fix: add default models to Anthropic and make sure template is updated (

#5839)

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

src/backend/base/langflow/api/v1/endpoints.py

@@ -8,16 +8,7 @@
 from uuid import UUID
 
 import sqlalchemy as sa
-from fastapi import (
-    APIRouter,
-    BackgroundTasks,
-    Body,
-    Depends,
-    HTTPException,
-    Request,
-    UploadFile,
-    status,
-)
+from fastapi import APIRouter, BackgroundTasks, Body, Depends, HTTPException, Request, UploadFile, status
 from fastapi.encoders import jsonable_encoder
 from fastapi.responses import StreamingResponse
 from loguru import logger
@@ -36,11 +27,7 @@
     UploadFileResponse,
 )
 from langflow.custom.custom_component.component import Component
-from langflow.custom.utils import (
-    build_custom_component_template,
-    get_instance_name,
-    update_component_build_config,
-)
+from langflow.custom.utils import build_custom_component_template, get_instance_name, update_component_build_config
 from langflow.events.event_manager import create_stream_tokens_event_manager
 from langflow.exceptions.api import APIException, InvalidChatInputError
 from langflow.exceptions.serialization import SerializationError
@@ -55,16 +42,9 @@
 from langflow.services.cache.utils import save_uploaded_file
 from langflow.services.database.models.flow import Flow
 from langflow.services.database.models.flow.model import FlowRead
-from langflow.services.database.models.flow.utils import (
-    get_all_webhook_components_in_flow,
-)
+from langflow.services.database.models.flow.utils import get_all_webhook_components_in_flow
 from langflow.services.database.models.user.model import User, UserRead
-from langflow.services.deps import (
-    get_session_service,
-    get_settings_service,
-    get_task_service,
-    get_telemetry_service,
-)
+from langflow.services.deps import get_session_service, get_settings_service, get_task_service, get_telemetry_service
 from langflow.services.settings.feature_flags import FEATURE_FLAGS
 from langflow.services.telemetry.schema import RunPayload
 from langflow.utils.version import get_version_info
@@ -720,7 +700,6 @@ async def custom_component_update(
             user_id=user.id,
         )
 
-        template_data = code_request.model_dump().get("template", {}).copy()
         component_node["tool_mode"] = code_request.tool_mode
 
         if hasattr(cc_instance, "set_attributes"):
@@ -749,12 +728,6 @@ async def custom_component_update(
         )
         component_node["template"] = updated_build_config
 
-        # Preserve previous field values by merging filtered template data into
-        # the component node's template. Only include entries where the value
-        # is a dictionary containing the key "value".
-        filtered_data = {k: v for k, v in template_data.items() if isinstance(v, dict) and "value" in v}
-        component_node["template"] |= filtered_data
-
         if isinstance(cc_instance, Component):
             await cc_instance.run_and_validate_update_outputs(
                 frontend_node=component_node,

src/backend/base/langflow/base/models/model_input_constants.py

@@ -8,6 +8,7 @@
 from langflow.components.models.nvidia import NVIDIAModelComponent
 from langflow.components.models.openai import OpenAIModelComponent
 from langflow.inputs.inputs import InputTypes, SecretStrInput
+from langflow.template.field.base import Input
 
 
 class ModelProvidersDict(TypedDict):
@@ -24,7 +25,7 @@ def get_filtered_inputs(component_class):
     return [process_inputs(input_) for input_ in component_instance.inputs if input_.name not in base_input_names]
 
 
-def process_inputs(component_data):
+def process_inputs(component_data: Input):
     if isinstance(component_data, SecretStrInput):
         component_data.value = ""
         component_data.load_from_db = False
@@ -61,8 +62,8 @@ def add_combobox_true(component_input):
     return component_input
 
 
-def create_input_fields_dict(inputs, prefix):
-    return {f"{prefix}{input_.name}": input_ for input_ in inputs}
+def create_input_fields_dict(inputs: list[Input], prefix: str) -> dict[str, Input]:
+    return {f"{prefix}{input_.name}": input_.to_dict() for input_ in inputs}
 
 
 def _get_openai_inputs_and_fields():
@@ -73,7 +74,7 @@ def _get_openai_inputs_and_fields():
     except ImportError as e:
         msg = "OpenAI is not installed. Please install it with `pip install langchain-openai`."
         raise ImportError(msg) from e
-    return openai_inputs, {input_.name: input_ for input_ in openai_inputs}
+    return openai_inputs, create_input_fields_dict(openai_inputs, "")
 
 
 def _get_azure_inputs_and_fields():
@@ -204,10 +205,4 @@ def _get_amazon_bedrock_inputs_and_fields():
 MODEL_PROVIDERS = list(MODEL_PROVIDERS_DICT.keys())
 ALL_PROVIDER_FIELDS: list[str] = [field for provider in MODEL_PROVIDERS_DICT.values() for field in provider["fields"]]
 
