feat(message): support sequencing of multiple streamable models (#8434)

* feat: update OpenAI model parameters handling for reasoning models

* feat: extend input_value type in LCModelComponent to support AsyncIterator and Iterator

* refactor: remove assert_streaming_sequence method and related checks from Graph class

* feat: add consume_iterator method to Message class for handling iterators

* test: add unit tests for OpenAIModelComponent functionality and integration

* feat: update OpenAIModelComponent to include temperature and seed parameters in build_model method

* feat: rename consume_iterator method to consume_iterator_in_text and update its implementation for handling text

* feat: add is_connected_to_chat_output method to Component class for improved message handling

* feat: refactor LCModelComponent methods to support asynchronous message handling and improve chat output integration

* refactor: remove consume_iterator_in_text method from Message class and clean up LCModelComponent input handling

* fix: update import paths for input components in multiple starter project JSON files

* fix: enhance error message formatting in ErrorMessage class to handle additional exception attributes

* refactor: remove validate_stream calls from generate_flow_events and Graph class to streamline flow processing

* fix: handle asyncio.CancelledError in aadd_messagetables to ensure proper session rollback and retry logic

* refactor: streamline message handling in LCModelComponent by replacing async invocation with synchronous calls and updating message text handling

* refactor: enhance message handling in LCModelComponent by introducing lf_message for improved return value management and updating properties for consistency

* feat: add _build_source method to Component class for enhanced source handling and flexibility in source object management

* feat: enhance LCModelComponent by adding _handle_stream method for improved streaming response handling and refactoring chat output integration

* feat: update MemoryComponent to enhance message retrieval and storage functionality, including new sender type handling and output options for text and dataframe formats

* test: refactor LanguageModelComponent tests to use ComponentTestBaseWithoutClient and add tests for Google model creation and error handling

* test: add fixtures for API keys and implement live API tests for OpenAI, Anthropic, and Google models

* fix: reorder JSON properties for consistency in starter projects

* Updated JSON files for various starter projects to ensure consistent ordering of properties, specifically moving "type" to follow "selected_output" for better readability and maintainability.
* Affected files: Basic Prompt Chaining.json, Blog Writer.json, Financial Report Parser.json, Hybrid Search RAG.json, SEO Keyword Generator.json.

* refactor: simplify input_value type in LCModelComponent

* Updated the input_value parameter in LCModelComponent to remove AsyncIterator and Iterator types, streamlining the input options to only str and Message for improved clarity and maintainability.
* This change enhances the documentation and understanding of the expected input types for the component.

* fix: clarify comment for handling source in Component class

* refactor: remove unnecessary mocking in OpenAI model integration tests

src/backend/base/langflow/api/build.py

@@ -214,7 +214,6 @@ async def generate_flow_events(
             async with session_scope() as fresh_session:
                 graph = await create_graph(fresh_session, flow_id_str, flow_name)
 
-            graph.validate_stream()
             first_layer = sort_vertices(graph)
 
             for vertex_id in first_layer:

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

@@ -14,6 +14,7 @@ from langflow.field_typing import LanguageModel
 from langflow.inputs.inputs import BoolInput, InputTypes, MessageInput, MultilineInput
 from langflow.schema.message import Message
 from langflow.template.field.base import Output
+from langflow.utils.constants import MESSAGE_SENDER_AI
 
 # Enabled detailed thinking for NVIDIA reasoning models.
 #
@@ -82,12 +83,12 @@ class LCModelComponent(Component):
                 msg = f"Method '{method_name}' must be defined."
                 raise ValueError(msg)
 
-    def text_response(self) -> Message:
+    async def text_response(self) -> Message:
         input_value = self.input_value
         stream = self.stream
         system_message = self.system_message
         output = self.build_model()
-        result = self.get_chat_result(
+        result = await self.get_chat_result(
             runnable=output, stream=stream, input_value=input_value, system_message=system_message
         )
         self.status = result
@@ -167,7 +168,7 @@ class LCModelComponent(Component):
             status_message = f"Response: {message.content}"  # type: ignore[assignment]
         return status_message
 
