Decision models

Decision models#

Module for handling processing logic using decision models.

This module provides the class for using a decision model in document processing workflows. A decision model takes a text and a set of closed questions (yes/no, pick one option, rate on a scale) and returns the probabilities of the answers. It generates no text. It is used for what can be put as such questions: which paragraphs or sentences of a document belong to an aspect, and the concepts whose values come from a closed set of options. The probabilities are the model’s own estimate of each answer.

The requests are sent through the litellm library, which supports several providers of decision models. The module covers the configuration of a model, the requests to its API, and the tracking of token usage and costs.

class contextgem.public.decision_models.DocumentDecisionModel(**data)[source]#

Bases: _DocumentDecisionModel

Handles processing documents with a specific decision model.

A decision model answers closed questions on a text with probabilities and generates no text. It extracts aspects, by deciding on each paragraph whether the paragraph belongs to an aspect and, for an aspect with reference_depth="sentences", on each sentence of such a paragraph whether the sentence does, and the concepts whose values come from a closed set of options: BooleanConcept, LabelConcept and RatingConcept. Each extracted item has the probabilities of all the options in its probabilities attribute. A decision model can be used on its own or, with a role assigned, as a part of a model group, next to the LLMs that extract everything else.

Variables:

  • model (str) – Model identifier in format {model_provider}/{model_name}, e.g. "typesafe/jev-latest". The model has to be served by one of the providers of decision models that litellm supports: a model of another provider is rejected when the decision model is created. For a model deployed in Microsoft Foundry, the name of the deployment takes the place of the model name, i.e. "azure_ai/<deployment_name>".

  • api_key (str | None) – API key for authentication. Not required for self-hosted models. Not included in the serialized model data, i.e. to be set again after deserialization. Not shown when the model is printed, nor in the text of a validation error of the model, which names the invalid parameter and the reason without showing the values passed. The values are still returned by the errors() and json() methods of the error, unless include_input=False is passed, with '**********' in place of a key that is passed as a value, e.g. a rejected key or the key among the parameters that a model is created with, and in place of the value of any parameter that a model does not have, as it may be the key passed under a misspelled name. A model that is among the values is returned by errors() as it is, i.e. with the key as its attribute, and is written out by json() without its key. A key that is passed as the value of another parameter, e.g. of model, is returned as any other value.

  • api_base (str | None) – Base URL of the API endpoint, e.g. the address of a self-hosted server, or the endpoint of the Microsoft Foundry resource that the model is deployed in. Not included in the serialized model data, i.e. to be set again after deserialization.

  • role (str | None) – Free-form role label of the model (up to 64 printable characters), used for routing in a DocumentModelGroup: the model extracts the aspects/concepts with the same model_role. Defaults to None, in which case the model extracts the aspects/concepts without model_role. Ignored when the model is used on its own, as it then extracts all the aspects/concepts it is given.

  • num_retries_failed_request (int) – Number of retries when a request fails with a transient error (request timeout, rate limit, server or connection error). Retries are performed with exponential backoff and jitter. Non-transient errors (e.g. invalid request, failed authentication) are not retried. Defaults to 3.

  • timeout (int) – Timeout in seconds for API calls. Defaults to 600 seconds.

  • pricing_details (ModelPricing | None) – ModelPricing object with pricing details for cost calculation. Decision models are usually priced by the input tokens only, in which case the price of the output tokens is to be set to 0.0. Defaults to None.

  • auto_pricing (bool) – Enable automatic cost calculation using genai-prices. Cannot be set together with pricing_details. For an OpenAI model (openai/), the prices of the decision requests are used, which genai-prices has apart from the prices of the chat requests to the model. Not supported for a model for which genai-prices has the prices of a text-generating model, i.e. a price of output tokens, which are the prices of the chat requests to the model (e.g. openrouter/openai/gpt-6-luna), and for self-hosted models (strands_decider/): set pricing_details for them instead. For a model that genai-prices has no prices for, e.g. a model deployed in Microsoft Foundry, a warning is issued and the costs stay at zero: pricing_details is to be set instead for such a model too. Defaults to False.

  • auto_pricing_refresh (bool) – Whether genai-prices should auto-refresh its cached pricing data, e.g. to price a recently released model that the bundled pricing data does not include yet. Defaults to False.

  • is_fallback (bool) – Indicates whether the model is a fallback model. Defaults to False.

  • fallback_models (list[DocumentDecisionModel | DocumentLLM]) – Models to use as fallbacks if the current one fails, tried in the given order until one of them succeeds: decision models, LLMs, or both. Each must have is_fallback=True and the same role as the current model, and must differ from the current model and from each other in at least one config param. A fallback LLM extracts the same aspects/concepts with its prompts, i.e. its items have no probabilities, and is asked again when it returns invalid data, up to its max_retries_invalid_data times, while a fallback decision model is asked once. Defaults to an empty list.

  • decision_threshold (float) – The lowest probability of “yes” (above 0, up to 1) with which a yes/no question is taken as answered “yes”: a paragraph or a sentence belongs to an aspect, a BooleanConcept is True, a label of a LabelConcept with classification_type="multi_label" is selected. A lower threshold extracts more, a higher one extracts only what the model is surer of. Applies unless the aspect or the concept has a decision_threshold of its own. Does not apply to a LabelConcept with classification_type="multi_class" and to a RatingConcept, for which the most probable option is taken. Does not change the probabilities of the extracted items. Defaults to 0.5.

  • async_limiter (AsyncLimiter) – Controls frequency of async API requests for concurrent tasks. Defaults to allowing 10 acquisitions per 10-second period to prevent rate limit issues. With concurrency enabled, the requests for the paragraphs of a document, which aspects are extracted with, are paced by the limiter: consider a higher rate for long documents, if the rate limits of the model provider allow for it. The rate applies to all the requests to the model that are sent concurrently, i.e. the extraction calls that run at the same time share it. Requests to a fallback model are controlled by the limiter of that fallback model. See mjpieters/aiolimiter for configuration details.

Parameters:
Note:

For what a decision model extracts, how its requests are formed, how the extracted items get their values and probabilities, and the limits, see https://contextgem.shcherbak.ai/decision_models/decision_models/

Example:
Decision model definition#
from contextgem import (
    DocumentDecisionModel,
    DocumentLLM,
    DocumentModelGroup,
)


# Create a decision model. A decision model answers closed questions on a text
# with probabilities: it extracts aspects, and Boolean, label and rating concepts.
decision_model = DocumentDecisionModel(
    model="typesafe/jev-latest",
    api_key="your-typesafe-api-key",  # Replace with your actual API key
    role="decide",  # A label of your choice, needed for routing in a model group
    auto_pricing=True,  # optional, to calculate the costs with `genai-prices`
)

# Create a fallback LLM in case the decision model fails. It extracts
# the same aspects and concepts with its prompts, i.e. without the probabilities.
decision_model_fallback = DocumentLLM(
    model="openai/gpt-6-luna",
    api_key="your-openai-api-key",  # Replace with your actual API key
    role="decide",  # Must have the same role as the model it is a fallback for
    is_fallback=True,
)
decision_model.fallback_models = [decision_model_fallback]

# Create an LLM for everything else. This LLM has no role, i.e. it extracts
# the aspects and concepts that have no `model_role`.
llm = DocumentLLM(
    model="openai/gpt-6-luna",
    api_key="your-openai-api-key",  # Replace with your actual API key
)

# Create a DocumentModelGroup. The aspects and concepts with `model_role="decide"`
# are extracted by the decision model, and all the other ones by the LLM.
model_group = DocumentModelGroup(models=[llm, decision_model])

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

_eq_deserialized_model_config(other)#

Custom config equality method to compare this model with a deserialized instance.

Compares the __dict__ of both instances and performs specific checks for certain attributes that require special handling: first for what models of all kinds have, then for what only the specific kind of the model has (see _eq_deserialized_kind_specific_config).

Note that, by default, the reconstructed deserialized model will be only partially equal (==) to the original one, as the api credentials are redacted, and the async limiter and the async lock are not serialized and point to different objects in memory post-initialization. Also, usage and cost are reset by default pre-serialization.

Parameters:

other (contextgem.internal.base.models._DocumentModel) – Another model instance to compare with

Returns:

True if the instances are equal, False otherwise

Return type:

bool

property async_limiter: AsyncLimiter#

Gets the async rate limiter for this model.

Returns:

The AsyncLimiter instance controlling request rate limits.

Return type:

AsyncLimiter

extract_all(document, *, overwrite_existing=False, max_items_per_call=0, use_concurrency=False, max_paragraphs_to_analyze_per_call=0, max_images_to_analyze_per_call=0, raise_exception_on_extraction_error=True)#

Extracts all aspects and concepts from a document and its aspects.

