Update LLMS references to Models and Specs

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/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]: