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 typing import TYPE_CHECKING, Annotated
 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 @@ from langflow.api.v1.schemas import (
     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.auth.utils import api_key_security, get_current_active_us
 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.groq import GroqModel
 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 @@ except ImportError:
 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_input_constants import (
 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 @@ class AgentComponent(ToolCallingAgentComponent):
         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 @@ class AgentComponent(ToolCallingAgentComponent):
         # 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 @@ class AgentComponent(ToolCallingAgentComponent):
                     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 @@ class AnthropicModelComponent(LCModelComponent):
     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 @@ class Component(CustomComponent):
                     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:

src/backend/base/langflow/memory.py

@@ -254,7 +254,10 @@ async def astore_message(
         return []
 
     if not message.session_id or not message.sender or not message.sender_name:
-        msg = "All of session_id, sender, and sender_name must be provided."
+        msg = (
+            f"All of session_id, sender, and sender_name must be provided. Session ID: {message.session_id},"
+            f" Sender: {message.sender}, Sender Name: {message.sender_name}"
+        )
         raise ValueError(msg)
     if hasattr(message, "id") and message.id:
         # if message has an id and exist in the database, update it

src/backend/tests/base.py

@@ -1,6 +1,12 @@
+import inspect
 from typing import Any
+from unittest.mock import Mock
+from uuid import uuid4
 
 import pytest
+from langflow.custom.custom_component.component import Component
+from langflow.graph.graph.base import Graph
+from langflow.graph.vertex.base import Vertex
 from typing_extensions import TypedDict
 
 from tests.constants import SUPPORTED_VERSIONS
@@ -45,9 +51,20 @@ class ComponentTestBase:
         msg = f"{self.__class__.__name__} must implement the file_names_mapping fixture"
         raise NotImplementedError(msg)
 
+    def component_setup(self, component_class: type[Any], default_kwargs: dict[str, Any]) -> Component:
+        mock_vertex = Mock(spec=Vertex)
+        mock_vertex.graph = Mock(spec=Graph)
+        mock_vertex.graph.session_id = str(uuid4())
+        mock_vertex.graph.flow_id = str(uuid4())
+        source_code = inspect.getsource(component_class)
+        component_instance = component_class(_code=source_code, **default_kwargs)
+        component_instance._vertex = mock_vertex
+        return component_instance
+
     def test_latest_version(self, component_class: type[Any], default_kwargs: dict[str, Any]) -> None:
         """Test that the component works with the latest version."""
-        result = component_class(**default_kwargs)()
+        component_instance = self.component_setup(component_class, default_kwargs)
+        result = component_instance()
         assert result is not None, "Component returned None for the latest version."
 
     def test_all_versions_have_a_file_name_defined(self, file_names_mapping: list[VersionComponentMapping]) -> None:

src/backend/tests/unit/api/v1/test_endpoints.py

@@ -1,9 +1,12 @@
+import inspect
 from typing import Any
 
 from aiofile import async_open
 from fastapi import status
 from httpx import AsyncClient
 from langflow.api.v1.schemas import UpdateCustomComponentRequest
+from langflow.components.agents.agent import AgentComponent
+from langflow.custom.utils import build_custom_component_template
 