-MODEL_DYNAMIC_UPDATE_FIELDS = [
-    "api_key",
-    "model",
-    "tool_model_enabled",
-    "base_url",
-    "model_name",
-]
+MODEL_DYNAMIC_UPDATE_FIELDS = ["api_key", "model", "tool_model_enabled", "base_url", "model_name"]

src/backend/base/langflow/components/agents/agent.py

@@ -9,9 +9,7 @@
 from langflow.base.models.model_utils import get_model_name
 from langflow.components.helpers import CurrentDateComponent
 from langflow.components.helpers.memory import MemoryComponent
-from langflow.components.langchain_utilities.tool_calling import (
-    ToolCallingAgentComponent,
-)
+from langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent
 from langflow.custom.utils import update_component_build_config
 from langflow.io import BoolInput, DropdownInput, MultilineInput, Output
 from langflow.logging import logger
@@ -121,6 +119,8 @@ async def get_memory_data(self):
         memory_kwargs = {
             component_input.name: getattr(self, f"{component_input.name}") for component_input in self.memory_inputs
         }
+        # filter out empty values
+        memory_kwargs = {k: v for k, v in memory_kwargs.items() if v}
 
         return await MemoryComponent().set(**memory_kwargs).retrieve_messages()
 
@@ -177,13 +177,14 @@ async def update_build_config(
         # Iterate over all providers in the MODEL_PROVIDERS_DICT
         # Existing logic for updating build_config
         if field_name in ("agent_llm",):
+            build_config["agent_llm"]["value"] = field_value
             provider_info = MODEL_PROVIDERS_DICT.get(field_value)
             if provider_info:
                 component_class = provider_info.get("component_class")
                 if component_class and hasattr(component_class, "update_build_config"):
                     # Call the component class's update_build_config method
                     build_config = await update_component_build_config(
-                        component_class, build_config, field_value, field_name
+                        component_class, build_config, field_value, "model_name"
                     )
 
             provider_configs: dict[str, tuple[dict, list[dict]]] = {
@@ -261,6 +262,6 @@ async def update_build_config(
                     if isinstance(field_name, str) and isinstance(prefix, str):
                         field_name = field_name.replace(prefix, "")
                     build_config = await update_component_build_config(
-                        component_class, build_config, field_value, field_name
+                        component_class, build_config, field_value, "model_name"
                     )
-        return build_config
+        return {k: v.to_dict() if hasattr(v, "to_dict") else v for k, v in build_config.items()}

src/backend/base/langflow/components/models/anthropic.py

@@ -29,8 +29,9 @@ class AnthropicModelComponent(LCModelComponent):
         DropdownInput(
             name="model_name",
             display_name="Model Name",
-            options=[],
+            options=ANTHROPIC_MODELS,
             refresh_button=True,
+            value=ANTHROPIC_MODELS[0],
         ),
         SecretStrInput(
             name="api_key",
@@ -138,14 +139,16 @@ def _get_exception_message(self, exception: Exception) -> str | None:
     def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
         if field_name in ("base_url", "model_name", "tool_model_enabled", "api_key") and field_value:
             try:
-                if len(self.api_key) != 0:
+                if len(self.api_key) == 0:
+                    ids = ANTHROPIC_MODELS
+                else:
                     try:
                         ids = self.get_models(tool_model_enabled=self.tool_model_enabled)
                     except (ImportError, ValueError, requests.exceptions.RequestException) as e:
                         logger.exception(f"Error getting model names: {e}")
                         ids = ANTHROPIC_MODELS
-                    build_config["model_name"]["options"] = ids
-                    build_config["model_name"]["value"] = ids[0]
+                build_config["model_name"]["options"] = ids
+                build_config["model_name"]["value"] = ids[0]
             except Exception as e:
                 msg = f"Error getting model names: {e}"
                 raise ValueError(msg) from e

src/backend/base/langflow/components/models/groq.py

@@ -58,7 +58,8 @@ class GroqModel(LCModelComponent):
             name="model_name",
             display_name="Model",
             info="The name of the model to use.",
-            options=[],
+            options=GROQ_MODELS,
+            value=GROQ_MODELS[0],
             refresh_button=True,
             real_time_refresh=True,
         ),

src/backend/base/langflow/custom/custom_component/component.py

@@ -694,7 +694,11 @@ def _set_input_value(self, name: str, value: Any) -> None:
                     name, f"Input is connected to {input_value.__self__.display_name}.{input_value.__name__}"
                 )
                 raise ValueError(msg)
-            self._inputs[name].value = value
+            try:
+                self._inputs[name].value = value
+            except Exception as e:
+                msg = f"Error setting input value for {name}: {e}"
+                raise ValueError(msg) from e
             if hasattr(self._inputs[name], "load_from_db"):
                 self._inputs[name].load_from_db = False
         else: