memoryscope.core.models

class memoryscope.core.models.BaseModel(model_name: str, module_name: str, timeout: int | None = None, max_retries: int = 3, retry_interval: float = 1.0, kwargs_filter: bool = True, raise_exception: bool = True, **kwargs)

Bases: object

m_type: ModelEnum | None = None

__init__(model_name: str, module_name: str, timeout: int | None = None, max_retries: int = 3, retry_interval: float = 1.0, kwargs_filter: bool = True, raise_exception: bool = True, **kwargs)

property model

abstract before_call(model_response: ModelResponse, **kwargs)

abstract after_call(model_response: ModelResponse, **kwargs) → ModelResponse | Generator[ModelResponse, None, None]

call(stream: bool = False, **kwargs) → ModelResponse | Generator[ModelResponse, None, None]

async async_call(**kwargs) → ModelResponse

class memoryscope.core.models.DummyGenerationModel(model_name: str, module_name: str, timeout: int | None = None, max_retries: int = 3, retry_interval: float = 1.0, kwargs_filter: bool = True, raise_exception: bool = True, **kwargs)

Bases: BaseModel

The DummyGenerationModel class serves as a placeholder model for generating responses. It processes input prompts or sequences of messages, adapting them into a structure compatible with chat interfaces. It also facilitates the generation of mock (dummy) responses for testing, supporting both immediate and streamed output.

m_type: ModelEnum = 'generation_model'

before_call(model_response: ModelResponse, **kwargs)

Prepares the input data before making a call to the language model. It accepts either a ‘prompt’ directly or a list of ‘messages’. If ‘prompt’ is provided, it sets the data accordingly. If ‘messages’ are provided, it constructs a list of ChatMessage objects from the list. Raises an error if neither ‘prompt’ nor ‘messages’ are supplied.

Parameters:

• model_response – model_response

• **kwargs – Arbitrary keyword arguments including ‘prompt’ and ‘messages’.

Raises:

RuntimeError – When both ‘prompt’ and ‘messages’ inputs are not provided.

after_call(model_response: ModelResponse, stream: bool = False, **kwargs) → ModelResponse | Generator[ModelResponse, None, None]

Processes the model’s response post-call, optionally streaming the output or returning it as a whole.

This method modifies the input model_response by resetting its message content and, based on the stream parameter, either yields the response in a generated stream or returns the complete response directly.

Parameters:

• model_response (ModelResponse) – The initial response object to be processed.

• stream (bool, optional) – Flag indicating whether to stream the response. Defaults to False.

• **kwargs – Additional keyword arguments (not used in this implementation).

Returns:

If stream is True, a generator yielding updated ModelResponse objects;

otherwise, a modified ModelResponse object with the complete content.

Return type:

ModelResponse | ModelResponseGen

class memoryscope.core.models.LlamaIndexEmbeddingModel(*args, **kwargs)

Bases: BaseModel

Manages text embeddings utilizing the DashScopeEmbedding within the LlamaIndex framework, facilitating embedding operations for both sync and async modes, inheriting from BaseModel.

m_type: ModelEnum = 'embedding_model'

__init__(*args, **kwargs)

classmethod register_model(model_name: str, model_class: type)

Registers a new embedding model class with the model registry.

Parameters:

• model_name (str) – The name to register the model under.

• model_class (type) – The class of the model to register.

before_call(model_response: ModelResponse, **kwargs)

after_call(model_response: ModelResponse, **kwargs) → ModelResponse

class memoryscope.core.models.LlamaIndexGenerationModel(*args, **kwargs)

Bases: BaseModel

This class represents a generation model within the LlamaIndex framework, capable of processing input prompts or message histories, selecting an appropriate language model service from a registry, and generating text responses, with support for both streaming and non-streaming modes. It encapsulates logic for formatting these interactions within the context of a memory scope management system.

m_type: ModelEnum = 'generation_model'

__init__(*args, **kwargs)

before_call(model_response: ModelResponse, **kwargs)

Prepares the input data before making a call to the language model. It accepts either a ‘prompt’ directly or a list of ‘messages’. If ‘prompt’ is provided, it sets the data accordingly. If ‘messages’ are provided, it constructs a list of ChatMessage objects from the list. Raises an error if neither ‘prompt’ nor ‘messages’ are supplied.

Parameters:

• model_response – model_response

• **kwargs – Arbitrary keyword arguments including ‘prompt’ and ‘messages’.

Raises:

RuntimeError – When both ‘prompt’ and ‘messages’ inputs are not provided.

after_call(model_response: ModelResponse, stream: bool = False, **kwargs) → ModelResponse | Generator[ModelResponse, None, None]

class memoryscope.core.models.LlamaIndexRankModel(*args, **kwargs)

Bases: BaseModel

The LlamaIndexRankModel class is designed to rerank documents according to their relevance to a provided query, utilizing the DashScope Rerank model. It transforms document lists and queries into a compatible format for ranking, manages the ranking process, and allocates rank scores to individual documents.

m_type: ModelEnum = 'rank_model'

__init__(*args, **kwargs)

before_call(model_response: ModelResponse, **kwargs)

Prepares necessary data before the ranking call by extracting the query and documents, ensuring they are valid, and initializing nodes with dummy scores.

Parameters:

• model_response – model response

• **kwargs – Keyword arguments containing ‘query’ and ‘documents’.

after_call(model_response: ModelResponse, **kwargs) → ModelResponse

Processes the model response post-ranking, assigning calculated rank scores to each document based on their index in the original document list.

Parameters:

• model_response (ModelResponse) – The initial response from the ranking model.

• **kwargs – Additional keyword arguments (unused).

Returns:

Updated response with rank scores assigned to documents.

Return type:

ModelResponse