Merge branch 'zustand/io/migration' of github.com:logspace-ai/langflow into zustand/io/migration

src/backend/langflow/components/io/TextInput.py

@@ -9,9 +9,6 @@ class TextInput(CustomComponent):
     description = "Used to pass text input to the next component."
 
     field_config = {
-        "code": {
-            "show": False,
-        },
         "value": {"display_name": "Value"},
     }
 

src/backend/langflow/components/io/TextOutput.py

@@ -0,0 +1,19 @@
+from typing import Optional
+
+from langflow import CustomComponent
+from langflow.field_typing import Text
+
+
+class TextOutput(CustomComponent):
+    display_name = "Text Output"
+    description = "Used to pass text output to the next component."
+
+    field_config = {
+        "value": {"display_name": "Value"},
+    }
+
+    def build(self, value: Optional[str] = "") -> Text:
+        self.status = value
+        if not value:
+            value = ""
+        return value

src/backend/langflow/components/models/AmazonBedrockModel.py

@@ -1,13 +1,13 @@
 from typing import Optional
 
 from langchain_community.chat_models.bedrock import BedrockChat
-from langflow.field_typing import Text
 
 from langflow import CustomComponent
+from langflow.field_typing import Text
 
 
 class AmazonBedrockComponent(CustomComponent):
-    display_name: str = "Amazon Bedrock model"
+    display_name: str = "Amazon Bedrock Model"
     description: str = "Generate text using LLM model from Amazon Bedrock."
 
     def build_config(self):

src/backend/langflow/components/models/AnthropicModel.py

@@ -2,14 +2,13 @@ from typing import Optional
 
 from langchain_community.chat_models.anthropic import ChatAnthropic
 from pydantic.v1 import SecretStr
+
+from langflow import CustomComponent
 from langflow.field_typing import Text
 
 
-from langflow import CustomComponent
-
-
 class AnthropicLLM(CustomComponent):
-    display_name: str = "Anthropic model"
+    display_name: str = "AnthropicModel"
     description: str = "Generate text using Anthropic Chat&Completion large language models."
 
     def build_config(self):
@@ -67,7 +66,7 @@ class AnthropicLLM(CustomComponent):
         try:
             output = ChatAnthropic(
                 model_name=model,
-                anthropic_api_key=SecretStr(anthropic_api_key) if anthropic_api_key else None,
+                anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),
                 max_tokens_to_sample=max_tokens,  # type: ignore
                 temperature=temperature,
                 anthropic_api_url=api_endpoint,

src/backend/langflow/components/models/AzureOpenAIModel.py

@@ -7,7 +7,7 @@ from langflow import CustomComponent
 
 
 class AzureChatOpenAIComponent(CustomComponent):
-    display_name: str = "AzureOpenAI model"
+    display_name: str = "AzureOpenAIModel"
     description: str = "Generate text using LLM model from Azure OpenAI."
     documentation: str = "https://python.langchain.com/docs/integrations/llms/azure_openai"
     beta = False

src/backend/langflow/components/models/CTransformersModel.py

@@ -1,13 +1,13 @@
 from typing import Dict, Optional
 
 from langchain_community.llms.ctransformers import CTransformers
-from langflow.field_typing import Text
 
 from langflow import CustomComponent
+from langflow.field_typing import Text
 
 
 class CTransformersComponent(CustomComponent):
-    display_name = "CTransformers model"
+    display_name = "CTransformersModel"
     description = "Generate text using CTransformers LLM models"
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers"
 
@@ -31,7 +31,14 @@ class CTransformersComponent(CustomComponent):
             "inputs": {"display_name": "Input"},
         }
 
