an image of the artefact. Must be a signed or public URL that the OpenAI service can access.	None
max_characters	int	Character limit before content will be truncated.	200000
system_prompt	str | None	Override the default prompt with custom instructions.	None
api_key	str | None	API key for Azure resource. If not provided will default to environment variable AZURE_OPENAI_API_KEY.	None
azure_endpoint	str | None	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 | None	API version for Azure resource.	None
model	str | None	Model deployment name within the Azure resource. If not provided will default to environment variable AZURE_OPENAI_DEPLOYMENT.	None
max_retries	int	Number of retry attempts to the Azure OpenAI API service.	5
openai_client	AzureOpenAIClient | None	Optional AzureOpenAIClient instance to reuse. If not provided, a new client will be created. Providing a shared client instance improves performance in concurrent scenarios.	None

Returns:

Type	Description
dict[str, Any]	JSON-formatted dictionary containing: code: selected classification code. title: title / description of the classification code certainty: confidence score, either "low", "medium" or "high". explanation: concise explanation of why this code was chosen.

vector_similarity_search

vector_similarity_search(query, storage_location='./storage', parquet_name='vectors')

Perform vector cosine similarity search between an input query and a set of embeddings stored in a local parquet file.

Parameters:

Name	Type	Description	Default
query	str	The input string for which the similarity search needs to be performed.	required
storage_location	str	Storage location of the Parquet embeddings files.	'./storage'
parquet_name	str	The name of the Parquet embeddings files.	'vectors'

Returns:

Type	Description
dict[Any, Any]	A dictionary object of the most similar match.