This method performs comprehensive extraction by processing the document for aspects and concepts, then extracting concepts from each aspect. The operation can be configured for concurrent processing and customized extraction parameters.

This is the synchronous version of extract_all_async().

Parameters:

  • document (contextgem.internal.base.documents._Document) – The document to analyze.

  • overwrite_existing (bool) – Whether to overwrite already processed aspects and concepts with newly extracted information. Defaults to False.

  • max_items_per_call (int) – Maximum number of items with the same extraction params to process in each call. Defaults to 0 (all items in one call). If concurrency is enabled, defaults to 1. For complex tasks, you should not set a high value, in order to avoid prompt overloading.

  • use_concurrency (bool) – If True, enables concurrent processing of multiple items. Concurrency can considerably reduce processing time, but may cause rate limit errors with model providers. Use this option when API rate limits allow for multiple concurrent requests. Defaults to False.

  • max_paragraphs_to_analyze_per_call (int) – Maximum paragraphs to include in a single LLM prompt. A decision model is sent each paragraph in a request of its own for aspects, whatever the value. For concepts, it is sent a text with more paragraphs in parts of at most that many paragraphs, each in a request of its own: a concept then gets an extracted item for each part, and a concept with singular_occurrence=True is rejected with ValueError. Defaults to 0 (all paragraphs). Cannot be negative.

  • max_images_to_analyze_per_call (int) – Maximum images to include in a single LLM prompt. Defaults to 0 (all images). Cannot be negative. Relevant only for document-level concepts.

  • raise_exception_on_extraction_error (bool) – Whether to raise an exception if the extraction fails due to invalid data returned by a model or an error in the model API. If False, a warning will be issued instead, and no extracted items will be returned. Defaults to True.

Returns:

The document with extracted aspects and concepts.

Return type:

contextgem.internal.base.documents._Document

async extract_all_async(document, *, overwrite_existing=False, max_items_per_call=0, use_concurrency=False, max_paragraphs_to_analyze_per_call=0, max_images_to_analyze_per_call=0, raise_exception_on_extraction_error=True)#

Asynchronously extracts all aspects and concepts from a document and its aspects.

This method performs comprehensive extraction by processing the document for aspects and concepts, then extracting concepts from each aspect. The operation can be configured for concurrent processing and customized extraction parameters.

Parameters:

  • document (contextgem.internal.base.documents._Document) – The document to analyze.

  • overwrite_existing (bool) – Whether to overwrite already processed aspects and concepts with newly extracted information. Defaults to False.

  • max_items_per_call (int) – Maximum number of items with the same extraction params to process in each call. Defaults to 0 (all items in one call). If concurrency is enabled, defaults to 1. For complex tasks, you should not set a high value, in order to avoid prompt overloading.

  • use_concurrency (bool) – If True, enables concurrent processing of multiple items. Concurrency can considerably reduce processing time, but may cause rate limit errors with model providers. Use this option when API rate limits allow for multiple concurrent requests. Defaults to False.

  • max_paragraphs_to_analyze_per_call (int) – Maximum paragraphs to include in a single LLM prompt. A decision model is sent each paragraph in a request of its own for aspects, whatever the value. For concepts, it is sent a text with more paragraphs in parts of at most that many paragraphs, each in a request of its own: a concept then gets an extracted item for each part, and a concept with singular_occurrence=True is rejected with ValueError. Defaults to 0 (all paragraphs). Cannot be negative.

  • max_images_to_analyze_per_call (int) – Maximum images to include in a single LLM prompt. Defaults to 0 (all images). Cannot be negative. Relevant only for document-level concepts.

  • raise_exception_on_extraction_error (bool) – Whether to raise an exception if the extraction fails due to invalid data returned by a model or an error in the model API. If False, a warning will be issued instead, and no extracted items will be returned. Defaults to True.

Returns:

The document with extracted aspects and concepts.

Return type:

contextgem.internal.base.documents._Document

extract_aspects_from_document(document, *, from_aspects=None, overwrite_existing=False, max_items_per_call=0, use_concurrency=False, max_paragraphs_to_analyze_per_call=0, raise_exception_on_extraction_error=True)#

Extracts aspects from the provided document using predefined models.

If an aspect instance has extracted_items populated, the reference_paragraphs field will be automatically populated from these items. For an aspect with the “sentences” reference depth, the reference_sentences field is populated from them as well: with the extracted sentences, and not with the other sentences of the reference paragraphs.

