fix imports

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

@@ -1,5 +1,5 @@
-from .pick_best_chain import PickBest
-from .rl_chain_base import (
+from langchain.chains.rl_chain.pick_best_chain import PickBest
+from langchain.chains.rl_chain.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
 
-from . import rl_chain_base as base
+import langchain.chains.rl_chain.rl_chain_base as base
 
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain

libs/langchain/langchain/chains/rl_chain/rl_chain_base.py

@@ -6,9 +6,9 @@ from typing import Any, Dict, List, Optional, Tuple, Union, Sequence
 from abc import ABC, abstractmethod
 
 import vowpal_wabbit_next as vw
-from .vw_logger import VwLogger
-from .model_repository import ModelRepository
-from .metrics import MetricsTracker
+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

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

@@ -1,4 +1,4 @@
-import langchain.chains.rl_chain.pick_best_chain as pick_best_chain
+import langchain.chains.rl_chain as rl_chain
 from test_utils import MockEncoder
 import pytest
 from langchain.prompts.prompt import PromptTemplate
@@ -17,32 +17,32 @@ def setup():
 
 def test_multiple_ToSelectFrom_throws():
     llm, PROMPT = setup()
-    chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
+    chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
     actions = ["0", "1", "2"]
     with pytest.raises(ValueError):
         chain.run(
-            User=pick_best_chain.base.BasedOn("Context"),
-            action=pick_best_chain.base.ToSelectFrom(actions),
-            another_action=pick_best_chain.base.ToSelectFrom(actions),
+            User=rl_chain.BasedOn("Context"),
+            action=rl_chain.ToSelectFrom(actions),
+            another_action=rl_chain.ToSelectFrom(actions),
         )
 
 
 def test_missing_basedOn_from_throws():
     llm, PROMPT = setup()
-    chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
+    chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
     actions = ["0", "1", "2"]
     with pytest.raises(ValueError):
-        chain.run(action=pick_best_chain.base.ToSelectFrom(actions))
+        chain.run(action=rl_chain.ToSelectFrom(actions))
 
 
 def test_ToSelectFrom_not_a_list_throws():
     llm, PROMPT = setup()
-    chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
+    chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
     actions = {"actions": ["0", "1", "2"]}
     with pytest.raises(ValueError):
         chain.run(
-            User=pick_best_chain.base.BasedOn("Context"),
-            action=pick_best_chain.base.ToSelectFrom(actions),
+            User=rl_chain.BasedOn("Context"),
+            action=rl_chain.ToSelectFrom(actions),
         )
 
 
@@ -50,15 +50,15 @@ 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 = pick_best_chain.PickBest.from_llm(
+    chain = rl_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
-        selection_scorer=pick_best_chain.base.AutoSelectionScorer(llm=auto_val_llm),
+        selection_scorer=rl_chain.AutoSelectionScorer(llm=auto_val_llm),
     )
     actions = ["0", "1", "2"]
     response = chain.run(
-        User=pick_best_chain.base.BasedOn("Context"),
-        action=pick_best_chain.base.ToSelectFrom(actions),
+        User=rl_chain.BasedOn("Context"),
+        action=rl_chain.ToSelectFrom(actions),
     )
     assert response["response"] == "hey"
     selection_metadata = response["selection_metadata"]
@@ -71,15 +71,15 @@ 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 = pick_best_chain.PickBest.from_llm(
+    chain = rl_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
-        selection_scorer=pick_best_chain.base.AutoSelectionScorer(llm=auto_val_llm),
+        selection_scorer=rl_chain.AutoSelectionScorer(llm=auto_val_llm),
     )
     actions = ["0", "1", "2"]
     response = chain.run(
-        User=pick_best_chain.base.BasedOn("Context"),
-        action=pick_best_chain.base.ToSelectFrom(actions),
+        User=rl_chain.BasedOn("Context"),
+        action=rl_chain.ToSelectFrom(actions),
     )
     assert response["response"] == "hey"
     selection_metadata = response["selection_metadata"]
@@ -92,13 +92,13 @@ def test_update_with_delayed_score_force():
 
