Note from youtube trasncript.json

{"id":"fe880407-6b35-475e-bbdc-f3c6f6c9e993","data":{"nodes":[{"id":"TextInput-oysby","type":"genericNode","position":{"x":-31.136647088589143,"y":280.0839191460614},"data":{"type":"TextInput","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.io.text import TextComponent\nfrom langflow.io import MessageTextInput, Output, SecretStrInput\nfrom langflow.schema.message import Message\n\nimport os\nimport sys\nimport subprocess\nimport re\n\nfrom openai import OpenAI\nimport whisper\nfrom pytubefix import YouTube\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Youtube Downloader\"\n    description = \"Downloads and transcribes youtube videos.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"URL\",\n            info=\"URL to be transcribed.\",\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    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        openai_api_key = self.openai_api_key\n    \n        yt = YouTube(str(message.text))\n        \n        video = yt.streams.get_highest_resolution()\n        audio = yt.streams.get_audio_only()\n        audio.download(mp3=True)\n        audio_path = 'downloaded_audio'\n        path_file = 'downloaded_video'\n        safe_title = yt.title.replace(' ', '_')\n        filename = f\"{path_file}/{safe_title}.mp4\"\n        audio_filename = f\"{audio_path}/{safe_title}.mp4\"\n        \n        os.makedirs('downloaded_video/', exist_ok=True)\n        os.makedirs('downloaded_audio/', exist_ok=True)\n        \n        video.download(filename=filename)\n        audio.download(filename=audio_filename)\n        client = OpenAI(api_key=openai_api_key)\n        \n        audio_file= open(audio_filename, \"rb\")\n        transcription = client.audio.transcriptions.create(\n            model=\"whisper-1\", \n            file=audio_file\n        )\n        return transcription\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"input_value","display_name":"URL","advanced":false,"input_types":["Message"],"dynamic":false,"info":"URL to be transcribed.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"openai_api_key":{"load_from_db":false,"required":false,"placeholder":"","show":true,"value":"","name":"openai_api_key","display_name":"OpenAI API Key","advanced":false,"input_types":[],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"}},"description":"Downloads and transcribes youtube videos.","icon":"type","base_classes":["Message"],"display_name":"Text Input","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","openai_api_key"],"beta":false,"edited":true},"id":"TextInput-oysby","description":"Downloads and transcribes youtube videos.","display_name":"Text Input"},"selected":false,"width":384,"height":411,"positionAbsolute":{"x":-31.136647088589143,"y":280.0839191460614},"dragging":false},{"id":"Prompt-S4TUy","type":"genericNode","position":{"x":475.84030544600387,"y":127.85086994061817},"data":{"type":"Prompt","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":"apresente a transcrição de forma legivel, mantendo os timestamps relevantes\n{transcrição}","name":"template","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","load_from_db":false},"transcrição":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"transcrição","display_name":"transcrição","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","base_classes":["Message"],"display_name":"Prompt","documentation":"","custom_fields":{"template":["transcrição"]},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["template"],"beta":false,"edited":true},"id":"Prompt-S4TUy","description":"Create a prompt template with dynamic variables.","display_name":"Prompt"},"selected":false,"width":384,"height":431,"positionAbsolute":{"x":475.84030544600387,"y":127.85086994061817},"dragging":false},{"id":"OpenAIModel-Qpy4Z","type":"genericNode","position":{"x":2256.543022457784,"y":-359.24908010628997},"data":{"type":"OpenAIModel","node":{"template":{"_type":"Component","api_key":{"load_from_db":false,"required":false,"placeholder":"","show":true,"value":"","name":"api_key","display_name":"OpenAI API Key","advanced":false,"input_types":[],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"input_value","display_name":"Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageInput"},"json_mode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"json_mode","display_name":"JSON Mode","advanced":true,"dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","title_case":false,"type":"bool","_input_type":"BoolInput"},"max_tokens":{"trace_as_metadata":true,"range_spec":{"step_type":"float","min":0,"max":128000,"step":0.1},"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"max_tokens","display_name":"Max Tokens","advanced":true,"dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","title_case":false,"type":"int","_input_type":"IntInput"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":{},"name":"model_kwargs","display_name":"Model Kwargs","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict","_input_type":"DictInput"},"model_name":{"combobox":false,"trace_as_metadata":true,"options":["gpt-4o-mini","gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"required":false,"placeholder":"","show":true,"value":"gpt-4o-mini","name":"model_name","display_name":"Model Name","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"openai_api_base":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"openai_api_base","display_name":"OpenAI API Base","advanced":true,"dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","title_case":false,"type":"str","_input_type":"StrInput"},"output_schema":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"value":{},"name":"output_schema","display_name":"Schema","advanced":true,"dynamic":false,"info":"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.","title_case":false,"type":"dict","_input_type":"DictInput"},"seed":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":1,"name":"seed","display_name":"Seed","advanced":true,"dynamic":false,"info":"The seed controls the reproducibility of the job.","title_case":false,"type":"int","_input_type":"IntInput"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"stream","display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool","_input_type":"BoolInput"},"system_message":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"system_message","display_name":"System