dot product of encodings as default auto_embed

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

@@ -229,6 +229,9 @@ class VwPolicy(Policy):
 
 
 class Embedder(Generic[TEvent], ABC):
+    def __init__(self, *args: Any, **kwargs: Any):
+        pass
+
     @abstractmethod
     def format(self, event: TEvent) -> str:
         ...
@@ -498,8 +501,8 @@ 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)
+        # 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)

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

@@ -53,21 +53,25 @@ 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")
+            # model = SentenceTransformer("all-MiniLM-L6-v2")
 
         self.model = model
+        self.auto_embed = auto_embed
 
-    def format(self, event: PickBestEvent) -> str:
-        """
-        Converts the `BasedOn` and `ToSelectFrom` into a format that can be used by VW
-        """
+    @staticmethod
+    def _str(embedding):
+        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 +81,11 @@ 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 +105,97 @@ 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 = {}
+
+        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 = " ".join(nsa)
+                line_parts.append(f"|# {nsa}")
+
+            line_parts.append(f"|embedding {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 = " ".join(nsc)
+                shared.append(f"|@ {nsc}")
+
+        r = "shared " + " ".join(shared) + "\n" + "\n".join(action_lines)
+        print(r)
+        return r
+
+    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 +219,12 @@ 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]):
     """
@@ -154,12 +259,20 @@ class PickBest(base.RLChain[PickBestEvent]):
         *args: Any,
         **kwargs: Any,
     ):
+        auto_embed = kwargs.get("auto_embed", False)
+
         vw_cmd = kwargs.get("vw_cmd", [])
         if not vw_cmd:
-            vw_cmd = [
+            interactions = ["--interactions=::"]
+            if auto_embed:
+                interactions = [
+                    "--interactions=@#",
+                    "--ignore_linear=@",
+                    "--ignore_linear=#",
+                    "--noconstant",
+                ]
+            vw_cmd = interactions + [
                 "--cb_explore_adf",
-                "--quiet",
-                "--interactions=::",
                 "--coin",
                 "--squarecb",
             ]
@@ -172,7 +285,7 @@ class PickBest(base.RLChain[PickBestEvent]):
 
         feature_embedder = kwargs.get("feature_embedder", None)
         if not feature_embedder:
-            feature_embedder = PickBestFeatureEmbedder()
+            feature_embedder = PickBestFeatureEmbedder(auto_embed=auto_embed)
         kwargs["feature_embedder"] = feature_embedder
 
         super().__init__(*args, **kwargs)

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

@@ -26,7 +26,7 @@ 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 +43,7 @@ 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 +56,7 @@ 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 +75,7 @@ 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 +98,7 @@ 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 +121,7 @@ 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 +153,7 @@ 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 +166,11 @@ 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 +189,8 @@ def test_auto_embeddings_on() -> None:
     actions = [str1, str2, str3]
 
     response = chain.run(
-        User=rl_chain.BasedOn(ctx_str_1),
-        action=rl_chain.ToSelectFrom(actions),
+        User=rl_chain.EmbedAndKeep(rl_chain.BasedOn(ctx_str_1)),
+        action=rl_chain.EmbedAndKeep(rl_chain.ToSelectFrom(actions)),
     )
     selection_metadata = response["selection_metadata"]
     vw_str = feature_embedder.format(selection_metadata)
@@ -200,7 +200,7 @@ 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
     )
@@ -226,7 +226,7 @@ def test_default_auto_embedder_is_off() -> None:
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_default_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
     )
@@ -252,7 +252,7 @@ def test_default_embeddings_off() -> None:
 @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(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=True, model=MockEncoder())
     chain = pick_best_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=True
     )
@@ -291,7 +291,7 @@ 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 +310,7 @@ 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 +330,7 @@ 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 +348,7 @@ 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 +371,7 @@ 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 +9,7 @@ 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 +20,7 @@ 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 +30,7 @@ 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 +42,7 @@ 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 +58,7 @@ 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 +76,7 @@ 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 +100,7 @@ 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 +124,7 @@ 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 +137,7 @@ 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 +151,7 @@ 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 +165,7 @@ 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 +198,7 @@ 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 +231,7 @@ 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 +263,7 @@ 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 +298,7 @@ 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))