compass.plugin.one_shot.components.SchemaBasedTextExtractor

class SchemaBasedTextExtractor(llm_service, usage_tracker=None, **kwargs)

Bases: SchemaOutputLLMCaller, BaseTextExtractor

Schema-based text extractor

Parameters:

• llm_service (Service) – LLM service used for queries.

• usage_tracker (UsageTracker, optional) – Optional tracker instance to monitor token usage during LLM calls. By default, None.

• **kwargs –

  Keyword arguments to be passed to the underlying service processing function (i.e. llm_service.call(**kwargs)). Should not contain the following keys:

  • usage_sub_label

  • messages

  These arguments are provided by this caller object.

Methods

call(sys_msg, content, response_format[, ...])

Call LLM for structured data retrieval

Attributes

IN_LABEL	Identifier for text ingested by this class
OUTPUT_SCHEMA	Validation output schema
OUT_LABEL	Identifier for final text extracted by this class
SCHEMA	Extraction schema
TASK_DESCRIPTION	Task description to show in progress bar
TASK_ID	ID to use for this extraction for linking with LLM configs
parsers	Iterable of parsers provided by this extractor

abstract property SCHEMA

Extraction schema

Type:

dict

abstract property OUTPUT_SCHEMA

Validation output schema

Type:

dict

property parsers

Iterable of parsers provided by this extractor

Yields:

• name (str) – Name describing the type of text output by the parser.

• parser (callable()) – Async function that takes a text_chunks input and outputs parsed text.

abstract property IN_LABEL

Identifier for text ingested by this class

Type:

str

abstract property OUT_LABEL

Identifier for final text extracted by this class

Type:

str

TASK_DESCRIPTION = 'Condensing text for extraction'

Task description to show in progress bar

TASK_ID = 'text_extraction'

ID to use for this extraction for linking with LLM configs

async call(sys_msg, content, response_format, usage_sub_label=LLMUsageCategory.DEFAULT)

Call LLM for structured data retrieval

Parameters:

• sys_msg (str) – The LLM system message. If this text does not contain the instruction text “Return your answer as a dictionary in JSON format”, it will be added.

• content (str) – LLM call content (typically some text to extract info from).

• usage_sub_label (str, optional) – Label to store token usage under. By default, "default".

• response_format (dict) –

  Dictionary specifying the expected response format. This will be passed to the underlying LLM service (e.g. OpenAI) and should be formatted according to that service’s specifications for structured output. For example, for OpenAI GPT models, this should be a dictionary with the following keys:

  • type: Should be set to “json_schema” to indicate that the expected output is structured JSON.

  • json_schema: A dictionary specifying the expected JSON schema of the output. This should include the following keys:

    • name: A string name for this response format (e.g. “extracted_features”).

    • strict: A boolean indicating whether the LLM should strictly adhere to the provided schema (i.e. not include any keys not specified in the schema). If True, the LLM will be instructed to only include keys specified in the schema field. If False, the LLM may include additional keys not specified in the schema field.

    • schema: A dictionary specifying the expected JSON schema of the output. This should be formatted according to JSON Schema specifications, and should define the expected structure of the output JSON object. For example, it may specify that the output should be an object with certain required properties, and the expected data types of those properties.

Returns:

dict – Dictionary containing the LLM-extracted features. Dictionary may be empty if there was an error during the LLM call.