PickBestFeatureEmbedder#

class langchain_experimental.rl_chain.pick_best_chain.PickBestFeatureEmbedder(

auto_embed: bool,

model: Any | None = None,

*args: Any,

**kwargs: Any,

)[source]#

Embed the BasedOn and ToSelectFrom inputs into a format that can be used by the learning policy.

Parameters:

auto_embed (bool)
model (Optional[Any])
args (Any)
kwargs (Any)

model name

The type of embeddings to be used for feature representation. Defaults to BERT SentenceTransformer.

Type:: Any, optional

Methods

`__init__`(auto_embed[, model])
`format`(event)
`format_auto_embed_off`(event)	Converts the BasedOn and ToSelectFrom into a format that can be used by VW
`format_auto_embed_on`(event)
`get_context_and_action_embeddings`(event)
`get_indexed_dot_product`(context_emb, action_embs)
`get_label`(event)

__init__(

auto_embed: bool,

model: Any | None = None,

*args: Any,

**kwargs: Any,

)[source]#

Parameters:

auto_embed (bool)
model (Any | None)
args (Any)
kwargs (Any)

format( event: PickBestEvent, ) → str[source]#

Parameters:: event (PickBestEvent)
Return type:: str

format_auto_embed_off( event: PickBestEvent, ) → str[source]#

Converts the BasedOn and ToSelectFrom into a format that can be used by VW

Parameters:: event (PickBestEvent)
Return type:: str

format_auto_embed_on( event: PickBestEvent, ) → str[source]#

Parameters:: event (PickBestEvent)
Return type:: str

get_context_and_action_embeddings( event: PickBestEvent, ) → tuple[source]#

Parameters:: event (PickBestEvent)
Return type:: tuple

get_indexed_dot_product( context_emb: List, action_embs: List, ) → Dict[source]#

Parameters:

context_emb (List)
action_embs (List)

Return type:

Dict

get_label( event: PickBestEvent, ) → tuple[source]#

Parameters:: event (PickBestEvent)
Return type:: tuple