-    def get_chat_result(
+    async def get_chat_result(
         self,
         *,
         runnable: LanguageModel,
@@ -178,14 +179,14 @@ class LCModelComponent(Component):
         if getattr(self, "detailed_thinking", False):
             system_message = DETAILED_THINKING_PREFIX + (system_message or "")
 
-        return self._get_chat_result(
+        return await self._get_chat_result(
             runnable=runnable,
             stream=stream,
             input_value=input_value,
             system_message=system_message,
         )
 
-    def _get_chat_result(
+    async def _get_chat_result(
         self,
         *,
         runnable: LanguageModel,
@@ -193,11 +194,29 @@ class LCModelComponent(Component):
         input_value: str | Message,
         system_message: str | None = None,
     ) -> Message:
+        """Get chat result from a language model.
+
+        This method handles the core logic of getting a response from a language model,
+        including handling different input types, streaming, and error handling.
+
+        Args:
+            runnable (LanguageModel): The language model to use for generating responses
+            stream (bool): Whether to stream the response
+            input_value (str | Message): The input to send to the model
+            system_message (str | None, optional): System message to prepend. Defaults to None.
+
+        Returns:
+            The model response, either as a Message object or raw content
+
+        Raises:
+            ValueError: If the input message is empty or if there's an error during model invocation
+        """
         messages: list[BaseMessage] = []
         if not input_value and not system_message:
             msg = "The message you want to send to the model is empty."
             raise ValueError(msg)
         system_message_added = False
+        message = None
         if input_value:
             if isinstance(input_value, Message):
                 with warnings.catch_warnings():
@@ -219,6 +238,7 @@ class LCModelComponent(Component):
         if system_message and not system_message_added:
             messages.insert(0, SystemMessage(content=system_message))
         inputs: list | dict = messages or {}
+        lf_message = None
         try:
             # TODO: Depreciated Feature to be removed in upcoming release
             if hasattr(self, "output_parser") and self.output_parser is not None:
@@ -232,9 +252,10 @@ class LCModelComponent(Component):
                 }
             )
             if stream:
-                return runnable.stream(inputs)
-            message = runnable.invoke(inputs)
-            result = message.content if hasattr(message, "content") else message
+                lf_message, result = await self._handle_stream(runnable, inputs)
+            else:
+                message = runnable.invoke(inputs)
+                result = message.content if hasattr(message, "content") else message
             if isinstance(message, AIMessage):
                 status_message = self.build_status_message(message)
                 self.status = status_message
@@ -247,8 +268,41 @@ class LCModelComponent(Component):
             if message := self._get_exception_message(e):
                 raise ValueError(message) from e
             raise
+        return lf_message or Message(text=result)
 
-        return Message(text=result)
+    async def _handle_stream(self, runnable, inputs):
+        """Handle streaming responses from the language model.
+
+        Args:
+            runnable: The language model configured for streaming
+            inputs: The inputs to send to the model
+
+        Returns:
+            tuple: (Message object if connected to chat output, model result)
+        """
+        lf_message = None
+        if self.is_connected_to_chat_output():
+            # Add a Message
+            if hasattr(self, "graph"):
+                session_id = self.graph.session_id
+            elif hasattr(self, "_session_id"):
+                session_id = self._session_id
+            else:
+                session_id = None
+            model_message = Message(
+                text=runnable.stream(inputs),
+                sender=MESSAGE_SENDER_AI,
+                sender_name="AI",
+                properties={"icon": self.icon, "state": "partial"},
+                session_id=session_id,
+            )
+            model_message.properties.source = self._build_source(self._id, self.display_name, self)
+            lf_message = await self.send_message(model_message)
+            result = lf_message.text
+        else:
+            message = runnable.invoke(inputs)
+            result = message.content if hasattr(message, "content") else message
+        return lf_message, result
 