-    def build(self, model: str, model_file: str, inputs: str, model_type: str, config: Optional[Dict] = None) -> Text:
+    def build(
+        self,
+        model: str,
+        model_file: str,
+        inputs: str,
+        model_type: str,
+        config: Optional[Dict] = None,
+    ) -> Text:
         output = CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)
         message = output.invoke(inputs)
         result = message.content if hasattr(message, "content") else message

src/backend/langflow/components/models/CohereModel.py

@@ -1,18 +1,33 @@
 from langchain_community.chat_models.cohere import ChatCohere
+
 from langflow import CustomComponent
 from langflow.field_typing import Text
 
 
 class CohereComponent(CustomComponent):
-    display_name = "Cohere model"
+    display_name = "CohereModel"
     description = "Generate text using Cohere large language models."
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
 
     def build_config(self):
         return {
-            "cohere_api_key": {"display_name": "Cohere API Key", "type": "password", "password": True},
-            "max_tokens": {"display_name": "Max Tokens", "default": 256, "type": "int", "show": True},
-            "temperature": {"display_name": "Temperature", "default": 0.75, "type": "float", "show": True},
+            "cohere_api_key": {
+                "display_name": "Cohere API Key",
+                "type": "password",
+                "password": True,
+            },
+            "max_tokens": {
+                "display_name": "Max Tokens",
+                "default": 256,
+                "type": "int",
+                "show": True,
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "default": 0.75,
+                "type": "float",
+                "show": True,
+            },
             "inputs": {"display_name": "Input"},
         }
 
@@ -23,8 +38,13 @@ class CohereComponent(CustomComponent):
         max_tokens: int = 256,
         temperature: float = 0.75,
     ) -> Text:
-        output = ChatCohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature)
+        output = ChatCohere(
+            cohere_api_key=cohere_api_key,
+            max_tokens=max_tokens,
+            temperature=temperature,
+        )
         message = output.invoke(inputs)
         result = message.content if hasattr(message, "content") else message
         self.status = result
         return result
+        return result

src/backend/langflow/components/models/GoogleGenerativeAIModel.py

@@ -1,14 +1,14 @@
 from typing import Optional
 
 from langchain_google_genai import ChatGoogleGenerativeAI  # type: ignore
-from langflow import CustomComponent
-from langflow.field_typing import RangeSpec
 from pydantic.v1.types import SecretStr
-from langflow.field_typing import Text
+
+from langflow import CustomComponent
+from langflow.field_typing import RangeSpec, Text
 
 
 class GoogleGenerativeAIComponent(CustomComponent):
-    display_name: str = "Google Generative AI model"
+    display_name: str = "Google Generative AIModel"
     description: str = "Generate text using Google Generative AI to generate text."
     documentation: str = "http://docs.langflow.org/components/custom"
 

src/backend/langflow/components/models/LlamaCppModel.py

@@ -1,11 +1,13 @@
-from typing import Optional, List, Dict, Any
-from langflow import CustomComponent
+from typing import Any, Dict, List, Optional
+
 from langchain_community.llms.llamacpp import LlamaCpp
+
+from langflow import CustomComponent
 from langflow.field_typing import Text
 
 
 class LlamaCppComponent(CustomComponent):
-    display_name = "LlamaCpp model"
+    display_name = "LlamaCppModel"
     description = "Generate text using llama.cpp model."
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp"
 
@@ -17,7 +19,10 @@ class LlamaCppComponent(CustomComponent):
             "echo": {"display_name": "Echo", "advanced": True},
             "f16_kv": {"display_name": "F16 KV", "advanced": True},
             "grammar_path": {"display_name": "Grammar Path", "advanced": True},
-            "last_n_tokens_size": {"display_name": "Last N Tokens Size", "advanced": True},
+            "last_n_tokens_size": {
+                "display_name": "Last N Tokens Size",
+                "advanced": True,
+            },
             "logits_all": {"display_name": "Logits All", "advanced": True},
             "logprobs": {"display_name": "Logprobs", "advanced": True},
             "lora_base": {"display_name": "Lora Base", "advanced": True},
@@ -134,3 +139,5 @@ class LlamaCppComponent(CustomComponent):
         result = message.content if hasattr(message, "content") else message
         self.status = result
         return result
+        self.status = result
+        return result

src/backend/langflow/components/models/OllamaModel.py

@@ -12,7 +12,7 @@ from langflow.field_typing import Text
 
 
 class ChatOllamaComponent(CustomComponent):
-    display_name = "ChatOllama model"
+    display_name = "ChatOllamaModel"
     description = "Generate text using Local LLM for chat with Ollama."
 
     def build_config(self) -> dict:

src/backend/langflow/components/models/VertexAiModel.py

@@ -7,7 +7,7 @@ from langflow.field_typing import Text
 
 
 class ChatVertexAIComponent(CustomComponent):
-    display_name = "ChatVertexAI model"
+    display_name = "ChatVertexAIModel"
     description = "Generate text using Vertex AI Chat large language models API."
 
