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infra: rm unused # noqa violations (#22049)

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Updating #21137
pull/21845/head^2
Bagatur 3 weeks ago committed by GitHub
parent 45ed5f3f51
commit 50186da0a1
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GPG Key ID: B5690EEEBB952194
149 changed files with 212 additions and 214 deletions
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2
.github/scripts/check_diff.py vendored
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@ -91,4 +91,4 @@ if __name__ == "__main__":
}
for key, value in outputs.items():
json_output = json.dumps(value)
print(f"{key}={json_output}") # noqa: T201
print(f"{key}={json_output}")

2
.github/scripts/get_min_versions.py vendored
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@ -76,4 +76,4 @@ if __name__ == "__main__":
print(
" ".join([f"{lib}=={version}" for lib, version in min_versions.items()])
) # noqa: T201
)

2
.github/workflows/extract_ignored_words_list.py vendored
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@ -7,4 +7,4 @@ ignore_words_list = (
pyproject_toml.get("tool", {}).get("codespell", {}).get("ignore-words-list")
)
print(f"::set-output name=ignore_words_list::{ignore_words_list}") # noqa: T201
print(f"::set-output name=ignore_words_list::{ignore_words_list}")

4
cookbook/rag_with_quantized_embeddings.ipynb
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@ -39,12 +39,10 @@
"from langchain_community.document_loaders.recursive_url_loader import (\n",
" RecursiveUrlLoader,\n",
")\n",
"\n",
"# noqa\n",
"from langchain_community.vectorstores import Chroma\n",
"\n",
"# For our example, we'll load docs from the web\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter # noqa\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"DOCSTORE_DIR = \".\"\n",
"DOCSTORE_ID_KEY = \"doc_id\""

2
docs/api_reference/create_api_rst.py
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@ -187,7 +187,7 @@ def _load_package_modules(
modules_by_namespace[top_namespace] = _module_members
except ImportError as e:
print(f"Error: Unable to import module '{namespace}' with error: {e}") # noqa: T201
print(f"Error: Unable to import module '{namespace}' with error: {e}")
return modules_by_namespace

4
docs/docs/how_to/graph_mapping.ipynb
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@ -300,7 +300,7 @@
"Entities in the question map to the following database values:\n",
"{entities_list}\n",
"Question: {question}\n",
"Cypher query:\"\"\" # noqa: E501\n",
"Cypher query:\"\"\"\n",
"\n",
"cypher_prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
@ -377,7 +377,7 @@
"response_template = \"\"\"Based on the the question, Cypher query, and Cypher response, write a natural language response:\n",
"Question: {question}\n",
"Cypher query: {query}\n",
"Cypher Response: {response}\"\"\" # noqa: E501\n",
"Cypher Response: {response}\"\"\"\n",
"\n",
"response_prompt = ChatPromptTemplate.from_messages(\n",
" [\n",

2
docs/docs/how_to/sql_csv.ipynb
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@ -503,7 +503,7 @@
}
],
"source": [
"chain = prompt | llm_with_tools | parser | tool # noqa\n",
"chain = prompt | llm_with_tools | parser | tool\n",
"chain.invoke({\"question\": \"What's the correlation between age and fare\"})"
]
},

2
docs/docs/how_to/sql_large_db.ipynb
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@ -262,7 +262,7 @@
" return tables\n",
"\n",
"\n",
"table_chain = category_chain | get_tables # noqa\n",
"table_chain = category_chain | get_tables\n",
"table_chain.invoke({\"input\": \"What are all the genres of Alanis Morisette songs\"})"
]
},

2
docs/docs/integrations/chat/anthropic.ipynb
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@ -670,7 +670,7 @@
" \"type\": \"image_url\",\n",
" \"image_url\": {\n",
" # langchain logo\n",
" \"url\": f\"data:image/png;base64,{img_base64}\", # noqa: E501\n",
" \"url\": f\"data:image/png;base64,{img_base64}\",\n",
" },\n",
" },\n",
" {\"type\": \"text\", \"text\": \"What is this logo for?\"},\n",

4
docs/docs/integrations/chat_loaders/discord.ipynb
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@ -95,7 +95,7 @@
" \"\"\"\n",
" self.path = path\n",
" self._message_line_regex = re.compile(\n",
" r\"(.+?) — (\\w{3,9} \\d{1,2}(?:st|nd|rd|th)?(?:, \\d{4})? \\d{1,2}:\\d{2} (?:AM|PM)|Today at \\d{1,2}:\\d{2} (?:AM|PM)|Yesterday at \\d{1,2}:\\d{2} (?:AM|PM))\", # noqa\n",
" r\"(.+?) — (\\w{3,9} \\d{1,2}(?:st|nd|rd|th)?(?:, \\d{4})? \\d{1,2}:\\d{2} (?:AM|PM)|Today at \\d{1,2}:\\d{2} (?:AM|PM)|Yesterday at \\d{1,2}:\\d{2} (?:AM|PM))\",\n",
" flags=re.DOTALL,\n",
" )\n",
"\n",
@ -120,7 +120,7 @@
" current_content = []\n",
" for line in lines:\n",
" if re.match(\n",
" r\".+? — (\\d{2}/\\d{2}/\\d{4} \\d{1,2}:\\d{2} (?:AM|PM)|Today at \\d{1,2}:\\d{2} (?:AM|PM)|Yesterday at \\d{1,2}:\\d{2} (?:AM|PM))\", # noqa\n",
" r\".+? — (\\d{2}/\\d{2}/\\d{4} \\d{1,2}:\\d{2} (?:AM|PM)|Today at \\d{1,2}:\\d{2} (?:AM|PM)|Yesterday at \\d{1,2}:\\d{2} (?:AM|PM))\",\n",
" line,\n",
" ):\n",
" if current_sender and current_content:\n",

2
docs/docs/integrations/chat_loaders/wechat.ipynb
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@ -94,7 +94,7 @@
" \"\"\"\n",
" self.path = path\n",
" self._message_line_regex = re.compile(\n",
" r\"(?P<sender>.+?) (?P<timestamp>\\d{4}/\\d{2}/\\d{2} \\d{1,2}:\\d{2} (?:AM|PM))\", # noqa\n",
" r\"(?P<sender>.+?) (?P<timestamp>\\d{4}/\\d{2}/\\d{2} \\d{1,2}:\\d{2} (?:AM|PM))\",\n",
" # flags=re.DOTALL,\n",
" )\n",
"\n",

2
docs/docs/integrations/document_loaders/example_data/source_code/example.py
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@ -3,7 +3,7 @@ class MyClass:
self.name = name
def greet(self):
print(f"Hello, {self.name}!") # noqa: T201
print(f"Hello, {self.name}!")
def main():

