diff --git a/src/backend/base/langflow/base/models/model.py b/src/backend/base/langflow/base/models/model.py index f7c47c648..4333b231b 100644 --- a/src/backend/base/langflow/base/models/model.py +++ b/src/backend/base/langflow/base/models/model.py @@ -33,7 +33,7 @@ class LCModelComponent(Component): info="System message to pass to the model.", advanced=False, ), - BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=False), + BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True), ] outputs = [ diff --git a/src/backend/base/langflow/components/models/anthropic.py b/src/backend/base/langflow/components/models/anthropic.py index feb1514af..bfaa7eec1 100644 --- a/src/backend/base/langflow/components/models/anthropic.py +++ b/src/backend/base/langflow/components/models/anthropic.py @@ -48,6 +48,7 @@ class AnthropicModelComponent(LCModelComponent): value=0.1, info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].", range_spec=RangeSpec(min=0, max=1, step=0.01), + advanced=True, ), MessageTextInput( name="base_url", diff --git a/src/backend/base/langflow/components/models/azure_openai.py b/src/backend/base/langflow/components/models/azure_openai.py index a6aa5b047..60eaed6cd 100644 --- a/src/backend/base/langflow/components/models/azure_openai.py +++ b/src/backend/base/langflow/components/models/azure_openai.py @@ -56,6 +56,7 @@ class AzureChatOpenAIComponent(LCModelComponent): value=0.7, range_spec=RangeSpec(min=0, max=2, step=0.01), info="Controls randomness. Lower values are more deterministic, higher values are more creative.", + advanced=True, ), IntInput( name="max_tokens", diff --git a/src/backend/base/langflow/components/models/cohere.py b/src/backend/base/langflow/components/models/cohere.py index 22c43b373..182dc9f4c 100644 --- a/src/backend/base/langflow/components/models/cohere.py +++ b/src/backend/base/langflow/components/models/cohere.py @@ -30,6 +30,7 @@ class CohereComponent(LCModelComponent): value=0.75, range_spec=RangeSpec(min=0, max=2, step=0.01), info="Controls randomness. Lower values are more deterministic, higher values are more creative.", + advanced=True, ), ] diff --git a/src/backend/base/langflow/components/models/deepseek.py b/src/backend/base/langflow/components/models/deepseek.py index c01cf5756..a2253d1ee 100644 --- a/src/backend/base/langflow/components/models/deepseek.py +++ b/src/backend/base/langflow/components/models/deepseek.py @@ -64,6 +64,7 @@ class DeepSeekModelComponent(LCModelComponent): info="Controls randomness in responses", value=1.0, range_spec=RangeSpec(min=0, max=2, step=0.01), + advanced=True, ), IntInput( name="seed", diff --git a/src/backend/base/langflow/components/models/groq.py b/src/backend/base/langflow/components/models/groq.py index 5d94df090..28c76a9af 100644 --- a/src/backend/base/langflow/components/models/groq.py +++ b/src/backend/base/langflow/components/models/groq.py @@ -6,7 +6,7 @@ from langflow.base.models.groq_constants import GROQ_MODELS from langflow.base.models.model import LCModelComponent from langflow.field_typing import LanguageModel from langflow.field_typing.range_spec import RangeSpec -from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, MessageTextInput, SecretStrInput, SliderInput +from langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput class GroqModel(LCModelComponent): @@ -34,18 +34,13 @@ class GroqModel(LCModelComponent): info="The maximum number of tokens to generate.", advanced=True, ), - FloatInput( - name="temperature", - display_name="Temperature", - info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].", - value=0.1, - ), SliderInput( name="temperature", display_name="Temperature", value=0.1, info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].", range_spec=RangeSpec(min=0, max=1, step=0.01), + advanced=True, ), IntInput( name="n", diff --git a/src/backend/base/langflow/components/models/lmstudiomodel.py b/src/backend/base/langflow/components/models/lmstudiomodel.py index 6a503f33f..1680699c2 100644 --- a/src/backend/base/langflow/components/models/lmstudiomodel.py +++ b/src/backend/base/langflow/components/models/lmstudiomodel.py @@ -75,7 +75,12 @@ class LMStudioModelComponent(LCModelComponent): advanced=True, value="LMSTUDIO_API_KEY", ), - FloatInput(name="temperature", display_name="Temperature", value=0.1), + FloatInput( + name="temperature", + display_name="Temperature", + value=0.1, + advanced=True, + ), IntInput( name="seed", display_name="Seed", diff --git a/src/backend/base/langflow/components/models/mistral.py b/src/backend/base/langflow/components/models/mistral.py index ed66ab529..155b0a1cc 100644 --- a/src/backend/base/langflow/components/models/mistral.py +++ b/src/backend/base/langflow/components/models/mistral.py @@ -52,8 +52,8 @@ class MistralAIModelComponent(LCModelComponent): FloatInput( name="temperature", display_name="Temperature", - advanced=False, - value=0.5, + value=0.1, + advanced=True, ), IntInput( name="max_retries", diff --git a/src/backend/base/langflow/components/models/nvidia.py b/src/backend/base/langflow/components/models/nvidia.py index 132a4df0c..faeebe6f9 100644 --- a/src/backend/base/langflow/components/models/nvidia.py +++ b/src/backend/base/langflow/components/models/nvidia.py @@ -60,6 +60,7 @@ class NVIDIAModelComponent(LCModelComponent): value=0.1, info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].", range_spec=RangeSpec(min=0, max=1, step=0.01), + advanced=True, ), IntInput( name="seed", diff --git a/src/backend/base/langflow/components/models/ollama.py b/src/backend/base/langflow/components/models/ollama.py index b5b8f52a8..ddd48ef10 100644 --- a/src/backend/base/langflow/components/models/ollama.py +++ b/src/backend/base/langflow/components/models/ollama.py @@ -35,7 +35,11 @@ class ChatOllamaComponent(LCModelComponent): real_time_refresh=True, ), SliderInput( - name="temperature", display_name="Temperature", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01) + name="temperature", + display_name="Temperature", + value=0.1, + range_spec=RangeSpec(min=0, max=1, step=0.01), + advanced=True, ), MessageTextInput( name="format", display_name="Format", info="Specify the format of the output (e.g., json).", advanced=True diff --git a/src/backend/base/langflow/components/models/openai_chat_model.py b/src/backend/base/langflow/components/models/openai_chat_model.py index 0f68acf8b..1fe36c05e 100644 --- a/src/backend/base/langflow/components/models/openai_chat_model.py +++ b/src/backend/base/langflow/components/models/openai_chat_model.py @@ -60,7 +60,11 @@ class OpenAIModelComponent(LCModelComponent): required=True, ), SliderInput( - name="temperature", display_name="Temperature", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01) + name="temperature", + display_name="Temperature", + value=0.1, + range_spec=RangeSpec(min=0, max=1, step=0.01), + advanced=True, ), IntInput( name="seed", diff --git a/src/backend/base/langflow/components/models/openrouter.py b/src/backend/base/langflow/components/models/openrouter.py index dc7aa4544..c0292cf68 100644 --- a/src/backend/base/langflow/components/models/openrouter.py +++ b/src/backend/base/langflow/components/models/openrouter.py @@ -67,6 +67,7 @@ class OpenRouterComponent(LCModelComponent): value=0.7, range_spec=RangeSpec(min=0, max=2, step=0.01), info="Controls randomness. Lower values are more deterministic, higher values are more creative.", + advanced=True, ), IntInput( name="max_tokens", diff --git a/src/backend/base/langflow/components/models/sambanova.py b/src/backend/base/langflow/components/models/sambanova.py index b59333001..f5eb22f5a 100644 --- a/src/backend/base/langflow/components/models/sambanova.py +++ b/src/backend/base/langflow/components/models/sambanova.py @@ -56,7 +56,11 @@ class SambaNovaComponent(LCModelComponent): info="Model top_p", ), SliderInput( - name="temperature", display_name="Temperature", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01) + name="temperature", + display_name="Temperature", + value=0.1, + range_spec=RangeSpec(min=0, max=2, step=0.01), + advanced=True, ), ] diff --git a/src/backend/base/langflow/components/models/xai.py b/src/backend/base/langflow/components/models/xai.py index bb6b04bee..81bf3892b 100644 --- a/src/backend/base/langflow/components/models/xai.py +++ b/src/backend/base/langflow/components/models/xai.py @@ -64,7 +64,11 @@ class XAIModelComponent(LCModelComponent): required=True, ), SliderInput( - name="temperature", display_name="Temperature", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01) + name="temperature", + display_name="Temperature", + value=0.1, + range_spec=RangeSpec(min=0, max=2, step=0.01), + advanced=True, ), IntInput( name="seed", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json index 76181069c..89520a9f2 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json @@ -1354,7 +1354,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -1524,7 +1524,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1566,7 +1566,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", @@ -1736,7 +1736,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -1906,7 +1906,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1948,7 +1948,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", @@ -2118,7 +2118,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -2288,7 +2288,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -2330,7 +2330,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json index abcc3c1ee..adbf6a743 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json @@ -974,7 +974,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -1144,7 +1144,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1186,7 +1186,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json index 70b28bfee..fb169f66d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json @@ -1079,7 +1079,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -1249,7 +1249,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1291,7 +1291,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json index 570056441..9a75bfffa 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json @@ -2093,7 +2093,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any\n\nimport requests\nfrom loguru import logger\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass AnthropicModelComponent(LCModelComponent):\n display_name = \"Anthropic\"\n description = \"Generate text using Anthropic Chat&Completion LLMs with prefill support.\"\n icon = \"Anthropic\"\n name = \"AnthropicModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n value=4096,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=ANTHROPIC_MODELS,\n refresh_button=True,\n value=ANTHROPIC_MODELS[0],\n combobox=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Anthropic API Key\",\n info=\"Your Anthropic API key.\",\n value=None,\n required=True,\n real_time_refresh=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n ),\n MessageTextInput(\n name=\"base_url\",\n display_name=\"Anthropic API URL\",\n info=\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\n value=\"https://api.anthropic.com\",\n real_time_refresh=True,\n ),\n BoolInput(\n name=\"tool_model_enabled\",\n display_name=\"Enable Tool Models\",\n info=(\n \"Select if you want to use models that can work with tools. If yes, only those models will be shown.\"\n ),\n advanced=False,\n value=False,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"prefill\", display_name=\"Prefill\", info=\"Prefill text to guide the model's response.\", advanced=True\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n try:\n output = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n max_tokens_to_sample=self.max_tokens,\n temperature=self.temperature,\n anthropic_api_url=self.base_url,\n streaming=self.stream,\n )\n except Exception as e:\n msg = \"Could not connect to Anthropic API.