Message","advanced":true,"input_types":["Message"],"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"temperature":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":0.1,"name":"temperature","display_name":"Temperature","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"}},"description":"Generates text using OpenAI LLMs.","icon":"OpenAI","base_classes":["LanguageModel","Message"],"display_name":"OpenAI","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text_output","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true},{"types":["LanguageModel"],"selected":"LanguageModel","name":"model_output","display_name":"Language Model","method":"build_model","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","system_message","stream","max_tokens","model_kwargs","json_mode","output_schema","model_name","openai_api_base","api_key","temperature","seed"],"beta":false,"edited":true},"id":"OpenAIModel-Qpy4Z","description":"Generates text using OpenAI LLMs.","display_name":"OpenAI"},"selected":false,"width":384,"height":639,"positionAbsolute":{"x":2256.543022457784,"y":-359.24908010628997},"dragging":false},{"id":"Prompt-xfJhC","type":"genericNode","position":{"x":1704.6800425967353,"y":-120.91316805350746},"data":{"type":"Prompt","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":"IDENTITY and PURPOSE\nYou identify tags from text content for the mind mapping tools. Carefully consider the topics and content of the text and identify at least 5 subjects / ideas to be used as tags. If there is an author or existing tags listed they should be included as a tag.\n\nOUTPUT INSTRUCTIONS\nOnly output a single line\n\nOnly output the tags in lowercase separated by spaces. Tag devem vir depois de #. como por exemplo #tag\n\nEach tag should be lower case\n\nTags should not contain spaces. If a tag contains a space replace it with an underscore.\n\nDo not give warnings or notes; only output the requested info.\n\nDo not repeat tags\n\n\n\nEnsure you follow ALL these instructions when creating your output.\n\nINPUT\nINPUT: {transcrição}\n\nhere is an example of tags:\n\n#AI #ArtificialIntelligence #MachineLearning #NarrowAI #GeneralAI #AGI #TechIndustry #Marketing #Hype #Smartwatch #CoPilotPC #MSI #GPTOmni #LanguageModel #Algorithms #DataProcessing #TrainingData #ReinforcementLearning #GenerativeModels #ImageGeneration #VideoGeneration #AudioGeneration #NeuralNetworks #HandwritingRecognition #WebTrafficAnalysis #VideoGames #Chatbots #Tesla #AutonomousVehicles #SelfDriving #Hardware #Software #UserSafety #ClassAction #MarketingBuzzwords #ComputerCognition #SummarizationEngines #GuessingMachines #Tokens #ContextualAwareness #StableDiffusion #GenerativeAI #Distrust #Manscaped #PerformancePackage #TechTips","name":"template","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","load_from_db":false},"transcrição":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"transcrição","display_name":"transcrição","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","base_classes":["Message"],"display_name":"Prompt","documentation":"","custom_fields":{"template":["transcrição"]},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["template"],"beta":false,"edited":true},"id":"Prompt-xfJhC","description":"Create a prompt template with dynamic variables.","display_name":"Prompt"},"selected":false,"width":384,"height":431,"positionAbsolute":{"x":1704.6800425967353,"y":-120.91316805350746},"dragging":false},{"id":"OpenAIModel-I67FN","type":"genericNode","position":{"x":2256.319363263364,"y":389.3415848237813},"data":{"type":"OpenAIModel","node":{"template":{"_type":"Component","api_key":{"load_from_db":false,"required":false,"placeholder":"","show":true,"value":"","name":"api_key","display_name":"OpenAI API Key","advanced":false,"input_types":[],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"input_value","display_name":"Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageInput"},"json_mode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"json_mode","display_name":"JSON Mode","advanced":true,"dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","title_case":false,"type":"bool","_input_type":"BoolInput"},"max_tokens":{"trace_as_metadata":true,"range_spec":{"step_type":"float","min":0,"max":128000,"step":0.1},"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"max_tokens","display_name":"Max Tokens","advanced":true,"dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","title_case":false,"type":"int","_input_type":"IntInput"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":{},"name":"model_kwargs","display_name":"Model Kwargs","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict","_input_type":"DictInput"},"model_name":{"combobox":false,"trace_as_metadata":true,"options":["gpt-4o-mini","gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"required":false,"placeholder":"","show":true,"value":"gpt-4o-mini","name":"model_name","display_name":"Model Name","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"openai_api_base":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"openai_api_base","display_name":"OpenAI API Base","advanced":true,"dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","title_case":false,"type":"str","_input_type":"StrInput"},"output_schema":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"value":{},"name":"output_schema","display_name":"Schema","advanced":true,"dynamic":false,"info":"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.","title_case":false,"type":"dict","_input_type":"DictInput"},"seed":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":1,"name":"seed","display_name":"Seed","advanced":true,"dynamic":false,"info":"The seed controls the reproducibility of the job.","title_case":false,"type":"int","_input_type":"IntInput"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"stream","display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool","_input_type":"BoolInput"},"system_message":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"system_message","display_name":"System Message","advanced":true,"input_types":["Message"],"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"temperature":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":0.1,"name":"temperature","display_name":"Temperature","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"}},"description":"Generates text using OpenAI