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refactor: Update OpenAIModelComponent inputs and model response method

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This commit updates the inputs of the `OpenAIModelComponent` class in the `OpenAIModel.py` file. It replaces the `Input` class with specific input classes such as `StrInput`, `IntInput`, `DictInput`, `DropdownInput`, `BoolInput`, and `SecretStrInput`. This change improves the organization and separation of concerns in the codebase, making it easier to understand and maintain.

Additionally, the commit renames the `model_response` method to `build_model` for better clarity and consistency. The method is responsible for constructing the language model using the specified input values and returning the built model.

These updates enhance the functionality and maintainability of the `OpenAIModelComponent` class.
This commit is contained in:
ogabrielluiz 2024-06-12 13:54:12 -03:00
commit f65380f12c
7 changed files with 40 additions and 42 deletions
Download patch file Download diff file Expand all files Collapse all files

40
src/backend/base/langflow/components/models/OpenAIModel.py
View file

@ -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,

6
src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json
View file

@ -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,

6
src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json
View file

@ -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,

6
src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json
View file

@ -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,

6
src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json
View file

@ -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,

12
src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json
View file

@ -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,

6
src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
View file

@ -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,