This is the synchronous version of extract_aspects_from_document_async().

Parameters:

  • document (contextgem.internal.base.documents._Document) – The document from which aspects are to be extracted.

  • from_aspects (collections.abc.Sequence[contextgem.internal.base.aspects._Aspect] | None) – Existing aspects to use as a base for extraction. If None, uses all document’s aspects. An aspect that is given more than once is extracted once.

  • overwrite_existing (bool) – Whether to overwrite already processed aspects with newly extracted information. Defaults to False.

  • max_items_per_call (int) – Maximum items with the same extraction params to process per call. Defaults to 0 (all items in single call). For complex tasks, you should not set a value, to avoid prompt overloading. If concurrency is enabled, defaults to 1 (each item processed separately).

  • use_concurrency (bool) – If True, enables concurrent processing of multiple items. Concurrency can considerably reduce processing time, but may cause rate limit errors with model providers. Use this option when API rate limits allow for multiple concurrent requests. Defaults to False.

  • max_paragraphs_to_analyze_per_call (int) – Maximum paragraphs to analyze in a single LLM prompt. Changes nothing for a decision model, which is sent each paragraph in a request of its own. Defaults to 0 (all paragraphs). Cannot be negative.

  • raise_exception_on_extraction_error (bool) – Whether to raise an exception if the extraction fails due to invalid data returned by a model or an error in the model API. If False, a warning will be issued instead, and no extracted items will be returned. Defaults to True.

Returns:

List of processed _Aspect objects with extracted items.

Return type:

list[contextgem.internal.base.aspects._Aspect]

async extract_aspects_from_document_async(document, *, from_aspects=None, overwrite_existing=False, max_items_per_call=0, use_concurrency=False, max_paragraphs_to_analyze_per_call=0, raise_exception_on_extraction_error=True)#

Extracts aspects from the provided document using predefined models asynchronously.

If an aspect instance has extracted_items populated, the reference_paragraphs field will be automatically populated from these items. For an aspect with the “sentences” reference depth, the reference_sentences field is populated from them as well: with the extracted sentences, and not with the other sentences of the reference paragraphs.

Parameters:

  • document (contextgem.internal.base.documents._Document) – The document from which aspects are to be extracted.

  • from_aspects (collections.abc.Sequence[contextgem.internal.base.aspects._Aspect] | None) – Existing aspects to use as a base for extraction. If None, uses all document’s aspects. An aspect that is given more than once is extracted once.

  • overwrite_existing (bool) – Whether to overwrite already processed aspects with newly extracted information. Defaults to False.

  • max_items_per_call (int) – Maximum number of items with the same extraction params to process per call. Defaults to 0 (all items in one call). If concurrency is enabled, defaults to 1. For complex tasks, you should not set a high value, in order to avoid prompt overloading.

  • use_concurrency (bool) – If True, enables concurrent processing of multiple items. Concurrency can considerably reduce processing time, but may cause rate limit errors with model providers. Use this option when API rate limits allow for multiple concurrent requests. Defaults to False.

  • max_paragraphs_to_analyze_per_call (int) – Maximum paragraphs to analyze in a single LLM prompt. Changes nothing for a decision model, which is sent each paragraph in a request of its own. Defaults to 0 (all paragraphs). Cannot be negative.

  • raise_exception_on_extraction_error (bool) – Whether to raise an exception if the extraction fails due to invalid data returned by a model or an error in the model API. If False, a warning will be issued instead, and no extracted items will be returned. Defaults to True.

Returns:

List of processed _Aspect objects with extracted items.

Return type:

list[contextgem.internal.base.aspects._Aspect]

extract_concepts_from_aspect(aspect, document, *, from_concepts=None, overwrite_existing=False, max_items_per_call=0, use_concurrency=False, max_paragraphs_to_analyze_per_call=0, raise_exception_on_extraction_error=True)#

Extracts concepts associated with a given aspect in a document.

This method processes an aspect to extract related concepts using models. If the aspect has not been previously processed, a ValueError is raised.

This is the synchronous version of extract_concepts_from_aspect_async().

Parameters:

  • aspect (contextgem.internal.base.aspects._Aspect) – The aspect from which to extract concepts.

  • document (contextgem.internal.base.documents._Document) – The document that contains the aspect.

  • from_concepts (collections.abc.Sequence[contextgem.internal.base.concepts._Concept] | None) – List of existing concepts to process. Defaults to None. A concept that is given more than once is extracted once.

