fix lock, imports, deps, test w deps, typo, formatting

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

@@ -2,25 +2,22 @@ from __future__ import annotations
 
 import logging
 import os
-from typing import Any, Dict, List, Optional, Tuple, Union, Sequence
 from abc import ABC, abstractmethod
-
-import vowpal_wabbit_next as vw
-from langchain.chains.rl_chain.vw_logger import VwLogger
-from langchain.chains.rl_chain.model_repository import ModelRepository
-from langchain.chains.rl_chain.metrics import MetricsTracker
-from langchain.prompts import BasePromptTemplate
-
-from langchain.pydantic_v1 import Extra, BaseModel, root_validator
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
+from langchain.chains.rl_chain.metrics import MetricsTracker
+from langchain.chains.rl_chain.model_repository import ModelRepository
+from langchain.chains.rl_chain.vw_logger import VwLogger
 from langchain.prompts import (
+    BasePromptTemplate,
     ChatPromptTemplate,
-    SystemMessagePromptTemplate,
     HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
 )
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 
 logger = logging.getLogger(__name__)
 
@@ -87,7 +84,9 @@ def EmbedAndKeep(anything):
 # helper functions
 
 
-def parse_lines(parser: vw.TextFormatParser, input_str: str) -> List[vw.Example]:
+def parse_lines(parser: "vw.TextFormatParser", input_str: str) -> List["vw.Example"]:
+    import vowpal_wabbit_next as vw
+
     return [parser.parse_line(line) for line in input_str.split("\n")]
 
 
@@ -100,7 +99,8 @@ def get_based_on_and_to_select_from(inputs: Dict[str, Any]):
 
     if not to_select_from:
         raise ValueError(
-            "No variables using 'ToSelectFrom' found in the inputs. Please include at least one variable containing a list to select from."
+            "No variables using 'ToSelectFrom' found in the inputs. \
+                Please include at least one variable containing a list to select from."
         )
 
     based_on = {
@@ -173,14 +173,17 @@ class VwPolicy(Policy):
         self.vw_logger = vw_logger
 
     def predict(self, event: Event) -> Any:
+        import vowpal_wabbit_next as vw
+
         text_parser = vw.TextFormatParser(self.workspace)
         return self.workspace.predict_one(
             parse_lines(text_parser, self.feature_embedder.format(event))
         )
 
     def learn(self, event: Event):
-        vw_ex = self.feature_embedder.format(event)
+        import vowpal_wabbit_next as vw
 
+        vw_ex = self.feature_embedder.format(event)
         text_parser = vw.TextFormatParser(self.workspace)
         multi_ex = parse_lines(text_parser, vw_ex)
         self.workspace.learn_one(multi_ex)
@@ -216,7 +219,7 @@ class AutoSelectionScorer(SelectionScorer, BaseModel):
     @staticmethod
     def get_default_system_prompt() -> SystemMessagePromptTemplate:
         return SystemMessagePromptTemplate.from_template(
-            "PLEASE RESPOND ONLY WITH A SIGNLE FLOAT AND NO OTHER TEXT EXPLANATION\n You are a strict judge that is called on to rank a response based on given criteria.\
+            "PLEASE RESPOND ONLY WITH A SINGLE FLOAT AND NO OTHER TEXT EXPLANATION\n You are a strict judge that is called on to rank a response based on given criteria.\
                     You must respond with your ranking by providing a single float within the range [0, 1], 0 being very bad response and 1 being very good response."
         )
 

libs/langchain/langchain/chains/rl_chain/metrics.py

@@ -1,4 +1,3 @@
-import pandas as pd
 from typing import Optional
 
 
@@ -23,5 +22,7 @@ class MetricsTracker:
         if self._step > 0 and self._i % self._step == 0:
             self._history.append({"step": self._i, "score": self.score})
 
