remove dead code and unused dependencies (#1488)
* added esLint to frontend project * Remove formModalPropsType from index.ts * Remove chat input component * Delete codeBlock and fileComponent components * Remove unused code for BuildTrigger component * Refactor Chat component and remove unused code * Delete chatTrigger component * Delete unused SVG and PNG files * Remove LightTooltipComponent * Remove LoadingSpinner component * Delete RadialProgressComponent * Delete ReactTooltipComponent * Remove TooltipComponent * Delete ElementStack component * Remove ToggleComponent * Remove unused dependencies from package.json * Update package json * add accordion * change accordion sidebar to shadcn * added million lint * Remove MillionCompiler plugin from Vite config * Refactor authContext autoLogin dependency * Add console.log statements for debugging * Refactored position of elements in IO branch * fix assets imports * fix re-render on pageComponent * Add StrictMode to root render and update key in ExtraSidebarComponent * Add showCanvas state and useEffect to update it to improve performance * Refactor FlowPage component and remove ExtraSidebar from main area * Refactor FlowToolbar to prevent Unnecessary re-render * get node position with zustand in NodeToolbarComponent * Fix ShareModal rendering issue * Remove ExtraSidebar component and fix CodeAreaComponent bug * Refactor: Use useMemo to avoid unnecessary render * merge zustandIo * Remove console.log statements * Update package-lock.json and refactor extraSidebarComponent * update package json * Remove unused msgpack wheel file and add new nvidia_nvjitlink_cu12 wheel file * Fix import formatting in loading.py * Imported missing module and removed unused import --------- Co-authored-by: cristhianzl <cristhian.lousa@gmail.com> Co-authored-by: Lucas Oliveira <lucas.edu.oli@hotmail.com> Co-authored-by: igorrCarvalho <igorsilvabhz6@gmail.com> Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
This commit is contained in:
anovazzi1
GitHub
parent
e564991040
commit
e61896bc1f
71 changed files with 3496 additions and 3399 deletions
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@ -13,8 +13,8 @@ class FileComponent(CustomComponent):
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def build_config(self) -> Dict[str, Any]:
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return {
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"path": {
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"display_name": "Path",
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"paths": {
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"display_name": "Paths",
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"field_type": "file",
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"file_types": TEXT_FILE_TYPES,
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"info": f"Supported file types: {', '.join(TEXT_FILE_TYPES)}",
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30
src/backend/base/langflow/components/inputs/JSONInput.py
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30
src/backend/base/langflow/components/inputs/JSONInput.py
Normal file
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@ -0,0 +1,30 @@
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import ast
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import json
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from langflow import CustomComponent
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from langflow.schema import Record
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class JSONInputComponent(CustomComponent):
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display_name = "JSON Input"
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description = "Load a JSON object as input."
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def build_config(self):
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return {
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"json_str": {
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"display_name": "JSON String",
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"multiline": True,
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"info": "The JSON string to load.",
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}
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}
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def build(self, json_str: str) -> Record:
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try:
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data = json.loads(json_str)
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except json.JSONDecodeError:
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try:
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data = ast.literal_eval(json_str)
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except (SyntaxError, ValueError):
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raise ValueError("Invalid JSON string.")
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record = Record(data=data)
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return record
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@ -1,7 +1,6 @@
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import ast
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import json
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from typing import AsyncIterator, Callable, Dict, Iterator, List, Optional, Union
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import yaml
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from langchain_core.messages import AIMessage
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from loguru import logger
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@ -1,4 +1,4 @@
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from typing import TYPE_CHECKING, Any, Awaitable, Callable, List, Optional, Tuple, Type, Union, cast
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from typing import TYPE_CHECKING, Any, Callable, Coroutine, List, Optional, Tuple, Union
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from pydantic.v1 import BaseModel, Field, create_model
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from sqlmodel import select
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@ -63,30 +63,28 @@ def find_flow(flow_name: str, user_id: str) -> Optional[str]:
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async def run_flow(
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inputs: Optional[Union[dict, List[dict]]] = None,
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inputs: Union[dict, List[dict]] = None,
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tweaks: Optional[dict] = None,
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flow_id: Optional[str] = None,
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flow_name: Optional[str] = None,
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user_id: Optional[str] = None,
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) -> Any:
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if not user_id:
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raise ValueError("Session is invalid")
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graph = await load_flow(user_id, flow_id, flow_name, tweaks)
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if inputs is None:
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inputs = []
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inputs_list: list[dict[str, str]] = []
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inputs_list = []
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inputs_components = []
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types = []
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for input_dict in inputs:
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inputs_list.append({INPUT_FIELD_NAME: cast(str, input_dict.get("input_value", ""))})
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inputs_list.append({INPUT_FIELD_NAME: input_dict.get("input_value")})
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inputs_components.append(input_dict.get("components", []))
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types.append(input_dict.get("type", []))
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return await graph.arun(inputs_list, inputs_components=inputs_components, types=types)
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def generate_function_for_flow(inputs: List["Vertex"], flow_id: str) -> Callable[..., Awaitable[Any]]:
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def generate_function_for_flow(inputs: List["Vertex"], flow_id: str) -> Coroutine:
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"""
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Generate a dynamic flow function based on the given inputs and flow ID.
