fix linting errors

libs/experimental/langchain_experimental/rl_chain/base.py

@@ -19,19 +19,20 @@ from typing import (
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
-from langchain_experimental.rl_chain.metrics import (
-    MetricsTrackerAverage,
-    MetricsTrackerRollingWindow,
-)
-from langchain_experimental.rl_chain.model_repository import ModelRepository
-from langchain_experimental.rl_chain.vw_logger import VwLogger
 from langchain.prompts import (
     BasePromptTemplate,
     ChatPromptTemplate,
     HumanMessagePromptTemplate,
     SystemMessagePromptTemplate,
 )
+
 from langchain_experimental.pydantic_v1 import BaseModel, Extra, root_validator
+from langchain_experimental.rl_chain.metrics import (
+    MetricsTrackerAverage,
+    MetricsTrackerRollingWindow,
+)
+from langchain_experimental.rl_chain.model_repository import ModelRepository
+from langchain_experimental.rl_chain.vw_logger import VwLogger
 
 if TYPE_CHECKING:
     import vowpal_wabbit_next as vw

libs/experimental/langchain_experimental/rl_chain/pick_best_chain.py

@@ -3,12 +3,13 @@ from __future__ import annotations
 import logging
 from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
-import langchain_experimental.rl_chain.base as base
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.llm import LLMChain
 from langchain.prompts import BasePromptTemplate
 
+import langchain_experimental.rl_chain.base as base
+
 logger = logging.getLogger(__name__)
 
 # sentinel object used to distinguish between

libs/experimental/tests/integration_tests/chains/rl_chain/test_pick_best_chain_call.py

@@ -1,12 +1,12 @@
 from typing import Any, Dict
 
 import pytest
+from langchain.chat_models import FakeListChatModel
+from langchain.prompts.prompt import PromptTemplate
 from test_utils import MockEncoder, MockEncoderReturnsList
 
 import langchain_experimental.rl_chain.base as rl_chain
 import langchain_experimental.rl_chain.pick_best_chain as pick_best_chain
-from langchain.chat_models import FakeListChatModel
-from langchain.prompts.prompt import PromptTemplate
 
 encoded_keyword = "[encoded]"
 
@@ -90,11 +90,13 @@ def test_update_with_delayed_score_with_auto_validator_throws() -> None:
         User=rl_chain.BasedOn("Context"),
         action=rl_chain.ToSelectFrom(actions),
     )
-    assert response["response"] == "hey" # type: ignore
-    selection_metadata = response["selection_metadata"] # type: ignore
-    assert selection_metadata.selected.score == 3.0 # type: ignore
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 3.0  # type: ignore
     with pytest.raises(RuntimeError):
-        chain.update_with_delayed_score(chain_response=response, score=100)  # type: ignore
+        chain.update_with_delayed_score(
+            chain_response=response, score=100  # type: ignore
+        )
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
@@ -209,7 +211,7 @@ def test_everything_embedded() -> None:
         action=rl_chain.EmbedAndKeep(rl_chain.ToSelectFrom(actions)),
     )
     selection_metadata = response["selection_metadata"]  # type: ignore
-    vw_str = feature_embedder.format(selection_metadata) # type: ignore
+    vw_str = feature_embedder.format(selection_metadata)  # type: ignore
     assert vw_str == expected
 
 
@@ -237,7 +239,7 @@ def test_default_auto_embedder_is_off() -> None:
         action=pick_best_chain.base.ToSelectFrom(actions),
     )
     selection_metadata = response["selection_metadata"]  # type: ignore
-    vw_str = feature_embedder.format(selection_metadata) # type: ignore
+    vw_str = feature_embedder.format(selection_metadata)  # type: ignore
     assert vw_str == expected
 
 
@@ -264,8 +266,8 @@ def test_default_w_embeddings_off() -> None:
         User=rl_chain.BasedOn(ctx_str_1),
         action=rl_chain.ToSelectFrom(actions),
     )
-    selection_metadata = response["selection_metadata"] # type: ignore
-    vw_str = feature_embedder.format(selection_metadata) # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    vw_str = feature_embedder.format(selection_metadata)  # type: ignore
     assert vw_str == expected
 
 
@@ -292,8 +294,8 @@ def test_default_w_embeddings_on() -> None:
         User=rl_chain.BasedOn(ctx_str_1),
         action=rl_chain.ToSelectFrom(actions),
     )
-    selection_metadata = response["selection_metadata"] # type: ignore
-    vw_str = feature_embedder.format(selection_metadata) # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    vw_str = feature_embedder.format(selection_metadata)  # type: ignore
     assert vw_str == expected
 
 
@@ -324,8 +326,8 @@ def test_default_embeddings_mixed_w_explicit_user_embeddings() -> None:
         User2=rl_chain.BasedOn(ctx_str_2),
         action=rl_chain.ToSelectFrom(actions),
     )
-    selection_metadata = response["selection_metadata"] # type: ignore
-    vw_str = feature_embedder.format(selection_metadata) # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    vw_str = feature_embedder.format(selection_metadata)  # type: ignore
     assert vw_str == expected
 
 
@@ -345,9 +347,9 @@ def test_default_no_scorer_specified() -> None:
         action=rl_chain.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
-    assert response["response"] == "hey" # type: ignore
-    selection_metadata = response["selection_metadata"] # type: ignore
-    assert selection_metadata.selected.score == 100.0 # type: ignore
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 100.0  # type: ignore
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
@@ -366,9 +368,9 @@ def test_explicitly_no_scorer() -> None:
         action=rl_chain.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
-    assert response["response"] == "hey" # type: ignore
-    selection_metadata = response["selection_metadata"] # type: ignore
-    assert selection_metadata.selected.score is None # type: ignore
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score is None  # type: ignore
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
@@ -388,9 +390,9 @@ def test_auto_scorer_with_user_defined_llm() -> None:
         action=rl_chain.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
-    assert response["response"] == "hey" # type: ignore
-    selection_metadata = response["selection_metadata"] # type: ignore
-    assert selection_metadata.selected.score == 300.0 # type: ignore
+    assert response["response"] == "hey"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 300.0  # type: ignore
 
 
 @pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
@@ -434,24 +436,24 @@ def test_activate_and_deactivate_scorer() -> None:
         action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
     )
     # chain llm used for both basic prompt and for scoring
-    assert response["response"] == "hey1" # type: ignore
-    selection_metadata = response["selection_metadata"] # type: ignore
-    assert selection_metadata.selected.score == 300.0 # type: ignore
+    assert response["response"] == "hey1"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 300.0  # type: ignore
 
     chain.deactivate_selection_scorer()
     response = chain.run(
         User=pick_best_chain.base.BasedOn("Context"),
         action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
     )
-    assert response["response"] == "hey2" # type: ignore
-    selection_metadata = response["selection_metadata"] # type: ignore
-    assert selection_metadata.selected.score is None # type: ignore
+    assert response["response"] == "hey2"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score is None  # type: ignore
 
     chain.activate_selection_scorer()
     response = chain.run(
         User=pick_best_chain.base.BasedOn("Context"),
         action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
     )
-    assert response["response"] == "hey3" # type: ignore
-    selection_metadata = response["selection_metadata"] # type: ignore
-    assert selection_metadata.selected.score == 400.0 # type: ignore
+    assert response["response"] == "hey3"  # type: ignore
+    selection_metadata = response["selection_metadata"]  # type: ignore
+    assert selection_metadata.selected.score == 400.0  # type: ignore