-    def to_pandas(self) -> pd.DataFrame:
+    def to_pandas(self) -> "pd.DataFrame":
+        import pandas as pd
+
         return pd.DataFrame(self._history)

libs/langchain/langchain/chains/rl_chain/model_repository.py

@@ -1,11 +1,10 @@
-from pathlib import Path
-import shutil
 import datetime
-import vowpal_wabbit_next as vw
-from typing import Union, Sequence
-import os
 import glob
 import logging
+import os
+import shutil
+from pathlib import Path
+from typing import Sequence, Union
 
 logger = logging.getLogger(__name__)
 
@@ -35,14 +34,18 @@ class ModelRepository:
     def has_history(self) -> bool:
         return len(glob.glob(str(self.folder / "model-????????-??????.vw"))) > 0
 
-    def save(self, workspace: vw.Workspace) -> None:
+    def save(self, workspace: "vw.Workspace") -> None:
+        import vowpal_wabbit_next as vw
+
         with open(self.model_path, "wb") as f:
             logger.info(f"storing rl_chain model in: {self.model_path}")
             f.write(workspace.serialize())
         if self.with_history:  # write history
             shutil.copyfile(self.model_path, self.folder / f"model-{self.get_tag()}.vw")
 
-    def load(self, commandline: Sequence[str]) -> vw.Workspace:
+    def load(self, commandline: Sequence[str]) -> "vw.Workspace":
+        import vowpal_wabbit_next as vw
+
         model_data = None
         if self.model_path.exists():
             with open(self.model_path, "rb") as f:

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

@@ -1,19 +1,15 @@
 from __future__ import annotations
 
-import langchain.chains.rl_chain.base as base
+import logging
+from typing import Any, Dict, List, Optional, Tuple
 
+import langchain.chains.rl_chain.base as base
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
-from sentence_transformers import SentenceTransformer
 from langchain.prompts import BasePromptTemplate
 
-import logging
-
 logger = logging.getLogger(__name__)
 
 # sentinel object used to distinguish between user didn't supply anything or user explicitly supplied None
@@ -23,7 +19,7 @@ SENTINEL = object()
 class PickBestFeatureEmbedder(base.Embedder):
     """
     Contextual Bandit Text Embedder class that embeds the based_on and to_select_from into a format that can be used by VW
-    
+
     Attributes:
         model name (Any, optional): The type of embeddings to be used for feature representation. Defaults to BERT SentenceTransformer.
     """
@@ -32,6 +28,8 @@ class PickBestFeatureEmbedder(base.Embedder):
         super().__init__(*args, **kwargs)
 
         if model is None:
+            from sentence_transformers import SentenceTransformer
+
             model = SentenceTransformer("bert-base-nli-mean-tokens")
 
         self.model = model
@@ -67,7 +65,7 @@ class PickBestFeatureEmbedder(base.Embedder):
             )
 
         example_string = ""
-        example_string += f"shared "
+        example_string += "shared "
         for context_item in context_emb:
             for ns, based_on in context_item.items():
                 example_string += f"|{ns} {' '.join(based_on) if isinstance(based_on, list) else based_on} "
@@ -190,6 +188,8 @@ class PickBest(base.RLChain):
     def _call_after_predict_before_llm(
         self, inputs: Dict[str, Any], event: Event, prediction: List[Tuple[int, float]]
     ) -> Tuple[Dict[str, Any], PickBest.Event]:
+        import numpy as np
+
         prob_sum = sum(prob for _, prob in prediction)
         probabilities = [prob / prob_sum for _, prob in prediction]
         ## sample from the pmf
@@ -237,7 +237,7 @@ class PickBest(base.RLChain):
         Attributes:
             inputs: (Dict, required) The inputs to the chain. The inputs must contain a input variables that are wrapped in BasedOn and ToSelectFrom. BasedOn is the based_on that will be used for selecting an ToSelectFrom action that will be passed to the LLM prompt.
             run_manager: (CallbackManagerForChainRun, optional) The callback manager to use for this run. If not provided, a default callback manager is used.
-            
+
         Returns:
             A dictionary containing:
                 - `response`: The response generated by the LLM (Language Model).

libs/langchain/pyproject.toml

@@ -338,6 +338,7 @@ extended_testing = [
  "xmltodict",
  "faiss-cpu",
  "openapi-schema-pydantic",
+ "vowpal-wabbit-next"
 ]
 
