Merge pull request #10 from VowpalWabbit/dot_prods_auto_embed

Dot prods auto embed

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

@@ -15,6 +15,7 @@ from langchain.chains.rl_chain.base import (
 from langchain.chains.rl_chain.pick_best_chain import (
     PickBest,
     PickBestEvent,
+    PickBestFeatureEmbedder,
     PickBestSelected,
 )
 
@@ -37,6 +38,7 @@ __all__ = [
     "PickBest",
     "PickBestEvent",
     "PickBestSelected",
+    "PickBestFeatureEmbedder",
     "Embed",
     "BasedOn",
     "ToSelectFrom",

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

@@ -118,8 +118,7 @@ def get_based_on_and_to_select_from(inputs: Dict[str, Any]) -> Tuple[Dict, Dict]
 
     if not to_select_from:
         raise ValueError(
-            "No variables using 'ToSelectFrom' found in the inputs. \
+            "No variables using 'ToSelectFrom' found in the inputs. Please include at least one variable containing a list to select from."  # noqa: E501
-                Please include at least one variable containing a list to select from."
         )
 
     based_on = {
@@ -229,6 +228,9 @@ class VwPolicy(Policy):
 
 
 class Embedder(Generic[TEvent], ABC):
+    def __init__(self, *args: Any, **kwargs: Any):
+        pass
+
     @abstractmethod
     def format(self, event: TEvent) -> str:
         ...
@@ -300,9 +302,7 @@ class AutoSelectionScorer(SelectionScorer[Event], BaseModel):
             return resp
         except Exception as e:
             raise RuntimeError(
-                f"The auto selection scorer did not manage to score the response, \
+                f"The auto selection scorer did not manage to score the response, there is always the option to try again or tweak the reward prompt. Error: {e}"  # noqa: E501
-                    there is always the option to try again or tweak the reward prompt.\
-                         Error: {e}"
             )
 
 
@@ -316,7 +316,7 @@ class RLChain(Chain, Generic[TEvent]):
         - selection_scorer (Union[SelectionScorer, None]): Scorer for the selection. Can be set to None.
         - policy (Optional[Policy]): The policy used by the chain to learn to populate a dynamic prompt.
         - auto_embed (bool): Determines if embedding should be automatic. Default is False.
-        - metrics (Optional[MetricsTracker]): Tracker for metrics, can be set to None.
+        - metrics (Optional[Union[MetricsTrackerRollingWindow, MetricsTrackerAverage]]): Tracker for metrics, can be set to None.
 
     Initialization Attributes:
         - feature_embedder (Embedder): Embedder used for the `BasedOn` and `ToSelectFrom` inputs.
@@ -325,7 +325,8 @@ class RLChain(Chain, Generic[TEvent]):
         - vw_cmd (List[str], optional): Command line arguments for the VW model.
         - 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.
+        - metrics_step (int): Step for the metrics tracker. Default is -1. If set without metrics_window_size, average metrics will be tracked, otherwise rolling window metrics will be tracked.
+        - metrics_window_size (int): Window size for the metrics tracker. Default is -1. If set, rolling window metrics will be tracked.
 
     Notes:
         The class initializes the VW model using the provided arguments. If `selection_scorer` is not provided, a warning is logged, indicating that no reinforcement learning will occur unless the `update_with_delayed_score` method is called.
@@ -423,8 +424,7 @@ class RLChain(Chain, Generic[TEvent]):
         """  # noqa: E501
         if self._can_use_selection_scorer() and not force_score:
             raise RuntimeError(
-                "The selection scorer is set, and force_score was not set to True. \
+                "The selection scorer is set, and force_score was not set to True. Please set force_score=True to use this function."  # noqa: E501
-                    Please set force_score=True to use this function."
             )
         if self.metrics:
             self.metrics.on_feedback(score)
@@ -458,9 +458,7 @@ class RLChain(Chain, Generic[TEvent]):
             or self.selected_based_on_input_key in inputs.keys()
         ):
             raise ValueError(
-                f"The rl chain does not accept '{self.selected_input_key}' \
+                f"The rl chain does not accept '{self.selected_input_key}' or '{self.selected_based_on_input_key}' as input keys, they are reserved for internal use during auto reward."  # noqa: E501
-                    or '{self.selected_based_on_input_key}' as input keys, \
-                        they are reserved for internal use during auto reward."
             )
 