     def build_config(self):

src/backend/langflow/graph/graph/base.py

@@ -13,6 +13,7 @@ from langflow.graph.vertex.types import (
     ChatVertex,
     FileToolVertex,
     LLMVertex,
+    RoutingVertex,
     ToolkitVertex,
 )
 from langflow.interface.tools.constants import FILE_TOOLS
@@ -402,10 +403,12 @@ class Graph:
         node_name = node_id.split("-")[0]
         if node_name in ["ChatOutput", "ChatInput"]:
             return ChatVertex
-        if node_name in lazy_load_vertex_dict.VERTEX_TYPE_MAP:
-            return lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_name]
+        elif node_name in ["ShouldRunNext"]:
+            return RoutingVertex
         elif node_base_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP:
             return lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_base_type]
+        elif node_name in lazy_load_vertex_dict.VERTEX_TYPE_MAP:
+            return lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_name]
 
         if node_type in FILE_TOOLS:
             return FileToolVertex

src/backend/langflow/graph/vertex/types.py

@@ -67,7 +67,7 @@ class LLMVertex(StatelessVertex):
     class_built_object = None
 
     def __init__(self, data: Dict, graph, params: Optional[Dict] = None):
-        super().__init__(data, graph=graph, base_type="llms", params=params)
+        super().__init__(data, graph=graph, base_type="models", params=params)
         self.steps: List[Callable] = [self._custom_build]
 
     async def _custom_build(self, *args, **kwargs):

src/backend/langflow/interface/importing/utils.py

@@ -35,7 +35,7 @@ def import_by_type(_type: str, name: str) -> Any:
     func_dict = {
         "agents": import_agent,
         "prompts": import_prompt,
-        "llms": {"llm": import_llm, "chat": import_chat_llm},
+        "models": {"llm": import_llm, "chat": import_chat_llm},
         "tools": import_tool,
         "chains": import_chain,
         "toolkits": import_toolkit,
@@ -50,7 +50,7 @@ def import_by_type(_type: str, name: str) -> Any:
         "retrievers": import_retriever,
         "custom_components": import_custom_component,
     }
-    if _type == "llms":
+    if _type == "models":
         key = "chat" if "chat" in name.lower() else "llm"
         loaded_func = func_dict[_type][key]  # type: ignore
     else:

src/backend/langflow/interface/initialize/loading.py

@@ -19,7 +19,11 @@ from langflow.interface.custom.utils import get_function
 from langflow.interface.custom_lists import CUSTOM_NODES
 from langflow.interface.importing.utils import import_by_type
 from langflow.interface.initialize.llm import initialize_vertexai
-from langflow.interface.initialize.utils import handle_format_kwargs, handle_node_type, handle_partial_variables
+from langflow.interface.initialize.utils import (
+    handle_format_kwargs,
+    handle_node_type,
+    handle_partial_variables,
+)
 from langflow.interface.initialize.vector_store import vecstore_initializer
 from langflow.interface.output_parsers.base import output_parser_creator
 from langflow.interface.retrievers.base import retriever_creator
@@ -105,7 +109,7 @@ async def instantiate_based_on_type(class_object, base_type, node_type, params,
         return instantiate_chains(node_type, class_object, params)
     elif base_type == "output_parsers":
         return instantiate_output_parser(node_type, class_object, params)
-    elif base_type == "llms":
+    elif base_type == "models":
         return instantiate_llm(node_type, class_object, params)
     elif base_type == "retrievers":
         return instantiate_retriever(node_type, class_object, params)

src/backend/langflow/interface/llms/base.py

@@ -1,16 +1,16 @@
 from typing import Dict, List, Optional, Type
 
+from loguru import logger
+
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import llm_type_to_cls_dict
 from langflow.services.deps import get_settings_service
-
 from langflow.template.frontend_node.llms import LLMFrontendNode
-from loguru import logger
 from langflow.utils.util import build_template_from_class
 
 
 class LLMCreator(LangChainTypeCreator):
-    type_name: str = "llms"
+    type_name: str = "models"
 