2
docs/scripts/arxiv_references.py
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@ -389,7 +389,7 @@ class ArxivAPIWrapper(BaseModel):
Returns:
List of ArxivPaper objects.
""" # noqa: E501
"""
def cut_authors(authors: list) -> list[str]:
if len(authors) > 3:

2
docs/scripts/generate_api_reference_links.py
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@ -186,7 +186,7 @@ def replace_imports(file):
data = code_block_re.sub(replacer, data)
# if all_imports:
# print(f"Adding {len(all_imports)} links for imports in {file}") # noqa: T201
# print(f"Adding {len(all_imports)} links for imports in {file}")
with open(file, "w") as f:
f.write(data)
return all_imports

4
docs/scripts/model_feat_table.py
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@ -116,7 +116,7 @@ Each LLM integration can optionally provide native implementations for async, st
{table}
""" # noqa: E501
"""
CHAT_MODEL_TEMPLATE = """\
---
@ -133,7 +133,7 @@ The following table shows all the chat models that support one or more advanced
{table}
""" # noqa: E501
"""
def get_llm_table():

2
libs/cli/tests/unit_tests/migrate/test_replace_imports.py
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@ -17,7 +17,7 @@ ReplaceImportsCodemod = generate_import_replacer(
"langchain_to_core",
"community_to_core",
]
) # type: ignore[attr-defined] # noqa: E501
) # type: ignore[attr-defined]
class TestReplaceImportsCommand(CodemodTest):

2
libs/community/langchain_community/callbacks/manager.py
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@ -28,7 +28,7 @@ bedrock_anthropic_callback_var: (ContextVar)[
wandb_tracing_callback_var: ContextVar[Optional[WandbTracer]] = ContextVar(
"tracing_wandb_callback", default=None
)
comet_tracing_callback_var: ContextVar[Optional[CometTracer]] = ContextVar( # noqa: E501
comet_tracing_callback_var: ContextVar[Optional[CometTracer]] = ContextVar(
"tracing_comet_callback", default=None
)

2
libs/community/langchain_community/chat_loaders/langsmith.py
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@ -143,7 +143,7 @@ class LangSmithDatasetChatLoader(BaseChatLoader):
:return: Iterator of chat sessions containing messages.
"""
from langchain_community.adapters import openai as oai_adapter # noqa: E402
from langchain_community.adapters import openai as oai_adapter
data = self.client.read_dataset_openai_finetuning(
dataset_name=self.dataset_name

6
libs/community/langchain_community/chat_message_histories/cosmos_db.py
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@ -62,7 +62,7 @@ class CosmosDBChatMessageHistory(BaseChatMessageHistory):
self.messages: List[BaseMessage] = []
try:
from azure.cosmos import ( # pylint: disable=import-outside-toplevel # noqa: E501
from azure.cosmos import ( # pylint: disable=import-outside-toplevel
CosmosClient,
)
except ImportError as exc:
@ -91,7 +91,7 @@ class CosmosDBChatMessageHistory(BaseChatMessageHistory):
Use this function or the context manager to make sure your database is ready.
"""
try:
from azure.cosmos import ( # pylint: disable=import-outside-toplevel # noqa: E501
from azure.cosmos import ( # pylint: disable=import-outside-toplevel
PartitionKey,
)
except ImportError as exc:
@ -128,7 +128,7 @@ class CosmosDBChatMessageHistory(BaseChatMessageHistory):
if not self._container:
raise ValueError("Container not initialized")
try:
from azure.cosmos.exceptions import ( # pylint: disable=import-outside-toplevel # noqa: E501
from azure.cosmos.exceptions import ( # pylint: disable=import-outside-toplevel
CosmosHttpResponseError,
)
except ImportError as exc:

2
libs/community/langchain_community/chat_models/azure_openai.py
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@ -85,7 +85,7 @@ class AzureChatOpenAI(ChatOpenAI):
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
""" # noqa: E501
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.

2
libs/community/langchain_community/chat_models/azureml_endpoint.py
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@ -233,7 +233,7 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint):
endpoint_api_key="my-api-key",
content_formatter=chat_content_formatter,
)
""" # noqa: E501
"""
@property
def _identifying_params(self) -> Dict[str, Any]:

6
libs/community/langchain_community/document_loaders/__init__.py
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@ -214,7 +214,7 @@ if TYPE_CHECKING:
GitHubIssuesLoader,
)
from langchain_community.document_loaders.glue_catalog import (
GlueCatalogLoader, # noqa: F401
GlueCatalogLoader,
)
from langchain_community.document_loaders.google_speech_to_text import (
GoogleSpeechToTextLoader,
@ -332,8 +332,8 @@ if TYPE_CHECKING:
OracleAutonomousDatabaseLoader,
)
from langchain_community.document_loaders.oracleai import (
OracleDocLoader, # noqa: F401
OracleTextSplitter, # noqa: F401
OracleDocLoader,
OracleTextSplitter,
)
from langchain_community.document_loaders.org_mode import (
UnstructuredOrgModeLoader,

2
libs/community/langchain_community/document_loaders/blackboard.py
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@ -31,7 +31,7 @@ class BlackboardLoader(WebBaseLoader):
)
documents = loader.load()
""" # noqa: E501
"""
def __init__(
self,

2
libs/community/langchain_community/document_loaders/evernote.py
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@ -34,7 +34,7 @@ class EverNoteLoader(BaseLoader):
notes into a single long Document.
If this is set to True (default) then the only metadata on the document will be
the 'source' which contains the file name of the export.
""" # noqa: E501
"""
def __init__(self, file_path: Union[str, Path], load_single_document: bool = True):
"""Initialize with file path."""

