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, filename, content=None, metadata=None, image_url=None, max_characters=8000, system_prompt=None, api_key=None, azure_endpoint=None, api_version=None, model=None, max_retries=5)

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	Title of the artefact.	required
description	str	Description of the artefact.	required
content	str	Main body content of the artefact.	None
metadata	Dict	Metadata properties about the artefact.	None
image_url	str	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.	8000
system_prompt	str	Override the default prompt with custom instructions.	None
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
max_retries	int	Number of retry attempts to the Azure OpenAI API service.	5

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.