Delete unused files and components
This commit is contained in:
Gabriel Luiz Freitas Almeida
parent
a347fecf39
commit
830d063ca1
9 changed files with 0 additions and 344 deletions
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@ -1,70 +0,0 @@
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from typing import List, Union
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from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
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from langflow import CustomComponent
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from langflow.field_typing import BaseMemory, Text, Tool
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class LCAgentComponent(CustomComponent):
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def build_config(self):
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return {
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"lc": {
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"display_name": "LangChain",
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"info": "The LangChain to interact with.",
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},
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"handle_parsing_errors": {
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"display_name": "Handle Parsing Errors",
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"info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
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"advanced": True,
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},
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"output_key": {
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"display_name": "Output Key",
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"info": "The key to use to get the output from the agent.",
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"advanced": True,
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},
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"memory": {
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"display_name": "Memory",
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"info": "Memory to use for the agent.",
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},
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"tools": {
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"display_name": "Tools",
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"info": "Tools the agent can use.",
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},
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"input_value": {
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"display_name": "Input",
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"info": "Input text to pass to the agent.",
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},
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}
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async def run_agent(
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self,
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agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor],
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inputs: str,
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input_variables: list[str],
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tools: List[Tool],
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memory: BaseMemory = None,
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handle_parsing_errors: bool = True,
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output_key: str = "output",
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) -> Text:
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if isinstance(agent, AgentExecutor):
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runnable = agent
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else:
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runnable = AgentExecutor.from_agent_and_tools(
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agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=handle_parsing_errors
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)
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input_dict = {"input": inputs}
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for var in input_variables:
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if var not in ["agent_scratchpad", "input"]:
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input_dict[var] = ""
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result = await runnable.ainvoke(input_dict)
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self.status = result
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if output_key in result:
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return result.get(output_key)
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elif "output" not in result:
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if output_key != "output":
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raise ValueError(f"Output key not found in result. Tried '{output_key}' and 'output'.")
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else:
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raise ValueError("Output key not found in result. Tried 'output'.")
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return result.get("output")
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@ -1,3 +0,0 @@
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from .model import LCModelComponent
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__all__ = ["LCModelComponent"]
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@ -1,48 +0,0 @@
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from typing import Optional
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.language_models.llms import LLM
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from langchain_core.messages import HumanMessage, SystemMessage
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from langflow import CustomComponent
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class LCModelComponent(CustomComponent):
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display_name: str = "Model Name"
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description: str = "Model Description"
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def get_result(self, runnable: LLM, stream: bool, input_value: str):
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"""
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Retrieves the result from the output of a Runnable object.
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Args:
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output (Runnable): The output object to retrieve the result from.
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stream (bool): Indicates whether to use streaming or invocation mode.
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input_value (str): The input value to pass to the output object.
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Returns:
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The result obtained from the output object.
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"""
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if stream:
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result = runnable.stream(input_value)
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else:
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message = runnable.invoke(input_value)
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result = message.content if hasattr(message, "content") else message
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self.status = result
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return result
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def get_chat_result(
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self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
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):
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messages = []
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if input_value:
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messages.append(HumanMessage(input_value))
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if system_message:
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messages.append(SystemMessage(system_message))
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if stream:
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result = runnable.stream(messages)
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else:
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message = runnable.invoke(messages)
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result = message.content
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self.status = result
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return result
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@ -1,37 +0,0 @@
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from langchain_community.tools.searchapi import SearchAPIRun
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from langchain_community.utilities.searchapi import SearchApiAPIWrapper
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from langflow import CustomComponent
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from langflow.field_typing import Tool
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class SearchApiToolComponent(CustomComponent):
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display_name: str = "SearchApi Tool"
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description: str = "Real-time search engine results API."