6
libs/community/langchain_community/document_loaders/pebblo.py
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@ -208,9 +208,9 @@ class PebbloSafeLoader(BaseLoader):
if loading_end is True:
payload["loading_end"] = "true"
if "loader_details" in payload:
payload["loader_details"]["source_aggregate_size"] = ( # noqa
self.source_aggregate_size
)
payload["loader_details"][
"source_aggregate_size"
] = self.source_aggregate_size
payload = Doc(**payload).dict(exclude_unset=True)
load_doc_url = f"{self.classifier_url}{LOADER_DOC_URL}"
classified_docs = []

2
libs/community/langchain_community/document_transformers/__init__.py
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@ -81,7 +81,7 @@ _module_lookup = {
"GoogleTranslateTransformer": "langchain_community.document_transformers.google_translate", # noqa: E501
"Html2TextTransformer": "langchain_community.document_transformers.html2text",
"LongContextReorder": "langchain_community.document_transformers.long_context_reorder", # noqa: E501
"MarkdownifyTransformer": "langchain_community.document_transformers.markdownify", # noqa: E501
"MarkdownifyTransformer": "langchain_community.document_transformers.markdownify",
"NucliaTextTransformer": "langchain_community.document_transformers.nuclia_text_transform", # noqa: E501
"OpenAIMetadataTagger": "langchain_community.document_transformers.openai_functions", # noqa: E501
"get_stateful_documents": "langchain_community.document_transformers.embeddings_redundant_filter", # noqa: E501

2
libs/community/langchain_community/document_transformers/openai_functions.py
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@ -57,7 +57,7 @@ class OpenAIMetadataTagger(BaseDocumentTransformer, BaseModel):
new_documents = []
for document in documents:
extracted_metadata: Dict = self.tagging_chain.run(document.page_content) # type: ignore[assignment] # noqa: E501
extracted_metadata: Dict = self.tagging_chain.run(document.page_content) # type: ignore[assignment]
new_document = Document(
page_content=document.page_content,
metadata={**extracted_metadata, **document.metadata},

2
libs/community/langchain_community/embeddings/__init__.py
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@ -173,7 +173,7 @@ if TYPE_CHECKING:
QuantizedBiEncoderEmbeddings,
)
from langchain_community.embeddings.oracleai import (
OracleEmbeddings, # noqa: F401
OracleEmbeddings,
)
from langchain_community.embeddings.premai import (
PremAIEmbeddings,

2
libs/community/langchain_community/embeddings/azure_openai.py
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@ -44,7 +44,7 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
""" # noqa: E501
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.

2
libs/community/langchain_community/embeddings/elasticsearch.py
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@ -25,7 +25,7 @@ class ElasticsearchEmbeddings(Embeddings):
In Elasticsearch you need to have an embedding model loaded and deployed.
- https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model.html
- https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-deploy-models.html
""" # noqa: E501
"""
def __init__(
self,

2
libs/community/langchain_community/embeddings/infinity.py
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@ -182,7 +182,7 @@ class TinyAsyncOpenAIInfinityEmbeddingClient: #: :meta private:
length_sorted_idx = np.argsort([-sorter(sen) for sen in texts])
texts_sorted = [texts[idx] for idx in length_sorted_idx]
return texts_sorted, lambda unsorted_embeddings: [ # noqa E731
return texts_sorted, lambda unsorted_embeddings: [ # E731
unsorted_embeddings[idx] for idx in np.argsort(length_sorted_idx)
]

2
libs/community/langchain_community/embeddings/localai.py
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@ -254,7 +254,7 @@ class LocalAIEmbeddings(BaseModel, Embeddings):
openai.proxy = {
"http": self.openai_proxy,
"https": self.openai_proxy,
} # type: ignore[assignment] # noqa: E501
} # type: ignore[assignment]
return openai_args
def _embedding_func(self, text: str, *, engine: str) -> List[float]:

2
libs/community/langchain_community/embeddings/openai.py
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@ -390,7 +390,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
openai.proxy = {
"http": self.openai_proxy,
"https": self.openai_proxy,
} # type: ignore[assignment] # noqa: E501
} # type: ignore[assignment]
return openai_args
# please refer to

4
libs/community/langchain_community/embeddings/yandex.py
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@ -107,13 +107,13 @@ class YandexGPTEmbeddings(BaseModel, Embeddings):
raise ValueError("'doc_model_uri' or 'folder_id' must be provided.")
values[
"doc_model_uri"
] = f"emb://{values['folder_id']}/{values['doc_model_name']}/{values['model_version']}" # noqa: E501
] = f"emb://{values['folder_id']}/{values['doc_model_name']}/{values['model_version']}"
if not values.get("model_uri"):
if values["folder_id"] == "":
raise ValueError("'model_uri' or 'folder_id' must be provided.")
values[
"model_uri"
] = f"emb://{values['folder_id']}/{values['model_name']}/{values['model_version']}" # noqa: E501
] = f"emb://{values['folder_id']}/{values['model_name']}/{values['model_version']}"
if values["disable_request_logging"]:
values["_grpc_metadata"].append(
(

8
libs/community/langchain_community/graphs/arangodb_graph.py
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@ -174,9 +174,9 @@ def get_arangodb_client(
"Unable to import arango, please install with `pip install python-arango`."
) from e
_url: str = url or os.environ.get("ARANGODB_URL", "http://localhost:8529") # type: ignore[assignment] # noqa: E501
_dbname: str = dbname or os.environ.get("ARANGODB_DBNAME", "_system") # type: ignore[assignment] # noqa: E501
_username: str = username or os.environ.get("ARANGODB_USERNAME", "root") # type: ignore[assignment] # noqa: E501
_password: str = password or os.environ.get("ARANGODB_PASSWORD", "") # type: ignore[assignment] # noqa: E501
_url: str = url or os.environ.get("ARANGODB_URL", "http://localhost:8529") # type: ignore[assignment]
_dbname: str = dbname or os.environ.get("ARANGODB_DBNAME", "_system") # type: ignore[assignment]
_username: str = username or os.environ.get("ARANGODB_USERNAME", "root") # type: ignore[assignment]
_password: str = password or os.environ.get("ARANGODB_PASSWORD", "") # type: ignore[assignment]
return ArangoClient(_url).db(_dbname, _username, _password, verify=True)

2
libs/community/langchain_community/llms/azureml_endpoint.py
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@ -496,7 +496,7 @@ class AzureMLOnlineEndpoint(BaseLLM, AzureMLBaseEndpoint):
timeout=120,
content_formatter=content_formatter,
)
""" # noqa: E501
"""
@property
def _identifying_params(self) -> Mapping[str, Any]:

6
libs/community/langchain_community/llms/openai.py
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@ -604,7 +604,7 @@ class BaseOpenAI(BaseLLM):
if self.openai_proxy:
import openai
openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[assignment] # noqa: E501
openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[assignment]
return {**openai_creds, **self._default_params}
@property
@ -800,7 +800,7 @@ class AzureOpenAI(BaseOpenAI):
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
""" # noqa: E501
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.
@ -1055,7 +1055,7 @@ class OpenAIChat(BaseLLM):
if openai_organization:
openai.organization = openai_organization
if openai_proxy:
openai.proxy = {"http": openai_proxy, "https": openai_proxy} # type: ignore[assignment] # noqa: E501
openai.proxy = {"http": openai_proxy, "https": openai_proxy} # type: ignore[assignment]
except ImportError:
raise ImportError(
"Could not import openai python package. "