LLMs.","icon":"OpenAI","base_classes":["LanguageModel","Message"],"display_name":"OpenAI","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text_output","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true},{"types":["LanguageModel"],"selected":"LanguageModel","name":"model_output","display_name":"Language Model","method":"build_model","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","system_message","stream","max_tokens","model_kwargs","json_mode","output_schema","model_name","openai_api_base","api_key","temperature","seed"],"beta":false,"edited":true},"id":"OpenAIModel-I67FN","description":"Generates text using OpenAI LLMs.","display_name":"OpenAI"},"selected":false,"width":384,"height":639,"positionAbsolute":{"x":2256.319363263364,"y":389.3415848237813},"dragging":false},{"id":"Prompt-LvE8T","type":"genericNode","position":{"x":1705.9648302702362,"y":589.5437835526025},"data":{"type":"Prompt","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":"### Prompt para Resumo de Vídeos do YouTube a partir de Transcrições\n\n**IDENTIDADE E PROPÓSITO**\nVocê é um sistema de escrita que cria notas completas a partir de transcrições e descrição de vídeos do YouTube, perfeitas para estudo, mesmo que o leitor nunca tenha visto o vídeo original.\n\n**SEÇÕES DE SAÍDA**\n\nVocê extrai o conteúdo, incluindo quem está apresentando e o conteúdo discutido, mantendo todas as informações de forma compreensível para alguém que não viu o vídeo original. O resumo deve ser tão longo quanto necessário, não sendo um resumo curto. Escreva este resumo em uma seção chamada **RESUMO**.\n\n**INSTRUÇÕES DE SAÍDA**\n\nVocê deve apenas usar Markdown.\nVocê sempre escreve em português brasileiro.\nNão forneça avisos ou notas; apenas produza as seções solicitadas.\nNão comece itens com as mesmas palavras de abertura.\nQuando for interessante adicione listas ou bullet points, como por exemplo para listas de compras para uma receita, lista de exercicios em um video de treino, lista de pontos turisticos...\n\n**Passo 1: Leia a transcrição**\n\n1. Leia rapidamente a transcrição e a descrição para ter uma noção do tópico e da estrutura geral.\n2. Leia a transcrição e a descrição com atenção, destacando pontos importantes e tomando notas.\n3. Revise a transcrição novamente para confirmar a compreensão dos pontos-chave e releia qualquer trecho particularmente importante ou difícil.\n\n**Passo 2: Divida o texto em seções**\n\nPara tornar o texto mais gerenciável e entender seus subpontos, divida-o em seções menores.\n\n**Passo 3: Identifique os pontos-chave em cada seção**\n\nPasse por cada seção e selecione os pontos mais importantes. O que seu leitor precisa saber para entender o argumento ou a conclusão geral do vídeo?\n\nLembre-se de que um resumo não envolve parafrasear cada parágrafo. Seu objetivo é extrair os pontos essenciais, deixando de lado qualquer informação de fundo ou detalhe suplementar.\n\n**Passo 4: Escreva o resumo**\n\nAgora que você conhece os pontos-chave que o vídeo deseja comunicar, coloque-os em suas próprias palavras.\n\nPara evitar plágio e mostrar que você entendeu o vídeo, é essencial parafrasear adequadamente as ideias do autor. Não copie e cole partes da transcrição, nem mesmo uma frase ou duas.\n\nA melhor maneira de fazer isso é deixar a transcrição de lado e escrever sua própria compreensão dos pontos-chave do autor.\n\n**Exemplo de Resumo de Vídeo:**\n\n**Título do Vídeo: \"O Impacto da Inteligência Artificial na Saúde\"**\n\n**RESUMO:**\n\nNo vídeo, apresentado pelo Dr. João Silva, é discutido o impacto da inteligência artificial (IA) na saúde. O Dr. Silva começa destacando como a IA tem revolucionado o diagnóstico médico, citando exemplos de sistemas que analisam exames de imagem com maior precisão do que especialistas humanos. Ele menciona o caso de um software de IA que consegue detectar câncer de mama em estágios iniciais com uma taxa de acerto de 95%.\n\nAlém disso, o Dr. Silva explora a aplicação da IA na gestão hospitalar, onde algoritmos ajudam a otimizar o fluxo de pacientes e a alocação de recursos, resultando em uma maior eficiência e redução de custos. Outro ponto abordado é o uso de assistentes virtuais que, com base em IA, oferecem suporte aos pacientes em suas casas, monitorando sinais vitais e enviando alertas em casos de emergência.\n\nO vídeo também aborda as preocupações éticas relacionadas ao uso da IA na saúde. O Dr. Silva enfatiza a importância de garantir a privacidade dos dados dos pacientes e a necessidade de regulamentações claras para evitar abusos. Ele finaliza sugerindo que, apesar dos desafios, a integração da IA na saúde promete trazer benefícios significativos, melhorando a qualidade do atendimento e salvando vidas.\n\nEsse resumo cobre os principais pontos discutidos no vídeo, oferecendo uma visão clara e compreensível para quem não assistiu ao conteúdo original.\n\n**Passo 5: Verifique o resumo em relação à transcrição**\n\nFinalmente, releia a transcrição para garantir que:\n\n- Você representou com precisão o trabalho do autor.\n- Não perdeu nenhuma informação essencial.\n- A redação não está muito semelhante a nenhuma frase da transcrição original.\n\nUse um verificador de plágio, se necessário, para garantir que seu texto é completamente original e devidamente citado.\n\nINPUT:\nTranscrição:\n{transcrição}\n\nDescrição:\n{Descricao_do_video}","name":"template","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","load_from_db":false},"transcrição":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"transcrição","display_name":"transcrição","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"Descricao_do_video":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"Descricao_do_video","display_name":"Descricao_do_video","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","is_input":null,"is_output":null,"is_composition":null,"base_classes":["Message"],"name":"","display_name":"Prompt","documentation":"","custom_fields":{"template":["transcrição","Descricao_do_video"]},"output_types":[],"full_path":null,"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","hidden":false,"display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true}],"field_order":["template"],"beta":false,"error":null,"edited":true},"id":"Prompt-LvE8T","description":"Create a prompt template with dynamic