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documentation: str = "https://www.searchapi.io/docs/google"
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field_config = {
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"engine": {
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"display_name": "Engine",
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"field_type": "str",
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"info": "The search engine to use.",
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},
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"api_key": {
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"display_name": "API Key",
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"field_type": "str",
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"required": True,
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"password": True,
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"info": "The API key to use SearchApi.",
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},
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}
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def build(
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self,
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engine: str,
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api_key: str,
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) -> Tool:
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search_api_wrapper = SearchApiAPIWrapper(engine=engine, searchapi_api_key=api_key)
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tool = SearchAPIRun(api_wrapper=search_api_wrapper)
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self.status = tool
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return tool
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@ -1,103 +0,0 @@
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from contextlib import contextmanager
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from typing import TYPE_CHECKING, Generator
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from langflow.services import ServiceType, service_manager
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if TYPE_CHECKING:
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from sqlmodel import Session
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from langflow.services.cache.service import CacheService
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from langflow.services.chat.service import ChatService
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from langflow.services.credentials.service import CredentialService
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from langflow.services.database.service import DatabaseService
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from langflow.services.monitor.service import MonitorService
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from langflow.services.plugins.service import PluginService
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from langflow.services.session.service import SessionService
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from langflow.services.settings.service import SettingsService
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from langflow.services.socket.service import SocketIOService
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from langflow.services.storage.service import StorageService
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from langflow.services.store.service import StoreService
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from langflow.services.task.service import TaskService
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def get_socket_service() -> "SocketIOService":
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return service_manager.get(ServiceType.SOCKETIO_SERVICE) # type: ignore
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def get_storage_service() -> "StorageService":
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return service_manager.get(ServiceType.STORAGE_SERVICE) # type: ignore
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def get_credential_service() -> "CredentialService":
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return service_manager.get(ServiceType.CREDENTIAL_SERVICE) # type: ignore
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def get_plugins_service() -> "PluginService":
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return service_manager.get(ServiceType.PLUGIN_SERVICE) # type: ignore
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def get_settings_service() -> "SettingsService":
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try:
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return service_manager.get(ServiceType.SETTINGS_SERVICE) # type: ignore
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except ValueError:
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# initialize settings service
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from langflow.services.manager import initialize_settings_service
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initialize_settings_service()
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return service_manager.get(ServiceType.SETTINGS_SERVICE) # type: ignore
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def get_db_service() -> "DatabaseService":
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return service_manager.get(ServiceType.DATABASE_SERVICE) # type: ignore
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def get_session() -> Generator["Session", None, None]:
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db_service = get_db_service()
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yield from db_service.get_session()
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@contextmanager
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def session_scope():
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"""
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Context manager for managing a session scope.
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Yields:
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session: The session object.
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Raises:
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Exception: If an error occurs during the session scope.
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"""
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session = next(get_session())
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try:
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yield session
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session.commit()
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except:
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session.rollback()
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raise
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finally:
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session.close()
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def get_cache_service() -> "CacheService":
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return service_manager.get(ServiceType.CACHE_SERVICE) # type: ignore
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def get_session_service() -> "SessionService":
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return service_manager.get(ServiceType.SESSION_SERVICE) # type: ignore
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def get_monitor_service() -> "MonitorService":
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return service_manager.get(ServiceType.MONITOR_SERVICE) # type: ignore
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def get_task_service() -> "TaskService":
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return service_manager.get(ServiceType.TASK_SERVICE) # type: ignore
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def get_chat_service() -> "ChatService":
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return service_manager.get(ServiceType.CHAT_SERVICE) # type: ignore
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def get_store_service() -> "StoreService":
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return service_manager.get(ServiceType.STORE_SERVICE) # type: ignore
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@ -1,83 +0,0 @@
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import importlib
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import inspect
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from typing import TYPE_CHECKING, Type, get_type_hints
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from cachetools import LRUCache, cached
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from loguru import logger
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from langflow.services.schema import ServiceType
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if TYPE_CHECKING:
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from langflow.services.base import Service
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class ServiceFactory:
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def __init__(
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self,
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service_class,
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):
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self.service_class = service_class
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self.dependencies = infer_service_types(self, import_all_services_into_a_dict())
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def create(self, *args, **kwargs) -> "Service":
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raise self.service_class(*args, **kwargs)
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def hash_factory(factory: ServiceFactory) -> str:
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return factory.service_class.__name__
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def hash_dict(d: dict) -> str:
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return str(d)
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def hash_infer_service_types_args(factory_class: Type[ServiceFactory], available_services=None) -> str:
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factory_hash = hash_factory(factory_class)
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services_hash = hash_dict(available_services)
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return f"{factory_hash}_{services_hash}"
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@cached(cache=LRUCache(maxsize=10), key=hash_infer_service_types_args)
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def infer_service_types(factory_class: Type[ServiceFactory], available_services=None) -> "ServiceType":
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create_method = factory_class.create
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type_hints = get_type_hints(create_method, globalns=available_services)
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service_types = []
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for param_name, param_type in type_hints.items():
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# Skip the return type if it's included in type hints
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if param_name == "return":
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continue
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# Convert the type to the expected enum format directly without appending "_SERVICE"
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type_name = param_type.__name__.upper().replace("SERVICE", "_SERVICE")
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try:
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# Attempt to find a matching enum value
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service_type = ServiceType[type_name]
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service_types.append(service_type)
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except KeyError:
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raise ValueError(f"No matching ServiceType for parameter type: {param_type.__name__}")
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return service_types
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@cached(cache=LRUCache(maxsize=1))
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def import_all_services_into_a_dict():
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# Services are all in langflow.services.{service_name}.service
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# and are subclass of Service
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# We want to import all of them and put them in a dict
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# to use as globals
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from langflow.services.base import Service
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services = {}
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for service_type in ServiceType:
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try:
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service_name = ServiceType(service_type).value.replace("_service", "")
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module_name = f"langflow.services.{service_name}.service"
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module = importlib.import_module(module_name)
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for name, obj in inspect.getmembers(module, inspect.isclass):
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if issubclass(obj, Service) and obj is not Service:
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services[name] = obj
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break
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except Exception as exc:
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logger.exception(exc)
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raise RuntimeError("Could not initialize services. Please check your settings.") from exc
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return services
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