add dependency requirements to test file

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

@@ -471,7 +471,7 @@ class RLChain(Chain):
 
     def save_progress(self) -> None:
         """
-        This function should be called to save the state of the Vowpal Wabbit model.
+        This function should be called to save the state of the learned policy model.
         """
         self.policy.save()
 

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

@@ -9,7 +9,7 @@ from langchain.prompts.prompt import PromptTemplate
 encoded_text = "[ e n c o d e d ] "
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def setup():
     _PROMPT_TEMPLATE = """This is a dummy prompt that will be ignored by the fake llm"""
     PROMPT = PromptTemplate(input_variables=[], template=_PROMPT_TEMPLATE)
@@ -18,7 +18,7 @@ def setup():
     return llm, PROMPT
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_multiple_ToSelectFrom_throws():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
@@ -31,7 +31,7 @@ def test_multiple_ToSelectFrom_throws():
         )
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_missing_basedOn_from_throws():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
@@ -40,7 +40,7 @@ def test_missing_basedOn_from_throws():
         chain.run(action=rl_chain.ToSelectFrom(actions))
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_ToSelectFrom_not_a_list_throws():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
@@ -52,7 +52,7 @@ def test_ToSelectFrom_not_a_list_throws():
         )
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 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
@@ -74,7 +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")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_update_with_delayed_score_force():
     llm, PROMPT = setup()
     # this LLM returns a number so that the auto validator will return that
@@ -98,7 +98,7 @@ def test_update_with_delayed_score_force():
     assert selection_metadata.selected.score == 100.0
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_update_with_delayed_score():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(
@@ -116,7 +116,7 @@ def test_update_with_delayed_score():
     assert selection_metadata.selected.score == 100.0
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_user_defined_scorer():
     llm, PROMPT = setup()
 
@@ -138,7 +138,7 @@ def test_user_defined_scorer():
     assert selection_metadata.selected.score == 200.0
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_default_embeddings():
     llm, PROMPT = setup()
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
@@ -172,7 +172,7 @@ def test_default_embeddings():
     assert vw_str == expected
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_default_embeddings_off():
     llm, PROMPT = setup()
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
@@ -198,7 +198,7 @@ def test_default_embeddings_off():
     assert vw_str == expected
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_default_embeddings_mixed_w_explicit_user_embeddings():
     llm, PROMPT = setup()
     feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
@@ -233,7 +233,7 @@ def test_default_embeddings_mixed_w_explicit_user_embeddings():
     assert vw_str == expected
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_default_no_scorer_specified():
     _, PROMPT = setup()
     chain_llm = FakeListChatModel(responses=[100])
@@ -248,7 +248,7 @@ def test_default_no_scorer_specified():
     assert selection_metadata.selected.score == 100.0
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_explicitly_no_scorer():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(
@@ -264,7 +264,7 @@ def test_explicitly_no_scorer():
     assert selection_metadata.selected.score is None
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_auto_scorer_with_user_defined_llm():
     llm, PROMPT = setup()
     scorer_llm = FakeListChatModel(responses=[300])
@@ -283,7 +283,7 @@ def test_auto_scorer_with_user_defined_llm():
     assert selection_metadata.selected.score == 300.0
 
 
-@pytest.mark.requires("vowpal_wabbit_next")
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
 def test_calling_chain_w_reserved_inputs_throws():
     llm, PROMPT = setup()
     chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)