sherpa_ai.models package

Overview

The models package provides interfaces and wrappers for language models in Sherpa AI, enabling seamless integration with various LLM providers while adding enhanced functionality like logging and error handling.

Key Components

• SherpaBaseChatModel: Core interface for chat-based language models

• SherpaBaseModel: Base implementation for all model types

• ChatModelWithLogging: Chat model wrapper with integrated logging capabilities

Example Usage

from sherpa_ai.models.sherpa_base_chat_model import SherpaBaseChatModel

# Initialize a chat model
model = SherpaBaseChatModel(
    model_name="gpt-4",
    temperature=0.7,
    max_tokens=1000
)

# Generate a response to a user query
response = model.generate(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ]
)

print(response)

Submodules

Module	Description
sherpa_ai.models.chat_model_with_logging	Provides a chat model wrapper with integrated logging capabilities.
sherpa_ai.models.sherpa_base_chat_model	Implements the core interface for chat-based language models.
sherpa_ai.models.sherpa_base_model	Contains the base implementation for all model types in the system.

sherpa_ai.models.chat_model_with_logging module

Chat model with logging functionality for Sherpa AI.

This module provides a wrapper for chat models that adds detailed logging capabilities. It defines the ChatModelWithLogging class which logs all model interactions including inputs, outputs, and model information.

class sherpa_ai.models.chat_model_with_logging.ChatModelWithLogging(*args, **kwargs)

Bases: BaseChatModel

Chat model wrapper that adds detailed logging functionality.

This class wraps any chat model to add comprehensive logging of all interactions, including input messages, generated responses, and model information.

llm

The underlying chat model to wrap.

Type:

BaseChatModel

logger

Logger instance for recording interactions.

Type:

type(logger)

Example

>>> base_model = ChatOpenAI()
>>> model = ChatModelWithLogging(llm=base_model, logger=custom_logger)
>>> response = model.generate([Message("Hello")])
>>> # Logs will include input, output, and model info

llm: BaseChatModel

logger: Logger

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

sherpa_ai.models.sherpa_base_chat_model module

Chat model integration module for Sherpa AI.

This module provides chat model integration for the Sherpa AI system. It defines base and OpenAI-specific chat model classes with Sherpa enhancements like usage tracking and verbose logging.

class sherpa_ai.models.sherpa_base_chat_model.SherpaBaseChatModel(*args, **kwargs)

Bases: BaseChatModel

Base chat model with Sherpa-specific enhancements.

This class extends the base chat model to add Sherpa-specific functionality, including user-based token usage tracking and verbose logging capabilities.

user_id

ID of the user making model requests.

Type:

Optional[str]

verbose_logger

Logger for detailed operation tracking.

Type:

BaseVerboseLogger

Example

>>> model = SherpaBaseChatModel(user_id="user123")
>>> response = model.generate([Message("Hello")])
>>> print(response.generations[0].text)
'Hi there!'

user_id: str | None

session_id: str | None

agent_name: str | None

verbose_logger: BaseVerboseLogger

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sherpa_ai.models.sherpa_base_chat_model.SherpaChatOpenAI(*args, **kwargs)

Bases: ChatOpenAI

Enhanced OpenAI chat model with Sherpa-specific features.

This class extends the OpenAI chat model to add Sherpa-specific functionality, including user-based token usage tracking and verbose logging capabilities.

user_id

ID of the user making model requests.

Type:

Optional[str]

verbose_logger

Logger for detailed operation tracking.

Type:

BaseVerboseLogger

Example

>>> model = SherpaChatOpenAI(user_id="user123")
>>> response = model.generate([Message("Hello")])
>>> print(response.generations[0].text)
'Hi there!'

user_id: str | None

session_id: str | None

agent_name: str | None

verbose_logger: BaseVerboseLogger

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'populate_by_name': True, 'protected_namespaces': (), 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

sherpa_ai.models.sherpa_base_model module

Base OpenAI model integration for Sherpa AI.

This module provides the base OpenAI model integration for the Sherpa AI system. It defines the SherpaOpenAI class which extends OpenAI’s chat model with Sherpa-specific functionality like usage tracking.

class sherpa_ai.models.sherpa_base_model.SherpaOpenAI(*args, **kwargs)

Bases: ChatOpenAI

Enhanced OpenAI chat model with Sherpa-specific features.

This class extends the OpenAI chat model to add Sherpa-specific functionality, particularly user-based token usage tracking. It maintains compatibility with the base ChatOpenAI interface while adding user tracking capabilities.

user_id

ID of the user making model requests.

Type:

Optional[str]

Example

>>> model = SherpaOpenAI(user_id="user123")
>>> response = model.generate("What is the weather?")
>>> print(response.generations[0].text)
'The weather is sunny.'

user_id: str | None

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'populate_by_name': True, 'protected_namespaces': (), 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Module contents

Language model integration module for Sherpa AI.

This module provides language model integration for the Sherpa AI system. It exports the SherpaOpenAI and SherpaChatOpenAI classes which provide interfaces to OpenAI’s language models with Sherpa-specific enhancements.

Example

>>> from sherpa_ai.models import SherpaOpenAI, SherpaChatOpenAI
>>> model = SherpaOpenAI()
>>> chat_model = SherpaChatOpenAI()
>>> response = model.generate("Hello")
>>> chat_response = chat_model.chat("How are you?")

class sherpa_ai.models.SherpaOpenAI(*args, **kwargs)

Bases: ChatOpenAI

Enhanced OpenAI chat model with Sherpa-specific features.

This class extends the OpenAI chat model to add Sherpa-specific functionality, particularly user-based token usage tracking. It maintains compatibility with the base ChatOpenAI interface while adding user tracking capabilities.

user_id

ID of the user making model requests.

Type:

Optional[str]

Example

>>> model = SherpaOpenAI(user_id="user123")
>>> response = model.generate("What is the weather?")
>>> print(response.generations[0].text)
'The weather is sunny.'

user_id: str | None

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'populate_by_name': True, 'protected_namespaces': (), 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sherpa_ai.models.SherpaChatOpenAI(*args, **kwargs)

Bases: ChatOpenAI

Enhanced OpenAI chat model with Sherpa-specific features.

This class extends the OpenAI chat model to add Sherpa-specific functionality, including user-based token usage tracking and verbose logging capabilities.

user_id

ID of the user making model requests.

Type:

Optional[str]

verbose_logger

Logger for detailed operation tracking.

Type:

BaseVerboseLogger

Example

>>> model = SherpaChatOpenAI(user_id="user123")
>>> response = model.generate([Message("Hello")])
>>> print(response.generations[0].text)
'Hi there!'

user_id: str | None

session_id: str | None

agent_name: str | None

verbose_logger: BaseVerboseLogger

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'populate_by_name': True, 'protected_namespaces': (), 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].