 [tool.ruff]

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

@@ -1,13 +1,15 @@
-import langchain.chains.rl_chain.pick_best_chain as pick_best_chain
-import langchain.chains.rl_chain.base as rl_chain
-from test_utils import MockEncoder
 import pytest
-from langchain.prompts.prompt import PromptTemplate
+from test_utils import MockEncoder
+
+import langchain.chains.rl_chain.base as rl_chain
+import langchain.chains.rl_chain.pick_best_chain as pick_best_chain
 from langchain.chat_models import FakeListChatModel
+from langchain.prompts.prompt import PromptTemplate
 
 encoded_text = "[ e n c o d e d ] "
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def setup():
     _PROMPT_TEMPLATE = """This is a dummy prompt that will be ignored by the fake llm"""
     PROMPT = PromptTemplate(input_variables=[], template=_PROMPT_TEMPLATE)
@@ -16,6 +18,7 @@ def setup():
     return llm, PROMPT
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_multiple_ToSelectFrom_throws():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
@@ -28,6 +31,7 @@ def test_multiple_ToSelectFrom_throws():
         )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_missing_basedOn_from_throws():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
@@ -36,6 +40,7 @@ def test_missing_basedOn_from_throws():
         chain.run(action=rl_chain.ToSelectFrom(actions))
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_ToSelectFrom_not_a_list_throws():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
@@ -47,6 +52,7 @@ def test_ToSelectFrom_not_a_list_throws():
         )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_update_with_delayed_score_with_auto_validator_throws():
     llm, PROMPT = setup()
     # this LLM returns a number so that the auto validator will return that
@@ -68,6 +74,7 @@ def test_update_with_delayed_score_with_auto_validator_throws():
         chain.update_with_delayed_score(event=selection_metadata, score=100)
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_update_with_delayed_score_force():
     llm, PROMPT = setup()
     # this LLM returns a number so that the auto validator will return that
@@ -91,6 +98,7 @@ def test_update_with_delayed_score_force():
     assert selection_metadata.selected.score == 100.0
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_update_with_delayed_score():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(
@@ -108,6 +116,7 @@ def test_update_with_delayed_score():
     assert selection_metadata.selected.score == 100.0
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_user_defined_scorer():
     llm, PROMPT = setup()
 
@@ -129,6 +138,7 @@ def test_user_defined_scorer():
     assert selection_metadata.selected.score == 200.0
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_default_embeddings():
     llm, PROMPT = setup()
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
@@ -162,6 +172,7 @@ def test_default_embeddings():
     assert vw_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_default_embeddings_off():
     llm, PROMPT = setup()
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
@@ -187,6 +198,7 @@ def test_default_embeddings_off():
     assert vw_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_default_embeddings_mixed_w_explicit_user_embeddings():
     llm, PROMPT = setup()
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
@@ -221,6 +233,7 @@ def test_default_embeddings_mixed_w_explicit_user_embeddings():
     assert vw_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_default_no_scorer_specified():
     _, PROMPT = setup()
     chain_llm = FakeListChatModel(responses=[100])
@@ -235,6 +248,7 @@ def test_default_no_scorer_specified():
     assert selection_metadata.selected.score == 100.0
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_explicitly_no_scorer():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(
@@ -250,6 +264,7 @@ def test_explicitly_no_scorer():
     assert selection_metadata.selected.score == None
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_auto_scorer_with_user_defined_llm():
     llm, PROMPT = setup()
     scorer_llm = FakeListChatModel(responses=[300])
@@ -268,15 +283,14 @@ def test_auto_scorer_with_user_defined_llm():
     assert selection_metadata.selected.score == 300.0
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_calling_chain_w_reserved_inputs_throws():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
     with pytest.raises(ValueError):
         chain.run(
             User=rl_chain.BasedOn("Context"),
-            rl_chain_selected_based_on=rl_chain.ToSelectFrom(
-                ["0", "1", "2"]
-            ),
+            rl_chain_selected_based_on=rl_chain.ToSelectFrom(["0", "1", "2"]),
         )
 
     with pytest.raises(ValueError):