  • overwrite_existing (bool) – Whether to overwrite already processed concepts with newly extracted information. Defaults to False.

  • max_items_per_call (int) – Maximum number of items with the same extraction params to process in each call. Defaults to 0 (all items in one call). If concurrency is enabled, defaults to 1. For complex tasks, you should not set a high value, in order to avoid prompt overloading.

  • use_concurrency (bool) – If True, enables concurrent processing of multiple items. Concurrency can considerably reduce processing time, but may cause rate limit errors with model providers. Use this option when API rate limits allow for multiple concurrent requests. Defaults to False.

  • max_paragraphs_to_analyze_per_call (int) – Maximum paragraphs to include in a single LLM prompt. A decision model is sent a text with more paragraphs in parts of at most that many paragraphs, each in a request of its own: a concept then gets an extracted item for each part, and a concept with singular_occurrence=True is rejected with ValueError. Defaults to 0 (all paragraphs). Cannot be negative.

  • raise_exception_on_extraction_error (bool) – Whether to raise an exception if the extraction fails due to invalid data returned by a model or an error in the model API. If False, a warning will be issued instead, and no extracted items will be returned. Defaults to True.

Returns:

List of processed concept objects.

Return type:

list[contextgem.internal.base.concepts._Concept]

async extract_concepts_from_aspect_async(aspect, document, *, from_concepts=None, overwrite_existing=False, max_items_per_call=0, use_concurrency=False, max_paragraphs_to_analyze_per_call=0, raise_exception_on_extraction_error=True)#

Asynchronously extracts concepts from a specified aspect using models.

This method processes an aspect to extract related concepts using models. If the aspect has not been previously processed, a ValueError is raised.

Parameters:

  • aspect (contextgem.internal.base.aspects._Aspect) – The aspect from which to extract concepts.

  • document (contextgem.internal.base.documents._Document) – The document that contains the aspect.

  • from_concepts (collections.abc.Sequence[contextgem.internal.base.concepts._Concept] | None) – List of existing concepts to process. Defaults to None. A concept that is given more than once is extracted once.

  • overwrite_existing (bool) – Whether to overwrite already processed concepts with newly extracted information. Defaults to False.

  • max_items_per_call (int) – Maximum number of items with the same extraction params to process in each call. Defaults to 0 (all items in one call). If concurrency is enabled, defaults to 1. For complex tasks, you should not set a high value, in order to avoid prompt overloading.

  • use_concurrency (bool) – If True, enables concurrent processing of multiple items. Concurrency can considerably reduce processing time, but may cause rate limit errors with model providers. Use this option when API rate limits allow for multiple concurrent requests. Defaults to False.

  • max_paragraphs_to_analyze_per_call (int) – Maximum paragraphs to include in a single LLM prompt. A decision model is sent a text with more paragraphs in parts of at most that many paragraphs, each in a request of its own: a concept then gets an extracted item for each part, and a concept with singular_occurrence=True is rejected with ValueError. Defaults to 0 (all paragraphs). Cannot be negative.

  • raise_exception_on_extraction_error (bool) – Whether to raise an exception if the extraction fails due to invalid data returned by a model or an error in the model API. If False, a warning will be issued instead, and no extracted items will be returned. Defaults to True.

Returns:

List of processed concept objects.

Return type:

list[contextgem.internal.base.concepts._Concept]

extract_concepts_from_document(document, *, from_concepts=None, overwrite_existing=False, max_items_per_call=0, use_concurrency=False, max_paragraphs_to_analyze_per_call=0, max_images_to_analyze_per_call=0, raise_exception_on_extraction_error=True)#

Extracts concepts from the provided document using predefined models.

This is the synchronous version of extract_concepts_from_document_async().

Parameters:

  • document (contextgem.internal.base.documents._Document) – The document from which concepts are to be extracted.

  • from_concepts (collections.abc.Sequence[contextgem.internal.base.concepts._Concept] | None) – Existing concepts to use as a base for extraction. If None, uses all document’s concepts. A concept that is given more than once is extracted once.

  • overwrite_existing (bool) – Whether to overwrite already processed concepts with newly extracted information. Defaults to False.

  • max_items_per_call (int) – Maximum items with the same extraction params to process per call. Defaults to 0 (all items in single call). For complex tasks, you should not set a value, to avoid prompt overloading. If concurrency is enabled, defaults to 1 (each item processed separately).