     @abstractmethod
     def build_model(self) -> LanguageModel:  # type: ignore[type-var]

src/backend/base/langflow/components/languagemodels/openai_chat_model.py

@@ -103,17 +103,15 @@ class OpenAIModelComponent(LCModelComponent):
             "max_tokens": self.max_tokens or None,
             "model_kwargs": self.model_kwargs or {},
             "base_url": self.openai_api_base or "https://api.openai.com/v1",
-            "seed": self.seed,
             "max_retries": self.max_retries,
             "timeout": self.timeout,
-            "temperature": self.temperature if self.temperature is not None else 0.1,
         }
 
         logger.info(f"Model name: {self.model_name}")
-        if self.model_name in OPENAI_REASONING_MODEL_NAMES:
-            logger.info("Getting reasoning model parameters")
-            parameters.pop("temperature")
-            parameters.pop("seed")
+        if self.model_name not in OPENAI_REASONING_MODEL_NAMES:
+            parameters["temperature"] = self.temperature if self.temperature is not None else 0.1
+            parameters["seed"] = self.seed
+
         output = ChatOpenAI(**parameters)
         if self.json_mode:
             output = output.bind(response_format={"type": "json_object"})

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

@@ -160,6 +160,22 @@ class Component(CustomComponent):
         self._set_output_types(list(self._outputs_map.values()))
         self.set_class_code()
 
+    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:
+        source_dict = {}
+        if id_:
+            source_dict["id"] = id_
+        if display_name:
+            source_dict["display_name"] = display_name
+        if source:
+            # Handle case where source is a ChatOpenAI and other models objects
+            if hasattr(source, "model_name"):
+                source_dict["source"] = source.model_name
+            elif hasattr(source, "model"):
+                source_dict["source"] = str(source.model)
+            else:
+                source_dict["source"] = str(source)
+        return Source(**source_dict)
+
     def get_incoming_edge_by_target_param(self, target_param: str) -> str | None:
         """Get the source vertex ID for an incoming edge that targets a specific parameter.
 
@@ -1354,12 +1370,15 @@ class Component(CustomComponent):
                 )
             )
 
+    def is_connected_to_chat_output(self) -> bool:
+        return has_chat_output(self.graph.get_vertex_neighbors(self._vertex))
+
     def _should_skip_message(self, message: Message) -> bool:
         """Check if the message should be skipped based on vertex configuration and message type."""
         return (
             self._vertex is not None
             and not (self._vertex.is_output or self._vertex.is_input)
-            and not has_chat_output(self.graph.get_vertex_neighbors(self._vertex))
+            and not self.is_connected_to_chat_output()
             and not isinstance(message, ErrorMessage)
         )
 

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

@@ -1218,8 +1218,6 @@ class Graph:
             if vertex.id in self.cycle_vertices:
                 self.run_manager.add_to_cycle_vertices(vertex.id)
 
-        self.assert_streaming_sequence()
-
     def _get_edges_as_list_of_tuples(self) -> list[tuple[str, str]]:
         """Returns the edges of the graph as a list of tuples.
 
@@ -1940,24 +1938,11 @@ class Graph:
         vertex_instance.set_top_level(self.top_level_vertices)
         return vertex_instance
 
-    def assert_streaming_sequence(self) -> None:
-        for i in self.edges:
-            source = self.get_vertex(i.source_id)
-            if "stream" in source.params and source.params["stream"] is True:
-                target = self.get_vertex(i.target_id)
-                if target.vertex_type != "ChatOutput":
-                    msg = (
-                        "Error: A 'streaming' vertex cannot be followed by a non-'chat output' vertex."
-                        "Disable streaming to run the flow."
-                    )
-                    raise Exception(msg)  # noqa: TRY002
-
     def prepare(self, stop_component_id: str | None = None, start_component_id: str | None = None):
         self.initialize()
         if stop_component_id and start_component_id:
             msg = "You can only provide one of stop_component_id or start_component_id"
             raise ValueError(msg)
-        self.validate_stream()
 
         if stop_component_id or start_component_id:
             try:

src/backend/base/langflow/memory.py

@@ -153,15 +153,16 @@ async def aupdate_messages(messages: Message | list[Message]) -> list[Message]:
 
 async def aadd_messagetables(messages: list[MessageTable], session: AsyncSession):
     try:
-        for message in messages:
-            session.add(message)
         try:
+            for message in messages:
+                session.add(message)
             await session.commit()
             # This is a hack.
             # We are doing this because build_public_tmp causes the CancelledError to be raised
             # while build_flow does not.
         except asyncio.CancelledError:
-            await session.commit()
+            await session.rollback()
+            return await aadd_messagetables(messages, session)
         for message in messages:
             await session.refresh(message)
     except asyncio.CancelledError as e:

src/backend/base/langflow/schema/message.py

@@ -403,12 +403,18 @@ class ErrorMessage(Message):
         """Format the error reason without markdown."""
         if hasattr(exception, "body") and isinstance(exception.body, dict) and "message" in exception.body:
             reason = f"{exception.body.get('message')}\n"
+        elif hasattr(exception, "_message"):
+            reason = f"{exception._message()}\n" if callable(exception._message) else f"{exception._message}\n"
         elif hasattr(exception, "code"):
             reason = f"Code: {exception.code}\n"
         elif hasattr(exception, "args") and exception.args:
             reason = f"{exception.args[0]}\n"
         elif isinstance(exception, ValidationError):
             reason = f"{exception!s}\n"
+        elif hasattr(exception, "detail"):
+            reason = f"{exception.detail}\n"
+        elif hasattr(exception, "message"):
+            reason = f"{exception.message}\n"
         else:
             reason = "An unknown error occurred.\n"
         return reason