     def _can_use_selection_scorer(self) -> bool:
@@ -498,9 +496,6 @@ class RLChain(Chain, Generic[TEvent]):
     ) -> Dict[str, Any]:
         _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
 
-        if self.auto_embed:
-            inputs = prepare_inputs_for_autoembed(inputs=inputs)
-
         event: TEvent = self._call_before_predict(inputs=inputs)
         prediction = self.active_policy.predict(event=event)
         if self.metrics:
@@ -573,8 +568,7 @@ def embed_string_type(
 
     if namespace is None:
         raise ValueError(
-            "The default namespace must be \
+            "The default namespace must be provided when embedding a string or _Embed object."  # noqa: E501
-                provided when embedding a string or _Embed object."
         )
 
     return {namespace: keep_str + encoded}

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

@@ -53,21 +53,24 @@ class PickBestFeatureEmbedder(base.Embedder[PickBestEvent]):
         model name (Any, optional): The type of embeddings to be used for feature representation. Defaults to BERT SentenceTransformer.
     """  # noqa E501
 
-    def __init__(self, model: Optional[Any] = None, *args: Any, **kwargs: Any):
+    def __init__(
+        self, auto_embed: bool, model: Optional[Any] = None, *args: Any, **kwargs: Any
+    ):
         super().__init__(*args, **kwargs)
 
         if model is None:
             from sentence_transformers import SentenceTransformer
 
-            model = SentenceTransformer("bert-base-nli-mean-tokens")
+            model = SentenceTransformer("all-mpnet-base-v2")
 
         self.model = model
+        self.auto_embed = auto_embed
 
-    def format(self, event: PickBestEvent) -> str:
+    @staticmethod
-        """
+    def _str(embedding: List[float]) -> str:
-        Converts the `BasedOn` and `ToSelectFrom` into a format that can be used by VW
+        return " ".join([f"{i}:{e}" for i, e in enumerate(embedding)])
-        """
 
+    def get_label(self, event: PickBestEvent) -> tuple:
         cost = None
         if event.selected:
             chosen_action = event.selected.index
@@ -77,7 +80,11 @@ class PickBestFeatureEmbedder(base.Embedder[PickBestEvent]):
                 else None
             )
             prob = event.selected.probability
+            return chosen_action, cost, prob
+        else:
+            return None, None, None
 