2
libs/community/langchain_community/llms/symblai_nebula.py
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@ -41,7 +41,7 @@ class Nebula(LLM):
nebula_service_path="NEBULA_SERVICE_PATH",
nebula_api_key="NEBULA_API_KEY",
)
""" # noqa: E501
"""
"""Key/value arguments to pass to the model. Reserved for future use"""
model_kwargs: Optional[dict] = None

2
libs/community/langchain_community/output_parsers/ernie_functions.py
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@ -164,7 +164,7 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
else:
fn_name = _result["name"]
_args = _result["arguments"]
pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args) # type: ignore # noqa: E501
pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args) # type: ignore
return pydantic_args

2
libs/community/langchain_community/retrievers/google_vertex_ai_search.py
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@ -190,7 +190,7 @@ class _BaseGoogleVertexAISearchRetriever(BaseModel):
print( # noqa: T201
"Make sure that your data store is using Advanced Website "
"Indexing.\n"
"https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features#advanced-website-indexing" # noqa: E501
"https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features#advanced-website-indexing"
)
return documents

2
libs/community/langchain_community/tools/bearly/tool.py
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@ -59,7 +59,7 @@ If you have any files outputted write them to "output/" relative to the executio
path. Output can only be read from the directory, stdout, and stdin. \
Do not use things like plot.show() as it will \
not work instead write them out `output/` and a link to the file will be returned. \
print() any output and results so you can capture the output.""" # noqa: T201
print() any output and results so you can capture the output."""
class FileInfo(BaseModel):

2
libs/community/langchain_community/tools/powerbi/tool.py
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@ -51,7 +51,7 @@ class QueryPowerBITool(BaseTool):
for var in llm_chain.prompt.input_variables:
if var not in ["tool_input", "tables", "schemas", "examples"]:
raise ValueError(
"LLM chain for QueryPowerBITool must have input variables ['tool_input', 'tables', 'schemas', 'examples'], found %s", # noqa: C0301 E501 # pylint: disable=C0301
"LLM chain for QueryPowerBITool must have input variables ['tool_input', 'tables', 'schemas', 'examples'], found %s", # noqa: E501 # pylint: disable=C0301
llm_chain.prompt.input_variables,
)
return llm_chain

2
libs/community/langchain_community/utilities/__init__.py
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@ -105,7 +105,7 @@ if TYPE_CHECKING:
OpenWeatherMapAPIWrapper,
)
from langchain_community.utilities.oracleai import (
OracleSummary, # noqa: F401
OracleSummary,
)
from langchain_community.utilities.outline import (
OutlineAPIWrapper,

4
libs/community/langchain_community/utilities/arxiv.py
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@ -103,7 +103,7 @@ class ArxivAPIWrapper(BaseModel):
Args:
query: a plaintext search query
""" # noqa: E501
"""
try:
if self.is_arxiv_identifier(query):
results = self.arxiv_search(
@ -142,7 +142,7 @@ class ArxivAPIWrapper(BaseModel):
Args:
query: a plaintext search query
""" # noqa: E501
"""
try:
if self.is_arxiv_identifier(query):
results = self.arxiv_search(

2
libs/community/langchain_community/utilities/awslambda.py
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@ -60,7 +60,7 @@ class LambdaWrapper(BaseModel):
query: an input to passed to the lambda
function as the ``body`` of a JSON
object.
""" # noqa: E501
"""
res = self.lambda_client.invoke(
FunctionName=self.function_name,
InvocationType="RequestResponse",

2
libs/community/langchain_community/utilities/powerbi.py
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@ -61,7 +61,7 @@ class PowerBIDataset(BaseModel):
"""Get the request url."""
if self.group_id:
return f"{BASE_URL}/groups/{self.group_id}/datasets/{self.dataset_id}/executeQueries" # noqa: E501 # pylint: disable=C0301
return f"{BASE_URL}/datasets/{self.dataset_id}/executeQueries" # noqa: E501 # pylint: disable=C0301
return f"{BASE_URL}/datasets/{self.dataset_id}/executeQueries" # pylint: disable=C0301
@property
def headers(self) -> Dict[str, str]:

2
libs/community/langchain_community/utilities/tavily_search.py
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@ -97,7 +97,7 @@ class TavilySearchAPIWrapper(BaseModel):
content: The content of the result.
score: The score of the result.
raw_content: The raw content of the result.
""" # noqa: E501
"""
raw_search_results = self.raw_results(
query,
max_results=max_results,

4
libs/community/langchain_community/utilities/twilio.py
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@ -40,7 +40,7 @@ class TwilioAPIWrapper(BaseModel):
Twilio also work here. You cannot, for example, spoof messages from a private
cell phone number. If you are using `messaging_service_sid`, this parameter
must be empty.
""" # noqa: E501
"""
class Config:
"""Configuration for this pydantic object."""
@ -77,6 +77,6 @@ class TwilioAPIWrapper(BaseModel):
SMS/MMS or
[Channel user address](https://www.twilio.com/docs/sms/channels#channel-addresses)
for other 3rd-party channels.
""" # noqa: E501
"""
message = self.client.messages.create(to, from_=self.from_number, body=body)
return message.sid

4
libs/community/langchain_community/vectorstores/__init__.py
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@ -182,7 +182,7 @@ if TYPE_CHECKING:
OpenSearchVectorSearch,
)
from langchain_community.vectorstores.oraclevs import (
OracleVS, # noqa: F401
OracleVS,
)
from langchain_community.vectorstores.pathway import (
PathwayVectorClient,
@ -448,7 +448,7 @@ _module_lookup = {
"MyScale": "langchain_community.vectorstores.myscale",
"MyScaleSettings": "langchain_community.vectorstores.myscale",
"Neo4jVector": "langchain_community.vectorstores.neo4j_vector",
"NeuralDBClientVectorStore": "langchain_community.vectorstores.thirdai_neuraldb", # noqa: E501
"NeuralDBClientVectorStore": "langchain_community.vectorstores.thirdai_neuraldb",
"NeuralDBVectorStore": "langchain_community.vectorstores.thirdai_neuraldb",
"OpenSearchVectorSearch": "langchain_community.vectorstores.opensearch_vector_search", # noqa: E501
"OracleVS": "langchain_community.vectorstores.oraclevs",

2
libs/community/langchain_community/vectorstores/jaguar.py
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@ -431,7 +431,7 @@ class Jaguar(VectorStore):
def prt(self, msg: str) -> None:
with open("/tmp/debugjaguar.log", "a") as file:
print(f"msg={msg}", file=file, flush=True) # noqa: T201
print(f"msg={msg}", file=file, flush=True)
def _parseMeta(self, nvmap: dict, filecol: str) -> Tuple[List[str], List[str], str]:
filepath = ""