src/backend/tests/unit/components/languagemodels/test_openai_model.py

@@ -0,0 +1,203 @@
+import os
+from unittest.mock import MagicMock, patch
+
+import pytest
+from langchain_openai import ChatOpenAI
+from langflow.components.languagemodels.openai_chat_model import OpenAIModelComponent
+
+from tests.base import ComponentTestBaseWithoutClient
+
+
+class TestOpenAIModelComponent(ComponentTestBaseWithoutClient):
+    @pytest.fixture
+    def component_class(self):
+        return OpenAIModelComponent
+
+    @pytest.fixture
+    def default_kwargs(self):
+        return {
+            "max_tokens": 1000,
+            "model_kwargs": {},
+            "json_mode": False,
+            "model_name": "gpt-4.1-nano",
+            "openai_api_base": "https://api.openai.com/v1",
+            "api_key": "test-api-key",
+            "temperature": 0.1,
+            "seed": 1,
+            "max_retries": 5,
+            "timeout": 700,
+        }
+
+    @pytest.fixture
+    def file_names_mapping(self):
+        # Provide an empty list or the actual mapping if versioned files exist
+        return []
+
+    @patch("langflow.components.languagemodels.openai_chat_model.ChatOpenAI")
+    async def test_build_model(self, mock_chat_openai, component_class, default_kwargs):
+        mock_instance = MagicMock()
+        mock_chat_openai.return_value = mock_instance
+        component = component_class(**default_kwargs)
+        model = component.build_model()
+
+        mock_chat_openai.assert_called_once_with(
+            api_key="test-api-key",
+            model_name="gpt-4.1-nano",
+            max_tokens=1000,
+            model_kwargs={},
+            base_url="https://api.openai.com/v1",
+            seed=1,
+            max_retries=5,
+            timeout=700,
+            temperature=0.1,
+        )
+        assert model == mock_instance
+
+    @patch("langflow.components.languagemodels.openai_chat_model.ChatOpenAI")
+    async def test_build_model_reasoning_model(self, mock_chat_openai, component_class, default_kwargs):
+        mock_instance = MagicMock()
+        mock_chat_openai.return_value = mock_instance
+        default_kwargs["model_name"] = "o1"
+        component = component_class(**default_kwargs)
+        model = component.build_model()
+
+        # For reasoning models, temperature and seed should be excluded
+        mock_chat_openai.assert_called_once_with(
+            api_key="test-api-key",
+            model_name="o1",
+            max_tokens=1000,
+            model_kwargs={},
+            base_url="https://api.openai.com/v1",
+            max_retries=5,
+            timeout=700,
+        )
+        assert model == mock_instance
+
+        # Verify that temperature and seed are not in the parameters
+        args, kwargs = mock_chat_openai.call_args
+        assert "temperature" not in kwargs
+        assert "seed" not in kwargs
+
+    @patch("langflow.components.languagemodels.openai_chat_model.ChatOpenAI")
+    async def test_build_model_with_json_mode(self, mock_chat_openai, component_class, default_kwargs):
+        mock_instance = MagicMock()
+        mock_bound_instance = MagicMock()
+        mock_instance.bind.return_value = mock_bound_instance
+        mock_chat_openai.return_value = mock_instance
+
+        default_kwargs["json_mode"] = True
+        component = component_class(**default_kwargs)
+        model = component.build_model()
+
+        mock_chat_openai.assert_called_once()
+        mock_instance.bind.assert_called_once_with(response_format={"type": "json_object"})
+        assert model == mock_bound_instance
+
+    @patch("langflow.components.languagemodels.openai_chat_model.ChatOpenAI")
+    async def test_build_model_no_api_key(self, mock_chat_openai, component_class, default_kwargs):
+        mock_instance = MagicMock()
+        mock_chat_openai.return_value = mock_instance
+        default_kwargs["api_key"] = None
+        component = component_class(**default_kwargs)
+        component.build_model()
+
+        # When api_key is None, it should be passed as None to ChatOpenAI
+        args, kwargs = mock_chat_openai.call_args
+        assert kwargs["api_key"] is None
+
+    @patch("langflow.components.languagemodels.openai_chat_model.ChatOpenAI")
+    async def test_build_model_max_tokens_zero(self, mock_chat_openai, component_class, default_kwargs):
+        mock_instance = MagicMock()
+        mock_chat_openai.return_value = mock_instance
+        default_kwargs["max_tokens"] = 0
+        component = component_class(**default_kwargs)
+        component.build_model()
+
+        # When max_tokens is 0, it should be passed as None to ChatOpenAI
+        args, kwargs = mock_chat_openai.call_args
+        assert kwargs["max_tokens"] is None
+
+    async def test_get_exception_message_bad_request_error(self, component_class, default_kwargs):
+        component_class(**default_kwargs)
+
+        # Create a mock BadRequestError with a body attribute
+        mock_error = MagicMock()
+        mock_error.body = {"message": "test error message"}
+
+        # Test the method directly by patching the import
+        with patch("openai.BadRequestError", mock_error.__class__):
+            # Manually call isinstance to avoid mocking it
+            if hasattr(mock_error, "body"):
+                message = mock_error.body.get("message")
+                assert message == "test error message"
+
+    async def test_get_exception_message_no_openai_import(self, component_class, default_kwargs):
+        component = component_class(**default_kwargs)
+
+        # Test when openai module is not available
+        with patch.dict("sys.modules", {"openai": None}), patch("builtins.__import__", side_effect=ImportError):
+            message = component._get_exception_message(Exception("test"))
+            assert message is None
+
+    async def test_get_exception_message_other_exception(self, component_class, default_kwargs):
+        component = component_class(**default_kwargs)
+
+        # Create a regular exception (not BadRequestError)
+        regular_exception = ValueError("test error")
+
+        # Create a simple mock for BadRequestError that the exception won't match
+        class MockBadRequestError:
+            pass
+
+        with patch("openai.BadRequestError", MockBadRequestError):
+            message = component._get_exception_message(regular_exception)
+            assert message is None
+
+    async def test_update_build_config_reasoning_model(self, component_class, default_kwargs):
+        component = component_class(**default_kwargs)
+        build_config = {
+            "temperature": {"show": True},
+            "seed": {"show": True},
+        }
+
+        # Test with reasoning model
+        updated_config = component.update_build_config(build_config, "o1", "model_name")
+        assert updated_config["temperature"]["show"] is False
+        assert updated_config["seed"]["show"] is False
+
+        # Test with regular model
+        updated_config = component.update_build_config(build_config, "gpt-4", "model_name")
+        assert updated_config["temperature"]["show"] is True
+        assert updated_config["seed"]["show"] is True
+
+    def test_build_model_integration(self):
+        component = OpenAIModelComponent()
+        component.api_key = os.getenv("OPENAI_API_KEY")
+        component.model_name = "gpt-4.1-nano"
+        component.temperature = 0.2
+        component.max_tokens = 1000
+        component.seed = 42
+        component.max_retries = 3
+        component.timeout = 600
+        component.openai_api_base = "https://api.openai.com/v1"
+
+        model = component.build_model()
+        assert isinstance(model, ChatOpenAI)
+        assert model.model_name == "gpt-4.1-nano"
+        assert model.openai_api_base == "https://api.openai.com/v1"
+
+    def test_build_model_integration_reasoning(self):
+        component = OpenAIModelComponent()
+        component.api_key = os.getenv("OPENAI_API_KEY")
+        component.model_name = "o1"
+        component.temperature = 0.2  # This should be ignored for reasoning models
+        component.max_tokens = 1000
+        component.seed = 42  # This should be ignored for reasoning models
+        component.max_retries = 3
+        component.timeout = 600
+        component.openai_api_base = "https://api.openai.com/v1"
+
+        model = component.build_model()
+        assert isinstance(model, ChatOpenAI)
+        assert model.model_name == "o1"
+        assert model.openai_api_base == "https://api.openai.com/v1"