+    def get_context_and_action_embeddings(self, event: PickBestEvent) -> tuple:
         context_emb = base.embed(event.based_on, self.model) if event.based_on else None
         to_select_from_var_name, to_select_from = next(
             iter(event.to_select_from.items()), (None, None)
@@ -97,6 +104,95 @@ class PickBestFeatureEmbedder(base.Embedder[PickBestEvent]):
             raise ValueError(
                 "Context and to_select_from must be provided in the inputs dictionary"
             )
+        return context_emb, action_embs
+
+    def get_indexed_dot_product(self, context_emb: List, action_embs: List) -> Dict:
+        import numpy as np
+
+        unique_contexts = set()
+        for context_item in context_emb:
+            for ns, ee in context_item.items():
+                if isinstance(ee, list):
+                    for ea in ee:
+                        unique_contexts.add(f"{ns}={ea}")
+                else:
+                    unique_contexts.add(f"{ns}={ee}")
+
+        encoded_contexts = self.model.encode(list(unique_contexts))
+        context_embeddings = dict(zip(unique_contexts, encoded_contexts))
+
+        unique_actions = set()
+        for action in action_embs:
+            for ns, e in action.items():
+                if isinstance(e, list):
+                    for ea in e:
+                        unique_actions.add(f"{ns}={ea}")
+                else:
+                    unique_actions.add(f"{ns}={e}")
+
+        encoded_actions = self.model.encode(list(unique_actions))
+        action_embeddings = dict(zip(unique_actions, encoded_actions))
+
+        action_matrix = np.stack([v for k, v in action_embeddings.items()])
+        context_matrix = np.stack([v for k, v in context_embeddings.items()])
+        dot_product_matrix = np.dot(context_matrix, action_matrix.T)
+
+        indexed_dot_product: Dict = {}
+
+        for i, context_key in enumerate(context_embeddings.keys()):
+            indexed_dot_product[context_key] = {}
+            for j, action_key in enumerate(action_embeddings.keys()):
+                indexed_dot_product[context_key][action_key] = dot_product_matrix[i, j]
+
+        return indexed_dot_product
+
+    def format_auto_embed_on(self, event: PickBestEvent) -> str:
+        chosen_action, cost, prob = self.get_label(event)
+        context_emb, action_embs = self.get_context_and_action_embeddings(event)
+        indexed_dot_product = self.get_indexed_dot_product(context_emb, action_embs)
+
+        action_lines = []
+        for i, action in enumerate(action_embs):
+            line_parts = []
+            dot_prods = []
+            if cost is not None and chosen_action == i:
+                line_parts.append(f"{chosen_action}:{cost}:{prob}")
+            for ns, action in action.items():
+                line_parts.append(f"|{ns}")
+                elements = action if isinstance(action, list) else [action]
+                nsa = []
+                for elem in elements:
+                    line_parts.append(f"{elem}")
+                    ns_a = f"{ns}={elem}"
+                    nsa.append(ns_a)
+                    for k, v in indexed_dot_product.items():
+                        dot_prods.append(v[ns_a])
+                nsa_str = " ".join(nsa)
+                line_parts.append(f"|# {nsa_str}")
+
+            line_parts.append(f"|dotprod {self._str(dot_prods)}")
+            action_lines.append(" ".join(line_parts))
+
+        shared = []
+        for item in context_emb:
+            for ns, context in item.items():
+                shared.append(f"|{ns}")
+                elements = context if isinstance(context, list) else [context]
+                nsc = []
+                for elem in elements:
+                    shared.append(f"{elem}")
+                    nsc.append(f"{ns}={elem}")
+                nsc_str = " ".join(nsc)
+                shared.append(f"|@ {nsc_str}")
+
+        return "shared " + " ".join(shared) + "\n" + "\n".join(action_lines)
+
+    def format_auto_embed_off(self, event: PickBestEvent) -> str:
+        """
+        Converts the `BasedOn` and `ToSelectFrom` into a format that can be used by VW
+        """
+        chosen_action, cost, prob = self.get_label(event)
+        context_emb, action_embs = self.get_context_and_action_embeddings(event)
 
         example_string = ""
         example_string += "shared "
@@ -120,6 +216,12 @@ class PickBestFeatureEmbedder(base.Embedder[PickBestEvent]):
         # Strip the last newline
         return example_string[:-1]
 
+    def format(self, event: PickBestEvent) -> str:
+        if self.auto_embed:
+            return self.format_auto_embed_on(event)
+        else:
+            return self.format_auto_embed_off(event)
+
 