2
libs/community/langchain_community/vectorstores/momento_vector_index.py
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@ -397,7 +397,7 @@ class MomentoVectorIndex(VectorStore):
)
selected = [response.hits[i].metadata for i in mmr_selected]
return [
Document(page_content=metadata.pop(self.text_field, ""), metadata=metadata) # type: ignore # noqa: E501
Document(page_content=metadata.pop(self.text_field, ""), metadata=metadata) # type: ignore
for metadata in selected
]

4
libs/community/langchain_community/vectorstores/qdrant.py
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@ -1681,7 +1681,7 @@ class Qdrant(VectorStore):
f"Existing Qdrant collection {collection_name} uses named vectors. "
f"If you want to reuse it, please set `vector_name` to any of the "
f"existing named vectors: "
f"{', '.join(current_vector_config.keys())}." # noqa
f"{', '.join(current_vector_config.keys())}."
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
@ -1846,7 +1846,7 @@ class Qdrant(VectorStore):
f"Existing Qdrant collection {collection_name} uses named vectors. "
f"If you want to reuse it, please set `vector_name` to any of the "
f"existing named vectors: "
f"{', '.join(current_vector_config.keys())}." # noqa
f"{', '.join(current_vector_config.keys())}."
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)

8
libs/community/langchain_community/vectorstores/redis/base.py
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@ -383,7 +383,7 @@ class Redis(VectorStore):
# type check for metadata
if metadatas:
if isinstance(metadatas, list) and len(metadatas) != len(texts): # type: ignore # noqa: E501
if isinstance(metadatas, list) and len(metadatas) != len(texts): # type: ignore
raise ValueError("Number of metadatas must match number of texts")
if not (isinstance(metadatas, list) and isinstance(metadatas[0], dict)):
raise ValueError("Metadatas must be a list of dicts")
@ -704,7 +704,7 @@ class Redis(VectorStore):
# type check for metadata
if metadatas:
if isinstance(metadatas, list) and len(metadatas) != len(texts): # type: ignore # noqa: E501
if isinstance(metadatas, list) and len(metadatas) != len(texts): # type: ignore
raise ValueError("Number of metadatas must match number of texts")
if not (isinstance(metadatas, list) and isinstance(metadatas[0], dict)):
raise ValueError("Metadatas must be a list of dicts")
@ -832,7 +832,7 @@ class Redis(VectorStore):
# Perform vector search
# ignore type because redis-py is wrong about bytes
try:
results = self.client.ft(self.index_name).search(redis_query, params_dict) # type: ignore # noqa: E501
results = self.client.ft(self.index_name).search(redis_query, params_dict) # type: ignore
except redis.exceptions.ResponseError as e:
# split error message and see if it starts with "Syntax"
if str(e).split(" ")[0] == "Syntax":
@ -947,7 +947,7 @@ class Redis(VectorStore):
# Perform vector search
# ignore type because redis-py is wrong about bytes
try:
results = self.client.ft(self.index_name).search(redis_query, params_dict) # type: ignore # noqa: E501
results = self.client.ft(self.index_name).search(redis_query, params_dict) # type: ignore
except redis.exceptions.ResponseError as e:
# split error message and see if it starts with "Syntax"
if str(e).split(" ")[0] == "Syntax":

2
libs/community/langchain_community/vectorstores/timescalevector.py
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@ -66,7 +66,7 @@ class TimescaleVector(VectorStore):
collection_name=COLLECTION_NAME,
service_url=SERVICE_URL,
)
""" # noqa: E501
"""
def __init__(
self,

2
libs/community/tests/integration_tests/document_loaders/test_tensorflow_datasets.py
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@ -12,7 +12,7 @@ from langchain_community.document_loaders.tensorflow_datasets import (
)
if TYPE_CHECKING:
import tensorflow as tf # noqa: E402
import tensorflow as tf
def decode_to_str(item: tf.Tensor) -> str:

2
libs/community/tests/integration_tests/utilities/test_tensorflow_datasets.py
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@ -10,7 +10,7 @@ from langchain_core.pydantic_v1 import ValidationError
from langchain_community.utilities.tensorflow_datasets import TensorflowDatasets
if TYPE_CHECKING:
import tensorflow as tf # noqa: E402
import tensorflow as tf
def decode_to_str(item: tf.Tensor) -> str:

2
libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_add_texts.py
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@ -7,7 +7,7 @@ from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import ( # noqa
from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import (
qdrant_locations,
)

2
libs/community/tests/integration_tests/vectorstores/test_azure_cosmos_db.py
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@ -74,7 +74,7 @@ class TestAzureCosmosDBVectorSearch:
# insure the test collection is empty
collection = prepare_collection()
assert collection.count_documents({}) == 0 # type: ignore[index] # noqa: E501
assert collection.count_documents({}) == 0 # type: ignore[index]
@classmethod
def teardown_class(cls) -> None:

2
libs/community/tests/integration_tests/vectorstores/test_documentdb.py
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@ -70,7 +70,7 @@ class TestDocumentDBVectorSearch:
# insure the test collection is empty
collection = prepare_collection()
assert collection.count_documents({}) == 0 # type: ignore[index] # noqa: E501
assert collection.count_documents({}) == 0 # type: ignore[index]
@classmethod
def teardown_class(cls) -> None:

2
libs/community/tests/integration_tests/vectorstores/test_mongodb_atlas.py
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@ -34,7 +34,7 @@ class TestMongoDBAtlasVectorSearch:
def setup_class(cls) -> None:
# insure the test collection is empty
collection = get_collection()
assert collection.count_documents({}) == 0 # type: ignore[index] # noqa: E501
assert collection.count_documents({}) == 0 # type: ignore[index]
@classmethod
def teardown_class(cls) -> None:

4
libs/community/tests/unit_tests/document_loaders/parsers/language/test_lua.py
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@ -33,7 +33,7 @@ end"""
@pytest.mark.skip(
reason=(
"Flakey. To be investigated. See "
"https://github.com/langchain-ai/langchain/actions/runs/7907779756/job/21585580650." # noqa: E501
"https://github.com/langchain-ai/langchain/actions/runs/7907779756/job/21585580650."
)
)
def test_extract_functions_classes(self) -> None:
@ -46,7 +46,7 @@ end"""
reason=(
"Flakey. To be investigated. See "
"https://github.com/langchain-ai/langchain/actions/runs/7923203031/job/21632416298?pr=17599 " # noqa: E501
"and https://github.com/langchain-ai/langchain/actions/runs/7923784089/job/2163420864." # noqa: E501
"and https://github.com/langchain-ai/langchain/actions/runs/7923784089/job/2163420864."
)
)
def test_simplify_code(self) -> None:

2
libs/community/tests/unit_tests/llms/test_llamafile.py
Unescape Escape View File

@ -42,7 +42,7 @@ def mock_response() -> requests.Response:
def mock_response_stream(): # type: ignore[no-untyped-def]
mock_response = deque(
[
b'data: {"content":"the","multimodal":false,"slot_id":0,"stop":false}\n\n', # noqa
b'data: {"content":"the","multimodal":false,"slot_id":0,"stop":false}\n\n',
b'data: {"content":" quick","multimodal":false,"slot_id":0,"stop":false}\n\n', # noqa
]
)

2
libs/community/tests/unit_tests/test_cache.py
Unescape Escape View File

@ -22,7 +22,7 @@ except ImportError:
from langchain.globals import get_llm_cache, set_llm_cache
from langchain_core.outputs import Generation, LLMResult
from langchain_community.cache import SQLAlchemyCache # noqa: E402
from langchain_community.cache import SQLAlchemyCache
from tests.unit_tests.llms.fake_llm import FakeLLM

2
libs/community/tests/unit_tests/test_document_transformers.py
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@ -3,7 +3,7 @@ import pytest
pytest.importorskip("langchain_community")
from langchain_community.document_transformers.embeddings_redundant_filter import ( # noqa: E402,E501
from langchain_community.document_transformers.embeddings_redundant_filter import ( # noqa: E402
_filter_similar_embeddings,
)
from langchain_community.utils.math import cosine_similarity # noqa: E402

2
libs/core/langchain_core/messages/ai.py
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@ -110,7 +110,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["AIMessageChunk"] = "AIMessageChunk" # type: ignore[assignment] # noqa: E501
type: Literal["AIMessageChunk"] = "AIMessageChunk" # type: ignore[assignment]
tool_call_chunks: List[ToolCallChunk] = []
"""If provided, tool call chunks associated with the message."""

2
libs/core/langchain_core/messages/human.py
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@ -28,7 +28,7 @@ class HumanMessageChunk(HumanMessage, BaseMessageChunk):
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["HumanMessageChunk"] = "HumanMessageChunk" # type: ignore[assignment] # noqa: E501
type: Literal["HumanMessageChunk"] = "HumanMessageChunk" # type: ignore[assignment]
@classmethod
def get_lc_namespace(cls) -> List[str]:

2
libs/core/langchain_core/messages/system.py
Unescape Escape View File

@ -25,7 +25,7 @@ class SystemMessageChunk(SystemMessage, BaseMessageChunk):
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["SystemMessageChunk"] = "SystemMessageChunk" # type: ignore[assignment] # noqa: E501
type: Literal["SystemMessageChunk"] = "SystemMessageChunk" # type: ignore[assignment]
@classmethod
def get_lc_namespace(cls) -> List[str]:

2
libs/core/langchain_core/output_parsers/openai_functions.py
Unescape Escape View File

@ -205,7 +205,7 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
else:
fn_name = _result["name"]
_args = _result["arguments"]
pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args) # type: ignore # noqa: E501
pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args) # type: ignore
return pydantic_args

2
libs/core/langchain_core/outputs/chat_generation.py
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@ -61,7 +61,7 @@ class ChatGenerationChunk(ChatGeneration):
message: BaseMessageChunk
# Override type to be ChatGeneration, ignore mypy error as this is intentional
type: Literal["ChatGenerationChunk"] = "ChatGenerationChunk" # type: ignore[assignment] # noqa: E501
type: Literal["ChatGenerationChunk"] = "ChatGenerationChunk" # type: ignore[assignment]
"""Type is used exclusively for serialization purposes."""
@classmethod

2
libs/core/langchain_core/prompts/chat.py
Unescape Escape View File

@ -431,7 +431,7 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate):
if isinstance(tmpl, str):
text: str = tmpl
else:
text = cast(_TextTemplateParam, tmpl)["text"] # type: ignore[assignment] # noqa: E501
text = cast(_TextTemplateParam, tmpl)["text"] # type: ignore[assignment]
prompt.append(
PromptTemplate.from_template(
text, template_format=template_format

2
libs/core/langchain_core/runnables/base.py
Unescape Escape View File

@ -517,7 +517,7 @@ class Runnable(Generic[Input, Output], ABC):
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
""" # noqa: E501
"""
from langchain_core.runnables.passthrough import RunnablePick
return self | RunnablePick(keys)

4
libs/core/langchain_core/runnables/graph_ascii.py
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@ -244,8 +244,8 @@ def draw_ascii(vertices: Mapping[str, str], edges: Sequence[LangEdge]) -> str:
# NOTE: coordinates might me negative, so we need to shift
# everything to the positive plane before we actually draw it.
Xs = [] # noqa: N806
Ys = [] # noqa: N806
Xs = []
Ys = []
sug = _build_sugiyama_layout(vertices, edges)

4
libs/core/langchain_core/runnables/history.py
Unescape Escape View File

@ -214,7 +214,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):
config={"configurable": {"user_id": "123", "conversation_id": "1"}}
)
""" # noqa: E501
"""
get_session_history: GetSessionHistoryCallable
input_messages_key: Optional[str] = None
@ -297,7 +297,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):
into the get_session_history factory.
**kwargs: Arbitrary additional kwargs to pass to parent class
``RunnableBindingBase`` init.
""" # noqa: E501
"""
history_chain: Runnable = RunnableLambda(
self._enter_history, self._aenter_history
).with_config(run_name="load_history")

4
libs/core/langchain_core/tracers/context.py
Unescape Escape View File

@ -33,10 +33,10 @@ if TYPE_CHECKING:
tracing_callback_var: Any = None
tracing_v2_callback_var: ContextVar[Optional[LangChainTracer]] = ContextVar(
"tracing_callback_v2", default=None
) # noqa: E501
)
run_collector_var: ContextVar[Optional[RunCollectorCallbackHandler]] = ContextVar(
"run_collector", default=None
) # noqa: E501
)
@contextmanager

2
libs/core/langchain_core/utils/input.py
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@ -38,6 +38,6 @@ def print_text(
) -> None:
"""Print text with highlighting and no end characters."""
text_to_print = get_colored_text(text, color) if color else text
print(text_to_print, end=end, file=file) # noqa: T201
print(text_to_print, end=end, file=file)
if file:
file.flush() # ensure all printed content are written to file