src/backend/tests/unit/components/models/test_language_model_component.py

@@ -1,15 +1,18 @@
-from unittest.mock import MagicMock, patch
+import os
 
 import pytest
+from langchain_anthropic import ChatAnthropic
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_openai import ChatOpenAI
 from langflow.base.models.anthropic_constants import ANTHROPIC_MODELS
+from langflow.base.models.google_generative_ai_constants import GOOGLE_GENERATIVE_AI_MODELS
 from langflow.base.models.openai_constants import OPENAI_MODEL_NAMES
 from langflow.components.models.language_model import LanguageModelComponent
 
-from tests.base import ComponentTestBaseWithClient
+from tests.base import ComponentTestBaseWithoutClient
 
 
-@pytest.mark.usefixtures("client")
-class TestLanguageModelComponent(ComponentTestBaseWithClient):
+class TestLanguageModelComponent(ComponentTestBaseWithoutClient):
     @pytest.fixture
     def component_class(self):
         return LanguageModelComponent
@@ -29,6 +32,32 @@ class TestLanguageModelComponent(ComponentTestBaseWithClient):
     @pytest.fixture
     def file_names_mapping(self):
         """Return the file names mapping for version-specific files."""
+        # No version-specific files for this component
+        return []
+
+    @pytest.fixture
+    def openai_api_key(self):
+        """Fixture to get OpenAI API key from environment variable."""
+        api_key = os.environ.get("OPENAI_API_KEY")
+        if not api_key:
+            pytest.skip("OPENAI_API_KEY environment variable not set")
+        return api_key
+
+    @pytest.fixture
+    def anthropic_api_key(self):
+        """Fixture to get Anthropic API key from environment variable."""
+        api_key = os.environ.get("ANTHROPIC_API_KEY")
+        if not api_key:
+            pytest.skip("ANTHROPIC_API_KEY environment variable not set")
+        return api_key
+
+    @pytest.fixture
+    def google_api_key(self):
+        """Fixture to get Google API key from environment variable."""
+        api_key = os.environ.get("GOOGLE_API_KEY")
+        if not api_key:
+            pytest.skip("GOOGLE_API_KEY environment variable not set")
+        return api_key
 
