rename rl_chain_base to base and update paths and imports

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

@@ -1,5 +1,5 @@
 from langchain.chains.rl_chain.pick_best_chain import PickBest
-from langchain.chains.rl_chain.rl_chain_base import (
+from langchain.chains.rl_chain.base import (
     Embed,
     BasedOn,
     ToSelectFrom,

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

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import langchain.chains.rl_chain.rl_chain_base as base
+import langchain.chains.rl_chain.base as base
 
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain

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

@@ -1,4 +1,5 @@
-import langchain.chains.rl_chain as rl_chain
+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
@@ -17,7 +18,7 @@ def setup():
 
 def test_multiple_ToSelectFrom_throws():
     llm, PROMPT = setup()
-    chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
+    chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
     actions = ["0", "1", "2"]
     with pytest.raises(ValueError):
         chain.run(
@@ -29,7 +30,7 @@ def test_multiple_ToSelectFrom_throws():
 
 def test_missing_basedOn_from_throws():
     llm, PROMPT = setup()
-    chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
+    chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
     actions = ["0", "1", "2"]
     with pytest.raises(ValueError):
         chain.run(action=rl_chain.ToSelectFrom(actions))
@@ -37,7 +38,7 @@ def test_missing_basedOn_from_throws():
 
 def test_ToSelectFrom_not_a_list_throws():
     llm, PROMPT = setup()
-    chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
+    chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
     actions = {"actions": ["0", "1", "2"]}
     with pytest.raises(ValueError):
         chain.run(
@@ -50,7 +51,7 @@ 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
     auto_val_llm = FakeListChatModel(responses=["3"])
-    chain = rl_chain.PickBest.from_llm(
+    chain = pick_best_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
         selection_scorer=rl_chain.AutoSelectionScorer(llm=auto_val_llm),
@@ -71,7 +72,7 @@ def test_update_with_delayed_score_force():
     llm, PROMPT = setup()
     # this LLM returns a number so that the auto validator will return that
     auto_val_llm = FakeListChatModel(responses=["3"])
-    chain = rl_chain.PickBest.from_llm(
+    chain = pick_best_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
         selection_scorer=rl_chain.AutoSelectionScorer(llm=auto_val_llm),
@@ -92,7 +93,7 @@ def test_update_with_delayed_score_force():
 
 def test_update_with_delayed_score():
     llm, PROMPT = setup()
-    chain = rl_chain.PickBest.from_llm(
+    chain = pick_best_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, selection_scorer=None
     )
     actions = ["0", "1", "2"]
@@ -115,7 +116,7 @@ def test_user_defined_scorer():
             score = 200
             return score
 
-    chain = rl_chain.PickBest.from_llm(
+    chain = pick_best_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, selection_scorer=CustomSelectionScorer()
     )
     actions = ["0", "1", "2"]
@@ -130,8 +131,8 @@ def test_user_defined_scorer():
 
 def test_default_embeddings():
     llm, PROMPT = setup()
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
-    chain = rl_chain.PickBest.from_llm(
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    chain = pick_best_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, feature_embedder=feature_embedder
     )
 
@@ -163,8 +164,8 @@ def test_default_embeddings():
 
 def test_default_embeddings_off():
     llm, PROMPT = setup()
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
-    chain = rl_chain.PickBest.from_llm(
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    chain = pick_best_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=False
     )
 
@@ -188,8 +189,8 @@ def test_default_embeddings_off():
 
 def test_default_embeddings_mixed_w_explicit_user_embeddings():
     llm, PROMPT = setup()
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
-    chain = rl_chain.PickBest.from_llm(
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    chain = pick_best_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, feature_embedder=feature_embedder
     )
 
@@ -223,7 +224,7 @@ def test_default_embeddings_mixed_w_explicit_user_embeddings():
 def test_default_no_scorer_specified():
     _, PROMPT = setup()
     chain_llm = FakeListChatModel(responses=[100])
-    chain = rl_chain.PickBest.from_llm(llm=chain_llm, prompt=PROMPT)
+    chain = pick_best_chain.PickBest.from_llm(llm=chain_llm, prompt=PROMPT)
     response = chain.run(
         User=rl_chain.BasedOn("Context"),
         action=rl_chain.ToSelectFrom(["0", "1", "2"]),
@@ -236,7 +237,7 @@ def test_default_no_scorer_specified():
 
 def test_explicitly_no_scorer():
     llm, PROMPT = setup()
-    chain = rl_chain.PickBest.from_llm(
+    chain = pick_best_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, selection_scorer=None
     )
     response = chain.run(
@@ -252,7 +253,7 @@ def test_explicitly_no_scorer():
 def test_auto_scorer_with_user_defined_llm():
     llm, PROMPT = setup()
     scorer_llm = FakeListChatModel(responses=[300])
-    chain = rl_chain.PickBest.from_llm(
+    chain = pick_best_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
         selection_scorer=rl_chain.AutoSelectionScorer(llm=scorer_llm),
@@ -269,7 +270,7 @@ def test_auto_scorer_with_user_defined_llm():
 
 def test_calling_chain_w_reserved_inputs_throws():
     llm, PROMPT = setup()
-    chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
+    chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
     with pytest.raises(ValueError):
         chain.run(
             User=rl_chain.BasedOn("Context"),

