diff --git a/src/backend/base/langflow/components/models/OpenAIModel.py b/src/backend/base/langflow/components/models/OpenAIModel.py index c5592c2c4..43b7e774d 100644 --- a/src/backend/base/langflow/components/models/OpenAIModel.py +++ b/src/backend/base/langflow/components/models/OpenAIModel.py @@ -1,5 +1,3 @@ -from typing import Optional - from langchain_openai import ChatOpenAI from pydantic.v1 import SecretStr @@ -7,7 +5,9 @@ from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent from langflow.base.models.openai_constants import MODEL_NAMES from langflow.field_typing import BaseLanguageModel, Text -from langflow.template import Input, Output +from langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, SecretStrInput, StrInput +from langflow.inputs.inputs import IntInput +from langflow.template import Output class OpenAIModelComponent(LCModelComponent): @@ -16,36 +16,34 @@ class OpenAIModelComponent(LCModelComponent): icon = "OpenAI" inputs = [ - Input(name="input_value", type=str, display_name="Input", input_types=["Text", "Record", "Prompt"]), - Input( + StrInput(name="input_value", display_name="Input", input_types=["Text", "Record", "Prompt"]), + IntInput( name="max_tokens", - type=Optional[int], display_name="Max Tokens", advanced=True, info="The maximum number of tokens to generate. Set to 0 for unlimited tokens.", ), - Input(name="model_kwargs", type=dict, display_name="Model Kwargs", advanced=True), - Input(name="model_name", type=str, display_name="Model Name", advanced=False, options=MODEL_NAMES), - Input( + DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True), + DropdownInput( + name="model_name", display_name="Model Name", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0] + ), + StrInput( name="openai_api_base", - type=Optional[str], display_name="OpenAI API Base", advanced=True, info="The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", ), - Input( + SecretStrInput( name="openai_api_key", - type=str, display_name="OpenAI API Key", info="The OpenAI API Key to use for the OpenAI model.", advanced=False, - password=True, + value="OPENAI_API_KEY", ), - Input(name="temperature", type=float, display_name="Temperature", advanced=False, default=0.1), - Input(name="stream", type=bool, display_name="Stream", info=STREAM_INFO_TEXT, advanced=True), - Input( + FloatInput(name="temperature", display_name="Temperature", value=0.1), + BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True), + StrInput( name="system_message", - type=Optional[str], display_name="System Message", info="System message to pass to the model.", advanced=True, @@ -53,19 +51,19 @@ class OpenAIModelComponent(LCModelComponent): ] outputs = [ Output(display_name="Text", name="text_output", method="text_response"), - Output(display_name="Language Model", name="model_output", method="model_response"), + Output(display_name="Language Model", name="model_output", method="build_model"), ] def text_response(self) -> Text: input_value = self.input_value stream = self.stream system_message = self.system_message - output = self.model_response() + output = self.build_model() result = self.get_chat_result(output, stream, input_value, system_message) self.status = result return result - def model_response(self) -> BaseLanguageModel: + def build_model(self) -> BaseLanguageModel: openai_api_key = self.openai_api_key temperature = self.temperature model_name = self.model_name @@ -80,7 +78,7 @@ class OpenAIModelComponent(LCModelComponent): output = ChatOpenAI( max_tokens=max_tokens or None, - model_kwargs=model_kwargs, + model_kwargs=model_kwargs or {}, model=model_name, base_url=openai_api_base, api_key=api_key, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index ab37eaca8..9e7179534 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -168,7 +168,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" + "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", field_type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" }, "template": { "advanced": false, @@ -293,7 +293,7 @@ { "cache": true, "display_name": "Language Model", - "method": "model_response", + "method": "build_model", "name": "model_output", "selected": "BaseLanguageModel", "types": [ @@ -320,7 +320,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.template import Input, Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\n Input(\n name=\"max_tokens\",\n type=Optional[int],\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n Input(name=\"model_kwargs\", type=dict, display_name=\"Model Kwargs\", advanced=True),\n Input(name=\"model_name\", type=str, display_name=\"Model Name\", advanced=False, options=MODEL_NAMES),\n Input(\n name=\"openai_api_base\",\n type=Optional[str],\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n Input(\n name=\"openai_api_key\",\n type=str,\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n password=True,\n ),\n Input(name=\"temperature\", type=float, display_name=\"Temperature\", advanced=False, default=0.1),\n Input(name=\"stream\", type=bool, display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n Input(\n name=\"system_message\",\n type=Optional[str],\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"model_response\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.model_response()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def model_response(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n return output\n" + "value": "from langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, SecretStrInput, StrInput\nfrom langflow.inputs.inputs import IntInput\nfrom langflow.template import Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\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 ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n DropdownInput(\n name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n StrInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n return output\n" }, "input_value": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index cef198313..7f07e0959 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -236,7 +236,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" + "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", field_type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" }, "instructions": { "advanced": false, @@ -707,7 +707,7 @@ { "cache": true, "display_name": "Language Model", - "method": "model_response", + "method": "build_model", "name": "model_output", "selected": "BaseLanguageModel", "types": [ @@ -734,7 +734,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.template import