fix all mypy errors

libs/langchain/langchain/chains/rl_chain/base.py

@@ -161,6 +161,9 @@ TEvent = TypeVar("TEvent", bound=Event)
 
 
 class Policy(ABC):
+    def __init__(self, **kwargs: Any):
+        pass
+
     @abstractmethod
     def predict(self, event: TEvent) -> Any:
         ...
@@ -233,7 +236,7 @@ class SelectionScorer(ABC, BaseModel):
 
 
 class AutoSelectionScorer(SelectionScorer, BaseModel):
-    llm_chain: Union[LLMChain, None] = None
+    llm_chain: LLMChain
     prompt: Union[BasePromptTemplate, None] = None
     scoring_criteria_template_str: Optional[str] = None
 
@@ -309,7 +312,7 @@ class RLChain(Generic[TEvent], Chain):
         - model_save_dir (str, optional): Directory for saving the VW model. Default is the current directory.
         - reset_model (bool): If set to True, the model starts training from scratch. Default is False.
         - vw_cmd (List[str], optional): Command line arguments for the VW model.
-        - policy (VwPolicy): Policy used by the chain.
+        - policy (Type[VwPolicy]): Policy used by the chain.
         - vw_logs (Optional[Union[str, os.PathLike]]): Path for the VW logs.
         - metrics_step (int): Step for the metrics tracker. Default is -1.
 
@@ -322,7 +325,7 @@ class RLChain(Generic[TEvent], Chain):
     output_key: str = "result"  #: :meta private:
     prompt: BasePromptTemplate
     selection_scorer: Union[SelectionScorer, None]
-    policy: Policy
+    active_policy: Policy
     auto_embed: bool = True
     selected_input_key = "rl_chain_selected"
     selected_based_on_input_key = "rl_chain_selected_based_on"
@@ -347,7 +350,9 @@ class RLChain(Generic[TEvent], Chain):
                     reinforcement learning will be done in the RL chain \
                         unless update_with_delayed_score is called."
             )
-        self.policy = policy(
+
+        if self.active_policy is None:
+            self.active_policy = policy(
                 model_repo=ModelRepository(
                     model_save_dir, with_history=True, reset=reset_model
                 ),
@@ -355,6 +360,7 @@ class RLChain(Generic[TEvent], Chain):
                 feature_embedder=feature_embedder,
                 vw_logger=VwLogger(vw_logs),
             )
+
         self.metrics = MetricsTracker(step=metrics_step)
 
     class Config:
@@ -427,8 +433,8 @@ class RLChain(Generic[TEvent], Chain):
         if self.metrics:
             self.metrics.on_feedback(score)
         self._call_after_scoring_before_learning(event=event, score=score)
-        self.policy.learn(event=event)
-        self.policy.log(event=event)
+        self.active_policy.learn(event=event)
+        self.active_policy.log(event=event)
 
     def set_auto_embed(self, auto_embed: bool) -> None:
         """
@@ -447,7 +453,7 @@ class RLChain(Generic[TEvent], Chain):
             inputs = prepare_inputs_for_autoembed(inputs=inputs)
 
         event: TEvent = self._call_before_predict(inputs=inputs)
-        prediction = self.policy.predict(event=event)
+        prediction = self.active_policy.predict(event=event)
         if self.metrics:
             self.metrics.on_decision()
 
@@ -484,8 +490,8 @@ class RLChain(Generic[TEvent], Chain):
         if self.metrics:
             self.metrics.on_feedback(score)
         event = self._call_after_scoring_before_learning(score=score, event=event)
-        self.policy.learn(event=event)
-        self.policy.log(event=event)
+        self.active_policy.learn(event=event)
+        self.active_policy.log(event=event)
 
         return {self.output_key: {"response": output, "selection_metadata": event}}
 
@@ -493,7 +499,7 @@ class RLChain(Generic[TEvent], Chain):
         """
         This function should be called to save the state of the learned policy model.
         """
-        self.policy.save()
+        self.active_policy.save()
 
     @property
     def _chain_type(self) -> str:
@@ -509,7 +515,7 @@ def is_stringtype_instance(item: Any) -> bool:
 
 def embed_string_type(
     item: Union[str, _Embed], model: Any, namespace: Optional[str] = None
-) -> Dict[str, str]:
+) -> Dict[str, Union[str, List[str]]]:
     """Helper function to embed a string or an _Embed object."""
     join_char = ""
     keep_str = ""
@@ -533,9 +539,9 @@ def embed_string_type(
     return {namespace: keep_str + join_char.join(map(str, encoded))}
 
 
-def embed_dict_type(item: Dict, model: Any) -> Dict[str, Union[str, List[str]]]:
+def embed_dict_type(item: Dict, model: Any) -> Dict[str, Any]:
     """Helper function to embed a dictionary item."""
-    inner_dict: Dict[str, Union[str, List[str]]] = {}
+    inner_dict: Dict[str, Any] = {}
     for ns, embed_item in item.items():
         if isinstance(embed_item, list):
             inner_dict[ns] = []
@@ -560,9 +566,7 @@ def embed_list_type(
 
 
 def embed(
-    to_embed: Union[
-        Union(str, _Embed(str)), Dict, List[Union(str, _Embed(str))], List[Dict]
-    ],
+    to_embed: Union[Union[str, _Embed], Dict, List[Union[str, _Embed]], List[Dict]],
     model: Any,
     namespace: Optional[str] = None,
 ) -> List[Dict[str, Union[str, List[str]]]]:

libs/langchain/langchain/chains/rl_chain/pick_best_chain.py

@@ -54,11 +54,16 @@ class PickBestFeatureEmbedder(base.Embedder[PickBest.Event]):
         to_select_from_var_name, to_select_from = next(
             iter(event.to_select_from.items()), (None, None)
         )
+
         action_embs = (
+            (
                 base.embed(to_select_from, self.model, to_select_from_var_name)
                 if event.to_select_from
                 else None
             )
+            if to_select_from
+            else None
+        )
 
         if not context_emb or not action_embs:
             raise ValueError(