variables.","display_name":"Prompt"},"selected":false,"width":384,"height":525,"positionAbsolute":{"x":1705.9648302702362,"y":589.5437835526025},"dragging":false},{"id":"Prompt-PG8hF","type":"genericNode","position":{"x":3064.9312274576805,"y":108.36256691248872},"data":{"type":"Prompt","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":"{Tags}\n\n{Resumo}\n{IDEAS}\n{Quotes}\n{recomendations}\n\nLink: {Link}","name":"template","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","load_from_db":false},"Tags":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"Tags","display_name":"Tags","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"Resumo":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"Resumo","display_name":"Resumo","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"IDEAS":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"IDEAS","display_name":"IDEAS","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"Quotes":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"Quotes","display_name":"Quotes","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"recomendations":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"recomendations","display_name":"recomendations","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"Link":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"Link","display_name":"Link","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","base_classes":["Message"],"display_name":"Prompt","documentation":"","custom_fields":{"template":["Tags","Resumo","IDEAS","Quotes","recomendations","Link"]},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["template"],"beta":false,"edited":true},"id":"Prompt-PG8hF","description":"Create a prompt template with dynamic variables.","display_name":"Prompt"},"selected":false,"width":384,"height":901,"positionAbsolute":{"x":3064.9312274576805,"y":108.36256691248872},"dragging":false},{"id":"TextInput-I5Dkp","type":"genericNode","position":{"x":3721.720593371355,"y":824.0679727151723},"data":{"type":"TextInput","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.io.text import TextComponent\nfrom langflow.io import MessageTextInput, Output\nfrom langflow.schema.message import Message\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Obsidian Note Creator\"\n    description = \"Creates note in Obsidian.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"note\",\n            display_name=\"Note\",\n            info=\"Nota criada.\",\n        ),\n        MessageTextInput(\n            name=\"path\",\n            display_name=\"Path\",\n            info=\"Caminho para pasta.\",\n        ),\n        MessageTextInput(\n            name=\"filename\",\n            display_name=\"File Name\",\n            info=\"Nome do arquivo.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Note\", name=\"text\", method=\"note_response\"),\n    ]\n\n    def note_response(self) -> Message:\n        message = Message(\n            text=self.note,\n        )\n        path = self.path\n        filename = self.filename\n        \n        path_file = (f'{path}/{filename}.md')\n        \n        with open(path_file, 'w') as file:\n            file.write(str(self.note))\n            \n        return message.text\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"filename":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"filename","display_name":"File Name","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Nome do arquivo.","title_case":false,"type":"str"},"note":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"note","display_name":"Note","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Nota criada.","title_case":false,"type":"str"},"path":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"path","display_name":"Path","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Caminho para pasta.","title_case":false,"type":"str"}},"description":"Creates note in Obsidian.","icon":"type","base_classes":["Message"],"display_name":"MD text save","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Note","method":"note_response","value":"__UNDEFINED__","cache":true}],"field_order":["note","path","filename"],"beta":false,"edited":true},"id":"TextInput-I5Dkp","description":"Creates note in Obsidian.","display_name":"MD text save"},"selected":false,"width":384,"height":505,"positionAbsolute":{"x":3721.720593371355,"y":824.0679727151723},"dragging":false},{"id":"OpenAIModel-cuJuy","type":"genericNode","position":{"x":2252.9951328979987,"y":1072.8781194713604},"data":{"type":"OpenAIModel","node":{"template":{"_type":"Component","api_key":{"load_from_db":false,"required":false,"placeholder":"","show":true,"value":"","name":"api_key","display_name":"OpenAI API Key","advanced":false,"input_types":[],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"input_value","display_name":"Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageInput"},"json_mode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"json_mode","display_name":"JSON Mode","advanced":true,"dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","title_case":false,"type":"bool","_input_type":"BoolInput"},"max_tokens":{"trace_as_metadata":true,"range_spec":{"step_type":"float","min":0,"max":128000,"step":0.1},"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"max_tokens","display_name":"Max Tokens","advanced":true,"dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","title_case":false,"type":"int","_input_type":"IntInput"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":{},"name":"model_kwargs","display_name":"Model Kwargs","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict","_input_type":"DictInput"},"model_name":{"combobox":false,"trace_as_metadata":true,"options":["gpt-4o-mini","gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"required":false,"placeholder":"","show":true,"value":"gpt-4o-mini","name":"model_name","display_name":"Model Name","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"openai_api_base":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"openai_api_base","display_name":"OpenAI API Base","advanced":true,"dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","title_case":false,"type":"str","_input_type":"StrInput"},"output_schema":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"value":{},"name":"output_schema","display_name":"Schema","advanced":true,"dynamic":false,"info":"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.","title_case":false,"type":"dict","_input_type":"DictInput"},"seed":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":1,"name":"seed","display_name":"Seed","advanced":true,"dynamic":false,"info":"The