Input, Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\n Input(\n name=\"max_tokens\",\n type=Optional[int],\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n Input(name=\"model_kwargs\", type=dict, display_name=\"Model Kwargs\", advanced=True),\n Input(name=\"model_name\", type=str, display_name=\"Model Name\", advanced=False, options=MODEL_NAMES),\n Input(\n name=\"openai_api_base\",\n type=Optional[str],\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n Input(\n name=\"openai_api_key\",\n type=str,\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n password=True,\n ),\n Input(name=\"temperature\", type=float, display_name=\"Temperature\", advanced=False, default=0.1),\n Input(name=\"stream\", type=bool, display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n Input(\n name=\"system_message\",\n type=Optional[str],\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"model_response\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.model_response()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def model_response(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n return output\n" + "value": "from langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, SecretStrInput, StrInput\nfrom langflow.inputs.inputs import IntInput\nfrom langflow.template import Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\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 ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n DropdownInput(\n name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n StrInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n return output\n" }, "input_value": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index 580f71e2d..1d3d9d309 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -253,7 +253,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" + "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", field_type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" }, "template": { "advanced": false, @@ -851,7 +851,7 @@ { "cache": true, "display_name": "Language Model", - "method": "model_response", + "method": "build_model", "name": "model_output", "selected": "BaseLanguageModel", "types": [ @@ -878,7 +878,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.template import Input, Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\n Input(\n name=\"max_tokens\",\n type=Optional[int],\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n Input(name=\"model_kwargs\", type=dict, display_name=\"Model Kwargs\", advanced=True),\n Input(name=\"model_name\", type=str, display_name=\"Model Name\", advanced=False, options=MODEL_NAMES),\n Input(\n name=\"openai_api_base\",\n type=Optional[str],\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n Input(\n name=\"openai_api_key\",\n type=str,\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n password=True,\n ),\n Input(name=\"temperature\", type=float, display_name=\"Temperature\", advanced=False, default=0.1),\n Input(name=\"stream\", type=bool, display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n Input(\n name=\"system_message\",\n type=Optional[str],\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"model_response\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.model_response()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def model_response(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n return output\n" + "value": "from langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, SecretStrInput, StrInput\nfrom langflow.inputs.inputs import IntInput\nfrom langflow.template import Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\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 ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n DropdownInput(\n name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n StrInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n return output\n" }, "input_value": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index 1530efe3d..162951304 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -834,7 +834,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" + "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", field_type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" }, "context": { "advanced": false, @@ -983,7 +983,7 @@ { "cache": true, "display_name": "Language Model", - "method": "model_response", + "method": "build_model", "name": "model_output", "selected": "BaseLanguageModel", "types": [ @@ -1010,7 +1010,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.template import Input, Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\n Input(\n name=\"max_tokens\",\n type=Optional[int],\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n Input(name=\"model_kwargs\", type=dict, display_name=\"Model Kwargs\", advanced=True),\n Input(name=\"model_name\", type=str, display_name=\"Model Name\", advanced=False, options=MODEL_NAMES),\n Input(\n name=\"openai_api_base\",\n type=Optional[str],\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n Input(\n name=\"openai_api_key\",\n type=str,\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n password=True,\n ),\n Input(name=\"temperature\", type=float, display_name=\"Temperature\", advanced=False, default=0.1),\n Input(name=\"stream\", type=bool, display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n Input(\n name=\"system_message\",\n type=Optional[str],\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"model_response\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.model_response()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def model_response(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n return output\n" + "value": "from langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, SecretStrInput, StrInput\nfrom langflow.inputs.inputs import IntInput\nfrom langflow.template import Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\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 ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n DropdownInput(\n name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n StrInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n return output\n" }, "input_value": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index 2bc93b124..547c692f1 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -320,7 +320,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" + "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", field_type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" }, "document": { "advanced": false, @@ -462,7 +462,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" + "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", field_type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" }, "summary": { "advanced": false, @@ -1207,7 +1207,7 @@ { "cache": true, "display_name": "Language Model", - "method": "model_response", + "method": "build_model", "name": "model_output", "selected": "BaseLanguageModel", "types": [ @@ -1234,7 +1234,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.template import Input, Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\n Input(\n name=\"max_tokens\",\n type=Optional[int],\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n Input(name=\"model_kwargs\", type=dict, display_name=\"Model Kwargs\", advanced=True),\n Input(name=\"model_name\", type=str, display_name=\"Model Name\", advanced=False, options=MODEL_NAMES),\n Input(\n name=\"openai_api_base\",\n type=Optional[str],\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n Input(\n name=\"openai_api_key\",\n type=str,\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n password=True,\n ),\n Input(name=\"temperature\", type=float, display_name=\"Temperature\", advanced=False, default=0.1),\n Input(name=\"stream\", type=bool, display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n Input(\n name=\"system_message\",\n type=Optional[str],\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"model_response\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.model_response()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def model_response(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n return output\n" + "value": "from langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, SecretStrInput, StrInput\nfrom langflow.inputs.inputs import IntInput\nfrom langflow.template import Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\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 ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n DropdownInput(\n name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n StrInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n return output\n" }, "input_value": { "advanced": false, @@ -1624,7 +1624,7 @@ { "cache": true, "display_name": "Language Model", - "method": "model_response", + "method": "build_model", "name": "model_output", "selected": "BaseLanguageModel", "types": [ @@ -1651,7 +1651,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.template import Input, Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\n Input(\n name=\"max_tokens\",\n type=Optional[int],\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n Input(name=\"model_kwargs\", type=dict, display_name=\"Model Kwargs\", advanced=True),\n Input(name=\"model_name\", type=str, display_name=\"Model Name\", advanced=False, options=MODEL_NAMES),\n Input(\n name=\"openai_api_base\",\n type=Optional[str],\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n Input(\n name=\"openai_api_key\",\n type=str,\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n password=True,\n ),\n Input(name=\"temperature\", type=float, display_name=\"Temperature\", advanced=False, default=0.1),\n Input(name=\"stream\", type=bool, display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n Input(\n name=\"system_message\",\n type=Optional[str],\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"model_response\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.model_response()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def model_response(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n return output\n" + "value": "from langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, SecretStrInput, StrInput\nfrom langflow.inputs.inputs import IntInput\nfrom langflow.template import Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\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 ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n DropdownInput(\n name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n StrInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n return output\n" }, "input_value": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index c7e633773..8cfae131d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -1175,7 +1175,7 @@ { "cache": true, "display_name": "Language Model", - "method": "model_response", + "method": "build_model", "name": "model_output", "selected": "BaseLanguageModel", "types": [ @@ -1202,7 +1202,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.template import Input, Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\n Input(\n name=\"max_tokens\",\n type=Optional[int],\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n Input(name=\"model_kwargs\", type=dict, display_name=\"Model Kwargs\", advanced=True),\n Input(name=\"model_name\", type=str, display_name=\"Model Name\", advanced=False, options=MODEL_NAMES),\n Input(\n name=\"openai_api_base\",\n type=Optional[str],\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n Input(\n name=\"openai_api_key\",\n type=str,\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n password=True,\n ),\n Input(name=\"temperature\", type=float, display_name=\"Temperature\", advanced=False, default=0.1),\n Input(name=\"stream\", type=bool, display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n Input(\n name=\"system_message\",\n type=Optional[str],\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"model_response\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.model_response()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def model_response(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n return output\n" + "value": "from langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, SecretStrInput, StrInput\nfrom langflow.inputs.inputs import IntInput\nfrom langflow.template import Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Record\", \"Prompt\"]),\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 ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n DropdownInput(\n name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n StrInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n return output\n" }, "input_value": { "advanced": false, @@ -1504,7 +1504,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" + "value": "from langflow.custom import Component\nfrom langflow.field_typing import Input, Output\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n Input(name=\"template\", field_type=Prompt, display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Prompt:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Prompt.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n" }, "context": { "advanced": false,