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

@@ -1,12 +1,13 @@
-import langchain.chains.rl_chain.pick_best_chain as pick_best_chain
-import langchain.chains.rl_chain.base as rl_chain
+import pytest
 from test_utils import MockEncoder
 
-import pytest
+import langchain.chains.rl_chain.base as rl_chain
+import langchain.chains.rl_chain.pick_best_chain as pick_best_chain
 
 encoded_text = "[ e n c o d e d ] "
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_missing_context_throws():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_action = {"action": ["0", "1", "2"]}
@@ -17,6 +18,7 @@ def test_pickbest_textembedder_missing_context_throws():
         feature_embedder.format(event)
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_missing_actions_throws():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     event = pick_best_chain.PickBest.Event(
@@ -26,6 +28,7 @@ def test_pickbest_textembedder_missing_actions_throws():
         feature_embedder.format(event)
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_no_label_no_emb():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_actions = {"action1": ["0", "1", "2"]}
@@ -37,6 +40,7 @@ def test_pickbest_textembedder_no_label_no_emb():
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_w_label_no_score_no_emb():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_actions = {"action1": ["0", "1", "2"]}
@@ -52,6 +56,7 @@ def test_pickbest_textembedder_w_label_no_score_no_emb():
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_w_full_label_no_emb():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_actions = {"action1": ["0", "1", "2"]}
@@ -69,6 +74,7 @@ def test_pickbest_textembedder_w_full_label_no_emb():
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_w_full_label_w_emb():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     str1 = "0"
@@ -92,6 +98,7 @@ def test_pickbest_textembedder_w_full_label_w_emb():
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_w_full_label_w_embed_and_keep():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     str1 = "0"
@@ -115,6 +122,7 @@ def test_pickbest_textembedder_w_full_label_w_embed_and_keep():
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_no_label_no_emb():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_actions = {"action1": [{"a": "0", "b": "0"}, "1", "2"]}
@@ -127,6 +135,7 @@ def test_pickbest_textembedder_more_namespaces_no_label_no_emb():
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_label_no_emb():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_actions = {"action1": [{"a": "0", "b": "0"}, "1", "2"]}
@@ -140,6 +149,7 @@ def test_pickbest_textembedder_more_namespaces_w_label_no_emb():
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_full_label_no_emb():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_actions = {"action1": [{"a": "0", "b": "0"}, "1", "2"]}
@@ -153,6 +163,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_no_emb():
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
@@ -168,9 +179,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb():
     encoded_ctx_str_1 = encoded_text + " ".join(char for char in ctx_str_1)
     encoded_ctx_str_2 = encoded_text + " ".join(char for char in ctx_str_2)
 
-    named_actions = {
-        "action1": rl_chain.Embed([{"a": str1, "b": str1}, str2, str3])
-    }
+    named_actions = {"action1": rl_chain.Embed([{"a": str1, "b": str1}, str2, str3])}
     context = {
         "context1": rl_chain.Embed(ctx_str_1),
         "context2": rl_chain.Embed(ctx_str_2),
@@ -185,6 +194,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb():
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_keep():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
@@ -201,9 +211,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_kee
     encoded_ctx_str_2 = encoded_text + " ".join(char for char in ctx_str_2)
 
     named_actions = {
-        "action1": rl_chain.EmbedAndKeep(
-            [{"a": str1, "b": str1}, str2, str3]
-        )
+        "action1": rl_chain.EmbedAndKeep([{"a": str1, "b": str1}, str2, str3])
     }
     context = {
         "context1": rl_chain.EmbedAndKeep(ctx_str_1),
@@ -219,6 +227,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_kee
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
@@ -252,6 +261,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb():
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_embed_and_keep():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
@@ -288,6 +298,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_embed_and_
     assert vw_ex_str == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_raw_features_underscored():
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     str1 = "this is a long string"

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

@@ -1,16 +1,18 @@
-import langchain.chains.rl_chain.base as base
+import pytest
 from test_utils import MockEncoder
 