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

@@ -1,4 +1,5 @@
-import langchain.chains.rl_chain as rl_chain
+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
@@ -7,9 +8,9 @@ encoded_text = "[ e n c o d e d ] "
 
 
 def test_pickbest_textembedder_missing_context_throws():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_action = {"action": ["0", "1", "2"]}
-    event = rl_chain.PickBest.Event(
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_action, based_on={}
     )
     with pytest.raises(ValueError):
@@ -17,8 +18,8 @@ def test_pickbest_textembedder_missing_context_throws():
 
 
 def test_pickbest_textembedder_missing_actions_throws():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
-    event = rl_chain.PickBest.Event(
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from={}, based_on={"context": "context"}
     )
     with pytest.raises(ValueError):
@@ -26,10 +27,10 @@ def test_pickbest_textembedder_missing_actions_throws():
 
 
 def test_pickbest_textembedder_no_label_no_emb():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_actions = {"action1": ["0", "1", "2"]}
     expected = """shared |context context \n|action1 0 \n|action1 1 \n|action1 2 """
-    event = rl_chain.PickBest.Event(
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on={"context": "context"}
     )
     vw_ex_str = feature_embedder.format(event)
@@ -37,11 +38,11 @@ def test_pickbest_textembedder_no_label_no_emb():
 
 
 def test_pickbest_textembedder_w_label_no_score_no_emb():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_actions = {"action1": ["0", "1", "2"]}
     expected = """shared |context context \n|action1 0 \n|action1 1 \n|action1 2 """
-    selected = rl_chain.PickBest.Selected(index=0, probability=1.0)
-    event = rl_chain.PickBest.Event(
+    selected = pick_best_chain.PickBest.Selected(index=0, probability=1.0)
+    event = pick_best_chain.PickBest.Event(
         inputs={},
         to_select_from=named_actions,
         based_on={"context": "context"},
@@ -52,13 +53,13 @@ def test_pickbest_textembedder_w_label_no_score_no_emb():
 
 
 def test_pickbest_textembedder_w_full_label_no_emb():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(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 """
     )
-    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = rl_chain.PickBest.Event(
+    selected = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = pick_best_chain.PickBest.Event(
         inputs={},
         to_select_from=named_actions,
         based_on={"context": "context"},
@@ -69,7 +70,7 @@ def test_pickbest_textembedder_w_full_label_no_emb():
 
 
 def test_pickbest_textembedder_w_full_label_w_emb():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     str1 = "0"
     str2 = "1"
     str3 = "2"
@@ -83,8 +84,8 @@ def test_pickbest_textembedder_w_full_label_w_emb():
     named_actions = {"action1": rl_chain.Embed([str1, str2, str3])}
     context = {"context": rl_chain.Embed(ctx_str_1)}
     expected = f"""shared |context {encoded_ctx_str_1} \n0:-0.0:1.0 |action1 {encoded_str1} \n|action1 {encoded_str2} \n|action1 {encoded_str3} """
-    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = rl_chain.PickBest.Event(
+    selected = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -92,7 +93,7 @@ def test_pickbest_textembedder_w_full_label_w_emb():
 
 
 def test_pickbest_textembedder_w_full_label_w_embed_and_keep():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     str1 = "0"
     str2 = "1"
     str3 = "2"
@@ -106,8 +107,8 @@ def test_pickbest_textembedder_w_full_label_w_embed_and_keep():
     named_actions = {"action1": rl_chain.EmbedAndKeep([str1, str2, str3])}
     context = {"context": rl_chain.EmbedAndKeep(ctx_str_1)}
     expected = f"""shared |context {ctx_str_1 + " " + encoded_ctx_str_1} \n0:-0.0:1.0 |action1 {str1 + " " + encoded_str1} \n|action1 {str2 + " " + encoded_str2} \n|action1 {str3 + " " + encoded_str3} """
-    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = rl_chain.PickBest.Event(
+    selected = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -115,11 +116,11 @@ def test_pickbest_textembedder_w_full_label_w_embed_and_keep():
 