  • use_concurrency (bool) – If True, enables concurrent processing of multiple items. Concurrency can considerably reduce processing time, but may cause rate limit errors with model providers. Use this option when API rate limits allow for multiple concurrent requests. Defaults to False.

  • max_paragraphs_to_analyze_per_call (int) – Maximum paragraphs to analyze in a single LLM prompt. A decision model is sent a text with more paragraphs in parts of at most that many paragraphs, each in a request of its own: a concept then gets an extracted item for each part, and a concept with singular_occurrence=True is rejected with ValueError. Defaults to 0 (all paragraphs). Cannot be negative.

  • max_images_to_analyze_per_call (int) – Maximum images to include in a single LLM prompt. Defaults to 0 (all images). Cannot be negative.

  • raise_exception_on_extraction_error (bool) – Whether to raise an exception if the extraction fails due to invalid data returned by a model or an error in the model API. If False, a warning will be issued instead, and no extracted items will be returned. Defaults to True.

Returns:

List of processed Concept objects with extracted items.

Return type:

list[contextgem.internal.base.concepts._Concept]

async extract_concepts_from_document_async(document, *, from_concepts=None, overwrite_existing=False, max_items_per_call=0, use_concurrency=False, max_paragraphs_to_analyze_per_call=0, max_images_to_analyze_per_call=0, raise_exception_on_extraction_error=True)#

Extracts concepts from the provided document using predefined models asynchronously.

This method processes a document to extract concepts using configured models.

Parameters:

  • document (contextgem.internal.base.documents._Document) – The document from which concepts are to be extracted.

  • from_concepts (collections.abc.Sequence[contextgem.internal.base.concepts._Concept] | None) – Existing concepts to use as a base for extraction. If None, uses all document’s concepts. A concept that is given more than once is extracted once.

  • overwrite_existing (bool) – Whether to overwrite already processed concepts with newly extracted information. Defaults to False.

  • max_items_per_call (int) – Maximum number of items with the same extraction params to process per call. Defaults to 0 (all items in one call). If concurrency is enabled, defaults to 1. For complex tasks, you should not set a high value, in order to avoid prompt overloading.

  • use_concurrency (bool) – If True, enables concurrent processing of multiple items. Concurrency can considerably reduce processing time, but may cause rate limit errors with model providers. Use this option when API rate limits allow for multiple concurrent requests. Defaults to False.

  • max_paragraphs_to_analyze_per_call (int) – Maximum paragraphs to analyze in a single LLM prompt. A decision model is sent a text with more paragraphs in parts of at most that many paragraphs, each in a request of its own: a concept then gets an extracted item for each part, and a concept with singular_occurrence=True is rejected with ValueError. Defaults to 0 (all paragraphs). Cannot be negative.

  • max_images_to_analyze_per_call (int) – Maximum images to include in a single LLM prompt. Defaults to 0 (all images). Cannot be negative.

  • raise_exception_on_extraction_error (bool) – Whether to raise an exception if the extraction fails due to invalid data returned by a model or an error in the model API. If False, a warning will be issued instead, and no extracted items will be returned. Defaults to True.

Returns:

List of processed Concept objects with extracted items.

Return type:

list[contextgem.internal.base.concepts._Concept]

classmethod from_dict(obj_dict)#

Reconstructs an instance of the class from a dictionary representation.

This method deserializes a dictionary containing the object’s attributes and values into a new instance of the class. It handles complex nested structures like aspects, concepts, and extracted items, properly reconstructing each component.

A deserialized model or model group has no API credentials, as they are not included in the serialized data, i.e. they are to be set again.

Parameters:

obj_dict (dict[str, typing.Any]) – Dictionary containing the serialized object data.

Returns:

A new instance of the class with restored attributes.

Return type:

typing.Self

classmethod from_disk(file_path)#

Loads an instance of the class from a JSON file stored on disk.

This method reads the JSON content from the specified file path and deserializes it into an instance of the class using the from_json method.

Parameters:

file_path (str | pathlib.Path) – Path to the JSON file to load (must end with ‘.json’). Can be a string or a Path object.

Returns:

An instance of the class populated with the data from the file.

Return type:

typing.Self

Raises:

  • ValueError – If the file path doesn’t end with ‘.json’, or if the file does not hold valid serialized data, e.g. it is not a valid UTF-8 text (UnicodeDecodeError) or JSON (json.JSONDecodeError), or the data fails validation (pydantic’s ValidationError). These errors are subclasses of ValueError.

