From a6f9dccc35d5420cd441383db0c5df1712c37bf8 Mon Sep 17 00:00:00 2001 From: olgavrou Date: Fri, 18 Aug 2023 03:42:17 -0400 Subject: [PATCH] rename rl_chain_base to base and update paths and imports --- .../langchain/chains/rl_chain/__init__.py | 2 +- .../rl_chain/{rl_chain_base.py => base.py} | 0 .../chains/rl_chain/pick_best_chain.py | 2 +- .../rl_chain/test_pick_best_chain_call.py | 37 ++++---- .../rl_chain/test_pick_best_text_embedder.py | 87 ++++++++++--------- .../rl_chain/test_rl_chain_base_embedder.py | 2 +- 6 files changed, 66 insertions(+), 64 deletions(-) rename libs/langchain/langchain/chains/rl_chain/{rl_chain_base.py => base.py} (100%) diff --git a/libs/langchain/langchain/chains/rl_chain/__init__.py b/libs/langchain/langchain/chains/rl_chain/__init__.py index 19856658138..d485c5d5063 100644 --- a/libs/langchain/langchain/chains/rl_chain/__init__.py +++ b/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, diff --git a/libs/langchain/langchain/chains/rl_chain/rl_chain_base.py b/libs/langchain/langchain/chains/rl_chain/base.py similarity index 100% rename from libs/langchain/langchain/chains/rl_chain/rl_chain_base.py rename to libs/langchain/langchain/chains/rl_chain/base.py diff --git a/libs/langchain/langchain/chains/rl_chain/pick_best_chain.py b/libs/langchain/langchain/chains/rl_chain/pick_best_chain.py index 5e9a4673f7b..28c0f509f02 100644 --- a/libs/langchain/langchain/chains/rl_chain/pick_best_chain.py +++ b/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 diff --git a/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py b/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py index dd7a2d3ea19..e0e5f5dc21e 100644 --- a/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py +++ b/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"), diff --git a/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_text_embedder.py b/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_text_embedder.py index eee384641c2..22097c6ef36 100644 --- a/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_text_embedder.py +++ b/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) diff --git a/libs/langchain/tests/unit_tests/chains/rl_chain/test_rl_chain_base_embedder.py b/libs/langchain/tests/unit_tests/chains/rl_chain/test_rl_chain_base_embedder.py index 073bab31ad9..fc3d02d9b07 100644 --- a/libs/langchain/tests/unit_tests/chains/rl_chain/test_rl_chain_base_embedder.py +++ b/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