\"\n raise ValueError(msg) from e\n\n return output\n\n def get_models(self, tool_model_enabled: bool | None = None) -> list[str]:\n try:\n import anthropic\n\n client = anthropic.Anthropic(api_key=self.api_key)\n models = client.models.list(limit=20).data\n model_ids = [model.id for model in models]\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n model_ids = ANTHROPIC_MODELS\n if tool_model_enabled:\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n for model in model_ids:\n model_with_tool = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n anthropic_api_url=self.base_url,\n )\n if not self.supports_tool_calling(model_with_tool):\n model_ids.remove(model)\n return model_ids\n\n def _get_exception_message(self, exception: Exception) -> str | None:\n \"\"\"Get a message from an Anthropic 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 try:\n from anthropic import BadRequestError\n except ImportError:\n return None\n if isinstance(exception, BadRequestError):\n message = exception.body.get(\"error\", {}).get(\"message\")\n if message:\n return message\n return None\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n if field_name in {\"base_url\", \"model_name\", \"tool_model_enabled\", \"api_key\"} and field_value:\n try:\n if len(self.api_key) == 0:\n ids = ANTHROPIC_MODELS\n else:\n try:\n ids = self.get_models(tool_model_enabled=self.tool_model_enabled)\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n ids = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"options\"] = ids\n build_config[\"model_name\"][\"value\"] = ids[0]\n except Exception as e:\n msg = f\"Error getting model names: {e}\"\n raise ValueError(msg) from e\n return build_config\n" + "value": "from typing import Any\n\nimport requests\nfrom loguru import logger\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass AnthropicModelComponent(LCModelComponent):\n display_name = \"Anthropic\"\n description = \"Generate text using Anthropic Chat&Completion LLMs with prefill support.\"\n icon = \"Anthropic\"\n name = \"AnthropicModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n value=4096,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=ANTHROPIC_MODELS,\n refresh_button=True,\n value=ANTHROPIC_MODELS[0],\n combobox=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Anthropic API Key\",\n info=\"Your Anthropic API key.\",\n value=None,\n required=True,\n real_time_refresh=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\n MessageTextInput(\n name=\"base_url\",\n display_name=\"Anthropic API URL\",\n info=\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\n value=\"https://api.anthropic.com\",\n real_time_refresh=True,\n ),\n BoolInput(\n name=\"tool_model_enabled\",\n display_name=\"Enable Tool Models\",\n info=(\n \"Select if you want to use models that can work with tools. If yes, only those models will be shown.\"\n ),\n advanced=False,\n value=False,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"prefill\", display_name=\"Prefill\", info=\"Prefill text to guide the model's response.\", advanced=True\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n try:\n output = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n max_tokens_to_sample=self.max_tokens,\n temperature=self.temperature,\n anthropic_api_url=self.base_url,\n streaming=self.stream,\n )\n except Exception as e:\n msg = \"Could not connect to Anthropic API.\"\n raise ValueError(msg) from e\n\n return output\n\n def get_models(self, tool_model_enabled: bool | None = None) -> list[str]:\n try:\n import anthropic\n\n client = anthropic.Anthropic(api_key=self.api_key)\n models = client.models.list(limit=20).data\n model_ids = [model.id for model in models]\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n model_ids = ANTHROPIC_MODELS\n if tool_model_enabled:\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n for model in model_ids:\n model_with_tool = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n anthropic_api_url=self.base_url,\n )\n if not self.supports_tool_calling(model_with_tool):\n model_ids.remove(model)\n return model_ids\n\n def _get_exception_message(self, exception: Exception) -> str | None:\n \"\"\"Get a message from an Anthropic 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 try:\n from anthropic import BadRequestError\n except ImportError:\n return None\n if isinstance(exception, BadRequestError):\n message = exception.body.get(\"error\", {}).get(\"message\")\n if message:\n return message\n return None\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n if field_name in {\"base_url\", \"model_name\", \"tool_model_enabled\", \"api_key\"} and field_value:\n try:\n if len(self.api_key) == 0:\n ids = ANTHROPIC_MODELS\n else:\n try:\n ids = self.get_models(tool_model_enabled=self.tool_model_enabled)\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n ids = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"options\"] = ids\n build_config[\"model_name\"][\"value\"] = ids[0]\n except Exception as e:\n msg = f\"Error getting model names: {e}\"\n raise ValueError(msg) from e\n return build_config\n" }, "input_value": { "_input_type": "MessageInput", @@ -2191,7 +2191,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -2233,7 +2233,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json b/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json index e31043445..9a73fced1 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json @@ -1508,7 +1508,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -1678,7 +1678,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1720,7 +1720,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json b/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json index 136370b8a..ce8f218e1 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json @@ -226,7 +226,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -396,7 +396,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -438,7 +438,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Graph Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Graph Vector Store RAG.json index 04206a2c1..1afcd9e5d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Graph Vector Store RAG.