fix: Refactor import statements and enhance error logging

src/backend/base/langflow/components/astra_assistants/astra_assistant_manager.py

@@ -2,7 +2,7 @@
 
 from astra_assistants.astra_assistants_manager import AssistantManager
 
-from langflow.components.astra_assistants.util import (
+from langflow.base.astra_assistants.util import (
     get_patched_openai_client,
     litellm_model_names,
     tool_names,

src/backend/base/langflow/components/astra_assistants/create_assistant.py

@@ -1,4 +1,4 @@
-from langflow.components.astra_assistants.util import get_patched_openai_client
+from langflow.base.astra_assistants.util import get_patched_openai_client
 from langflow.custom.custom_component.component_with_cache import ComponentWithCache
 from langflow.inputs import MultilineInput, StrInput
 from langflow.schema.message import Message

src/backend/base/langflow/components/astra_assistants/create_thread.py

@@ -1,4 +1,4 @@
-from langflow.components.astra_assistants.util import get_patched_openai_client
+from langflow.base.astra_assistants.util import get_patched_openai_client
 from langflow.custom.custom_component.component_with_cache import ComponentWithCache
 from langflow.inputs import MultilineInput
 from langflow.schema.message import Message

src/backend/base/langflow/components/astra_assistants/get_assistant.py

@@ -1,4 +1,4 @@
-from langflow.components.astra_assistants.util import get_patched_openai_client
+from langflow.base.astra_assistants.util import get_patched_openai_client
 from langflow.custom.custom_component.component_with_cache import ComponentWithCache
 from langflow.inputs import MultilineInput, StrInput
 from langflow.schema.message import Message

src/backend/base/langflow/components/astra_assistants/list_assistants.py

@@ -1,4 +1,4 @@
-from langflow.components.astra_assistants.util import get_patched_openai_client
+from langflow.base.astra_assistants.util import get_patched_openai_client
 from langflow.custom.custom_component.component_with_cache import ComponentWithCache
 from langflow.schema.message import Message
 from langflow.template.field.base import Output

src/backend/base/langflow/components/astra_assistants/run.py

@@ -4,7 +4,7 @@
 from openai import OpenAI
 from openai.lib.streaming import AssistantEventHandler
 
-from langflow.components.astra_assistants.util import get_patched_openai_client
+from langflow.base.astra_assistants.util import get_patched_openai_client
 from langflow.custom.custom_component.component_with_cache import ComponentWithCache
 from langflow.inputs import MultilineInput
 from langflow.schema import dotdict

src/backend/base/langflow/components/toolkits/Metaphor.py

@@ -1,36 +1,51 @@
-from langchain_community.agent_toolkits.base import BaseToolkit
-from langchain_core.tools import Tool, tool
+from langchain_core.tools import tool
 from metaphor_python import Metaphor  # type: ignore
 
-from langflow.custom import CustomComponent
+from langflow.custom import Component
+from langflow.field_typing import Tool
+from langflow.io import BoolInput, IntInput, Output, SecretStrInput
 
 
-class MetaphorToolkit(CustomComponent):
-    display_name: str = "Metaphor"
-    description: str = "Metaphor Toolkit"
+class MetaphorToolkit(Component):
+    display_name = "Metaphor"
+    description = "Metaphor Toolkit for search and content retrieval"
     documentation = "https://python.langchain.com/docs/integrations/tools/metaphor_search"
-    beta: bool = True
-    name = "Metaphor"
-    # api key should be password = True
-    field_config = {
-        "metaphor_api_key": {"display_name": "Metaphor API Key", "password": True},
-        "code": {"advanced": True},
-    }
-
-    def build(
-        self,
-        metaphor_api_key: str,
-        use_autoprompt: bool = True,
-        search_num_results: int = 5,
-        similar_num_results: int = 5,
-    ) -> Tool | BaseToolkit:
-        # If documents, then we need to create a Vectara instance using .from_documents
-        client = Metaphor(api_key=metaphor_api_key)
+    beta = True
+
+    inputs = [
+        SecretStrInput(
+            name="metaphor_api_key",
+            display_name="Metaphor API Key",
+            password=True,
+        ),
+        BoolInput(
+            name="use_autoprompt",
+            display_name="Use Autoprompt",
+            value=True,
+        ),
+        IntInput(
+            name="search_num_results",
+            display_name="Search Number of Results",
+            value=5,
+        ),
+        IntInput(
+            name="similar_num_results",
+            display_name="Similar Number of Results",
+            value=5,
+        ),
+    ]
+
+    outputs = [
+        Output(name="tools", display_name="Tools", method="build_toolkit"),
+    ]
+
+    def build_toolkit(self) -> Tool:
+        client = Metaphor(api_key=self.metaphor_api_key)
 
         @tool
         def search(query: str):
             """Call search engine with a query."""
-            return client.search(query, use_autoprompt=use_autoprompt, num_results=search_num_results)
+            return client.search(query, use_autoprompt=self.use_autoprompt, num_results=self.search_num_results)
 
         @tool
         def get_contents(ids: list[str]):
@@ -46,6 +61,6 @@ def find_similar(url: str):
 
             The url passed in should be a URL returned from `search`
             """
-            return client.find_similar(url, num_results=similar_num_results)
+            return client.find_similar(url, num_results=self.similar_num_results)
 
         return [search, get_contents, find_similar]  # type: ignore

src/backend/base/langflow/custom/directory_reader/directory_reader.py

@@ -78,7 +78,9 @@ def filter_loaded_components(self, data: dict, with_errors: bool) -> dict:
                         component_tuple = (*build_component(component), component)
                         components.append(component_tuple)
                 except Exception as e:
-                    logger.debug(f"Error while loading component { component['name']}")
+                    logger.debug(
+                        f"Error while loading component '{ component['name']}' from file '{component['file']}'"
+                    )
                     logger.debug(e)
                     continue
             items.append({"name": menu["name"], "path": menu["path"], "components": components})

src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, World).json

@@ -727,7 +727,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key) if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key) if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "advanced": false,