 def test_update_with_delayed_score():
     llm, PROMPT = setup()
-    chain = pick_best_chain.PickBest.from_llm(
+    chain = rl_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, selection_scorer=None
     )
     actions = ["0", "1", "2"]
     response = chain.run(
-        User=pick_best_chain.base.BasedOn("Context"),
-        action=pick_best_chain.base.ToSelectFrom(actions),
+        User=rl_chain.BasedOn("Context"),
+        action=rl_chain.ToSelectFrom(actions),
     )
     assert response["response"] == "hey"
     selection_metadata = response["selection_metadata"]
@@ -110,18 +110,18 @@ def test_update_with_delayed_score():
 def test_user_defined_scorer():
     llm, PROMPT = setup()
 
-    class CustomSelectionScorer(pick_best_chain.base.SelectionScorer):
+    class CustomSelectionScorer(rl_chain.SelectionScorer):
         def score_response(self, inputs, llm_response: str) -> float:
             score = 200
             return score
 
-    chain = pick_best_chain.PickBest.from_llm(
+    chain = rl_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, selection_scorer=CustomSelectionScorer()
     )
     actions = ["0", "1", "2"]
     response = chain.run(
-        User=pick_best_chain.base.BasedOn("Context"),
-        action=pick_best_chain.base.ToSelectFrom(actions),
+        User=rl_chain.BasedOn("Context"),
+        action=rl_chain.ToSelectFrom(actions),
     )
     assert response["response"] == "hey"
     selection_metadata = response["selection_metadata"]
@@ -130,8 +130,8 @@ def test_user_defined_scorer():
 
 def test_default_embeddings():
     llm, PROMPT = setup()
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
-    chain = pick_best_chain.PickBest.from_llm(
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    chain = rl_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, feature_embedder=feature_embedder
     )
 
@@ -153,8 +153,8 @@ def test_default_embeddings():
     actions = [str1, str2, str3]
 
     response = chain.run(
-        User=pick_best_chain.base.BasedOn(ctx_str_1),
-        action=pick_best_chain.base.ToSelectFrom(actions),
+        User=rl_chain.BasedOn(ctx_str_1),
+        action=rl_chain.ToSelectFrom(actions),
     )
     selection_metadata = response["selection_metadata"]
     vw_str = feature_embedder.format(selection_metadata)
@@ -163,8 +163,8 @@ def test_default_embeddings():
 
 def test_default_embeddings_off():
     llm, PROMPT = setup()
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
-    chain = pick_best_chain.PickBest.from_llm(
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    chain = rl_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=False
     )
 
@@ -178,8 +178,8 @@ def test_default_embeddings_off():
     actions = [str1, str2, str3]
 
     response = chain.run(
-        User=pick_best_chain.base.BasedOn(ctx_str_1),
-        action=pick_best_chain.base.ToSelectFrom(actions),
+        User=rl_chain.BasedOn(ctx_str_1),
+        action=rl_chain.ToSelectFrom(actions),
     )
     selection_metadata = response["selection_metadata"]
     vw_str = feature_embedder.format(selection_metadata)
@@ -188,8 +188,8 @@ def test_default_embeddings_off():
 
 def test_default_embeddings_mixed_w_explicit_user_embeddings():
     llm, PROMPT = setup()
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
-    chain = pick_best_chain.PickBest.from_llm(
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    chain = rl_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, feature_embedder=feature_embedder
     )
 
@@ -208,12 +208,12 @@ def test_default_embeddings_mixed_w_explicit_user_embeddings():
 
     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} """
 
-    actions = [str1, str2, pick_best_chain.base.Embed(str3)]
+    actions = [str1, str2, rl_chain.Embed(str3)]
 