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Graph Vector Store RAG.json @@ -1804,7 +1804,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -1960,7 +1960,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1998,7 +1998,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json index 7dc961df7..ed257ab31 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json @@ -1438,7 +1438,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -1608,7 +1608,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1650,7 +1650,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json index 2ae477ef0..60864311f 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json @@ -2716,7 +2716,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -2886,7 +2886,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -2928,7 +2928,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", @@ -3098,7 +3098,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -3268,7 +3268,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -3310,7 +3310,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json b/src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json index 2b778038d..1f2cc1c83 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json @@ -783,7 +783,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any\n\nimport requests\nfrom loguru import logger\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass AnthropicModelComponent(LCModelComponent):\n display_name = \"Anthropic\"\n description = \"Generate text using Anthropic Chat&Completion LLMs with prefill support.\"\n icon = \"Anthropic\"\n name = \"AnthropicModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n value=4096,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=ANTHROPIC_MODELS,\n refresh_button=True,\n value=ANTHROPIC_MODELS[0],\n combobox=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Anthropic API Key\",\n info=\"Your Anthropic API key.\",\n value=None,\n required=True,\n real_time_refresh=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n ),\n MessageTextInput(\n name=\"base_url\",\n display_name=\"Anthropic API URL\",\n info=\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\n value=\"https://api.anthropic.com\",\n real_time_refresh=True,\n ),\n BoolInput(\n name=\"tool_model_enabled\",\n display_name=\"Enable Tool Models\",\n info=(\n \"Select if you want to use models that can work with tools. If yes, only those models will be shown.\"\n ),\n advanced=False,\n value=False,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"prefill\", display_name=\"Prefill\", info=\"Prefill text to guide the model's response.\", advanced=True\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n try:\n output = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n max_tokens_to_sample=self.max_tokens,\n temperature=self.temperature,\n anthropic_api_url=self.base_url,\n streaming=self.stream,\n )\n except Exception as e:\n msg = \"Could not connect to Anthropic API.\"\n raise ValueError(msg) from e\n\n return output\n\n def get_models(self, tool_model_enabled: bool | None = None) -> list[str]:\n try:\n import anthropic\n\n client = anthropic.Anthropic(api_key=self.api_key)\n models = client.models.list(limit=20).data\n model_ids = [model.id for model in models]\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n model_ids = ANTHROPIC_MODELS\n if tool_model_enabled:\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n for model in model_ids:\n model_with_tool = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n anthropic_api_url=self.base_url,\n )\n if not self.supports_tool_calling(model_with_tool):\n model_ids.remove(model)\n return model_ids\n\n def _get_exception_message(self, exception: Exception) -> str | None:\n \"\"\"Get a message from an Anthropic 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 try:\n from anthropic import BadRequestError\n except ImportError:\n return None\n if isinstance(exception, BadRequestError):\n message = exception.body.get(\"error\", {}).get(\"message\")\n if message:\n return message\n return None\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n if field_name in {\"base_url\", \"model_name\", \"tool_model_enabled\", \"api_key\"} and field_value:\n try:\n if len(self.api_key) == 0:\n ids = ANTHROPIC_MODELS\n else:\n try:\n ids = self.get_models(tool_model_enabled=self.tool_model_enabled)\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n ids = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"options\"] = ids\n build_config[\"model_name\"][\"value\"] = ids[0]\n except Exception as e:\n msg = f\"Error getting model names: {e}\"\n raise ValueError(msg) from e\n return build_config\n" + "value": "from typing import Any\n\nimport requests\nfrom loguru import logger\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass AnthropicModelComponent(LCModelComponent):\n display_name = \"Anthropic\"\n description = \"Generate text using Anthropic Chat&Completion LLMs with prefill support.\"\n icon = \"Anthropic\"\n name = \"AnthropicModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n value=4096,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=ANTHROPIC_MODELS,\n refresh_button=True,\n value=ANTHROPIC_MODELS[0],\n combobox=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Anthropic API Key\",\n info=\"Your Anthropic API key.\",\n value=None,\n required=True,\n real_time_refresh=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\n MessageTextInput(\n name=\"base_url\",\n display_name=\"Anthropic API URL\",\n info=\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\n value=\"https://api.anthropic.com\",\n real_time_refresh=True,\n ),\n BoolInput(\n name=\"tool_model_enabled\",\n display_name=\"Enable Tool Models\",\n info=(\n \"Select if you want to use models that can work with tools. If yes, only those models will be shown.\"\n ),\n advanced=False,\n value=False,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"prefill\", display_name=\"Prefill\", info=\"Prefill text to guide the model's response.\", advanced=True\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n try:\n output = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n max_tokens_to_sample=self.max_tokens,\n temperature=self.temperature,\n anthropic_api_url=self.base_url,\n streaming=self.stream,\n )\n except Exception as e:\n msg = \"Could not connect to Anthropic API.