no errors in pick best chain

libs/langchain/langchain/chains/rl_chain/__init__.py

@@ -13,7 +13,7 @@ from langchain.chains.rl_chain.base import (
 from langchain.chains.rl_chain.pick_best_chain import PickBest
 
 
-def configure_logger():
+def configure_logger() -> None:
     logger = logging.getLogger(__name__)
     logger.setLevel(logging.INFO)
     ch = logging.StreamHandler()

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

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
 import langchain.chains.rl_chain.base as base
 from langchain.base_language import BaseLanguageModel
@@ -145,7 +145,6 @@ class PickBest(base.RLChain[PickBest.Event]):
 
     def __init__(
         self,
-        feature_embedder: Optional[PickBestFeatureEmbedder] = None,
         *args: Any,
         **kwargs: Any,
     ):
@@ -163,12 +162,14 @@ class PickBest(base.RLChain[PickBest.Event]):
                 raise ValueError(
                     "If vw_cmd is specified, it must include --cb_explore_adf"
                 )
-
         kwargs["vw_cmd"] = vw_cmd
+
+        feature_embedder = kwargs.get("feature_embedder", None)
         if not feature_embedder:
             feature_embedder = PickBestFeatureEmbedder()
+        kwargs["feature_embedder"] = feature_embedder
 
-        super().__init__(feature_embedder=feature_embedder, *args, **kwargs)
+        super().__init__(*args, **kwargs)
 
     def _call_before_predict(self, inputs: Dict[str, Any]) -> Event:
         context, actions = base.get_based_on_and_to_select_from(inputs=inputs)
@@ -223,10 +224,15 @@ class PickBest(base.RLChain[PickBest.Event]):
         next_chain_inputs = event.inputs.copy()
         # only one key, value pair in event.to_select_from
         value = next(iter(event.to_select_from.values()))
+        v = (
+            value[event.selected.index]
+            if event.selected
+            else event.to_select_from.values()
+        )
         next_chain_inputs.update(
             {
                 self.selected_based_on_input_key: str(event.based_on),
-                self.selected_input_key: value[event.selected.index],
+                self.selected_input_key: v,
             }
         )
         return next_chain_inputs, event
@@ -234,7 +240,8 @@ class PickBest(base.RLChain[PickBest.Event]):
     def _call_after_scoring_before_learning(
         self, event: Event, score: Optional[float]
     ) -> Event:
-        event.selected.score = score
+        if event.selected:
+            event.selected.score = score
         return event
 
     def _call(
@@ -248,33 +255,19 @@ class PickBest(base.RLChain[PickBest.Event]):
     def _chain_type(self) -> str:
         return "rl_chain_pick_best"
 
-    @classmethod
-    def from_chain(
-        cls,
-        llm_chain: Chain,
-        prompt: BasePromptTemplate,
-        selection_scorer=SENTINEL,
-        **kwargs: Any,
-    ):
-        if selection_scorer is SENTINEL:
-            selection_scorer = base.AutoSelectionScorer(llm=llm_chain.llm)
-        return PickBest(
-            llm_chain=llm_chain,
-            prompt=prompt,
-            selection_scorer=selection_scorer,
-            **kwargs,
-        )
-
     @classmethod
     def from_llm(
-        cls,
+        cls: Type[PickBest],
         llm: BaseLanguageModel,
         prompt: BasePromptTemplate,
-        selection_scorer=SENTINEL,
+        selection_scorer: Union[base.AutoSelectionScorer, object] = SENTINEL,
         **kwargs: Any,
-    ):
+    ) -> PickBest:
         llm_chain = LLMChain(llm=llm, prompt=prompt)
-        return PickBest.from_chain(
+        if selection_scorer is SENTINEL:
+            selection_scorer = base.AutoSelectionScorer(llm=llm_chain.llm)
+
+        return PickBest(
             llm_chain=llm_chain,
             prompt=prompt,
             selection_scorer=selection_scorer,