diff --git a/poetry.lock b/poetry.lock index cd1c9a441..968047d46 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4941,7 +4941,6 @@ asyncer = "^0.0.5" bcrypt = "4.0.1" cachetools = "^5.3.1" chardet = "^5.2.0" -crewai = "^0.36.0" cryptography = "^42.0.5" docstring-parser = "^0.16" duckdb = "^1.0.0" @@ -4954,7 +4953,6 @@ jq = {version = "^1.7.0", markers = "sys_platform != \"win32\""} langchain = "~0.2.0" langchain-experimental = "^0.0.61" langchainhub = "~0.1.15" -langwatch = "^0.1.3" loguru = "^0.7.1" multiprocess = "^0.70.14" nest-asyncio = "^1.6.0" @@ -5038,13 +5036,13 @@ requests = ">=2,<3" [[package]] name = "langwatch" -version = "0.1.8" +version = "0.1.9" description = "Python SDK for LangWatch for monitoring your LLMs" optional = false python-versions = "<4.0,>=3.9" files = [ - {file = "langwatch-0.1.8-py3-none-any.whl", hash = "sha256:e6e7ffe2f3cd61c477e8ff6de4ad3d1e06f63b0c8f02880d68293fc126ec2bf7"}, - {file = "langwatch-0.1.8.tar.gz", hash = "sha256:2ccc3a741ef9bf493946264ab8fff5cb33845e51d4426136218d62b1a4cbd26d"}, + {file = "langwatch-0.1.9-py3-none-any.whl", hash = "sha256:d082f70bb7f6aa67623281e3e624065932bc6e5729322637d8957cc63942a007"}, + {file = "langwatch-0.1.9.tar.gz", hash = "sha256:3f044b668239b0e4cc244635f8ceeb7e10948c736f6b60bcda7c26852308b2f8"}, ] [package.dependencies] @@ -11737,4 +11735,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "afe38792a9e202ab55558389026b64beb95d1437b2fd4bccfbb300cbc7ad0677" +content-hash = "53efe77ea0fe985d9319884d4e0ade4d1949f6348ecf75d6afa7297483dd314c" diff --git a/pyproject.toml b/pyproject.toml index c66d09dbe..448d5182e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -99,7 +99,8 @@ langchain-nvidia-ai-endpoints = "^0.1.2" langchain-google-calendar-tools = "^0.0.1" langchain-milvus = "^0.1.1" crewai = {extras = ["tools"], version = "^0.36.0"} -langwatch = "^0.1.7" +langwatch = "^0.1.9" +langsmith = "^0.1.86" [tool.poetry.group.dev.dependencies] diff --git a/src/backend/base/langflow/services/tracing/langsmith.py b/src/backend/base/langflow/services/tracing/langsmith.py new file mode 100644 index 000000000..8501477d0 --- /dev/null +++ b/src/backend/base/langflow/services/tracing/langsmith.py @@ -0,0 +1,151 @@ +import os +import traceback +import types +from datetime import datetime, timezone +from typing import TYPE_CHECKING, Any, Dict, Optional +from uuid import UUID + +from loguru import logger + +from langflow.schema.data import Data +from langflow.services.tracing.base import BaseTracer +from langflow.services.tracing.schema import Log + +if TYPE_CHECKING: + from langflow.graph.vertex.base import Vertex + + +class LangSmithTracer(BaseTracer): + def __init__(self, trace_name: str, trace_type: str, project_name: str, trace_id: UUID): + from langsmith.run_trees import RunTree + + self.trace_name = trace_name + self.trace_type = trace_type + self.project_name = project_name + self.trace_id = trace_id + try: + self._run_tree = RunTree( + project_name=self.project_name, + name=self.trace_name, + run_type=self.trace_type, + id=self.trace_id, + ) + self._run_tree.add_event({"name": "Start", "time": datetime.now(timezone.utc).isoformat()}) + self._children: dict[str, RunTree] = {} + self._ready = self.setup_langsmith() + except Exception as e: + logger.debug(f"Error setting up LangSmith tracer: {e}") + self._ready = False + + @property + def ready(self): + return self._ready + + def setup_langsmith(self): + try: + from langsmith import Client + + self._client = Client() + except ImportError: + logger.error("Could not import langsmith. Please install it with `pip install langsmith`.") + return False + os.environ["LANGCHAIN_TRACING_V2"] = "true" + return True + + def add_trace( + self, + trace_id: str, + trace_name: str, + trace_type: str, + inputs: Dict[str, Any], + metadata: Dict[str, Any] | None = None, + vertex: Optional["Vertex"] = None, + ): + if not self._ready: + return + processed_inputs = {} + if inputs: + processed_inputs = self._convert_to_langchain_types(inputs) + child = self._run_tree.create_child( + name=trace_name, + run_type=trace_type, # type: ignore[arg-type] + inputs=processed_inputs, + ) + if metadata: + child.add_metadata(self._convert_to_langchain_types(metadata)) + self._children[trace_name] = child + self._child_link: dict[str, str] = {} + + def _convert_to_langchain_types(self, io_dict: Dict[str, Any]): + converted = {} + for key, value in io_dict.items(): + converted[key] = self._convert_to_langchain_type(value) + return converted + + def _convert_to_langchain_type(self, value): + from langflow.schema.message import Message + + if isinstance(value, dict): + for key, _value in value.copy().items(): + _value = self._convert_to_langchain_type(_value) + value[key] = _value + elif isinstance(value, list): + value = [self._convert_to_langchain_type(v) for v in value] + elif isinstance(value, Message): + if "prompt" in value: + value = value.load_lc_prompt() + elif value.sender: + value = value.to_lc_message() + else: + value = value.to_lc_document() + elif isinstance(value, Data): + value = value.to_lc_document() + elif isinstance(value, types.GeneratorType): + # generator is not serializable, also we can't consume it + value = str(value) + return value + + def end_trace( + self, + trace_id: str, + trace_name: str, + outputs: Dict[str, Any] | None = None, + error: Exception | None = None, + logs: list[Log | dict] = [], + ): + child = self._children[trace_name] + raw_outputs = {} + processed_outputs = {} + if outputs: + raw_outputs = outputs + processed_outputs = self._convert_to_langchain_types(outputs) + if logs: + child.add_metadata(self._convert_to_langchain_types({"logs": {log.get("name"): log for log in logs}})) + child.add_metadata(self._convert_to_langchain_types({"outputs": raw_outputs})) + child.end(outputs=processed_outputs, error=self._error_to_string(error)) + if error: + child.patch() + else: + child.post() + self._child_link[trace_name] = child.get_url() + + def _error_to_string(self, error: Optional[Exception]): + error_message = None + if error: + string_stacktrace = traceback.format_exception(error) + error_message = f"{error.__class__.__name__}: {error}\n\n{string_stacktrace}" + return error_message + + def end( + self, + inputs: dict[str, Any], + outputs: Dict[str, Any], + error: Exception | None = None, + metadata: dict[str, Any] | None = None, + ): + self._run_tree.add_metadata({"inputs": inputs}) + if metadata: + self._run_tree.add_metadata(metadata) + self._run_tree.end(outputs=outputs, error=self._error_to_string(error)) + self._run_tree.post() + self._run_link = self._run_tree.get_url() diff --git a/src/backend/base/langflow/services/tracing/langwatch.py b/src/backend/base/langflow/services/tracing/langwatch.py new file mode 100644 index 000000000..834d2fb5e --- /dev/null +++ b/src/backend/base/langflow/services/tracing/langwatch.py @@ -0,0 +1,178 @@ +from typing import TYPE_CHECKING, Any, Dict, Optional, cast +from uuid import UUID + +import nanoid # type: ignore +from langwatch.tracer import ContextSpan +from loguru import logger + +from langflow.schema.data import Data +from langflow.services.tracing.base import BaseTracer +from langflow.services.tracing.schema import Log + +if TYPE_CHECKING: + from langwatch.tracer import ContextSpan + from langwatch.types import SpanTypes + + from langflow.graph.vertex.base import Vertex + + +class LangWatchTracer(BaseTracer): + flow_id: str + + def __init__(self, trace_name: str, trace_type: str, project_name: str, trace_id: UUID): + self.trace_name = trace_name + self.trace_type = trace_type + self.project_name = project_name + self.trace_id = trace_id + self.flow_id = trace_name.split(" - ")[-1] + + try: + self._ready = self.setup_langwatch() + + # import after setting up langwatch so we are sure to be available + import nanoid # type: ignore + + self.trace = self._client.trace( + trace_id=str(self.trace_id), + ) + self.spans: dict[str, "ContextSpan"] = {} + + name_without_id = " - ".join(trace_name.split(" - ")[0:-1]) + self.trace.root_span.update( + span_id=f"{self.flow_id}-{nanoid.generate(size=6)}", # nanoid