\"\n raise ValueError(msg) from e\n\n return output\n\n def get_models(self, tool_model_enabled: bool | None = None) -> list[str]:\n try:\n import anthropic\n\n client = anthropic.Anthropic(api_key=self.api_key)\n models = client.models.list(limit=20).data\n model_ids = [model.id for model in models]\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n model_ids = ANTHROPIC_MODELS\n if tool_model_enabled:\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n for model in model_ids:\n model_with_tool = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n anthropic_api_url=self.base_url,\n )\n if not self.supports_tool_calling(model_with_tool):\n model_ids.remove(model)\n return model_ids\n\n def _get_exception_message(self, exception: Exception) -> str | None:\n \"\"\"Get a message from an Anthropic 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 try:\n from anthropic import BadRequestError\n except ImportError:\n return None\n if isinstance(exception, BadRequestError):\n message = exception.body.get(\"error\", {}).get(\"message\")\n if message:\n return message\n return None\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n if field_name in {\"base_url\", \"model_name\", \"tool_model_enabled\", \"api_key\"} and field_value:\n try:\n if len(self.api_key) == 0:\n ids = ANTHROPIC_MODELS\n else:\n try:\n ids = self.get_models(tool_model_enabled=self.tool_model_enabled)\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n ids = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"options\"] = ids\n build_config[\"model_name\"][\"value\"] = ids[0]\n except Exception as e:\n msg = f\"Error getting model names: {e}\"\n raise ValueError(msg) from e\n return build_config\n" }, "input_value": { "_input_type": "MessageInput", @@ -881,7 +881,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -923,7 +923,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json index cd3ad6124..1241306b0 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json @@ -2424,7 +2424,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -2594,7 +2594,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -2636,7 +2636,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json index 203fa1ca0..abdcf7ce7 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json @@ -705,7 +705,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -875,7 +875,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -917,7 +917,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", @@ -1839,7 +1839,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -2009,7 +2009,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -2051,7 +2051,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json index 860246d6e..3eb66d375 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json @@ -1295,7 +1295,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -1465,7 +1465,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1507,7 +1507,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json index c4c026246..f70b64118 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json @@ -753,7 +753,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any\n\nimport requests\nfrom loguru import logger\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass AnthropicModelComponent(LCModelComponent):\n display_name = \"Anthropic\"\n description = \"Generate text using Anthropic Chat&Completion LLMs with prefill support.\"\n icon = \"Anthropic\"\n name = \"AnthropicModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n value=4096,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=ANTHROPIC_MODELS,\n refresh_button=True,\n value=ANTHROPIC_MODELS[0],\n combobox=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Anthropic API Key\",\n info=\"Your Anthropic API key.\",\n value=None,\n required=True,\n real_time_refresh=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n ),\n MessageTextInput(\n name=\"base_url\",\n display_name=\"Anthropic API URL\",\n info=\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\n value=\"https://api.anthropic.com\",\n real_time_refresh=True,\n ),\n BoolInput(\n name=\"tool_model_enabled\",\n display_name=\"Enable Tool Models\",\n info=(\n \"Select if you want to use models that can work with tools. If yes, only those models will be shown.\"\n ),\n advanced=False,\n value=False,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"prefill\", display_name=\"Prefill\", info=\"Prefill text to guide the model's response.\", advanced=True\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n try:\n output = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n max_tokens_to_sample=self.max_tokens,\n temperature=self.temperature,\n anthropic_api_url=self.base_url,\n streaming=self.stream,\n )\n except Exception as e:\n msg = \"Could not connect to Anthropic API.\"\n raise ValueError(msg) from e\n\n return output\n\n def get_models(self, tool_model_enabled: bool | None = None) -> list[str]:\n try:\n import anthropic\n\n client = anthropic.Anthropic(api_key=self.api_key)\n models = client.models.list(limit=20).data\n model_ids = [model.id for model in models]\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n model_ids = ANTHROPIC_MODELS\n if tool_model_enabled:\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n for model in model_ids:\n model_with_tool = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n anthropic_api_url=self.base_url,\n )\n if not self.supports_tool_calling(model_with_tool):\n model_ids.remove(model)\n return model_ids\n\n def _get_exception_message(self, exception: Exception) -> str | None:\n \"\"\"Get a message from an Anthropic 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 try:\n from anthropic import BadRequestError\n except ImportError:\n return None\n if isinstance(exception, BadRequestError):\n message = exception.body.get(\"error\", {}).get(\"message\")\n if message:\n return message\n return None\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n if field_name in {\"base_url\", \"model_name\", \"tool_model_enabled\", \"api_key\"} and field_value:\n try:\n if len(self.api_key) == 0:\n ids = ANTHROPIC_MODELS\n else:\n try:\n ids = self.get_models(tool_model_enabled=self.tool_model_enabled)\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n ids = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"options\"] = ids\n build_config[\"model_name\"][\"value\"] = ids[0]\n except Exception as e:\n msg = f\"Error getting model names: {e}\"\n raise ValueError(msg) from e\n return build_config\n" + "value": "from typing import Any\n\nimport requests\nfrom loguru import logger\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass AnthropicModelComponent(LCModelComponent):\n display_name = \"Anthropic\"\n description = \"Generate text using Anthropic Chat&Completion LLMs with prefill support.