2
libs/core/tests/unit_tests/runnables/test_utils.py
Unescape Escape View File

@ -42,7 +42,7 @@ def test_indent_lines_after_first(text: str, prefix: str, expected_output: str)
def test_nonlocals() -> None:
agent = RunnableLambda(lambda x: x * 2) # noqa: F841
agent = RunnableLambda(lambda x: x * 2)
def my_func(input: str, agent: Dict[str, str]) -> str:
return agent.get("agent_name", input)

2
libs/core/tests/unit_tests/test_messages.py
Unescape Escape View File

@ -143,7 +143,7 @@ def test_chat_message_chunks() -> None:
role="User", content=" indeed."
) == AIMessageChunk(
content="I am indeed."
), "Other MessageChunk + ChatMessageChunk should be a MessageChunk as the left side" # noqa: E501
), "Other MessageChunk + ChatMessageChunk should be a MessageChunk as the left side"
def test_function_message_chunks() -> None:

8
libs/core/tests/unit_tests/test_tools.py
Unescape Escape View File

@ -626,7 +626,7 @@ def test_exception_handling_callable() -> None:
expected = "foo bar"
def handling(e: ToolException) -> str:
return expected # noqa: E731
return expected
_tool = _FakeExceptionTool(handle_tool_error=handling)
actual = _tool.run({})
@ -657,7 +657,7 @@ async def test_async_exception_handling_callable() -> None:
expected = "foo bar"
def handling(e: ToolException) -> str:
return expected # noqa: E731
return expected
_tool = _FakeExceptionTool(handle_tool_error=handling)
actual = await _tool.arun({})
@ -723,7 +723,7 @@ def test_validation_error_handling_callable() -> None:
expected = "foo bar"
def handling(e: ValidationError) -> str:
return expected # noqa: E731
return expected
_tool = _MockStructuredTool(handle_validation_error=handling)
actual = _tool.run({})
@ -785,7 +785,7 @@ async def test_async_validation_error_handling_callable() -> None:
expected = "foo bar"
def handling(e: ValidationError) -> str:
return expected # noqa: E731
return expected
_tool = _MockStructuredTool(handle_validation_error=handling)
actual = await _tool.arun({})

2
libs/experimental/langchain_experimental/agents/__init__.py
Unescape Escape View File

@ -6,7 +6,7 @@ a language model is used as a reasoning engine to determine which actions
to take and in which order.
Agents select and use **Tools** and **Toolkits** for actions.
""" # noqa: E501
"""
from langchain_experimental.agents.agent_toolkits import (
create_csv_agent,
create_pandas_dataframe_agent,

2
libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py
Unescape Escape View File

@ -220,7 +220,7 @@ def create_pandas_dataframe_agent(
verbose=True
)
""" # noqa: E501
"""
try:
if engine == "modin":
import modin.pandas as pd

2
libs/experimental/langchain_experimental/chat_models/llm_wrapper.py
Unescape Escape View File

@ -149,7 +149,7 @@ class Llama2Chat(ChatWrapper):
class Mixtral(ChatWrapper):
"""See https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#instruction-format""" # noqa: E501
"""See https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#instruction-format"""
@property
def _llm_type(self) -> str:

10
libs/experimental/langchain_experimental/llm_bash/bash.py
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@ -88,7 +88,7 @@ class BashProcess:
Args:
Prompt(str): the bash command to execute
""" # noqa: E501
"""
pexpect = self._lazy_import_pexpect()
process = pexpect.spawn(
"env", ["-i", "bash", "--norc", "--noprofile"], encoding="utf-8"
@ -107,7 +107,7 @@ class BashProcess:
Args:
commands(List[str]): a list of commands to
execute in the session
""" # noqa: E501
"""
if isinstance(commands, str):
commands = [commands]
commands = ";".join(commands)
@ -125,7 +125,7 @@ class BashProcess:
Args:
command: The command to run
""" # noqa: E501
"""
try:
output = subprocess.run(
command,
@ -149,7 +149,7 @@ class BashProcess:
Args:
output: a process' output string
command: the executed command
""" # noqa: E501
"""
pattern = re.escape(command) + r"\s*\n"
output = re.sub(pattern, "", output, count=1)
return output.strip()
@ -161,7 +161,7 @@ class BashProcess:
Args:
command: the command to execute
""" # noqa: E501
"""
pexpect = self._lazy_import_pexpect()
if self.process is None:
raise ValueError("Process not initialized")

2
libs/experimental/langchain_experimental/pal_chain/__init__.py
Unescape Escape View File

@ -3,7 +3,7 @@
See the paper: https://arxiv.org/pdf/2211.10435.pdf.
This chain is vulnerable to [arbitrary code execution](https://github.com/langchain-ai/langchain/issues/5872).
""" # noqa: E501
"""
from langchain_experimental.pal_chain.base import PALChain
__all__ = ["PALChain"]

2
libs/experimental/langchain_experimental/recommenders/__init__.py
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@ -3,7 +3,7 @@
[Amazon Personalize](https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html)
is a fully managed machine learning service that uses your data to generate
item recommendations for your users.
""" # noqa: E501
"""
from langchain_experimental.recommenders.amazon_personalize import AmazonPersonalize
from langchain_experimental.recommenders.amazon_personalize_chain import (
AmazonPersonalizeChain,

2
libs/experimental/langchain_experimental/rl_chain/base.py
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@ -475,7 +475,7 @@ class RLChain(Chain, Generic[TEvent]):
def save_progress(self) -> None:
"""
This function should be called to save the state of the learned policy model.
""" # noqa: E501
"""
self.active_policy.save()
def _validate_inputs(self, inputs: Dict[str, Any]) -> None:

2
libs/experimental/tests/unit_tests/chat_models/test_llm_wrapper_mixtral.py
Unescape Escape View File

@ -25,7 +25,7 @@ def test_prompt(model: Mixtral) -> None:
actual = model.invoke(messages).content # type: ignore
expected = (
"<s>[INST] sys-msg\nusr-msg-1 [/INST] ai-msg-1 </s> [INST] usr-msg-2 [/INST]" # noqa: E501
"<s>[INST] sys-msg\nusr-msg-1 [/INST] ai-msg-1 </s> [INST] usr-msg-2 [/INST]"
)
assert actual == expected

2
libs/experimental/tests/unit_tests/python/test_python_2.py
Unescape Escape View File

@ -78,7 +78,7 @@ string = "racecar"
if string == string[::-1]:
print(string, "is a palindrome") # noqa: T201
else:
print(string, "is not a palindrome")""" # noqa: T201
print(string, "is not a palindrome")"""
tool = PythonAstREPLTool()
assert tool.run(program) == "racecar is a palindrome\n"