 class PickBest(base.RLChain[PickBestEvent]):
     """
@@ -154,26 +256,42 @@ class PickBest(base.RLChain[PickBestEvent]):
         *args: Any,
         **kwargs: Any,
     ):
-        vw_cmd = kwargs.get("vw_cmd", [])
+        auto_embed = kwargs.get("auto_embed", False)
-        if not vw_cmd:
+
-            vw_cmd = [
+        feature_embedder = kwargs.get("feature_embedder", None)
-                "--cb_explore_adf",
+        if feature_embedder:
-                "--quiet",
+            if "auto_embed" in kwargs:
-                "--interactions=::",
+                logger.warning(
-                "--coin",
+                    "auto_embed will take no effect when explicit feature_embedder is provided"  # noqa E501
-                "--squarecb",
+                )
-            ]
+            # turning auto_embed off for cli setting below
+            auto_embed = False
         else:
+            feature_embedder = PickBestFeatureEmbedder(auto_embed=auto_embed)
+        kwargs["feature_embedder"] = feature_embedder
+
+        vw_cmd = kwargs.get("vw_cmd", [])
+        if vw_cmd:
             if "--cb_explore_adf" not in vw_cmd:
                 raise ValueError(
                     "If vw_cmd is specified, it must include --cb_explore_adf"
                 )
-        kwargs["vw_cmd"] = vw_cmd
+        else:
+            interactions = ["--interactions=::"]
+            if auto_embed:
+                interactions = [
+                    "--interactions=@#",
+                    "--ignore_linear=@",
+                    "--ignore_linear=#",
+                ]
+            vw_cmd = interactions + [
+                "--cb_explore_adf",
+                "--coin",
+                "--squarecb",
+                "--quiet",
+            ]
 
-        feature_embedder = kwargs.get("feature_embedder", None)
+        kwargs["vw_cmd"] = vw_cmd
-        if not feature_embedder:
-            feature_embedder = PickBestFeatureEmbedder()
-        kwargs["feature_embedder"] = feature_embedder
 
         super().__init__(*args, **kwargs)
 
@@ -181,23 +299,17 @@ class PickBest(base.RLChain[PickBestEvent]):
         context, actions = base.get_based_on_and_to_select_from(inputs=inputs)
         if not actions:
             raise ValueError(
-                "No variables using 'ToSelectFrom' found in the inputs. \
+                "No variables using 'ToSelectFrom' found in the inputs. Please include at least one variable containing a list to select from."  # noqa E501
-                    Please include at least one variable containing \
-                        a list to select from."
             )
 
         if len(list(actions.values())) > 1:
             raise ValueError(
-                "Only one variable using 'ToSelectFrom' can be provided in the inputs \
+                "Only one variable using 'ToSelectFrom' can be provided in the inputs for the PickBest chain. Please provide only one variable containing a list to select from."  # noqa E501
-                    for the PickBest chain. Please provide only one variable \
-                        containing a list to select from."
             )
 
         if not context:
             raise ValueError(
-                "No variables using 'BasedOn' found in the inputs. \
+                "No variables using 'BasedOn' found in the inputs. Please include at least one variable containing information to base the selected of ToSelectFrom on."  # noqa E501
-                    Please include at least one variable containing information \
-                        to base the selected of ToSelectFrom on."
             )
 
         event = PickBestEvent(inputs=inputs, to_select_from=actions, based_on=context)

libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict
 
 import pytest
-from test_utils import MockEncoder
+from test_utils import MockEncoder, MockEncoderReturnsList
 