\"\n icon = \"Anthropic\"\n name = \"AnthropicModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n value=4096,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=ANTHROPIC_MODELS,\n refresh_button=True,\n value=ANTHROPIC_MODELS[0],\n combobox=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Anthropic API Key\",\n info=\"Your Anthropic API key.\",\n value=None,\n required=True,\n real_time_refresh=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\n MessageTextInput(\n name=\"base_url\",\n display_name=\"Anthropic API URL\",\n info=\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\n value=\"https://api.anthropic.com\",\n real_time_refresh=True,\n ),\n BoolInput(\n name=\"tool_model_enabled\",\n display_name=\"Enable Tool Models\",\n info=(\n \"Select if you want to use models that can work with tools. If yes, only those models will be shown.\"\n ),\n advanced=False,\n value=False,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"prefill\", display_name=\"Prefill\", info=\"Prefill text to guide the model's response.\", advanced=True\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n try:\n output = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n max_tokens_to_sample=self.max_tokens,\n temperature=self.temperature,\n anthropic_api_url=self.base_url,\n streaming=self.stream,\n )\n except Exception as e:\n msg = \"Could not connect to Anthropic API.\"\n raise ValueError(msg) from e\n\n return output\n\n def get_models(self, tool_model_enabled: bool | None = None) -> list[str]:\n try:\n import anthropic\n\n client = anthropic.Anthropic(api_key=self.api_key)\n models = client.models.list(limit=20).data\n model_ids = [model.id for model in models]\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n model_ids = ANTHROPIC_MODELS\n if tool_model_enabled:\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n for model in model_ids:\n model_with_tool = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n anthropic_api_url=self.base_url,\n )\n if not self.supports_tool_calling(model_with_tool):\n model_ids.remove(model)\n return model_ids\n\n def _get_exception_message(self, exception: Exception) -> str | None:\n \"\"\"Get a message from an Anthropic 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 try:\n from anthropic import BadRequestError\n except ImportError:\n return None\n if isinstance(exception, BadRequestError):\n message = exception.body.get(\"error\", {}).get(\"message\")\n if message:\n return message\n return None\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n if field_name in {\"base_url\", \"model_name\", \"tool_model_enabled\", \"api_key\"} and field_value:\n try:\n if len(self.api_key) == 0:\n ids = ANTHROPIC_MODELS\n else:\n try:\n ids = self.get_models(tool_model_enabled=self.tool_model_enabled)\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n ids = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"options\"] = ids\n build_config[\"model_name\"][\"value\"] = ids[0]\n except Exception as e:\n msg = f\"Error getting model names: {e}\"\n raise ValueError(msg) from e\n return build_config\n" }, "input_value": { "_input_type": "MessageInput", @@ -851,7 +851,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -893,7 +893,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].", @@ -1089,7 +1089,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any\n\nimport requests\nfrom loguru import logger\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass AnthropicModelComponent(LCModelComponent):\n display_name = \"Anthropic\"\n description = \"Generate text using Anthropic Chat&Completion LLMs with prefill support.\"\n icon = \"Anthropic\"\n name = \"AnthropicModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n value=4096,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=ANTHROPIC_MODELS,\n refresh_button=True,\n value=ANTHROPIC_MODELS[0],\n combobox=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Anthropic API Key\",\n info=\"Your Anthropic API key.\",\n value=None,\n required=True,\n real_time_refresh=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n ),\n MessageTextInput(\n name=\"base_url\",\n display_name=\"Anthropic API URL\",\n info=\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\n value=\"https://api.anthropic.com\",\n real_time_refresh=True,\n ),\n BoolInput(\n name=\"tool_model_enabled\",\n display_name=\"Enable Tool Models\",\n info=(\n \"Select if you want to use models that can work with tools. If yes, only those models will be shown.\"\n ),\n advanced=False,\n value=False,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"prefill\", display_name=\"Prefill\", info=\"Prefill text to guide the model's response.\", advanced=True\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n try:\n output = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n max_tokens_to_sample=self.max_tokens,\n temperature=self.temperature,\n anthropic_api_url=self.base_url,\n streaming=self.stream,\n )\n except Exception as e:\n msg = \"Could not connect to Anthropic API.