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@ -140,14 +138,12 @@ async def flow_function({func_args}):
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"""
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compiled_func = compile(func_body, "<string>", "exec")
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local_scope: dict = {}
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local_scope = {}
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exec(compiled_func, globals(), local_scope)
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return local_scope["flow_function"]
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def build_function_and_schema(
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flow_record: Record, graph: "Graph"
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) -> Tuple[Callable[..., Awaitable[Any]], Type[BaseModel]]:
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def build_function_and_schema(flow_record: Record, graph: "Graph") -> Tuple[Callable, BaseModel]:
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"""
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Builds a dynamic function and schema for a given flow.
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@ -182,7 +178,7 @@ def get_flow_inputs(graph: "Graph") -> List["Vertex"]:
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return inputs
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def build_schema_from_inputs(name: str, inputs: List["Vertex"]) -> Type[BaseModel]:
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def build_schema_from_inputs(name: str, inputs: List[tuple[str, str, str]]) -> BaseModel:
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"""
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Builds a schema from the given inputs.
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@ -200,4 +196,4 @@ def build_schema_from_inputs(name: str, inputs: List["Vertex"]) -> Type[BaseMode
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field_name = input_.display_name.lower().replace(" ", "_")
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description = input_.description
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fields[field_name] = (str, Field(default="", description=description))
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return create_model(name, **fields) # type: ignore
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return create_model(name, **fields)
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@ -193,7 +193,7 @@
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"list": false,
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"show": true,
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"multiline": true,
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"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"A component for creating prompts using templates\"\n icon = \"terminal-square\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
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"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"A component for creating prompts using templates\"\n icon = \"terminal-square\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
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"fileTypes": [],
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"file_path": "",
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"password": false,
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@ -797,7 +797,7 @@
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"list": false,
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"show": true,
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"multiline": true,
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"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI's models.\"\n icon = \"OpenAI\"\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": False,\n \"required\": False,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n \"required\": False,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"required\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": False,\n \"required\": False,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"advanced\": False,\n \"required\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"required\": False,\n \"value\": 0.7,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": \"Stream the response from the model.\",\n },\n }\n\n def build(\n self,\n input_value: Text,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n model_name: str = \"gpt-4-1106-preview\",\n openai_api_base: Optional[str] = None,\n openai_api_key: Optional[str] = None,\n temperature: float = 0.7,\n stream: bool = False,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_result(output=output, stream=stream, input_value=input_value)\n",
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"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.components.models.base.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI's models.\"\n icon = \"OpenAI\"\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": False,\n \"required\": False,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n \"required\": False,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"required\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": False,\n \"required\": False,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"advanced\": False,\n \"required\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"required\": False,\n \"value\": 0.7,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": \"Stream the response from the model.\",\n },\n }\n\n def build(\n self,\n input_value: Text,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n model_name: str = \"gpt-4-1106-preview\",\n openai_api_base: Optional[str] = None,\n openai_api_key: Optional[str] = None,\n temperature: float = 0.7,\n stream: bool = False,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_result(output=output, stream=stream, input_value=input_value)\n",