 
 async def test_get_version(client: AsyncClient):
@@ -46,3 +49,59 @@ async def test_update_component_outputs(client: AsyncClient, logged_in_headers:
     assert response.status_code == status.HTTP_200_OK
     output_names = [output["name"] for output in result["outputs"]]
     assert "tool_output" in output_names
+
+
+async def test_update_component_model_name_options(client: AsyncClient, logged_in_headers: dict):
+    """Test that model_name options are updated when selecting a provider."""
+    component = AgentComponent()
+    component_node, cc_instance = build_custom_component_template(
+        component,
+    )
+
+    # Initial template with OpenAI as the provider
+    template = component_node["template"]
+    current_model_names = template["model_name"]["options"]
+
+    # load the code from the file at langflow.components.agents.agent.py asynchronously
+    # we are at str/backend/tests/unit/api/v1/test_endpoints.py
+    # find the file by using the class AgentComponent
+    agent_component_file = inspect.getsourcefile(AgentComponent)
+    async with async_open(agent_component_file, encoding="utf-8") as f:
+        code = await f.read()
+
+    # Create the request to update the component
+    request = UpdateCustomComponentRequest(
+        code=code,
+        frontend_node=component_node,
+        field="agent_llm",
+        field_value="Anthropic",
+        template=template,
+    )
+
+    # Make the request to update the component
+    response = await client.post("api/v1/custom_component/update", json=request.model_dump(), headers=logged_in_headers)
+    result = response.json()
+
+    # Verify the response
+    assert response.status_code == status.HTTP_200_OK, f"Response: {response.json()}"
+    assert "template" in result
+    assert "model_name" in result["template"]
+    assert isinstance(result["template"]["model_name"]["options"], list)
+    assert len(result["template"]["model_name"]["options"]) > 0, (
+        f"Model names: {result['template']['model_name']['options']}"
+    )
+    assert current_model_names != result["template"]["model_name"]["options"], (
+        f"Current model names: {current_model_names}, New model names: {result['template']['model_name']['options']}"
+    )
+    # Now test with Custom provider
+    template["agent_llm"]["value"] = "Custom"
+    request.field_value = "Custom"
+    request.template = template
+
+    response = await client.post("api/v1/custom_component/update", json=request.model_dump(), headers=logged_in_headers)
+    result = response.json()
+
+    # Verify that model_name is not present for Custom provider
+    assert response.status_code == status.HTTP_200_OK
+    assert "template" in result
+    assert "model_name" not in result["template"]

