lwe.plugins Package

lwe.plugins Submodules

lwe.plugins.echo module

class lwe.plugins.echo.Echo(config=None, cache_manager=None)

Bases: Plugin

Simple echo plugin, echos back the text you give it

command_echo(arg)

Echo command, a simple plugin example

This command is provided as an example of extending functionality via a plugin.

Arguments:

text: The text to echo

Examples:

{COMMAND} one

default_config()

The default configuration for this plugin. This is called by the plugin manager after the plugin is initialized. The user can override these settings in their profile configuration, under the key ‘plugins.echo’.

get_shell_completions(_base_shell_completions)

Example of provided shell completions.

setup()

Setup the plugin. This is called by the plugin manager after the backend is initialized.

lwe.plugins.examples module

class lwe.plugins.examples.Examples(config=None, cache_manager=None)

Bases: Plugin

Easily install example configuration files

command_examples(arg)

List or install example files

The example files that come with the package can be listed and optionally installed in the currently running profile configuration.

Arguments:

list: List all example files, and where they will be installed. type: The type of example files to install (default is all configured types).

Examples:

{COMMAND} list {COMMAND} presets

confirm_install(example_types)

default_config()

get_examples(example_type)

get_shell_completions(_base_shell_completions)

Example of provided shell completions.

install_example_file(example_type, example_file)

Helper method to install a single example file.

install_examples(example_type=None)

Install example files.

install_examples_confirm(example_type=None)

setup()

lwe.plugins.provider_chat_openai module

class lwe.plugins.provider_chat_openai.CustomChatOpenAI(*args: ~typing.Any, name: str | None = None, cache: ~langchain_core.caches.BaseCache | bool | None = None, verbose: bool = <factory>, callbacks: list[~langchain_core.callbacks.base.BaseCallbackHandler] | ~langchain_core.callbacks.base.BaseCallbackManager | None = None, tags: list[str] | None = None, metadata: dict[str, ~typing.Any] | None = None, custom_get_token_ids: ~typing.Callable[[str], list[int]] | None = None, callback_manager: ~langchain_core.callbacks.base.BaseCallbackManager | None = None, rate_limiter: ~langchain_core.rate_limiters.BaseRateLimiter | None = None, disable_streaming: bool | ~typing.Literal['tool_calling'] = False, client: ~typing.Any = None, async_client: ~typing.Any = None, root_client: ~typing.Any = None, root_async_client: ~typing.Any = None, model: str = 'gpt-3.5-turbo', temperature: float | None = None, model_kwargs: dict[str, ~typing.Any] = <factory>, api_key: ~pydantic.types.SecretStr | None = <factory>, base_url: str | None = None, organization: str | None = None, openai_proxy: str | None = <factory>, timeout: float | tuple[float, float] | ~typing.Any | None = None, stream_usage: bool | None = None, max_retries: int | None = None, presence_penalty: float | None = None, frequency_penalty: float | None = None, seed: int | None = None, logprobs: bool | None = None, top_logprobs: int | None = None, logit_bias: dict[int, int] | None = None, streaming: bool = False, n: int | None = None, top_p: float | None = None, max_completion_tokens: int | None = None, reasoning_effort: str | None = None, reasoning: dict[str, ~typing.Any] | None = None, verbosity: str | None = None, tiktoken_model_name: str | None = None, default_headers: ~collections.abc.Mapping[str, str] | None = None, default_query: ~collections.abc.Mapping[str, object] | None = None, http_client: ~typing.Any | None = None, http_async_client: ~typing.Any | None = None, stop_sequences: list[str] | str | None = None, extra_body: ~collections.abc.Mapping[str, ~typing.Any] | None = None, include_response_headers: bool = False, disabled_params: dict[str, ~typing.Any] | None = None, include: list[str] | None = None, service_tier: str | None = None, store: bool | None = None, truncation: str | None = None, use_previous_response_id: bool = False, use_responses_api: bool | None = None, output_version: ~typing.Literal['v0', 'responses/v1'] = 'v0')

Bases: ChatOpenAI

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 lwe.plugins.provider_chat_openai.ProviderChatOpenai(config=None, cache_manager=None)

Bases: Provider

Access to OpenAI chat models via the OpenAI API

property capabilities

customization_config()

default_config()

property default_model

format_responses_content(content_list)

handle_non_streaming_response(response: AIMessage)

handle_streaming_chunk(chunk: AIMessageChunk | str, _previous_chunks: list[AIMessageChunk | str]) → str

is_legacy_reasoning_model(model_name)

is_reasoning_model(model_name)

llm_factory()

llm_pre_call(llm, messages)

llm_pre_init(customizations)

prepare_messages_method()

setup()

property static_models

lwe.plugins.provider_fake_llm module

class lwe.plugins.provider_fake_llm.CustomFakeMessagesListChatModel(*, name: str | None = None, cache: ~langchain_core.caches.BaseCache | bool | None = None, verbose: bool = <factory>, callbacks: list[~langchain_core.callbacks.base.BaseCallbackHandler] | ~langchain_core.callbacks.base.BaseCallbackManager | None = None, tags: list[str] | None = None, metadata: dict[str, ~typing.Any] | None = None, custom_get_token_ids: ~typing.Callable[[str], list[int]] | None = None, callback_manager: ~langchain_core.callbacks.base.BaseCallbackManager | None = None, rate_limiter: ~langchain_core.rate_limiters.BaseRateLimiter | None = None, disable_streaming: bool | ~typing.Literal['tool_calling'] = False, responses: ~typing.List[~langchain_core.messages.base.BaseMessage | ~langchain_core.messages.base.BaseMessageChunk | str] | ~typing.List[~typing.List[~langchain_core.messages.base.BaseMessage | ~langchain_core.messages.base.BaseMessageChunk | str]], sleep: float | None = None, i: int = 0, model_name: str)

Bases: FakeMessagesListChatModel

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].

model_name: str

class lwe.plugins.provider_fake_llm.FakeMessagesListChatModel(*args: ~typing.Any, name: str | None = None, cache: ~langchain_core.caches.BaseCache | bool | None = None, verbose: bool = <factory>, callbacks: list[~langchain_core.callbacks.base.BaseCallbackHandler] | ~langchain_core.callbacks.base.BaseCallbackManager | None = None, tags: list[str] | None = None, metadata: dict[str, ~typing.Any] | None = None, custom_get_token_ids: ~typing.Callable[[str], list[int]] | None = None, callback_manager: ~langchain_core.callbacks.base.BaseCallbackManager | None = None, rate_limiter: ~langchain_core.rate_limiters.BaseRateLimiter | None = None, disable_streaming: bool | ~typing.Literal['tool_calling'] = False, responses: ~typing.List[~langchain_core.messages.base.BaseMessage | ~langchain_core.messages.base.BaseMessageChunk | str] | ~typing.List[~typing.List[~langchain_core.messages.base.BaseMessage | ~langchain_core.messages.base.BaseMessageChunk | str]], sleep: float | None = None, i: int = 0)

Bases: BaseChatModel

Fake ChatModel for testing purposes.

i: int

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].

responses: List[BaseMessage | BaseMessageChunk | str] | List[List[BaseMessage | BaseMessageChunk | str]]

sleep: float | None

class lwe.plugins.provider_fake_llm.ProviderFakeLlm(config=None, **kwargs)

Bases: Provider

Fake LLM provider.

property capabilities

customization_config()

property default_model

llm_factory()

prepare_messages_method()

lwe.plugins Module contents