seed controls the reproducibility of the job.","title_case":false,"type":"int","_input_type":"IntInput"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"stream","display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool","_input_type":"BoolInput"},"system_message":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"system_message","display_name":"System Message","advanced":true,"input_types":["Message"],"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"temperature":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":0.1,"name":"temperature","display_name":"Temperature","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"}},"description":"Generates text using OpenAI LLMs.","icon":"OpenAI","base_classes":["LanguageModel","Message"],"display_name":"OpenAI","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text_output","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true},{"types":["LanguageModel"],"selected":"LanguageModel","name":"model_output","display_name":"Language Model","method":"build_model","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","system_message","stream","max_tokens","model_kwargs","json_mode","output_schema","model_name","openai_api_base","api_key","temperature","seed"],"beta":false,"edited":true},"id":"OpenAIModel-cuJuy","description":"Generates text using OpenAI LLMs.","display_name":"OpenAI"},"selected":false,"width":384,"height":639,"dragging":false,"positionAbsolute":{"x":2252.9951328979987,"y":1072.8781194713604}},{"id":"Prompt-wi5eq","type":"genericNode","position":{"x":1706.878479063876,"y":1149.4093064806011},"data":{"type":"Prompt","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":"IDENTITY and PURPOSE\nYou are a summarization system that extracts the most interesting, useful, and surprising aspects of an article.\n\nTake a step back and think step by step about how to achieve the best result possible as defined in the steps below. You have a lot of freedom to make this work well.\n\nOUTPUT SECTIONS\nYou extract the top ideas from the input in a section called IDEAS:.\n\nOUTPUT INSTRUCTIONS\nYou only output Markdown.\nVoce sempre escreve em portugues brasieiro\nDo not give warnings or notes; only output the requested sections.\n\nDo not start items with the same opening words.\n\n\nINPUT: {transcrição}\n\n\n\nhere is an exemple of a good output:\n\n# Ideias\n1. Os LLMs generativos evoluíram rapidamente nos últimos anos, resultando em ferramentas inovadoras como o ChatGPT.\n2. A arquitetura do Transformer, baseada em redes neurais profundas, é essencial para os LLMs generativos.\n3. O pré-treinamento do modelo de linguagem envolve a previsão do próximo token em uma sequência de texto.\n4. O processo de alinhamento, por meio do ajuste fino supervisionado e do aprendizado por reforço a partir do feedback humano, melhora a qualidade e a utilidade dos modelos.\n5. A comunicação eficaz sobre as tecnologias de LLMs é fundamental para o avanço da IA e a democratização do conhecimento.\n\n\n","name":"template","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","load_from_db":false},"transcrição":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"transcrição","display_name":"transcrição","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","base_classes":["Message"],"display_name":"Prompt","documentation":"","custom_fields":{"template":["transcrição"]},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["template"],"beta":false,"edited":true},"id":"Prompt-wi5eq","description":"Create a prompt template with dynamic variables.","display_name":"Prompt"},"selected":false,"width":384,"height":431,"dragging":false,"positionAbsolute":{"x":1706.878479063876,"y":1149.4093064806011}},{"id":"OpenAIModel-dZ7Hd","type":"genericNode","position":{"x":2242.5142049496517,"y":1755.1883188218392},"data":{"type":"OpenAIModel","node":{"template":{"_type":"Component","api_key":{"load_from_db":false,"required":false,"placeholder":"","show":true,"value":"","name":"api_key","display_name":"OpenAI API Key","advanced":false,"input_types":[],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"input_value","display_name":"Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageInput"},"json_mode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"json_mode","display_name":"JSON Mode","advanced":true,"dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","title_case":false,"type":"bool","_input_type":"BoolInput"},"max_tokens":{"trace_as_metadata":true,"range_spec":{"step_type":"float","min":0,"max":128000,"step":0.1},"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"max_tokens","display_name":"Max Tokens","advanced":true,"dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","title_case":false,"type":"int","_input_type":"IntInput"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":{},"name":"model_kwargs","display_name":"Model Kwargs","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict","_input_type":"DictInput"},"model_name":{"combobox":false,"trace_as_metadata":true,"options":["gpt-4o-mini","gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"required":false,"placeholder":"","show":true,"value":"gpt-4o-mini","name":"model_name","display_name":"Model Name","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"openai_api_base":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"openai_api_base","display_name":"OpenAI API Base","advanced":true,"dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","title_case":false,"type":"str","_input_type":"StrInput"},"output_schema":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"value":{},"name":"output_schema","display_name":"Schema","advanced":true,"dynamic":false,"info":"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.","title_case":false,"type":"dict","_input_type":"DictInput"},"seed":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":1,"name":"seed","display_name":"Seed","advanced":true,"dynamic":false,"info":"The seed controls the reproducibility of the job.","title_case":false,"type":"int","_input_type":"IntInput"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"stream","display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool","_input_type":"BoolInput"},"system_message":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"system_message","display_name":"System