 import langchain.chains.rl_chain.base as rl_chain
 import langchain.chains.rl_chain.pick_best_chain as pick_best_chain
@@ -26,7 +26,9 @@ def test_multiple_ToSelectFrom_throws() -> None:
     chain = pick_best_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     actions = ["0", "1", "2"]
     with pytest.raises(ValueError):
@@ -43,7 +45,9 @@ def test_missing_basedOn_from_throws() -> None:
     chain = pick_best_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     actions = ["0", "1", "2"]
     with pytest.raises(ValueError):
@@ -56,7 +60,9 @@ def test_ToSelectFrom_not_a_list_throws() -> None:
     chain = pick_best_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     actions = {"actions": ["0", "1", "2"]}
     with pytest.raises(ValueError):
@@ -75,7 +81,9 @@ def test_update_with_delayed_score_with_auto_validator_throws() -> None:
         llm=llm,
         prompt=PROMPT,
         selection_scorer=rl_chain.AutoSelectionScorer(llm=auto_val_llm),
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     actions = ["0", "1", "2"]
     response = chain.run(
@@ -98,7 +106,9 @@ def test_update_with_delayed_score_force() -> None:
         llm=llm,
         prompt=PROMPT,
         selection_scorer=rl_chain.AutoSelectionScorer(llm=auto_val_llm),
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     actions = ["0", "1", "2"]
     response = chain.run(
@@ -121,7 +131,9 @@ def test_update_with_delayed_score() -> None:
         llm=llm,
         prompt=PROMPT,
         selection_scorer=None,
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     actions = ["0", "1", "2"]
     response = chain.run(
@@ -153,7 +165,9 @@ def test_user_defined_scorer() -> None:
         llm=llm,
         prompt=PROMPT,
         selection_scorer=CustomSelectionScorer(),
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     actions = ["0", "1", "2"]
     response = chain.run(
@@ -166,11 +180,13 @@ def test_user_defined_scorer() -> None:
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
-def test_auto_embeddings_on() -> None:
+def test_everything_embedded() -> None:
     llm, PROMPT = setup()
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     chain = pick_best_chain.PickBest.from_llm(
-        llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=True
+        llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=False
     )
 
     str1 = "0"
@@ -189,8 +205,8 @@ def test_auto_embeddings_on() -> None:
     actions = [str1, str2, str3]
 
     response = chain.run(
-        User=rl_chain.BasedOn(ctx_str_1),
+        User=rl_chain.EmbedAndKeep(rl_chain.BasedOn(ctx_str_1)),
-        action=rl_chain.ToSelectFrom(actions),
+        action=rl_chain.EmbedAndKeep(rl_chain.ToSelectFrom(actions)),
     )
     selection_metadata = response["selection_metadata"]
     vw_str = feature_embedder.format(selection_metadata)
@@ -200,7 +216,9 @@ def test_auto_embeddings_on() -> None:
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_default_auto_embedder_is_off() -> None:
     llm, PROMPT = setup()
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     chain = pick_best_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, feature_embedder=feature_embedder
     )
@@ -224,9 +242,11 @@ def test_default_auto_embedder_is_off() -> None:
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
-def test_default_embeddings_off() -> None:
+def test_default_w_embeddings_off() -> None:
     llm, PROMPT = setup()
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     chain = pick_best_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=False
     )
@@ -250,29 +270,54 @@ def test_default_embeddings_off() -> None:
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
-def test_default_embeddings_mixed_w_explicit_user_embeddings() -> None:
+def test_default_w_embeddings_on() -> None:
     llm, PROMPT = setup()
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=True, model=MockEncoderReturnsList()
+    )
     chain = pick_best_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=True
     )
 
     str1 = "0"
     str2 = "1"
-    str3 = "2"
+    ctx_str_1 = "context1"
-    encoded_str1 = rl_chain.stringify_embedding(list(encoded_keyword + str1))
+    dot_prod = "dotprod 0:5.0"  # dot prod of [1.0, 2.0] and [1.0, 2.0]
-    encoded_str2 = rl_chain.stringify_embedding(list(encoded_keyword + str2))
-    encoded_str3 = rl_chain.stringify_embedding(list(encoded_keyword + str3))
 
+    expected = f"""shared |User {ctx_str_1} |@ User={ctx_str_1}\n|action {str1} |# action={str1} |{dot_prod}\n|action {str2} |# action={str2} |{dot_prod}"""  # noqa
+
+    actions = [str1, str2]
+
+    response = chain.run(
+        User=rl_chain.BasedOn(ctx_str_1),
+        action=rl_chain.ToSelectFrom(actions),
+    )
+    selection_metadata = response["selection_metadata"]
+    vw_str = feature_embedder.format(selection_metadata)
+    assert vw_str == expected
+
+
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
+def test_default_embeddings_mixed_w_explicit_user_embeddings() -> None:
+    llm, PROMPT = setup()
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=True, model=MockEncoderReturnsList()
+    )
+    chain = pick_best_chain.PickBest.from_llm(
+        llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=True
+    )
+
+    str1 = "0"
+    str2 = "1"
+    encoded_str2 = rl_chain.stringify_embedding([1.0, 2.0])
     ctx_str_1 = "context1"
     ctx_str_2 = "context2"
+    encoded_ctx_str_1 = rl_chain.stringify_embedding([1.0, 2.0])
+    dot_prod = "dotprod 0:5.0 1:5.0"  # dot prod of [1.0, 2.0] and [1.0, 2.0]
 