     async def test_update_build_config_openai(self, component_class, default_kwargs):
         component = component_class(**default_kwargs)
@@ -52,57 +81,69 @@ class TestLanguageModelComponent(ComponentTestBaseWithClient):
         assert updated_config["model_name"]["value"] == ANTHROPIC_MODELS[0]
         assert updated_config["api_key"]["display_name"] == "Anthropic API Key"
 
-    @patch("langflow.components.models.language_model.ChatOpenAI")
-    async def test_build_model_openai(self, mock_chat_openai, component_class, default_kwargs):
-        # Setup mock
-        mock_instance = MagicMock()
-        mock_chat_openai.return_value = mock_instance
+    async def test_update_build_config_google(self, component_class, default_kwargs):
+        component = component_class(**default_kwargs)
+        build_config = {
+            "model_name": {"options": [], "value": ""},
+            "api_key": {"display_name": "API Key"},
+        }
+        updated_config = component.update_build_config(build_config, "Google", "provider")
+        assert updated_config["model_name"]["options"] == GOOGLE_GENERATIVE_AI_MODELS
+        assert updated_config["model_name"]["value"] == GOOGLE_GENERATIVE_AI_MODELS[0]
+        assert updated_config["api_key"]["display_name"] == "Google API Key"
 
-        # Create and configure the component
+    async def test_openai_model_creation(self, component_class, default_kwargs):
+        """Test that the component returns an instance of ChatOpenAI for OpenAI provider."""
         component = component_class(**default_kwargs)
         component.provider = "OpenAI"
         component.model_name = "gpt-3.5-turbo"
-        component.api_key = "test-key"
+        component.api_key = "sk-test-key"  # Use a fake but correctly formatted key
         component.temperature = 0.5
         component.stream = False
 
-        # Build the model
+        # The API key will be invalid, but we should still get a ChatOpenAI instance
         model = component.build_model()
+        assert isinstance(model, ChatOpenAI)
+        assert model.model_name == "gpt-3.5-turbo"
+        assert model.temperature == 0.5
+        assert model.streaming is False
+        # API key is stored as a SecretStr object, so we can't directly compare values
 
-        # Verify the ChatOpenAI was called with the correct parameters
-        mock_chat_openai.assert_called_once_with(
-            model_name="gpt-3.5-turbo",
-            temperature=0.5,
-            streaming=False,
-            openai_api_key="test-key",
-        )
-        assert model == mock_instance
-
-    @patch("langflow.components.models.language_model.ChatAnthropic")
-    async def test_build_model_anthropic(self, mock_chat_anthropic, component_class, default_kwargs):
-        # Setup mock
-        mock_instance = MagicMock()
-        mock_chat_anthropic.return_value = mock_instance
-
-        # Create and configure the component
+    async def test_anthropic_model_creation(self, component_class, default_kwargs):
+        """Test that the component returns an instance of ChatAnthropic for Anthropic provider."""
         component = component_class(**default_kwargs)
         component.provider = "Anthropic"
-        component.model_name = ANTHROPIC_MODELS[0]  # Use the first model from the constants
-        component.api_key = "test-key"
+        component.model_name = ANTHROPIC_MODELS[0]
+        component.api_key = "sk-ant-test-key"  # Use a fake but plausible key
         component.temperature = 0.7
         component.stream = False
 
-        # Build the model
+        # The API key will be invalid, but we should still get a ChatAnthropic instance
         model = component.build_model()
+        assert isinstance(model, ChatAnthropic)
+        assert model.model == ANTHROPIC_MODELS[0]
+        assert model.temperature == 0.7
+        assert model.streaming is False
+        # API key is stored as a SecretStr object, so we can't directly compare values
 