Message","advanced":true,"input_types":["Message"],"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"temperature":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":0.1,"name":"temperature","display_name":"Temperature","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"}},"description":"Generates text using OpenAI LLMs.","icon":"OpenAI","base_classes":["LanguageModel","Message"],"display_name":"OpenAI","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text_output","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true},{"types":["LanguageModel"],"selected":"LanguageModel","name":"model_output","display_name":"Language Model","method":"build_model","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","system_message","stream","max_tokens","model_kwargs","json_mode","output_schema","model_name","openai_api_base","api_key","temperature","seed"],"beta":false,"edited":true},"id":"OpenAIModel-dZ7Hd","description":"Generates text using OpenAI LLMs.","display_name":"OpenAI"},"selected":false,"width":384,"height":639,"positionAbsolute":{"x":2242.5142049496517,"y":1755.1883188218392},"dragging":false},{"id":"Prompt-rl8Ry","type":"genericNode","position":{"x":1761.4457906935818,"y":1774.7521401261847},"data":{"type":"Prompt","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":"IDENTITY and PURPOSE\nYou are a summarization system that extracts the most interesting, useful, and surprising aspects of an article.\n\nTake a step back and think step by step about how to achieve the best result possible as defined in the steps below. You have a lot of freedom to make this work well.\n\nOUTPUT SECTIONS\n\nYou extract the most insightful and interesting quotes from the input into a section called QUOTES:. Use the exact quote text from the input.\n\nOUTPUT INSTRUCTIONS\nYou only output Markdown.\nVoce sempre escreve em portugues brasieiro\nDo not give warnings or notes; only output the requested sections.\nYou use zettelkasten method.\nDo not repeat ideas, quotes, facts, or resources.\nDo not start items with the same opening words.\n\n\nINPUT: {transcrição}\n\n\n\nhere is an exemple of a good output:\n\n# Citações\n- \"Quase todos os modelos de linguagem modernos são baseados na arquitetura do Transformer.\" \n- \"Os LLMs generativos usam principalmente uma arquitetura de Transformes somente decodificador.\"\n- \"O pré-treinamento do modelo de linguagem envolve a previsão do próximo token em uma sequência de texto.\"\n- \"O processo de alinhamento, que ensina um modelo de linguagem como gerar texto que se alinhe aos desejos de um usuário humano, é a resposta à pergunta acima.\"\n- \"A comunicação eficaz sobre essas tecnologias é fundamental para o avanço da IA e a democratização do conhecimento.\"\n\n\n","name":"template","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","load_from_db":false},"transcrição":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"transcrição","display_name":"transcrição","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","base_classes":["Message"],"display_name":"Prompt","documentation":"","custom_fields":{"template":["transcrição"]},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["template"],"beta":false,"edited":true},"id":"Prompt-rl8Ry","description":"Create a prompt template with dynamic variables.","display_name":"Prompt"},"selected":false,"width":384,"height":431,"positionAbsolute":{"x":1761.4457906935818,"y":1774.7521401261847},"dragging":false},{"id":"OpenAIModel-8WG98","type":"genericNode","position":{"x":2222.174726383388,"y":2458.938338554389},"data":{"type":"OpenAIModel","node":{"template":{"_type":"Component","api_key":{"load_from_db":false,"required":false,"placeholder":"","show":true,"value":"","name":"api_key","display_name":"OpenAI API Key","advanced":false,"input_types":[],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"input_value","display_name":"Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageInput"},"json_mode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"json_mode","display_name":"JSON Mode","advanced":true,"dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","title_case":false,"type":"bool","_input_type":"BoolInput"},"max_tokens":{"trace_as_metadata":true,"range_spec":{"step_type":"float","min":0,"max":128000,"step":0.1},"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"max_tokens","display_name":"Max Tokens","advanced":true,"dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","title_case":false,"type":"int","_input_type":"IntInput"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":{},"name":"model_kwargs","display_name":"Model Kwargs","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict","_input_type":"DictInput"},"model_name":{"combobox":false,"trace_as_metadata":true,"options":["gpt-4o-mini","gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"required":false,"placeholder":"","show":true,"value":"gpt-4o-mini","name":"model_name","display_name":"Model Name","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"openai_api_base":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"openai_api_base","display_name":"OpenAI API Base","advanced":true,"dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","title_case":false,"type":"str","_input_type":"StrInput"},"output_schema":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"value":{},"name":"output_schema","display_name":"Schema","advanced":true,"dynamic":false,"info":"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.","title_case":false,"type":"dict","_input_type":"DictInput"},"seed":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":1,"name":"seed","display_name":"Seed","advanced":true,"dynamic":false,"info":"The seed controls the reproducibility of the job.","title_case":false,"type":"int","_input_type":"IntInput"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"stream","display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool","_input_type":"BoolInput"},"system_message":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"system_message","display_name":"System Message","advanced":true,"input_types":["Message"],"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"temperature":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":0.1,"name":"temperature","display_name":"Temperature","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"}},"description":"Generates text using OpenAI LLMs.","icon":"OpenAI","base_classes":["LanguageModel","Message"],"display_name":"OpenAI","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text_output","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true},{"types":["LanguageModel"],"selected":"LanguageModel","name":"model_output","display_name":"Language Model","method":"build_model","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","system_message","stream","max_tokens","model_kwargs","json_mode","output_schema","model_name","openai_api_base","api_key","temperature","seed"],"beta":false,"edited":true},"id":"OpenAIModel-8WG98","description":"Generates text using OpenAI LLMs.","display_name":"OpenAI"},"selected":false,"width":384,"height":639,"positionAbsolute":{"x":2222.174726383388,"y":2458.938338554389},"dragging":false},{"id":"Prompt-gRi5J","type":"genericNode","position":{"x":1723.119176181996,"y":2341.712413320099},"data":{"type":"Prompt","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":"IDENTITY and PURPOSE\nYou are a summarization system that extracts the most interesting, useful, and surprising aspects of an article.