     response = chain.run(
-        User=pick_best_chain.base.BasedOn(pick_best_chain.base.Embed(ctx_str_1)),
-        User2=pick_best_chain.base.BasedOn(ctx_str_2),
-        action=pick_best_chain.base.ToSelectFrom(actions),
+        User=rl_chain.BasedOn(rl_chain.Embed(ctx_str_1)),
+        User2=rl_chain.BasedOn(ctx_str_2),
+        action=rl_chain.ToSelectFrom(actions),
     )
     selection_metadata = response["selection_metadata"]
     vw_str = feature_embedder.format(selection_metadata)
@@ -223,10 +223,10 @@ def test_default_embeddings_mixed_w_explicit_user_embeddings():
 def test_default_no_scorer_specified():
     _, PROMPT = setup()
     chain_llm = FakeListChatModel(responses=[100])
-    chain = pick_best_chain.PickBest.from_llm(llm=chain_llm, prompt=PROMPT)
+    chain = rl_chain.PickBest.from_llm(llm=chain_llm, prompt=PROMPT)
     response = chain.run(
-        User=pick_best_chain.base.BasedOn("Context"),
-        action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
+        User=rl_chain.BasedOn("Context"),
+        action=rl_chain.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
     assert response["response"] == "100"
@@ -236,12 +236,12 @@ def test_default_no_scorer_specified():
 
 def test_explicitly_no_scorer():
     llm, PROMPT = setup()
-    chain = pick_best_chain.PickBest.from_llm(
+    chain = rl_chain.PickBest.from_llm(
         llm=llm, prompt=PROMPT, selection_scorer=None
     )
     response = chain.run(
-        User=pick_best_chain.base.BasedOn("Context"),
-        action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
+        User=rl_chain.BasedOn("Context"),
+        action=rl_chain.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
     assert response["response"] == "hey"
@@ -252,14 +252,14 @@ def test_explicitly_no_scorer():
 def test_auto_scorer_with_user_defined_llm():
     llm, PROMPT = setup()
     scorer_llm = FakeListChatModel(responses=[300])
-    chain = pick_best_chain.PickBest.from_llm(
+    chain = rl_chain.PickBest.from_llm(
         llm=llm,
         prompt=PROMPT,
-        selection_scorer=pick_best_chain.base.AutoSelectionScorer(llm=scorer_llm),
+        selection_scorer=rl_chain.AutoSelectionScorer(llm=scorer_llm),
     )
     response = chain.run(
-        User=pick_best_chain.base.BasedOn("Context"),
-        action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
+        User=rl_chain.BasedOn("Context"),
+        action=rl_chain.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
     assert response["response"] == "hey"
@@ -269,17 +269,17 @@ def test_auto_scorer_with_user_defined_llm():
 
 def test_calling_chain_w_reserved_inputs_throws():
     llm, PROMPT = setup()
-    chain = pick_best_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
+    chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
     with pytest.raises(ValueError):
         chain.run(
-            User=pick_best_chain.base.BasedOn("Context"),
-            rl_chain_selected_based_on=pick_best_chain.base.ToSelectFrom(
+            User=rl_chain.BasedOn("Context"),
+            rl_chain_selected_based_on=rl_chain.ToSelectFrom(
                 ["0", "1", "2"]
             ),
         )
 
     with pytest.raises(ValueError):
         chain.run(
-            User=pick_best_chain.base.BasedOn("Context"),
-            rl_chain_selected=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
+            User=rl_chain.BasedOn("Context"),
+            rl_chain_selected=rl_chain.ToSelectFrom(["0", "1", "2"]),
         )

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

@@ -1,4 +1,4 @@
-import langchain.chains.rl_chain.pick_best_chain as pick_best_chain
+import langchain.chains.rl_chain as rl_chain
 from test_utils import MockEncoder
 
 import pytest
@@ -7,9 +7,9 @@ encoded_text = "[ e n c o d e d ] "
 
 
 def test_pickbest_textembedder_missing_context_throws():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
     named_action = {"action": ["0", "1", "2"]}
-    event = pick_best_chain.PickBest.Event(
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_action, based_on={}
     )
     with pytest.raises(ValueError):
@@ -17,8 +17,8 @@ def test_pickbest_textembedder_missing_context_throws():
 
 
 def test_pickbest_textembedder_missing_actions_throws():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
-    event = pick_best_chain.PickBest.Event(
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from={}, based_on={"context": "context"}
     )
     with pytest.raises(ValueError):
@@ -26,10 +26,10 @@ def test_pickbest_textembedder_missing_actions_throws():
 