src/backend/tests/unit/components/agents/test_agent_component.py

@@ -1,8 +1,99 @@
 import os
+from typing import Any
+from uuid import uuid4
 
 import pytest
+from langflow.base.models.model_input_constants import MODEL_PROVIDERS_DICT
 from langflow.components.agents.agent import AgentComponent
 from langflow.components.tools.calculator import CalculatorToolComponent
+from langflow.custom import Component
+from langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI
+
+from tests.base import ComponentTestBaseWithoutClient
+from tests.unit.mock_language_model import MockLanguageModel
+
+
+class TestAgentComponent(ComponentTestBaseWithoutClient):
+    @pytest.fixture
+    def component_class(self):
+        return AgentComponent
+
+    @pytest.fixture
+    def file_names_mapping(self):
+        return []
+
+    def component_setup(self, component_class: type[Any], default_kwargs: dict[str, Any]) -> Component:
+        component_instance = super().component_setup(component_class, default_kwargs)
+        # Mock _should_process_output method
+        component_instance._should_process_output = lambda output: False  # noqa: ARG005
+        return component_instance
+
+    @pytest.fixture
+    def default_kwargs(self):
+        return {
+            "_type": "Agent",
+            "add_current_date_tool": True,
+            "agent_description": "A helpful agent",
+            "agent_llm": MockLanguageModel(),
+            "handle_parsing_errors": True,
+            "input_value": "",
+            "max_iterations": 10,
+            "system_prompt": "You are a helpful assistant.",
+            "tools": [],
+            "verbose": True,
+            "session_id": str(uuid4()),
+            "sender": MESSAGE_SENDER_AI,
+            "sender_name": MESSAGE_SENDER_NAME_AI,
+        }
+
+    async def test_build_config_update(self, component_class, default_kwargs):
+        component = self.component_setup(component_class, default_kwargs)
+        frontend_node = component.to_frontend_node()
+        build_config = frontend_node["data"]["node"]["template"]
+        # Test updating build config for OpenAI
+        component.set(agent_llm="OpenAI")
+        updated_config = await component.update_build_config(build_config, "OpenAI", "agent_llm")
+        assert "agent_llm" in updated_config
+        assert updated_config["agent_llm"]["value"] == "OpenAI"
+        assert isinstance(updated_config["agent_llm"]["options"], list)
+        assert len(updated_config["agent_llm"]["options"]) > 0
+        assert all(provider in updated_config["agent_llm"]["options"] for provider in MODEL_PROVIDERS_DICT)
+        assert "Custom" in updated_config["agent_llm"]["options"]
+
+        # Verify model_name field is populated for OpenAI
+
+        assert "model_name" in updated_config
+        model_name_dict = updated_config["model_name"]
+        assert isinstance(model_name_dict["options"], list)
+        assert len(model_name_dict["options"]) > 0  # OpenAI should have available models
+        assert "gpt-4o" in model_name_dict["options"]
+
+        # Test Anthropic
+        component.set(agent_llm="Anthropic")
+        updated_config = await component.update_build_config(build_config, "Anthropic", "agent_llm")
+        assert "agent_llm" in updated_config
+        assert updated_config["agent_llm"]["value"] == "Anthropic"
+        assert isinstance(updated_config["agent_llm"]["options"], list)
+        assert len(updated_config["agent_llm"]["options"]) > 0
+        assert all(provider in updated_config["agent_llm"]["options"] for provider in MODEL_PROVIDERS_DICT)
+        assert "Anthropic" in updated_config["agent_llm"]["options"]
+        assert updated_config["agent_llm"]["input_types"] == []
+        assert any("sonnet" in option.lower() for option in updated_config["model_name"]["options"]), (
+            f"Options: {updated_config['model_name']['options']}"
+        )
+
+        # Test updating build config for Custom
+        updated_config = await component.update_build_config(build_config, "Custom", "agent_llm")
+        assert "agent_llm" in updated_config
+        assert updated_config["agent_llm"]["value"] == "Custom"
+        assert isinstance(updated_config["agent_llm"]["options"], list)
+        assert len(updated_config["agent_llm"]["options"]) > 0
+        assert all(provider in updated_config["agent_llm"]["options"] for provider in MODEL_PROVIDERS_DICT)
+        assert "Custom" in updated_config["agent_llm"]["options"]
+        assert updated_config["agent_llm"]["input_types"] == ["LanguageModel"]
+
+        # Verify model_name field is cleared for Custom
+        assert "model_name" not in updated_config
 
 
 @pytest.mark.api_key_required

src/backend/tests/unit/mock_language_model.py

@@ -1,17 +1,21 @@
 from unittest.mock import MagicMock
 
 from langchain_core.language_models import BaseLanguageModel
+from pydantic import BaseModel, Field
 from typing_extensions import override
 
 
-class MockLanguageModel(BaseLanguageModel):
+class MockLanguageModel(BaseLanguageModel, BaseModel):
     """A mock language model for testing purposes."""
 
-    def __init__(self, response_generator=None):
+    tools: list = Field(default_factory=list)
+    response_generator: callable = Field(default_factory=lambda: lambda msg: f"Response for {msg}")
+
+    def __init__(self, response_generator=None, **kwargs):
         """Initialize the mock model with an optional response generator function."""
-        super().__init__()
-        # Use object's __dict__ to bypass pydantic validation
-        object.__setattr__(self, "_response_generator", response_generator or (lambda msg: f"Response for {msg}"))
+        super().__init__(**kwargs)
+        if response_generator:
+            self.response_generator = response_generator
 
     @override
     def with_config(self, *args, **kwargs):
@@ -30,7 +34,7 @@ class MockLanguageModel(BaseLanguageModel):
         for msg_list in messages:
             content = msg_list[-1]["content"] if isinstance(msg_list, list) else msg_list
             mock_response = MagicMock()
-            mock_response.content = self._response_generator(content)
+            mock_response.content = self.response_generator(content)
             responses.append(mock_response)
         return responses
 
@@ -61,3 +65,8 @@ class MockLanguageModel(BaseLanguageModel):
     @override
     async def apredict_messages(self, *args, **kwargs):
         raise NotImplementedError
+
+    def bind_tools(self, tools):
+        """Bind tools to the model for testing."""
+        self.tools = tools
+        return self