     @property
     def frontend_node_class(self) -> Type[LLMFrontendNode]:

src/backend/langflow/services/auth/utils.py

@@ -11,7 +11,11 @@ from starlette.websockets import WebSocket
 
 from langflow.services.database.models.api_key.model import ApiKey
 from langflow.services.database.models.api_key.crud import check_key
-from langflow.services.database.models.user.crud import get_user_by_id, get_user_by_username, update_user_last_login_at
+from langflow.services.database.models.user.crud import (
+    get_user_by_id,
+    get_user_by_username,
+    update_user_last_login_at,
+)
 from langflow.services.database.models.user.model import User
 from langflow.services.deps import get_session, get_settings_service
 
@@ -323,7 +327,7 @@ def add_padding(s):
 
 def get_fernet(settings_service=Depends(get_settings_service)):
     SECRET_KEY = settings_service.auth_settings.SECRET_KEY
-    # It's important that your secret key is 32 url-safe base64-encoded bytes
+    # It's important that your secret key is 32 url-safe base64-encoded byte
     padded_secret_key = add_padding(SECRET_KEY)
     fernet = Fernet(padded_secret_key)
     return fernet

src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx

@@ -324,7 +324,9 @@ export default function ParameterComponent({
           ) : (
             title
           )}
-          <span className={(info === '' ? '' : 'ml-1 ') + " text-status-red"}>{required ? " *" : ""}</span>
+          <span className={(info === "" ? "" : "ml-1 ") + " text-status-red"}>
+            {required ? " *" : ""}
+          </span>
           <div className="">
             {info !== "" && (
               <ShadTooltip content={infoHtml.current}>

src/frontend/src/CustomNodes/GenericNode/index.tsx

@@ -37,6 +37,7 @@ export default function GenericNode({
   const flowPool = useFlowStore((state) => state.flowPool);
   const buildFlow = useFlowStore((state) => state.buildFlow);
   const setNode = useFlowStore((state) => state.setNode);
+  const getBuildStatus = useFlowStore((state) => state.getBuildStatus);
   const name = nodeIconsLucide[data.type] ? data.type : types[data.type];
   const [inputName, setInputName] = useState(false);
   const [nodeName, setNodeName] = useState(data.node!.display_name);
@@ -170,7 +171,6 @@ export default function GenericNode({
     validationStatus: validationStatusType | null
   ) => {
     const isValid = validationStatus && validationStatus.valid;
-
     if (isValid) {
       return "green-status";
     } else if (!isValid && buildStatus === BuildStatus.INACTIVE) {
@@ -179,10 +179,12 @@ export default function GenericNode({
       return "green-status";
     } else if (!isValid && buildStatus === BuildStatus.BUILT) {
       return "red-status";
-    } else if (!validationStatus) {
-      return "yellow-status";
-    } else {
+    } else if (!validationStatus && buildStatus === BuildStatus.TO_BUILD) {
+      return "green-status";
+    } else if (buildStatus === BuildStatus.BUILDING) {
       return "status-build-animation";
+    } else {
+      return "green-status";
     }
   };
 
@@ -205,6 +207,7 @@ export default function GenericNode({
     validationStatus: validationStatusType | null
   ) => {
     let isInvalid = validationStatus && !validationStatus.valid;
+
     if (buildStatus === BuildStatus.INACTIVE && isInvalid) {
       // INACTIVE should have its own class
       return "inactive-status";
@@ -224,11 +227,17 @@ export default function GenericNode({
     buildStatus: BuildStatus | undefined,
     validationStatus: validationStatusType | null
   ) => {
+    const specificClassFromBuildStatus = getSpecificClassFromBuildStatus(
+      buildStatus,
+      validationStatus
+    );
+    const baseBorderClass = getBaseBorderClass(selected);
+    const nodeSizeClass = getNodeSizeClass(showNode);
     return classNames(
-      getBaseBorderClass(selected),
-      getNodeSizeClass(showNode),
+      baseBorderClass,
+      nodeSizeClass,
       "generic-node-div",
-      getSpecificClassFromBuildStatus(buildStatus, validationStatus)
+      specificClassFromBuildStatus
     );
   };
 
@@ -265,7 +274,7 @@ export default function GenericNode({
         className={getNodeBorderClassName(
           selected,
           showNode,
-          data?.build_status,
+          getBuildStatus(data.id),
           validationStatus
         )}
       >
@@ -476,22 +485,26 @@ export default function GenericNode({
                 variant="outline"
                 className={"h-9 px-1.5"}
                 onClick={() => {
-                  if (data?.build_status === BuildStatus.BUILDING || isBuilding)
+                  if (
+                    getBuildStatus(data.id) === BuildStatus.BUILDING ||
+                    isBuilding
+                  )
                     return;
+
                   buildFlow(data.id);
                 }}
               >
                 <div>
                   <Tooltip
                     title={
-                      data?.build_status === BuildStatus.BUILDING ? (
+                      getBuildStatus(data.id) === BuildStatus.BUILDING ? (
                         <span>Building...</span>
                       ) : !validationStatus ? (
                         <span className="flex">
                           Build{" "}
                           <IconComponent
                             name="Play"
-                            className=" h-5 stroke-build-trigger stroke-2"
+                            className=" h-5 stroke-status-green stroke-2"
                           />{" "}
                           flow to validate status.
                         </span>
@@ -510,7 +523,7 @@ export default function GenericNode({
                   >
                     <div className="generic-node-status-position flex items-center justify-center">
                       {renderIconPlayOrPauseComponents(
-                        data?.build_status,
+                        getBuildStatus(data.id),
                         validationStatus
                       )}
                     </div>

src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx

@@ -394,7 +394,10 @@ export default function NodeToolbarComponent({
                   />{" "}
                   <span className="">Delete</span>{" "}
                   <span>
-                    <IconComponent name="Delete" className="text-red-300 absolute right-2 top-2 h-4 w-4"></IconComponent>
+                    <IconComponent
+                      name="Delete"
+                      className="absolute right-2 top-2 h-4 w-4 text-red-300"
+                    ></IconComponent>
                   </span>
                 </div>
               </SelectItem>

src/frontend/src/stores/flowStore.ts

@@ -416,7 +416,6 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
         useFlowStore.getState().updateBuildStatus(idList, BuildStatus.BUILDING);
       },
     });
-    get().revertAllVerticesToBuild();
   },
   getFlow: () => {
     return {
@@ -425,26 +424,11 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       viewport: get().reactFlowInstance?.getViewport()!,
     };
   },
-  updateBuildStatus: (nodeIdList: string[], status: BuildStatus) => {
-    nodeIdList.forEach((id) => {
-      const nodeToUpdate = get().nodes.find((node) => node.id === id);
-      if (nodeToUpdate) {
-        nodeToUpdate.data.build_status = status;
-        get().setNodes(get().nodes);
-      }
-    });
-  },
   updateVerticesBuild: (vertices: string[]) => {
     set({ verticesBuild: vertices });
   },
   verticesBuild: [],
-  revertAllVerticesToBuild: () => {
-    // set all vertices to TO_BUILD
-    const verticesIds = get()
-      .nodes.filter((node) => node.data.build_status === BuildStatus.BUILDING)
-      .map((node) => node.id);
-    get().updateBuildStatus(verticesIds, BuildStatus.TO_BUILD);
-  },
+
   removeFromVerticesBuild: (vertices: string[]) => {
     set({
       verticesBuild: get().verticesBuild.filter(
@@ -452,6 +436,25 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       ),
     });
   },
+  updateBuildStatus: (nodeIdList: string[], status: BuildStatus) => {
+    let newBuildStatus = cloneDeep(get().buildStatus);
+    // check if nodeIdList is an array
+    if (!Array.isArray(nodeIdList)) {
+      nodeIdList = [nodeIdList];
+    }
+    nodeIdList.forEach((id) => {
+      newBuildStatus[id] = status;
+    });
+    set({ buildStatus: newBuildStatus });
+  },
+  buildStatus: {},
+  getBuildStatus: (nodeId: string) => {
+    // if the node is not in the buildStatus object, set it to TO_BUILD
+    if (!get().buildStatus[nodeId]) {
+      get().buildStatus[nodeId] = BuildStatus.TO_BUILD;
+    }
+    return get().buildStatus[nodeId];
+  },
 }));
 
 export default useFlowStore;

src/frontend/src/style/applies.css

@@ -296,7 +296,7 @@
     @apply generic-node-status text-status-green;
   }
   .gray-status {
-    @apply generic-node-status text-muted-foreground;
+    @apply generic-node-status text-status-gray;
   }
 
   .red-status {

src/frontend/src/style/index.css

@@ -60,6 +60,7 @@
     --chat-send: #059669;
     --status-green: #4ade80;
     --status-blue: #2563eb;
+    --status-gray: #6b7280;
     --connection: #555;
   }
 

src/frontend/src/types/api/index.ts

@@ -1,5 +1,4 @@
 import { Edge, Node, Viewport } from "reactflow";
-import { BuildStatus } from "../../constants/enums";
 import { FlowType } from "../flow";
 //kind and class are just representative names to represent the actual structure of the object received by the API
 export type APIDataType = { [key: string]: APIKindType };
@@ -37,7 +36,6 @@ export type APIClassType = {
     | CustomFieldsType
     | boolean
     | undefined;
-  build_status?: BuildStatus;
 };
 
 export type TemplateVariableType = {

src/frontend/src/types/flow/index.ts

@@ -1,5 +1,4 @@
 import { ReactFlowJsonObject, XYPosition } from "reactflow";
-import { BuildStatus } from "../../constants/enums";
 import { APIClassType } from "../api/index";
 
 export type FlowType = {
@@ -29,7 +28,6 @@ export type NodeDataType = {
   node?: APIClassType;
   id: string;
   output_types?: string[];
-  build_status?: BuildStatus;
 };
 // FlowStyleType is the type of the style object that is used to style the
 // Flow card with an emoji and a color.

src/frontend/src/types/zustand/flow/index.ts

@@ -86,9 +86,10 @@ export type FlowStoreType = {
   unselectAll: () => void;
   buildFlow: (nodeId?: string) => Promise<void>;
   getFlow: () => { nodes: Node[]; edges: Edge[]; viewport: Viewport };
-  updateBuildStatus: (nodeId: string[], status: BuildStatus) => void;
   updateVerticesBuild: (vertices: string[]) => void;
   removeFromVerticesBuild: (vertices: string[]) => void;
-  revertAllVerticesToBuild: () => void;
   verticesBuild: string[];
+  updateBuildStatus: (nodeId: string[], status: BuildStatus) => void;
+  buildStatus: { [key: string]: BuildStatus };
+  getBuildStatus: (nodeId: string) => BuildStatus;
 };

src/frontend/src/utils/styleUtils.ts

@@ -25,6 +25,7 @@ import {
   Compass,
   Copy,
   Cpu,
+  Delete,
   Download,
   DownloadCloud,
   Edit,
@@ -117,7 +118,6 @@ import {
   X,
   XCircle,
   Zap,
-  Delete,
 } from "lucide-react";
 import { FaApple, FaGithub } from "react-icons/fa";
 import { AWSIcon } from "../icons/AWS";
@@ -195,7 +195,7 @@ export const gradients = [
 export const nodeColors: { [char: string]: string } = {
   prompts: "#4367BF",
   models: "#AA2411",
-  llms: "#6344BE",
+  model_specs: "#6344BE",
   chains: "#FE7500",
   Document: "#7AAE42",
   list: "#9AAE42",
@@ -224,7 +224,7 @@ export const nodeColors: { [char: string]: string } = {
 export const nodeNames: { [char: string]: string } = {
   prompts: "Prompts",
   models: "Language Models",
-  llms: "Model Settings",
+  model_specs: "Model Specs",
   chains: "Chains",
   agents: "Agents",
   tools: "Tools",
@@ -296,7 +296,7 @@ export const nodeIconsLucide: iconsType = {
   chains: Link,
   memories: Cpu,
   models: Bot,
-  llms: Lightbulb,
+  model_specs: Lightbulb,
   prompts: TerminalSquare,
   tools: Wrench,
   advanced: Laptop2,

src/frontend/tailwind.config.js

@@ -80,6 +80,7 @@ module.exports = {
         "status-green": "var(--status-green)",
         "status-red": "var(--status-red)",
         "status-yellow": "var(--status-yellow)",
+        "status-gray": "var(--status-gray)",
         "success-background": "var(--success-background)",
         "success-foreground": "var(--success-foreground)",
         "beta-background": "var(--beta-background)",