\"\n raise ValueError(msg) from e\n\n return output\n\n def get_models(self, tool_model_enabled: bool | None = None) -> list[str]:\n try:\n import anthropic\n\n client = anthropic.Anthropic(api_key=self.api_key)\n models = client.models.list(limit=20).data\n model_ids = [model.id for model in models]\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n model_ids = ANTHROPIC_MODELS\n if tool_model_enabled:\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n for model in model_ids:\n model_with_tool = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n anthropic_api_url=self.base_url,\n )\n if not self.supports_tool_calling(model_with_tool):\n model_ids.remove(model)\n return model_ids\n\n def _get_exception_message(self, exception: Exception) -> str | None:\n \"\"\"Get a message from an Anthropic 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 try:\n from anthropic import BadRequestError\n except ImportError:\n return None\n if isinstance(exception, BadRequestError):\n message = exception.body.get(\"error\", {}).get(\"message\")\n if message:\n return message\n return None\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n if field_name in {\"base_url\", \"model_name\", \"tool_model_enabled\", \"api_key\"} and field_value:\n try:\n if len(self.api_key) == 0:\n ids = ANTHROPIC_MODELS\n else:\n try:\n ids = self.get_models(tool_model_enabled=self.tool_model_enabled)\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n ids = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"options\"] = ids\n build_config[\"model_name\"][\"value\"] = ids[0]\n except Exception as e:\n msg = f\"Error getting model names: {e}\"\n raise ValueError(msg) from e\n return build_config\n" + "value": "from typing import Any\n\nimport requests\nfrom loguru import logger\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass AnthropicModelComponent(LCModelComponent):\n display_name = \"Anthropic\"\n description = \"Generate text using Anthropic Chat&Completion LLMs with prefill support.\"\n icon = \"Anthropic\"\n name = \"AnthropicModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n value=4096,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=ANTHROPIC_MODELS,\n refresh_button=True,\n value=ANTHROPIC_MODELS[0],\n combobox=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Anthropic API Key\",\n info=\"Your Anthropic API key.\",\n value=None,\n required=True,\n real_time_refresh=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\n MessageTextInput(\n name=\"base_url\",\n display_name=\"Anthropic API URL\",\n info=\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\n value=\"https://api.anthropic.com\",\n real_time_refresh=True,\n ),\n BoolInput(\n name=\"tool_model_enabled\",\n display_name=\"Enable Tool Models\",\n info=(\n \"Select if you want to use models that can work with tools. If yes, only those models will be shown.\"\n ),\n advanced=False,\n value=False,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"prefill\", display_name=\"Prefill\", info=\"Prefill text to guide the model's response.\", advanced=True\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n try:\n output = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n max_tokens_to_sample=self.max_tokens,\n temperature=self.temperature,\n anthropic_api_url=self.base_url,\n streaming=self.stream,\n )\n except Exception as e:\n msg = \"Could not connect to Anthropic API.\"\n raise ValueError(msg) from e\n\n return output\n\n def get_models(self, tool_model_enabled: bool | None = None) -> list[str]:\n try:\n import anthropic\n\n client = anthropic.Anthropic(api_key=self.api_key)\n models = client.models.list(limit=20).data\n model_ids = [model.id for model in models]\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n model_ids = ANTHROPIC_MODELS\n if tool_model_enabled:\n try:\n from langchain_anthropic.chat_models import ChatAnthropic\n except ImportError as e:\n msg = \"langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`.\"\n raise ImportError(msg) from e\n for model in model_ids:\n model_with_tool = ChatAnthropic(\n model=self.model_name,\n anthropic_api_key=self.api_key,\n anthropic_api_url=self.base_url,\n )\n if not self.supports_tool_calling(model_with_tool):\n model_ids.remove(model)\n return model_ids\n\n def _get_exception_message(self, exception: Exception) -> str | None:\n \"\"\"Get a message from an Anthropic 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 try:\n from anthropic import BadRequestError\n except ImportError:\n return None\n if isinstance(exception, BadRequestError):\n message = exception.body.get(\"error\", {}).get(\"message\")\n if message:\n return message\n return None\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n if field_name in {\"base_url\", \"model_name\", \"tool_model_enabled\", \"api_key\"} and field_value:\n try:\n if len(self.api_key) == 0:\n ids = ANTHROPIC_MODELS\n else:\n try:\n ids = self.get_models(tool_model_enabled=self.tool_model_enabled)\n except (ImportError, ValueError, requests.exceptions.RequestException) as e:\n logger.exception(f\"Error getting model names: {e}\")\n ids = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"options\"] = ids\n build_config[\"model_name\"][\"value\"] = ids[0]\n except Exception as e:\n msg = f\"Error getting model names: {e}\"\n raise ValueError(msg) from e\n return build_config\n" }, "input_value": { "_input_type": "MessageInput", @@ -1187,7 +1187,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1229,7 +1229,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json index a4ee4b33b..fc5503cdf 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json @@ -2295,7 +2295,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -2465,7 +2465,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -2507,7 +2507,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", @@ -2677,7 +2677,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -2847,7 +2847,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -2889,7 +2889,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json index 901b5248b..5b7180244 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json @@ -960,7 +960,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -1130,7 +1130,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1172,7 +1172,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json index f458fd7bc..817e0b81c 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json @@ -1896,7 +1896,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -2066,7 +2066,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -2108,7 +2108,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json index 7f35f7624..ea4b97759 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json @@ -3019,7 +3019,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -3189,7 +3189,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -3231,7 +3231,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json index f236e4bdc..2d8e79438 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json @@ -815,7 +815,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" + "value": "from 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 BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\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(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\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 DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[1],\n combobox=True,\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 required=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\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 IntInput(\n name=\"max_retries\",\n display_name=\"Max Retries\",\n info=\"The maximum number of retries to make when generating.