-        # Verify the ChatAnthropic was called with the correct parameters
-        mock_chat_anthropic.assert_called_once_with(
-            model=ANTHROPIC_MODELS[0],
-            temperature=0.7,
-            streaming=False,
-            anthropic_api_key="test-key",
-        )
-        assert model == mock_instance
+    async def test_google_model_creation(self, component_class, default_kwargs):
+        """Test that the component returns an instance of ChatGoogleGenerativeAI for Google provider."""
+        component = component_class(**default_kwargs)
+        component.provider = "Google"
+        component.model_name = GOOGLE_GENERATIVE_AI_MODELS[0]
+        component.api_key = "google-test-key"  # Use a fake but plausible key
+        component.temperature = 0.7
+        component.stream = False
+
+        # The API key will be invalid, but we should still get a ChatGoogleGenerativeAI instance
+        model = component.build_model()
+        assert isinstance(model, ChatGoogleGenerativeAI)
+        # Google model automatically prepends "models/" to the model name
+        assert model.model == f"models/{GOOGLE_GENERATIVE_AI_MODELS[0]}"
+        assert model.temperature == 0.7
+        # Google model uses 'stream' instead of 'streaming'
+        # Skip this check for Google model since it has a different interface
+        # API key is stored as a SecretStr object, so we can't directly compare values
 
     async def test_build_model_openai_missing_api_key(self, component_class, default_kwargs):
         component = component_class(**default_kwargs)
@@ -120,9 +161,59 @@ class TestLanguageModelComponent(ComponentTestBaseWithClient):
         with pytest.raises(ValueError, match="Anthropic API key is required when using Anthropic provider"):
             component.build_model()
 
+    async def test_build_model_google_missing_api_key(self, component_class, default_kwargs):
+        component = component_class(**default_kwargs)
+        component.provider = "Google"
+        component.api_key = None
+
+        with pytest.raises(ValueError, match="Google API key is required when using Google provider"):
+            component.build_model()
+
     async def test_build_model_unknown_provider(self, component_class, default_kwargs):
         component = component_class(**default_kwargs)
         component.provider = "Unknown"
 
         with pytest.raises(ValueError, match="Unknown provider: Unknown"):
             component.build_model()
+
+    async def test_openai_live_api(self, component_class, default_kwargs, openai_api_key):
+        """Test that the component can create a model with a real API key."""
+        component = component_class(**default_kwargs)
+        component.provider = "OpenAI"
+        component.model_name = "gpt-3.5-turbo"
+        component.api_key = openai_api_key
+        component.temperature = 0.1
+        component.stream = False
+
+        model = component.build_model()
+        assert isinstance(model, ChatOpenAI)
+        # We could attempt a simple call here, but that would increase test time
+        # and might fail due to network issues, so we'll just verify the instance
+
+    async def test_anthropic_live_api(self, component_class, default_kwargs, anthropic_api_key):
+        """Test that the component can create a model with a real API key."""
+        component = component_class(**default_kwargs)
+        component.provider = "Anthropic"
+        component.model_name = ANTHROPIC_MODELS[0]
+        component.api_key = anthropic_api_key
+        component.temperature = 0.1
+        component.stream = False
+
+        model = component.build_model()
+        assert isinstance(model, ChatAnthropic)
+        # We could attempt a simple call here, but that would increase test time
+        # and might fail due to network issues, so we'll just verify the instance
+
+    async def test_google_live_api(self, component_class, default_kwargs, google_api_key):
+        """Test that the component can create a model with a real API key."""
+        component = component_class(**default_kwargs)
+        component.provider = "Google"
+        component.model_name = GOOGLE_GENERATIVE_AI_MODELS[0]
+        component.api_key = google_api_key
+        component.temperature = 0.1
+        component.stream = False
+
+        model = component.build_model()
+        assert isinstance(model, ChatGoogleGenerativeAI)
+        # We could attempt a simple call here, but that would increase test time
+        # and might fail due to network issues, so we'll just verify the instance