 
 def test_pickbest_textembedder_no_label_no_emb():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(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.PickBest.Event(
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on={"context": "context"}
     )
     vw_ex_str = feature_embedder.format(event)
@@ -37,11 +37,11 @@ def test_pickbest_textembedder_no_label_no_emb():
 
 
 def test_pickbest_textembedder_w_label_no_score_no_emb():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(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.PickBest.Selected(index=0, probability=1.0)
-    event = pick_best_chain.PickBest.Event(
+    selected = rl_chain.PickBest.Selected(index=0, probability=1.0)
+    event = rl_chain.PickBest.Event(
         inputs={},
         to_select_from=named_actions,
         based_on={"context": "context"},
@@ -52,13 +52,13 @@ def test_pickbest_textembedder_w_label_no_score_no_emb():
 
 
 def test_pickbest_textembedder_w_full_label_no_emb():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_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 = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = pick_best_chain.PickBest.Event(
+    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = rl_chain.PickBest.Event(
         inputs={},
         to_select_from=named_actions,
         based_on={"context": "context"},
@@ -69,7 +69,7 @@ def test_pickbest_textembedder_w_full_label_no_emb():
 
 
 def test_pickbest_textembedder_w_full_label_w_emb():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
     str1 = "0"
     str2 = "1"
     str3 = "2"
@@ -80,11 +80,11 @@ def test_pickbest_textembedder_w_full_label_w_emb():
     ctx_str_1 = "context1"
     encoded_ctx_str_1 = encoded_text + " ".join(char for char in ctx_str_1)
 
-    named_actions = {"action1": pick_best_chain.base.Embed([str1, str2, str3])}
-    context = {"context": pick_best_chain.base.Embed(ctx_str_1)}
+    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 = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = pick_best_chain.PickBest.Event(
+    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -92,7 +92,7 @@ def test_pickbest_textembedder_w_full_label_w_emb():
 
 
 def test_pickbest_textembedder_w_full_label_w_embed_and_keep():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
     str1 = "0"
     str2 = "1"
     str3 = "2"
@@ -103,11 +103,11 @@ def test_pickbest_textembedder_w_full_label_w_embed_and_keep():
     ctx_str_1 = "context1"
     encoded_ctx_str_1 = encoded_text + " ".join(char for char in ctx_str_1)
 
-    named_actions = {"action1": pick_best_chain.base.EmbedAndKeep([str1, str2, str3])}
-    context = {"context": pick_best_chain.base.EmbedAndKeep(ctx_str_1)}
+    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 = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = pick_best_chain.PickBest.Event(
+    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -115,11 +115,11 @@ def test_pickbest_textembedder_w_full_label_w_embed_and_keep():
 
 
 def test_pickbest_textembedder_more_namespaces_no_label_no_emb():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_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 = pick_best_chain.PickBest.Event(
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context
     )
     vw_ex_str = feature_embedder.format(event)
@@ -127,12 +127,12 @@ def test_pickbest_textembedder_more_namespaces_no_label_no_emb():
 
 
 def test_pickbest_textembedder_more_namespaces_w_label_no_emb():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_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 = pick_best_chain.PickBest.Selected(index=0, probability=1.0)
-    event = pick_best_chain.PickBest.Event(
+    selected = rl_chain.PickBest.Selected(index=0, probability=1.0)
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -140,12 +140,12 @@ def test_pickbest_textembedder_more_namespaces_w_label_no_emb():
 
 
 def test_pickbest_textembedder_more_namespaces_w_full_label_no_emb():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_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 = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = pick_best_chain.PickBest.Event(
+    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -153,7 +153,7 @@ 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 = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
     str1 = "0"
     str2 = "1"
@@ -168,16 +168,16 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb():
     encoded_ctx_str_2 = encoded_text + " ".join(char for char in ctx_str_2)
 
     named_actions = {
-        "action1": pick_best_chain.base.Embed([{"a": str1, "b": str1}, str2, str3])
+        "action1": rl_chain.Embed([{"a": str1, "b": str1}, str2, str3])
     }
     context = {
-        "context1": pick_best_chain.base.Embed(ctx_str_1),
-        "context2": pick_best_chain.base.Embed(ctx_str_2),
+        "context1": rl_chain.Embed(ctx_str_1),
+        "context2": rl_chain.Embed(ctx_str_2),
     }
     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 = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = pick_best_chain.PickBest.Event(
+    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -185,7 +185,7 @@ 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 = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
     str1 = "0"
     str2 = "1"
@@ -200,18 +200,18 @@ 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": pick_best_chain.base.EmbedAndKeep(
+        "action1": rl_chain.EmbedAndKeep(
             [{"a": str1, "b": str1}, str2, str3]
         )
     }
     context = {
-        "context1": pick_best_chain.base.EmbedAndKeep(ctx_str_1),
-        "context2": pick_best_chain.base.EmbedAndKeep(ctx_str_2),
+        "context1": rl_chain.EmbedAndKeep(ctx_str_1),
+        "context2": rl_chain.EmbedAndKeep(ctx_str_2),
     }
     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 = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = pick_best_chain.PickBest.Event(
+    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -219,7 +219,7 @@ 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 = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
     str1 = "0"
     str2 = "1"
@@ -235,16 +235,16 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb():
 
     named_actions = {
         "action1": [
-            {"a": str1, "b": pick_best_chain.base.Embed(str1)},
+            {"a": str1, "b": rl_chain.Embed(str1)},
             str2,
-            pick_best_chain.base.Embed(str3),
+            rl_chain.Embed(str3),
         ]
     }
-    context = {"context1": ctx_str_1, "context2": pick_best_chain.base.Embed(ctx_str_2)}
+    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 = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = pick_best_chain.PickBest.Event(
+    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -252,7 +252,7 @@ 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 = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
 
     str1 = "0"
     str2 = "1"
@@ -268,19 +268,19 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_embed_and_
 
     named_actions = {
         "action1": [
-            {"a": str1, "b": pick_best_chain.base.EmbedAndKeep(str1)},
+            {"a": str1, "b": rl_chain.EmbedAndKeep(str1)},
             str2,
-            pick_best_chain.base.EmbedAndKeep(str3),
+            rl_chain.EmbedAndKeep(str3),
         ]
     }
     context = {
         "context1": ctx_str_1,
-        "context2": pick_best_chain.base.EmbedAndKeep(ctx_str_2),
+        "context2": rl_chain.EmbedAndKeep(ctx_str_2),
     }
     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 = pick_best_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
-    event = pick_best_chain.PickBest.Event(
+    selected = rl_chain.PickBest.Selected(index=0, probability=1.0, score=0.0)
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context, selected=selected
     )
     vw_ex_str = feature_embedder.format(event)
@@ -288,7 +288,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_embed_and_
 
 
 def test_raw_features_underscored():
-    feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
+    feature_embedder = rl_chain.PickBestFeatureEmbedder(model=MockEncoder())
     str1 = "this is a long string"
     str1_underscored = str1.replace(" ", "_")
     encoded_str1 = encoded_text + " ".join(char for char in str1)
@@ -303,27 +303,27 @@ def test_raw_features_underscored():
     expected_no_embed = (
         f"""shared |context {ctx_str_underscored} \n|action {str1_underscored} """
     )
-    event = pick_best_chain.PickBest.Event(
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context
     )
     vw_ex_str = feature_embedder.format(event)
     assert vw_ex_str == expected_no_embed
 
     # Just embeddings
-    named_actions = {"action": pick_best_chain.base.Embed([str1])}
-    context = {"context": pick_best_chain.base.Embed(ctx_str)}
+    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 = pick_best_chain.PickBest.Event(
+    event = rl_chain.PickBest.Event(
         inputs={}, to_select_from=named_actions, based_on=context
     )
     vw_ex_str = feature_embedder.format(event)
     assert vw_ex_str == expected_embed
 
     # Embeddings and raw features
-    named_actions = {"action": pick_best_chain.base.EmbedAndKeep([str1])}
-    context = {"context": pick_best_chain.base.EmbedAndKeep(ctx_str)}
+    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 = pick_best_chain.PickBest.Event(
+    event = rl_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.rl_chain_base as base
+import langchain.chains.rl_chain as base
 from test_utils import MockEncoder
 
 import pytest