  • TypeError – If the serialized data is of another class.

  • OSError – If the file cannot be read, e.g. it does not exist. The error is the one raised by the file operation, e.g. FileNotFoundError or PermissionError.

classmethod from_json(json_string)#

Creates an instance of the class from a JSON string representation.

This method deserializes the provided JSON string into a dictionary and uses the from_dict method to construct the class instance. It validates that the class name in the serialized data matches the current class.

Parameters:

json_string (str) – JSON string containing the serialized object data.

Returns:

A new instance of the class with restored state.

Return type:

typing.Self

Raises:

  • json.JSONDecodeError – If the string is not a valid JSON. The error gives the position of what is invalid, and does not hold the string.

  • ValueError – If the JSON value is not an object with a class name, i.e. not serialized data.

  • TypeError – If the class name in the serialized data doesn’t match.

get_cost()#

Retrieves the accumulated cost information of the model and of its fallback models, if configured.

This method collects cost statistics for the current model instance and for its fallback models (if configured), providing insights into API usage expenses.

Returns:

A list of cost statistics containers for the model and its fallback models.

Return type:

list[contextgem.internal.data_models._ModelCostOutputContainer]

get_usage()#

Retrieves the usage information of the model and of its fallback models, if configured.

This method collects token usage statistics for the current model instance and for its fallback models (if configured), providing insights into API consumption.

Returns:

A list of usage statistics containers for the model and its fallback models.

Return type:

list[contextgem.internal.data_models._ModelUsageOutputContainer]

property is_group: bool#

Returns False indicating this is a single model, not a group.

Returns:

Always False for single models.

Return type:

bool

property list_roles: list[str | None]#

Returns a list containing the role of this model.

(For a single model, this returns a list with just one element - the model’s role, or None if the model has no role. For model groups, the method implementation returns roles of all models in the group.)

Returns:

A list containing the role of this model.

Return type:

list[str | None]

reset_usage_and_cost()#

Resets the usage and cost statistics for the model and its fallback models (if configured).

This method clears accumulated usage and cost data, which is useful when processing multiple documents sequentially and tracking metrics for each document separately.

Returns:

None

Return type:

None

to_dict()#

Transforms the current object into a dictionary representation.

Converts the object to a dictionary that includes: - All public attributes - Special handling for specific public and private attributes

When a model or model group is serialized, its API credentials and usage/cost stats are removed.

Returns:

A dictionary representation of the current object with all necessary data for serialization

Return type:

dict[str, typing.Any]

to_disk(file_path)#

Saves the serialized instance to a JSON file at the specified path.

This method converts the instance to a dictionary representation using to_dict(), then writes it to disk as a formatted JSON file with UTF-8 encoding.

Parameters:

file_path (str | pathlib.Path) – Path where the JSON file should be saved (must end with ‘.json’). Can be a string or a Path object.

Return type:

None

Returns:

None

Raises:

  • ValueError – If the file path doesn’t end with ‘.json’.

  • OSError – If the file cannot be written, e.g. its directory does not exist. The error is the one raised by the file operation, e.g. FileNotFoundError or PermissionError.

to_json()#

Converts the object to its JSON string representation.

Serializes the object into a JSON-formatted string using the dictionary representation provided by the to_dict() method.

Returns:

A JSON string representation of the object.

Return type:

str

decision_threshold: Annotated[float, Strict(strict=True)]#
model: Annotated[str, Strict(strict=True), StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=None, min_length=1, max_length=None, pattern=None, ascii_only=None)]#
api_key: Annotated[str, Strict(strict=True), StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=None, min_length=1, max_length=None, pattern=None, ascii_only=None)] | None#
api_base: Annotated[str, Strict(strict=True), StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=None, min_length=1, max_length=None, pattern=None, ascii_only=None)] | None#
role: Annotated[str, Strict(strict=True), AfterValidator(func=_validate_model_role)] | None#
num_retries_failed_request: Annotated[int, Strict(strict=True)]#
timeout: Annotated[int, Strict(strict=True)]#
pricing_details: _ModelPricing | None#
is_fallback: Annotated[bool, Strict(strict=True)]#
fallback_models: list[_DocumentModel]#
auto_pricing: Annotated[bool, Strict(strict=True)]#
auto_pricing_refresh: Annotated[bool, Strict(strict=True)]#