to make the span_id globally unique, which is required for LangWatch for now + name=name_without_id, + type=self._convert_trace_type(trace_type), + ) + except Exception as e: + logger.debug(f"Error setting up LangWatch tracer: {e}") + self._ready = False + + @property + def ready(self): + return self._ready + + def setup_langwatch(self): + try: + import langwatch + + self._client = langwatch + except ImportError: + logger.error("Could not import langwatch. Please install it with `pip install langwatch`.") + return False + return True + + def _convert_trace_type(self, trace_type: str): + trace_type_: "SpanTypes" = ( + cast("SpanTypes", trace_type) + if trace_type in ["span", "llm", "chain", "tool", "agent", "guardrail", "rag"] + else "span" + ) + return trace_type_ + + def add_trace( + self, + trace_id: str, + trace_name: str, + trace_type: str, + inputs: Dict[str, Any], + metadata: Dict[str, Any] | None = None, + vertex: Optional["Vertex"] = None, + ): + # If user is not using session_id, then it becomes the same as flow_id, but + # we don't want to have an infinite thread with all the flow messages + if "session_id" in inputs and inputs["session_id"] != self.flow_id: + self.trace.update(metadata=(self.trace.metadata or {}) | {"thread_id": inputs["session_id"]}) + + name_without_id = " (".join(trace_name.split(" (")[0:-1]) + + trace_type_ = self._convert_trace_type(trace_type) + self.spans[trace_id] = self.trace.span( + span_id=f"{trace_id}-{nanoid.generate(size=6)}", # Add a nanoid to make the span_id globally unique, which is required for LangWatch for now + name=name_without_id, + type=trace_type_, + parent=( + [span for key, span in self.spans.items() for edge in vertex.incoming_edges if key == edge.source_id][ + -1 + ] + if vertex and len(vertex.incoming_edges) > 0 + else self.trace.root_span + ), + input=self._convert_to_langwatch_types(inputs), + ) + + if trace_type_ == "llm" and "model_name" in inputs: + self.spans[trace_id].update(model=inputs["model_name"]) + + def end_trace( + self, + trace_id: str, + trace_name: str, + outputs: Dict[str, Any] | None = None, + error: Exception | None = None, + logs: list[Log | dict] = [], + ): + if self.spans.get(trace_id): + # Workaround for when model is used just as a component not actually called as an LLM, + # to prevent LangWatch from calculating the cost based on it when it was in fact never called + if ( + self.spans[trace_id].type == "llm" + and outputs + and "model_output" in outputs + and "text_output" not in outputs + ): + self.spans[trace_id].update(metrics={"prompt_tokens": 0, "completion_tokens": 0}) + + self.spans[trace_id].end(output=self._convert_to_langwatch_types(outputs), error=error) + + def end( + self, + inputs: dict[str, Any], + outputs: Dict[str, Any], + error: Exception | None = None, + metadata: dict[str, Any] | None = None, + ): + self.trace.root_span.end( + input=self._convert_to_langwatch_types(inputs), + output=self._convert_to_langwatch_types(outputs), + error=error, + ) + + if metadata and "flow_name" in metadata: + self.trace.update(metadata=(self.trace.metadata or {}) | {"labels": [f"Flow: {metadata['flow_name']}"]}) + self.trace.deferred_send_spans() + + def _convert_to_langwatch_types(self, io_dict: Optional[Dict[str, Any]]): + from langwatch.utils import autoconvert_typed_values + + if io_dict is None: + return None + converted = {} + for key, value in io_dict.items(): + converted[key] = self._convert_to_langwatch_type(value) + return autoconvert_typed_values(converted) + + def _convert_to_langwatch_type(self, value): + from langwatch.langchain import langchain_message_to_chat_message, langchain_messages_to_chat_messages + + from langflow.schema.message import BaseMessage, Message + + if isinstance(value, dict): + for key, _value in value.copy().items(): + _value = self._convert_to_langwatch_type(_value) + value[key] = _value + elif isinstance(value, list): + value = [self._convert_to_langwatch_type(v) for v in value] + elif isinstance(value, Message): + if "prompt" in value: + prompt = value.load_lc_prompt() + if len(prompt.input_variables) == 0 and all(isinstance(m, BaseMessage) for m in prompt.messages): + value = langchain_messages_to_chat_messages([cast(list[BaseMessage], prompt.messages)]) + else: + value = cast(dict, value.load_lc_prompt()) + elif value.sender: + value = langchain_message_to_chat_message(value.to_lc_message()) + else: + value = cast(dict, value.to_lc_document()) + elif isinstance(value, Data): + value = cast(dict, value.to_lc_document()) + return value diff --git a/src/backend/base/langflow/services/tracing/service.py b/src/backend/base/langflow/services/tracing/service.py index 3b31ced6c..b524eee7d 100644 --- a/src/backend/base/langflow/services/tracing/service.py +++ b/src/backend/base/langflow/services/tracing/service.py @@ -1,27 +1,23 @@ import asyncio import os -import traceback -import types from collections import defaultdict from contextlib import asynccontextmanager -from datetime import datetime, timezone -from typing import TYPE_CHECKING, Any, Dict, Optional, cast +from typing import TYPE_CHECKING, Any, Dict, Optional from uuid import UUID from loguru import logger -from langflow.schema.data import Data from langflow.services.base import Service from langflow.services.tracing.base import BaseTracer +from langflow.services.tracing.langsmith import LangSmithTracer +from langflow.services.tracing.langwatch import LangWatchTracer from langflow.services.tracing.schema import Log if TYPE_CHECKING: - from langflow.services.monitor.service import MonitorService - from langflow.services.settings.service import SettingsService from langflow.custom.custom_component.component import Component from langflow.graph.vertex.base import Vertex - from langwatch.tracer import ContextSpan - from langwatch.types import SpanTypes + from langflow.services.monitor.service import MonitorService + from langflow.services.settings.service import SettingsService class TracingService(Service): @@ -221,305 +217,3 @@ class TracingService(Service): if "api_key" in key: inputs[key] = "*****" # avoid logging api_keys for security reasons return inputs - - -class LangSmithTracer(BaseTracer): - def __init__(self, trace_name: str, trace_type: str, project_name: str, trace_id: UUID): - from langsmith.run_trees import RunTree - - self.trace_name = trace_name - self.trace_type = trace_type - self.project_name = project_name - self.trace_id = trace_id - try: - self._run_tree = RunTree( - project_name=self.project_name, - name=self.trace_name, - run_type=self.trace_type, - id=self.trace_id, - ) - self._run_tree.add_event({"name": "Start", "time": datetime.now(timezone.utc).isoformat()}) - self._children: dict[str, RunTree] = {} - self._ready = self.setup_langsmith() - except Exception as e: - logger.debug(f"Error setting up LangSmith tracer: {e}") - self._ready = False - - @property - def ready(self): - return self._ready - - def setup_langsmith(self): - try: - from langsmith import Client - - self._client = Client() - except ImportError: - logger.error("Could not import langsmith. Please install it with `pip install langsmith`.") - return False - os.environ["LANGCHAIN_TRACING_V2"] = "true" - return True - - def add_trace( - self, - trace_id: str, - trace_name: str, - trace_type: str, - inputs: Dict[str, Any], - metadata: Dict[str, Any] | None = None, - vertex: Optional["Vertex"] = None, - ): - if not self._ready: - return - processed_inputs = {} - if inputs: - processed_inputs = self._convert_to_langchain_types(inputs) - child = self._run_tree.create_child( - name=trace_name, - run_type=trace_type, # type: ignore[arg-type] - inputs=processed_inputs, - ) - if metadata: - child.add_metadata(self._convert_to_langchain_types(metadata)) - self._children[trace_name] = child - self._child_link: dict[str, str] = {} - - def _convert_to_langchain_types(self, io_dict: Dict[str, Any]): - converted = {} - for key, value in io_dict.items(): - converted[key] = self._convert_to_langchain_type(value) - return converted - - def _convert_to_langchain_type(self, value): - from langflow.schema.message import Message - - if isinstance(value, dict): - for key, _value in value.copy().items(): - _value = self._convert_to_langchain_type(_value) - value[key] = _value - elif isinstance(value, list): - value = [self._convert_to_langchain_type(v) for v in value] - elif isinstance(value, Message): - if "prompt" in value: - value = value.load_lc_prompt() - elif value.sender: - value = value.to_lc_message() - else: - value = value.to_lc_document() - elif isinstance(value, Data): - value = value.to_lc_document() - elif isinstance(value, types.GeneratorType): - # generator is not serializable, also we can't consume it - value = str(value) - return value - - def end_trace( - self, - trace_id: str, - trace_name: str, - outputs: Dict[str, Any] | None = None, - error: Exception | None = None, - logs: list[Log | dict] = [], - ): - child = self._children[trace_name] - raw_outputs = {} - processed_outputs = {} - if outputs: - raw_outputs = outputs - processed_outputs = self._convert_to_langchain_types(outputs) - if logs: - child.add_metadata(self._convert_to_langchain_types({"logs": {log.get("name"): log for log in logs}})) - child.add_metadata(self._convert_to_langchain_types({"outputs": raw_outputs})) - child.end(outputs=processed_outputs, error=self._error_to_string(error)) - if error: - child.patch() - else: - child.post() - self._child_link[trace_name] = child.get_url() - - def _error_to_string(self, error: Optional[Exception]): - error_message = None - if error: - string_stacktrace = traceback.format_exception(error) - error_message = f"{error.__class__.__name__}: {error}\n\n{string_stacktrace}" - return error_message - - def end( - self, - inputs: dict[str, Any], - outputs: Dict[str, Any], - error: Exception | None = None, - metadata: dict[str, Any] | None = None, - ): - self._run_tree.add_metadata({"inputs": inputs}) - if metadata: - self._run_tree.add_metadata(metadata) - self._run_tree.end(outputs=outputs, error=self._error_to_string(error)) - self._run_tree.post() - self._run_link = self._run_tree.get_url() - - -class LangWatchTracer(BaseTracer): - flow_id: str - - def __init__(self, trace_name: str, trace_type: str, project_name: str, trace_id: UUID): - self.trace_name = trace_name - self.trace_type = trace_type - self.project_name = project_name - self.trace_id = trace_id - self.flow_id = trace_name.split(" - ")[-1] - - try: - self._ready = self.setup_langwatch() - - # import after setting up langwatch so we are sure to be available - import nanoid # type: ignore - - self.trace = self._client.trace( - trace_id=str(self.trace_id), - ) - self.spans: dict[str, "ContextSpan"] = {} - - name_without_id = " - ".join(trace_name.split(" - ")[0:-1]) - self.trace.root_span.update( - span_id=f"{self.flow_id}-{nanoid.generate(size=6)}", # nanoid to make the span_id globally unique, which is required for LangWatch for now - name=name_without_id, - type=self._convert_trace_type(trace_type), - ) - except Exception as e: - logger.debug(f"Error setting up LangWatch tracer: {e}") - self._ready = False - - @property - def ready(self): - return self._ready - - def setup_langwatch(self): - try: - import langwatch - - self._client = langwatch - except ImportError: - logger.error("Could not import langwatch. Please install it with `pip install langwatch`.") - return False - return True - - def _convert_trace_type(self, trace_type: str): - trace_type_: "SpanTypes" = ( - cast("SpanTypes", trace_type) - if trace_type in ["span", "llm", "chain", "tool", "agent", "guardrail", "rag"] - else "span" - ) - return trace_type_ - - def add_trace( - self, - trace_id: str, - trace_name: str, - trace_type: str, - inputs: Dict[str, Any], - metadata: Dict[str, Any] | None = None, - vertex: Optional["Vertex"] = None, - ): - import nanoid - - # If user is not using session_id, then it becomes the same as flow_id, but - # we don't want to have an infinite thread with all the flow messages - if "session_id" in inputs and inputs["session_id"] != self.flow_id: - self.trace.update(metadata=(self.trace.metadata or {}) | {"thread_id": inputs["session_id"]}) - - name_without_id = " (".join(trace_name.split(" (")[0:-1]) - - trace_type_ = self._convert_trace_type(trace_type) - self.spans[trace_id] = self.trace.span( - span_id=f"{trace_id}-{nanoid.generate(size=6)}", # Add a nanoid to make the span_id globally unique, which is required for LangWatch for now - name=name_without_id, - type=trace_type_, - parent=( - [span for key, span in self.spans.items() for edge in vertex.incoming_edges if key == edge.source_id][ - -1 - ] - if vertex and len(vertex.incoming_edges) > 0 - else self.trace.root_span - ), - input=self._convert_to_langwatch_types(inputs), - ) - - if trace_type_ == "llm" and "model_name" in inputs: - self.spans[trace_id].update(model=inputs["model_name"]) - - def end_trace( - self, - trace_id: str, - trace_name: str, - outputs: Dict[str, Any] | None = None, - error: Exception | None = None, - logs: list[Log | dict] = [], - ): - if self.spans.get(trace_id): - # Workaround for when model is used just as a component not actually called as an LLM, - # to prevent LangWatch from calculating the cost based on it when it was in fact never called - if ( - self.spans[trace_id].type == "llm" - and outputs - and "model_output" in outputs - and "text_output" not in outputs - ): - self.spans[trace_id].update(metrics={"prompt_tokens": 0, "completion_tokens": 0}) - - self.spans[trace_id].end(output=self._convert_to_langwatch_types(outputs), error=error) - - def end( - self, - inputs: dict[str, Any], - outputs: Dict[str, Any], - error: Exception | None = None, - metadata: dict[str, Any] | None = None, - ): - self.trace.root_span.end( - input=self._convert_to_langwatch_types(inputs), - output=self._convert_to_langwatch_types(outputs), - error=error, - ) - - if metadata and "flow_name" in metadata: - self.trace.update(metadata=(self.trace.metadata or {}) | {"labels": [f"Flow: {metadata['flow_name']}"]}) - self.trace.deferred_send_spans() - - def _convert_to_langwatch_types(self, io_dict: Optional[Dict[str, Any]]): - from langwatch.utils import autoconvert_typed_values - - if io_dict is None: - return None - converted = {} - for key, value in io_dict.items(): - converted[key] = self._convert_to_langwatch_type(value) - return autoconvert_typed_values(converted) - - def _convert_to_langwatch_type(self, value): - from langflow.schema.message import Message, BaseMessage - from langwatch.langchain import ( - langchain_messages_to_chat_messages, - langchain_message_to_chat_message, - ) - - if isinstance(value, dict): - for key, _value in value.copy().items(): - _value = self._convert_to_langwatch_type(_value) - value[key] = _value - elif isinstance(value, list): - value = [self._convert_to_langwatch_type(v) for v in value] - elif isinstance(value, Message): - if "prompt" in value: - prompt = value.load_lc_prompt() - if len(prompt.input_variables) == 0 and all(isinstance(m, BaseMessage) for m in prompt.messages): - value = langchain_messages_to_chat_messages([cast(list[BaseMessage], prompt.messages)]) - else: - value = cast(dict, value.load_lc_prompt()) - elif value.sender: - value = langchain_message_to_chat_message(value.to_lc_message()) - else: - value = cast(dict, value.to_lc_document()) - elif isinstance(value, Data): - value = cast(dict, value.to_lc_document()) - return value diff --git a/src/backend/base/poetry.lock b/src/backend/base/poetry.lock index d606edd69..c947248a0 100644 --- a/src/backend/base/poetry.lock +++ b/src/backend/base/poetry.lock @@ -173,34 +173,6 @@ doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphin test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] trio = ["trio (>=0.23)"] -[[package]] -name = "appdirs" -version = "1.4.4" -description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." -optional = false -python-versions = "*" -files = [ - {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"}, - {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"}, -] - 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