rl_chain.pick_best_chain.PickBestEvent(...)
rl_chain.pick_best_chain.PickBestFeatureEmbedder(...)
Text Embedder class that embeds the BasedOn and ToSelectFrom inputs into a format that can be used by the learning policy
rl_chain.pick_best_chain.PickBestRandomPolicy(...)
rl_chain.pick_best_chain.PickBestSelected([...])
rl_chain.vw_logger.VwLogger(path)
Functions¶
rl_chain.base.BasedOn(anything)
rl_chain.base.Embed(anything[, keep])
rl_chain.base.EmbedAndKeep(anything)
rl_chain.base.ToSelectFrom(anything)
rl_chain.base.embed(to_embed, model[, namespace])
Embeds the actions or context using the SentenceTransformer model (or a model that has an encode function)
rl_chain.base.embed_dict_type(item, model)
Helper function to embed a dictionary item.
rl_chain.base.embed_list_type(item, model[, ...])
rl_chain.base.embed_string_type(item, model)
Helper function to embed a string or an _Embed object.
rl_chain.base.get_based_on_and_to_select_from(inputs)
rl_chain.base.is_stringtype_instance(item)
Helper function to check if an item is a string.
rl_chain.base.parse_lines(parser, input_str)
rl_chain.base.prepare_inputs_for_autoembed(inputs)
go over all the inputs and if something is either wrapped in _ToSelectFrom or _BasedOn, and if their inner values are not already _Embed, then wrap them in EmbedAndKeep while retaining their _ToSelectFrom or _BasedOn status
rl_chain.base.stringify_embedding(embedding)
langchain_experimental.smart_llm¶
Generalized implementation of SmartGPT (origin: https://youtu.be/wVzuvf9D9BU)
Classes¶
smart_llm.base.SmartLLMChain