-    encoded_ctx_str_1 = rl_chain.stringify_embedding(list(encoded_keyword + ctx_str_1))
+    expected = f"""shared |User {encoded_ctx_str_1} |@ User={encoded_ctx_str_1} |User2 {ctx_str_2} |@ User2={ctx_str_2}\n|action {str1} |# action={str1} |{dot_prod}\n|action {encoded_str2} |# action={encoded_str2} |{dot_prod}"""  # noqa
-    encoded_ctx_str_2 = rl_chain.stringify_embedding(list(encoded_keyword + ctx_str_2))
 
-    expected = f"""shared |User {encoded_ctx_str_1} |User2 {ctx_str_2 + " " + encoded_ctx_str_2} \n|action {str1 + " " + encoded_str1} \n|action {str2 + " " + encoded_str2} \n|action {encoded_str3} """  # noqa
+    actions = [str1, rl_chain.Embed(str2)]
-
-    actions = [str1, str2, rl_chain.Embed(str3)]
 
     response = chain.run(
         User=rl_chain.BasedOn(rl_chain.Embed(ctx_str_1)),
@@ -291,7 +336,9 @@ def test_default_no_scorer_specified() -> None:
     chain = pick_best_chain.PickBest.from_llm(
         llm=chain_llm,
         prompt=PROMPT,
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     response = chain.run(
         User=rl_chain.BasedOn("Context"),
@@ -310,7 +357,9 @@ def test_explicitly_no_scorer() -> None:
         llm=llm,
         prompt=PROMPT,
         selection_scorer=None,
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     response = chain.run(
         User=rl_chain.BasedOn("Context"),
@@ -330,7 +379,9 @@ def test_auto_scorer_with_user_defined_llm() -> None:
         llm=llm,
         prompt=PROMPT,
         selection_scorer=rl_chain.AutoSelectionScorer(llm=scorer_llm),
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     response = chain.run(
         User=rl_chain.BasedOn("Context"),
@@ -348,7 +399,9 @@ def test_calling_chain_w_reserved_inputs_throws() -> None:
     chain = pick_best_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     with pytest.raises(ValueError):
         chain.run(
@@ -371,7 +424,9 @@ def test_activate_and_deactivate_scorer() -> None:
         llm=llm,
         prompt=PROMPT,
         selection_scorer=pick_best_chain.base.AutoSelectionScorer(llm=scorer_llm),
-        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(
+            auto_embed=False, model=MockEncoder()
+        ),
     )
     response = chain.run(
         User=pick_best_chain.base.BasedOn("Context"),

libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_text_embedder.py

@@ -9,7 +9,9 @@ encoded_keyword = "[encoded]"
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_missing_context_throws() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     named_action = {"action": ["0", "1", "2"]}
     event = pick_best_chain.PickBestEvent(
         inputs={}, to_select_from=named_action, based_on={}
@@ -20,7 +22,9 @@ def test_pickbest_textembedder_missing_context_throws() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_missing_actions_throws() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     event = pick_best_chain.PickBestEvent(
         inputs={}, to_select_from={}, based_on={"context": "context"}
     )
@@ -30,7 +34,9 @@ def test_pickbest_textembedder_missing_actions_throws() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_no_label_no_emb() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     named_actions = {"action1": ["0", "1", "2"]}
     expected = """shared |context context \n|action1 0 \n|action1 1 \n|action1 2 """
     event = pick_best_chain.PickBestEvent(
@@ -42,7 +48,9 @@ def test_pickbest_textembedder_no_label_no_emb() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_w_label_no_score_no_emb() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     named_actions = {"action1": ["0", "1", "2"]}
     expected = """shared |context context \n|action1 0 \n|action1 1 \n|action1 2 """
     selected = pick_best_chain.PickBestSelected(index=0, probability=1.0)
@@ -58,7 +66,9 @@ def test_pickbest_textembedder_w_label_no_score_no_emb() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_w_full_label_no_emb() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     named_actions = {"action1": ["0", "1", "2"]}
     expected = (
         """shared |context context \n0:-0.0:1.0 |action1 0 \n|action1 1 \n|action1 2 """
@@ -76,7 +86,9 @@ def test_pickbest_textembedder_w_full_label_no_emb() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_w_full_label_w_emb() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     str1 = "0"
     str2 = "1"
     str3 = "2"
@@ -100,7 +112,9 @@ def test_pickbest_textembedder_w_full_label_w_emb() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_w_full_label_w_embed_and_keep() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     str1 = "0"
     str2 = "1"
     str3 = "2"
@@ -124,7 +138,9 @@ def test_pickbest_textembedder_w_full_label_w_embed_and_keep() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_no_label_no_emb() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     named_actions = {"action1": [{"a": "0", "b": "0"}, "1", "2"]}
     context = {"context1": "context1", "context2": "context2"}
     expected = """shared |context1 context1 |context2 context2 \n|a 0 |b 0 \n|action1 1 \n|action1 2 """  # noqa: E501
@@ -137,7 +153,9 @@ def test_pickbest_textembedder_more_namespaces_no_label_no_emb() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_label_no_emb() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     named_actions = {"action1": [{"a": "0", "b": "0"}, "1", "2"]}
     context = {"context1": "context1", "context2": "context2"}
     expected = """shared |context1 context1 |context2 context2 \n|a 0 |b 0 \n|action1 1 \n|action1 2 """  # noqa: E501
@@ -151,7 +169,9 @@ def test_pickbest_textembedder_more_namespaces_w_label_no_emb() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_full_label_no_emb() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     named_actions = {"action1": [{"a": "0", "b": "0"}, "1", "2"]}
     context = {"context1": "context1", "context2": "context2"}
     expected = """shared |context1 context1 |context2 context2 \n0:-0.0:1.0 |a 0 |b 0 \n|action1 1 \n|action1 2 """  # noqa: E501
@@ -165,7 +185,9 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_no_emb() -> None:
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
 
     str1 = "0"
     str2 = "1"
@@ -198,7 +220,9 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb() -> None
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_keep() -> (
     None
 ):
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
 
     str1 = "0"
     str2 = "1"
@@ -231,7 +255,9 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_kee
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
 
     str1 = "0"
     str2 = "1"
@@ -263,7 +289,9 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb() -> N
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emakeep() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
 
     str1 = "0"
     str2 = "1"
@@ -298,7 +326,9 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emakeep()
 
 @pytest.mark.requires("vowpal_wabbit_next")
 def test_raw_features_underscored() -> None:
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(
+        auto_embed=False, model=MockEncoder()
+    )
     str1 = "this is a long string"
     str1_underscored = str1.replace(" ", "_")
     encoded_str1 = rl_chain.stringify_embedding(list(encoded_keyword + str1))

libs/langchain/tests/unit_tests/chains/rl_chain/test_utils.py

@@ -1,3 +1,15 @@
+from typing import Any, List
+
+
 class MockEncoder:
     def encode(self, to_encode: str) -> str:
         return "[encoded]" + to_encode
+
+
+class MockEncoderReturnsList:
+    def encode(self, to_encode: Any) -> List:
+        if isinstance(to_encode, str):
+            return [1.0, 2.0]
+        elif isinstance(to_encode, List):
+            return [[1.0, 2.0] for _ in range(len(to_encode))]
+        raise ValueError("Invalid input type for unit test")