-import pytest
+import langchain.chains.rl_chain.base as base
 
 encoded_text = "[ e n c o d e d ] "
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_simple_context_str_no_emb():
     expected = [{"a_namespace": "test"}]
     assert base.embed("test", MockEncoder(), "a_namespace") == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_simple_context_str_w_emb():
     str1 = "test"
     encoded_str1 = " ".join(char for char in str1)
@@ -25,6 +27,7 @@ def test_simple_context_str_w_emb():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_simple_context_str_w_nested_emb():
     # nested embeddings, innermost wins
     str1 = "test"
@@ -42,11 +45,13 @@ def test_simple_context_str_w_nested_emb():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_context_w_namespace_no_emb():
     expected = [{"test_namespace": "test"}]
     assert base.embed({"test_namespace": "test"}, MockEncoder()) == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_context_w_namespace_w_emb():
     str1 = "test"
     encoded_str1 = " ".join(char for char in str1)
@@ -61,6 +66,7 @@ def test_context_w_namespace_w_emb():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_context_w_namespace_w_emb2():
     str1 = "test"
     encoded_str1 = " ".join(char for char in str1)
@@ -75,6 +81,7 @@ def test_context_w_namespace_w_emb2():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_context_w_namespace_w_some_emb():
     str1 = "test1"
     str2 = "test2"
@@ -103,6 +110,7 @@ def test_context_w_namespace_w_some_emb():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_simple_action_strlist_no_emb():
     str1 = "test1"
     str2 = "test2"
@@ -111,6 +119,7 @@ def test_simple_action_strlist_no_emb():
     assert base.embed([str1, str2, str3], MockEncoder(), "a_namespace") == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_simple_action_strlist_w_emb():
     str1 = "test1"
     str2 = "test2"
@@ -138,6 +147,7 @@ def test_simple_action_strlist_w_emb():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_simple_action_strlist_w_some_emb():
     str1 = "test1"
     str2 = "test2"
@@ -170,6 +180,7 @@ def test_simple_action_strlist_w_some_emb():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_action_w_namespace_no_emb():
     str1 = "test1"
     str2 = "test2"
@@ -192,6 +203,7 @@ def test_action_w_namespace_no_emb():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_action_w_namespace_w_emb():
     str1 = "test1"
     str2 = "test2"
@@ -233,6 +245,7 @@ def test_action_w_namespace_w_emb():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_action_w_namespace_w_emb2():
     str1 = "test1"
     str2 = "test2"
@@ -278,6 +291,7 @@ def test_action_w_namespace_w_emb2():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_action_w_namespace_w_some_emb():
     str1 = "test1"
     str2 = "test2"
@@ -318,6 +332,7 @@ def test_action_w_namespace_w_some_emb():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_action_w_namespace_w_emb_w_more_than_one_item_in_first_dict():
     str1 = "test1"
     str2 = "test2"
@@ -368,6 +383,7 @@ def test_action_w_namespace_w_emb_w_more_than_one_item_in_first_dict():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_one_namespace_w_list_of_features_no_emb():
     str1 = "test1"
     str2 = "test2"
@@ -375,6 +391,7 @@ def test_one_namespace_w_list_of_features_no_emb():
     assert base.embed({"test_namespace": [str1, str2]}, MockEncoder()) == expected
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_one_namespace_w_list_of_features_w_some_emb():
     str1 = "test1"
     str2 = "test2"
@@ -386,21 +403,25 @@ def test_one_namespace_w_list_of_features_w_some_emb():
     )
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_nested_list_features_throws():
     with pytest.raises(ValueError):
         base.embed({"test_namespace": [[1, 2], [3, 4]]}, MockEncoder())
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_dict_in_list_throws():
     with pytest.raises(ValueError):
         base.embed({"test_namespace": [{"a": 1}, {"b": 2}]}, MockEncoder())
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_nested_dict_throws():
     with pytest.raises(ValueError):
         base.embed({"test_namespace": {"a": {"b": 1}}}, MockEncoder())
 
 
+@pytest.mark.requires("vowpal_wabbit_next")
 def test_list_of_tuples_throws():
     with pytest.raises(ValueError):
         base.embed({"test_namespace": [("a", 1), ("b", 2)]}, MockEncoder())