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"fileTypes": [],
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"file_path": "",
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"password": false,
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@ -2,6 +2,7 @@ import inspect
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import json
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from typing import TYPE_CHECKING, Any, Callable, Dict, Sequence, Type
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import orjson
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from langchain.agents import agent as agent_module
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from langchain.agents.agent import AgentExecutor
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0
src/backend/langflow/base/agents/__init__.py
Normal file
0
src/backend/langflow/base/agents/__init__.py
Normal file
70
src/backend/langflow/base/agents/agent.py
Normal file
70
src/backend/langflow/base/agents/agent.py
Normal file
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@ -0,0 +1,70 @@
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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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3
src/backend/langflow/base/models/__init__.py
Normal file
3
src/backend/langflow/base/models/__init__.py
Normal file
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@ -0,0 +1,3 @@
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from .model import LCModelComponent
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__all__ = ["LCModelComponent"]
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48
src/backend/langflow/base/models/model.py
Normal file
48
src/backend/langflow/base/models/model.py
Normal file
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@ -0,0 +1,48 @@
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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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0
src/backend/langflow/components/models/base/__init__.py
Normal file
0
src/backend/langflow/components/models/base/__init__.py
Normal file
37
src/backend/langflow/components/tools/SearchAPITool.py
Normal file
37
src/backend/langflow/components/tools/SearchAPITool.py
Normal file
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@ -0,0 +1,37 @@
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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(
|
||||
self,
|
||||
engine: str,
|
||||
api_key: str,
|
||||
) -> Tool:
|
||||
search_api_wrapper = SearchApiAPIWrapper(engine=engine, searchapi_api_key=api_key)
|
||||
|
||||
tool = SearchAPIRun(api_wrapper=search_api_wrapper)
|
||||
|
||||
self.status = tool
|
||||
return tool
|
||||
0
src/backend/langflow/components/tools/SearchApi.py
Normal file
0
src/backend/langflow/components/tools/SearchApi.py
Normal file
103
src/backend/langflow/services/deps.py
Normal file
103
src/backend/langflow/services/deps.py
Normal file
|
|
@ -0,0 +1,103 @@
|
|||
from contextlib import contextmanager
|
||||
from typing import TYPE_CHECKING, Generator
|
||||
|
||||
from langflow.services import ServiceType, service_manager
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from sqlmodel import Session
|
||||
|
||||
from langflow.services.cache.service import CacheService
|
||||
from langflow.services.chat.service import ChatService
|
||||
from langflow.services.credentials.service import CredentialService
|
||||
from langflow.services.database.service import DatabaseService
|
||||
from langflow.services.monitor.service import MonitorService
|
||||
from langflow.services.plugins.service import PluginService
|
||||
from langflow.services.session.service import SessionService
|
||||
from langflow.services.settings.service import SettingsService
|
||||
from langflow.services.socket.service import SocketIOService
|
||||
from langflow.services.storage.service import StorageService
|
||||
from langflow.services.store.service import StoreService
|
||||
from langflow.services.task.service import TaskService
|
||||
|
||||
|
||||
def get_socket_service() -> "SocketIOService":
|
||||
return service_manager.get(ServiceType.SOCKETIO_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_storage_service() -> "StorageService":
|
||||
return service_manager.get(ServiceType.STORAGE_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_credential_service() -> "CredentialService":
|
||||
return service_manager.get(ServiceType.CREDENTIAL_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_plugins_service() -> "PluginService":
|
||||
return service_manager.get(ServiceType.PLUGIN_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_settings_service() -> "SettingsService":
|
||||
try:
|
||||
return service_manager.get(ServiceType.SETTINGS_SERVICE) # type: ignore
|
||||
except ValueError:
|
||||
# initialize settings service
|
||||
from langflow.services.manager import initialize_settings_service
|
||||
|
||||
initialize_settings_service()
|
||||
return service_manager.get(ServiceType.SETTINGS_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_db_service() -> "DatabaseService":
|
||||
return service_manager.get(ServiceType.DATABASE_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_session() -> Generator["Session", None, None]:
|
||||
db_service = get_db_service()
|
||||
yield from db_service.get_session()
|
||||
|
||||
|
||||
@contextmanager
|
||||
def session_scope():
|
||||
"""
|
||||
Context manager for managing a session scope.
|
||||
|
||||
Yields:
|
||||
session: The session object.
|
||||
|
||||
Raises:
|
||||
Exception: If an error occurs during the session scope.