2
libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/openai_functions.py
Unescape Escape View File

@ -1,4 +1,4 @@
from typing import Any, List, Optional # noqa: E501
from typing import Any, List, Optional
from langchain_core.language_models import BaseLanguageModel
from langchain_core.memory import BaseMemory

4
libs/langchain/langchain/callbacks/tracers/logging.py
Unescape Escape View File

@ -33,8 +33,8 @@ class LoggingCallbackHandler(FunctionCallbackHandler):
text: str,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None, # noqa: ARG002
**kwargs: Any, # noqa: ARG002
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
try:
crumbs_str = f"[{self.get_breadcrumbs(run=self._get_run(run_id=run_id))}] "

6
libs/langchain/langchain/chains/openai_functions/openapi.py
Unescape Escape View File

@ -76,7 +76,7 @@ def _openapi_params_to_json_schema(params: List[Parameter], spec: OpenAPISpec) -
if p.param_schema:
schema = spec.get_schema(p.param_schema)
else:
media_type_schema = list(p.content.values())[0].media_type_schema # type: ignore # noqa: E501
media_type_schema = list(p.content.values())[0].media_type_schema # type: ignore
schema = spec.get_schema(media_type_schema)
if p.description and not schema.description:
schema.description = p.description
@ -237,7 +237,7 @@ class SimpleRequestChain(Chain):
else:
try:
response = api_response.json()
except Exception: # noqa: E722
except Exception:
response = api_response.text
return {self.output_key: response}
@ -280,7 +280,7 @@ def get_openapi_chain(
break
except ImportError as e:
raise e
except Exception: # noqa: E722
except Exception:
pass
if isinstance(spec, str):
raise ValueError(f"Unable to parse spec from source {spec}")

2
libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py
Unescape Escape View File

@ -141,7 +141,7 @@ class TrajectoryEvalChain(AgentTrajectoryEvaluator, LLMEvalChain):
)
print(result["score"]) # noqa: T201
# 0
""" # noqa: E501
"""
agent_tools: Optional[List[BaseTool]] = None
"""A list of tools available to the agent."""

4
libs/langchain/langchain/evaluation/criteria/eval_chain.py
Unescape Escape View File

@ -142,7 +142,7 @@ def resolve_criteria(
>>> criterion = "relevance"
>>> CriteriaEvalChain.resolve_criteria(criteria)
{'relevance': 'Is the submission referring to a real quote from the text?'}
""" # noqa: E501
"""
if criteria is None:
return {
"helpfulness": _SUPPORTED_CRITERIA[Criteria.HELPFULNESS],
@ -307,7 +307,7 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
>>> criterion = "relevance"
>>> CriteriaEvalChain.resolve_criteria(criteria)
{'relevance': 'Is the submission referring to a real quote from the text?'}
""" # noqa: E501
"""
return resolve_criteria(criteria)
@classmethod

2
libs/langchain/langchain/evaluation/loading.py
Unescape Escape View File

@ -56,7 +56,7 @@ def load_dataset(uri: str) -> List[Dict]:
from langchain.evaluation import load_dataset
ds = load_dataset("llm-math")
""" # noqa: E501
"""
try:
from datasets import load_dataset
except ImportError:

2
libs/langchain/langchain/evaluation/scoring/eval_chain.py
Unescape Escape View File

@ -439,7 +439,7 @@ class LabeledScoreStringEvalChain(ScoreStringEvalChain):
Raises:
ValueError: If the input variables are not as expected.
""" # noqa: E501
"""
expected_input_vars = {
"prediction",
"input",

2
libs/langchain/langchain/smith/evaluation/config.py
Unescape Escape View File

@ -133,7 +133,7 @@ class RunEvalConfig(BaseModel):
as `EvaluatorType.QA`, the evaluator type string ("qa"), or a configuration for a
given evaluator
(e.g.,
:class:`RunEvalConfig.QA <langchain.smith.evaluation.config.RunEvalConfig.QA>`).""" # noqa: E501
:class:`RunEvalConfig.QA <langchain.smith.evaluation.config.RunEvalConfig.QA>`)."""
custom_evaluators: Optional[List[CUSTOM_EVALUATOR_TYPE]] = None
"""Custom evaluators to apply to the dataset run."""
batch_evaluators: Optional[List[BATCH_EVALUATOR_LIKE]] = None

2
libs/langchain/langchain/smith/evaluation/runner_utils.py
Unescape Escape View File

@ -225,7 +225,7 @@ def _wrap_in_chain_factory(
return lambda: RunnableLambda(constructor)
else:
# Typical correct case
return constructor # noqa
return constructor
return llm_or_chain_factory

2
libs/partners/anthropic/tests/integration_tests/test_experimental.py
Unescape Escape View File

@ -165,5 +165,5 @@ def test_anthropic_complex_structured_output() -> None:
{
"email": "From: Erick. The email is about the new project. The tone is positive. The action items are to send the report and to schedule a meeting." # noqa: E501
}
) # noqa: E501
)
assert isinstance(response, Email)

2
libs/partners/mongodb/tests/integration_tests/test_vectorstores.py
Unescape Escape View File

@ -51,7 +51,7 @@ class TestMongoDBAtlasVectorSearch:
# insure the test collection is empty
collection = get_collection()
if collection.count_documents({}):
collection.delete_many({}) # type: ignore[index] # noqa: E501
collection.delete_many({}) # type: ignore[index]
@classmethod
def teardown_class(cls) -> None:

2
libs/partners/mongodb/tests/unit_tests/test_vectorstores.py
Unescape Escape View File

@ -46,7 +46,7 @@ class TestMongoDBAtlasVectorSearch:
def setup_class(cls) -> None:
# ensure the test collection is empty
collection = get_collection()
assert collection.count_documents({}) == 0 # type: ignore[index] # noqa: E501
assert collection.count_documents({}) == 0 # type: ignore[index]
@classmethod
def teardown_class(cls) -> None:

4
libs/partners/openai/langchain_openai/chat_models/azure.py
Unescape Escape View File

@ -81,7 +81,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
""" # noqa: E501
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.
@ -178,7 +178,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
'azure_endpoint="https://xxx.openai.azure.com/", '
'azure_deployment="my-deployment"\n\n'
"Or you can equivalently specify:\n\n"
'base_url="https://xxx.openai.azure.com/openai/deployments/my-deployment"' # noqa: E501
'base_url="https://xxx.openai.azure.com/openai/deployments/my-deployment"'
)
client_params = {
"api_version": values["openai_api_version"],

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