rl_chain

RL (Reinforcement Learning) Chain leverages the Vowpal Wabbit (VW) models for reinforcement learning with a context, with the goal of modifying the prompt before the LLM call.

[Vowpal Wabbit](https://vowpalwabbit.org/) provides fast, efficient, and flexible online machine learning techniques for reinforcement learning, supervised learning, and more.

Classes

rl_chain.base.AutoSelectionScorer	Auto selection scorer.
rl_chain.base.Embedder(*args, **kwargs)	Abstract class to represent an embedder.
rl_chain.base.Event(inputs[, selected])	Abstract class to represent an event.
rl_chain.base.Policy(**kwargs)	Abstract class to represent a policy.
rl_chain.base.RLChain	Chain that leverages the Vowpal Wabbit (VW) model as a learned policy for reinforcement learning.
rl_chain.base.Selected()	Abstract class to represent the selected item.
rl_chain.base.SelectionScorer	Abstract class to grade the chosen selection or the response of the llm.
rl_chain.base.VwPolicy(model_repo, vw_cmd, ...)	Vowpal Wabbit policy.
rl_chain.metrics.MetricsTrackerAverage(step)	Metrics Tracker Average.
rl_chain.metrics.MetricsTrackerRollingWindow(...)	Metrics Tracker Rolling Window.
rl_chain.model_repository.ModelRepository(folder)	Model Repository.
rl_chain.pick_best_chain.PickBest	Chain that leverages the Vowpal Wabbit (VW) model for reinforcement learning with a context, with the goal of modifying the prompt before the LLM call.
rl_chain.pick_best_chain.PickBestEvent(...)	Event class for PickBest chain.
rl_chain.pick_best_chain.PickBestFeatureEmbedder(...)	Embed the BasedOn and ToSelectFrom inputs into a format that can be used by the learning policy.
rl_chain.pick_best_chain.PickBestRandomPolicy(...)	Random policy for PickBest chain.
rl_chain.pick_best_chain.PickBestSelected([...])	Selected class for PickBest chain.
rl_chain.vw_logger.VwLogger(path)	Vowpal Wabbit custom logger.

Functions

rl_chain.base.BasedOn(anything)	Wrap a value to indicate that it should be based on.
rl_chain.base.Embed(anything[, keep])	Wrap a value to indicate that it should be embedded.
rl_chain.base.EmbedAndKeep(anything)	Wrap a value to indicate that it should be embedded and kept.
rl_chain.base.ToSelectFrom(anything)	Wrap a value to indicate that it should be selected from.
rl_chain.base.get_based_on_and_to_select_from(inputs)	Get the BasedOn and ToSelectFrom from the inputs.
rl_chain.base.parse_lines(parser, input_str)	Parse the input string into a list of examples.
rl_chain.base.prepare_inputs_for_autoembed(inputs)	Prepare the inputs for auto embedding.
rl_chain.helpers.embed(to_embed, model[, ...])	Embed the actions or context using the SentenceTransformer model (or a model that has an encode function).
rl_chain.helpers.embed_dict_type(item, model)	Embed a dictionary item.
rl_chain.helpers.embed_list_type(item, model)	Embed a list item.
rl_chain.helpers.embed_string_type(item, model)	Embed a string or an _Embed object.
rl_chain.helpers.is_stringtype_instance(item)	Check if an item is a string.
rl_chain.helpers.stringify_embedding(embedding)	Convert an embedding to a string.