|
||||
|
||||
"""
|
||||
session = next(get_session())
|
||||
try:
|
||||
yield session
|
||||
session.commit()
|
||||
except:
|
||||
session.rollback()
|
||||
raise
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
|
||||
def get_cache_service() -> "CacheService":
|
||||
return service_manager.get(ServiceType.CACHE_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_session_service() -> "SessionService":
|
||||
return service_manager.get(ServiceType.SESSION_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_monitor_service() -> "MonitorService":
|
||||
return service_manager.get(ServiceType.MONITOR_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_task_service() -> "TaskService":
|
||||
return service_manager.get(ServiceType.TASK_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_chat_service() -> "ChatService":
|
||||
return service_manager.get(ServiceType.CHAT_SERVICE) # type: ignore
|
||||
|
||||
|
||||
def get_store_service() -> "StoreService":
|
||||
return service_manager.get(ServiceType.STORE_SERVICE) # type: ignore
|
||||
83
src/backend/langflow/services/factory.py
Normal file
83
src/backend/langflow/services/factory.py
Normal file
|
|
@ -0,0 +1,83 @@
|
|||
import importlib
|
||||
import inspect
|
||||
from typing import TYPE_CHECKING, Type, get_type_hints
|
||||
|
||||
from cachetools import LRUCache, cached
|
||||
from loguru import logger
|
||||
|
||||
from langflow.services.schema import ServiceType
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.services.base import Service
|
||||
|
||||
|
||||
class ServiceFactory:
|
||||
def __init__(
|
||||
self,
|
||||
service_class,
|
||||
):
|
||||
self.service_class = service_class
|
||||
self.dependencies = infer_service_types(self, import_all_services_into_a_dict())
|
||||
|
||||
def create(self, *args, **kwargs) -> "Service":
|
||||
raise self.service_class(*args, **kwargs)
|
||||
|
||||
|
||||
def hash_factory(factory: ServiceFactory) -> str:
|
||||
return factory.service_class.__name__
|
||||
|
||||
|
||||
def hash_dict(d: dict) -> str:
|
||||
return str(d)
|
||||
|
||||
|
||||
def hash_infer_service_types_args(factory_class: Type[ServiceFactory], available_services=None) -> str:
|
||||
factory_hash = hash_factory(factory_class)
|
||||
services_hash = hash_dict(available_services)
|
||||
return f"{factory_hash}_{services_hash}"
|
||||
|
||||
|
||||
@cached(cache=LRUCache(maxsize=10), key=hash_infer_service_types_args)
|
||||
def infer_service_types(factory_class: Type[ServiceFactory], available_services=None) -> "ServiceType":
|
||||
create_method = factory_class.create
|
||||
type_hints = get_type_hints(create_method, globalns=available_services)
|
||||
service_types = []
|
||||
for param_name, param_type in type_hints.items():
|
||||
# Skip the return type if it's included in type hints
|
||||
if param_name == "return":
|
||||
continue
|
||||
|
||||
# Convert the type to the expected enum format directly without appending "_SERVICE"
|
||||
type_name = param_type.__name__.upper().replace("SERVICE", "_SERVICE")
|
||||
|
||||
try:
|
||||
# Attempt to find a matching enum value
|
||||
service_type = ServiceType[type_name]
|
||||
service_types.append(service_type)
|
||||
except KeyError:
|
||||
raise ValueError(f"No matching ServiceType for parameter type: {param_type.__name__}")
|
||||
return service_types
|
||||
|
||||
|
||||
@cached(cache=LRUCache(maxsize=1))
|
||||
def import_all_services_into_a_dict():
|
||||
# Services are all in langflow.services.{service_name}.service
|
||||
# and are subclass of Service
|
||||
# We want to import all of them and put them in a dict
|
||||
# to use as globals
|
||||
from langflow.services.base import Service
|
||||
|
||||
services = {}
|
||||
for service_type in ServiceType:
|
||||
try:
|
||||
service_name = ServiceType(service_type).value.replace("_service", "")
|
||||
module_name = f"langflow.services.{service_name}.service"
|
||||
module = importlib.import_module(module_name)
|
||||
for name, obj in inspect.getmembers(module, inspect.isclass):
|
||||
if issubclass(obj, Service) and obj is not Service:
|
||||
services[name] = obj
|
||||
break
|
||||
except Exception as exc:
|
||||
logger.exception(exc)
|
||||
raise RuntimeError("Could not initialize services. Please check your settings.") from exc
|
||||
return services
|
||||
Loading…
Add table
Add a link
Reference in a new issue