\",\n advanced=True,\n value=5,\n ),\n IntInput(\n name=\"timeout\",\n display_name=\"Timeout\",\n info=\"The timeout for requests to OpenAI completion API.\",\n advanced=True,\n value=700,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\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 = self.json_mode\n seed = self.seed\n max_retries = self.max_retries\n timeout = self.timeout\n\n api_key = SecretStr(openai_api_key).get_secret_value() 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 max_retries=max_retries,\n request_timeout=timeout,\n )\n if json_mode:\n output = output.bind(response_format={\"type\": \"json_object\"})\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"Get a message from an OpenAI exception.\n\n Args:\n e (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\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\")\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -985,7 +985,7 @@ }, "stream": { "_input_type": "BoolInput", - "advanced": false, + "advanced": true, "display_name": "Stream", "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", @@ -1027,7 +1027,7 @@ }, "temperature": { "_input_type": "SliderInput", - "advanced": false, + "advanced": true, "display_name": "Temperature", "dynamic": false, "info": "", diff --git a/src/frontend/tests/core/features/freeze-path.spec.ts b/src/frontend/tests/core/features/freeze-path.spec.ts index 30a86ec81..329d7ade8 100644 --- a/src/frontend/tests/core/features/freeze-path.spec.ts +++ b/src/frontend/tests/core/features/freeze-path.spec.ts @@ -35,14 +35,7 @@ test( await page.getByTestId("dropdown_str_model_name").click(); await page.getByTestId("gpt-4o-1-option").click(); - await page.waitForSelector('[data-testid="default_slider_display_value"]', { - timeout: 1000, - }); - await page.getByTestId("fit_view").click(); - await page - .getByTestId("default_slider_display_value") - .click({ force: true }); await page.waitForSelector('[data-testid="button_run_chat output"]', { timeout: 1000, @@ -68,11 +61,9 @@ test( await page.getByText("Close").last().click(); - await page.waitForSelector('[data-testid="default_slider_display_value"]', { - timeout: 1000, - }); - - await moveSlider(page, "right", false); + // Change model to force different output + await page.getByTestId("dropdown_str_model_name").click(); + await page.getByTestId("gpt-4o-mini-0-option").click(); await page.waitForSelector('[data-testid="button_run_chat output"]', { timeout: 1000, diff --git a/src/frontend/tests/core/unit/intComponent.spec.ts b/src/frontend/tests/core/unit/intComponent.spec.ts index b39bddc8f..bc52d81cb 100644 --- a/src/frontend/tests/core/unit/intComponent.spec.ts +++ b/src/frontend/tests/core/unit/intComponent.spec.ts @@ -90,7 +90,7 @@ test("IntComponent", { tag: ["@release", "@workspace"] }, async ({ page }) => { await page.locator('//*[@id="showtemperature"]').click(); expect( await page.locator('//*[@id="showtemperature"]').isChecked(), - ).toBeFalsy(); + ).toBeTruthy(); await page.locator('//*[@id="showmodel_kwargs"]').click(); expect( @@ -110,7 +110,7 @@ test("IntComponent", { tag: ["@release", "@workspace"] }, async ({ page }) => { await page.locator('//*[@id="showtemperature"]').click(); expect( await page.locator('//*[@id="showtemperature"]').isChecked(), - ).toBeTruthy(); + ).toBeFalsy(); await page.locator('//*[@id="showmodel_kwargs"]').click(); expect( @@ -130,7 +130,7 @@ test("IntComponent", { tag: ["@release", "@workspace"] }, async ({ page }) => { await page.locator('//*[@id="showtemperature"]').click(); expect( await page.locator('//*[@id="showtemperature"]').isChecked(), - ).toBeFalsy(); + ).toBeTruthy(); await page.getByText("Close").last().click(); diff --git a/src/frontend/tests/core/unit/sliderComponent.spec.ts b/src/frontend/tests/core/unit/sliderComponent.spec.ts index 657c7d901..5a9bd54bf 100644 --- a/src/frontend/tests/core/unit/sliderComponent.spec.ts +++ b/src/frontend/tests/core/unit/sliderComponent.spec.ts @@ -35,15 +35,31 @@ test( let cleanCode = await extractAndCleanCode(page); - // Replace the import statement - cleanCode = cleanCode.replace( - 'name="temperature", display_name="Temperature", value=0.1, range_spec=RangeSpec(min=0, max=1, step=0.01)', - 'name="temperature", display_name="Temperature", value=0.2, range_spec=RangeSpec(min=3, max=30, step=1), min_label="test", max_label="test2", min_label_icon="pencil-ruler", max_label_icon="palette", slider_buttons=False, slider_buttons_options=[], slider_input=False,', + // Replace the multiline string in the code + const newCode = cleanCode.replace( + `name="temperature", + display_name="Temperature", + value=0.1, + range_spec=RangeSpec(min=0, max=1, step=0.01), + advanced=True,`, + `name="temperature", + display_name="Temperature", + value=0.2, + range_spec=RangeSpec(min=3, max=30, step=1), + min_label="test", + max_label="test2", + min_label_icon="pencil-ruler", + max_label_icon="palette", + slider_buttons=False, + slider_buttons_options=[], + slider_input=False, + advanced=False,`, ); - + // make sure codes are different + expect(cleanCode).not.toEqual(newCode); await page.locator("textarea").last().press(`ControlOrMeta+a`); await page.keyboard.press("Backspace"); - await page.locator("textarea").last().fill(cleanCode); + await page.locator("textarea").last().fill(newCode); await page.locator('//*[@id="checkAndSaveBtn"]').click(); await page.getByTestId("fit_view").click();