\n\nTake a step back and think step by step about how to achieve the best result possible as defined in the steps below. You have a lot of freedom to make this work well.\n\nOUTPUT SECTIONS\n\nYou extract the most insightful and interesting recommendations that can be collected from the content into a section called RECOMMENDATIONS.\n\nOUTPUT INSTRUCTIONS\nYou only output Markdown.\nVoce sempre escreve em portugues brasieiro\nDo not give warnings or notes; only output the requested sections.\nYou use zettelkasten method.\nDo not repeat ideas, quotes, facts, or resources.\nDo not start items with the same opening words.\n\n\nINPUT: {transcrição}\n\n\n\nhere is an exemple of a good output:\n\n# Recomendações\n1. Aprofundar o conhecimento sobre a arquitetura do Transformer e sua aplicação em LLMs generativos.\n2. Explorar o processo de pré-treinamento do modelo de linguagem e sua importância para a qualidade dos LLMs.\n3. Compreender o processo de alinhamento e como ele melhora a utilidade e a relevância dos modelos de linguagem.\n4. Estudar a comunicação eficaz sobre tecnologias de IA para promover o avanço e a democratização do conhecimento nesta área.\n\n","name":"template","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","load_from_db":false},"transcrição":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"transcrição","display_name":"transcrição","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","base_classes":["Message"],"display_name":"Prompt","documentation":"","custom_fields":{"template":["transcrição"]},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["template"],"beta":false,"edited":true},"id":"Prompt-gRi5J","description":"Create a prompt template with dynamic variables.","display_name":"Prompt"},"selected":false,"width":384,"height":431,"positionAbsolute":{"x":1723.119176181996,"y":2341.712413320099},"dragging":false},{"id":"URL-ocRCv","type":"genericNode","position":{"x":-1350.7227034200353,"y":598.3826741324156},"data":{"type":"URL","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.io import MessageTextInput, Output\nfrom langflow.schema import Data\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            is_list=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"\n        Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"urls":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":true,"required":false,"placeholder":"","show":true,"value":[""],"name":"urls","display_name":"URLs","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Enter one or more URLs, separated by commas.","title_case":false,"type":"str"}},"description":"Fetch content from one or more URLs.","icon":"layout-template","base_classes":["Data"],"display_name":"URL","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"data","display_name":"Data","method":"fetch_content","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["urls"],"beta":false,"edited":true},"id":"URL-ocRCv","description":"Fetch content from one or more URLs.","display_name":"URL"},"selected":false,"width":384,"height":317,"positionAbsolute":{"x":-1350.7227034200353,"y":598.3826741324156},"dragging":false},{"id":"ParseData-Kkfcu","type":"genericNode","position":{"x":-600.0658641293143,"y":439.69563809262},"data":{"type":"ParseData","node":{"template":{"_type":"Component","data":{"trace_as_input":true,"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"data","display_name":"Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"The data to convert to text.","title_case":false,"type":"other"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"sep":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"\n","name":"sep","display_name":"Separator","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str"},"template":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"{source}","name":"template","display_name":"Template","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.","title_case":false,"type":"str"}},"description":"Convert Data into plain text following a specified template.","icon":"braces","base_classes":["Message"],"display_name":"Parse Data","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Text","method":"parse_data","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["data","template","sep"],"beta":false,"edited":true},"id":"ParseData-Kkfcu","description":"Convert Data into plain text following a specified template.","display_name":"Parse Data"},"selected":false,"width":384,"height":401,"positionAbsolute":{"x":-600.0658641293143,"y":439.69563809262},"dragging":false},{"id":"ParseData-L5zdq","type":"genericNode","position":{"x":582.2499944072395,"y":1431.4910998181635},"data":{"type":"ParseData","node":{"template":{"_type":"Component","data":{"trace_as_input":true,"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"data","display_name":"Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"The data to convert to text.","title_case":false,"type":"other"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"sep":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"\n","name":"sep","display_name":"Separator","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str"},"template":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"{title}","name":"template","display_name":"Template","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.","title_case":false,"type":"str"}},"description":"Convert Data into plain text following a specified template.","icon":"braces","base_classes":["Message"],"display_name":"Parse Data","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Text","method":"parse_data","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["data","template","sep"],"beta":false,"edited":true},"id":"ParseData-L5zdq","description":"Convert Data into plain text following a specified template.","display_name":"Parse Data"},"selected":false,"width":384,"height":401,"positionAbsolute":{"x":582.2499944072395,"y":1431.4910998181635},"dragging":false},{"id":"ParseData-RboQU","type":"genericNode","position":{"x":588.1350961925507,"y":991.5328913750561},"data":{"type":"ParseData","node":{"template":{"_type":"Component","data":{"trace_as_input":true,"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"data","display_name":"Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"The data to convert to text.","title_case":false,"type":"other"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"sep":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"\n","name":"sep","display_name":"Separator","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str"},"template":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"{description}","name":"template","display_name":"Template","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.","title_case":false,"type":"str"}},"description":"Convert Data into plain text following a specified template.","icon":"braces","base_classes":["Message"],"display_name":"Parse Data","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Text","method":"parse_data","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["data","template","sep"],"beta":false,"edited":true},"id":"ParseData-RboQU","description":"Convert Data into plain text following a specified template.","display_name":"Parse Data"},"selected":false,"width":384,"height":401,"positionAbsolute":{"x":588.1350961925507,"y":991.5328913750561},"dragging":false},{"id":"ChatInput-AyPZT","type":"genericNode","position":{"x":-1902.2675151390572,"y":568.9954924137987},"data":{"type":"ChatInput","node":{"template":{"_type":"Component","files":{"trace_as_metadata":true,"file_path":"","fileTypes":["txt","md","mdx","csv","json","yaml","yml","xml","html","htm","pdf","docx","py","sh","sql","js","ts","tsx","jpg","jpeg","png","bmp","image"],"list":true,"required":false,"placeholder":"","show":true,"value":"","name":"files","display_name":"Files","advanced":true,"dynamic":false,"info":"Files to be sent with the message.","title_case":false,"type":"file"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"input_value","display_name":"Text","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Message to be passed as input.","title_case":false,"type":"str"},"sender":{"trace_as_metadata":true,"options":["Machine","User"],"required":false,"placeholder":"","show":true,"value":"User","name":"sender","display_name":"Sender Type","advanced":true,"dynamic":false,"info":"Type of sender.","title_case":false,"type":"str"},"sender_name":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"User","name":"sender_name","display_name":"Sender Name","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Name of the sender.","title_case":false,"type":"str"},"session_id":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"session_id","display_name":"Session ID","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Session ID for the message.","title_case":false,"type":"str"}},"description":"Get chat inputs from the Playground.","icon":"ChatInput","base_classes":["Message"],"display_name":"Chat Input","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"message","display_name":"Message","method":"message_response","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["input_value","sender","sender_name","session_id","files"],"beta":false,"edited":false},"id":"ChatInput-AyPZT"},"selected":true,"width":384,"height":317,"positionAbsolute":{"x":-1902.2675151390572,"y":568.9954924137987},"dragging":false},{"id":"OpenAIModel-4kwNZ","type":"genericNode","position":{"x":1012.058370404311,"y":-44.02135615582229},"data":{"type":"OpenAIModel","node":{"template":{"_type":"Component","api_key":{"load_from_db":false,"required":false,"placeholder":"","show":true,"value":"","name":"api_key","display_name":"OpenAI API Key","advanced":false,"input_types":[],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"input_value","display_name":"Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageInput"},"json_mode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"json_mode","display_name":"JSON Mode","advanced":true,"dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","title_case":false,"type":"bool","_input_type":"BoolInput"},"max_tokens":{"trace_as_metadata":true,"range_spec":{"step_type":"float","min":0,"max":128000,"step":0.1},"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"max_tokens","display_name":"Max Tokens","advanced":true,"dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","title_case":false,"type":"int","_input_type":"IntInput"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":{},"name":"model_kwargs","display_name":"Model Kwargs","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict","_input_type":"DictInput"},"model_name":{"combobox":false,"trace_as_metadata":true,"options":["gpt-4o-mini","gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"required":false,"placeholder":"","show":true,"value":"gpt-4o-mini","name":"model_name","display_name":"Model Name","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"openai_api_base":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"openai_api_base","display_name":"OpenAI API Base","advanced":true,"dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","title_case":false,"type":"str","_input_type":"StrInput"},"output_schema":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"value":{},"name":"output_schema","display_name":"Schema","advanced":true,"dynamic":false,"info":"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.","title_case":false,"type":"dict","_input_type":"DictInput"},"seed":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":1,"name":"seed","display_name":"Seed","advanced":true,"dynamic":false,"info":"The seed controls the reproducibility of the job.","title_case":false,"type":"int","_input_type":"IntInput"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"stream","display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. 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