 
 def test_pickbest_textembedder_more_namespaces_no_label_no_emb():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(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 """
-    event = rl_chain.PickBest.Event(
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context
     )
     vw_ex_str = feature_embedder.format(event)
@@ -127,12 +128,12 @@ def test_pickbest_textembedder_more_namespaces_no_label_no_emb():
 
 
 def test_pickbest_textembedder_more_namespaces_w_label_no_emb():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(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 """
-    selected = rl_chain.PickBest.Selected(index=0, probability=1.0)
-    event = rl_chain.PickBest.Event(
+    selected = pick_best_chain.PickBest.Selected(index=0, probability=1.0)
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -140,12 +141,12 @@ def test_pickbest_textembedder_more_namespaces_w_label_no_emb():
 
 
 def test_pickbest_textembedder_more_namespaces_w_full_label_no_emb():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(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 """
-    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = rl_chain.PickBest.Event(
+    selected = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -153,7 +154,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_no_emb():
 
 
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
     str1 = "0"
     str2 = "1"
@@ -176,8 +177,8 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb():
     }
     expected = f"""shared |context1 {encoded_ctx_str_1} |context2 {encoded_ctx_str_2} \n0:-0.0:1.0 |a {encoded_str1} |b {encoded_str1} \n|action1 {encoded_str2} \n|action1 {encoded_str3} """
 
-    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = rl_chain.PickBest.Event(
+    selected = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -185,7 +186,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb():
 
 
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_keep():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
     str1 = "0"
     str2 = "1"
@@ -210,8 +211,8 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_kee
     }
     expected = f"""shared |context1 {ctx_str_1 + " " + encoded_ctx_str_1} |context2 {ctx_str_2 + " " + encoded_ctx_str_2} \n0:-0.0:1.0 |a {str1 + " " + encoded_str1} |b {str1 + " " + encoded_str1} \n|action1 {str2 + " " + encoded_str2} \n|action1 {str3 + " " + encoded_str3} """
 
-    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = rl_chain.PickBest.Event(
+    selected = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -219,7 +220,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_kee
 
 
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
     str1 = "0"
     str2 = "1"
@@ -243,8 +244,8 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb():
     context = {"context1": ctx_str_1, "context2": rl_chain.Embed(ctx_str_2)}
     expected = f"""shared |context1 {ctx_str_1} |context2 {encoded_ctx_str_2} \n0:-0.0:1.0 |a {str1} |b {encoded_str1} \n|action1 {str2} \n|action1 {encoded_str3} """
 
-    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = rl_chain.PickBest.Event(
+    selected = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -252,7 +253,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb():
 
 
 def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_embed_and_keep():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
     str1 = "0"
     str2 = "1"
@@ -279,8 +280,8 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_embed_and_
     }
     expected = f"""shared |context1 {ctx_str_1} |context2 {ctx_str_2 + " " + encoded_ctx_str_2} \n0:-0.0:1.0 |a {str1} |b {str1 + " " + encoded_str1} \n|action1 {str2} \n|action1 {str3 + " " + encoded_str3} """
 
-    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = rl_chain.PickBest.Event(
+    selected = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -288,7 +289,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_embed_and_
 
 
 def test_raw_features_underscored():
-    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
     str1 = "this is a long string"
     str1_underscored = str1.replace(" ", "_")
     encoded_str1 = encoded_text + " ".join(char for char in str1)
@@ -303,7 +304,7 @@ def test_raw_features_underscored():
     expected_no_embed = (
         f"""shared |context {ctx_str_underscored} \n|action {str1_underscored} """
     )
-    event = rl_chain.PickBest.Event(
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context
     )
     vw_ex_str = feature_embedder.format(event)
@@ -313,7 +314,7 @@ def test_raw_features_underscored():
     named_actions = {"action": rl_chain.Embed([str1])}
     context = {"context": rl_chain.Embed(ctx_str)}
     expected_embed = f"""shared |context {encoded_ctx_str} \n|action {encoded_str1} """
-    event = rl_chain.PickBest.Event(
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context
     )
     vw_ex_str = feature_embedder.format(event)
@@ -323,7 +324,7 @@ def test_raw_features_underscored():
     named_actions = {"action": rl_chain.EmbedAndKeep([str1])}
     context = {"context": rl_chain.EmbedAndKeep(ctx_str)}
     expected_embed_and_keep = f"""shared |context {ctx_str_underscored + " " + encoded_ctx_str} \n|action {str1_underscored + " " + encoded_str1} """
-    event = rl_chain.PickBest.Event(
+    event = pick_best_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context
     )
     vw_ex_str = feature_embedder.format(event)

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

@@ -1,4 +1,4 @@
-import langchain.chains.rl_chain as base
+import langchain.chains.rl_chain.base as base
 from test_utils import MockEncoder
 
 import pytest