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ContentSmith AI.json
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{"description":"ContentSmith AI is an intelligent content generation system powered by Retrieval-Augmented Generation (RAG). It is designed for optimizing social media engagement, it transforms analytics from TikTok profiles into actionable insights and automates content creation, helping users efficiently craft engaging posts, summaries, and trend reports based on real-time data and historical performance.","icon_bg_color":null,"updated_at":"2024-09-20T08:41:49+00:00","webhook":false,"id":"8ce54eed-411a-423e-b55f-1d04158d6d45","user_id":"048c600e-a187-445b-8ddd-d9184e8d7332","name":"ContentSmith AI","icon":null,"is_component":false,"endpoint_name":null,"data":{"nodes":[{"data":{"description":"Get chat inputs from the Playground.","display_name":"Chat Input","id":"ChatInput-Q4qqI","node":{"base_classes":["Message"],"beta":false,"conditional_paths":[],"custom_fields":{},"description":"Get chat inputs from the Playground.","display_name":"Chat Input","documentation":"","edited":false,"field_order":["input_value","should_store_message","sender","sender_name","session_id","files"],"frozen":false,"icon":"ChatInput","output_types":[],"outputs":[{"cache":true,"display_name":"Message","method":"message_response","name":"message","selected":"Message","types":["Message"],"value":"__UNDEFINED__"}],"pinned":false,"template":{"_type":"Component","code":{"advanced":true,"dynamic":true,"fileTypes":[],"file_path":"","info":"","list":false,"load_from_db":false,"multiline":true,"name":"code","password":false,"placeholder":"","required":true,"show":true,"title_case":false,"type":"code","value":"from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, MESSAGE_SENDER_NAME_USER\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 BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_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=MESSAGE_SENDER_NAME_USER,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n 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\n if (\n self.session_id\n and isinstance(message, Message)\n and isinstance(message.text, str)\n and self.should_store_message\n ):\n store_message(\n message,\n flow_id=self.graph.flow_id,\n )\n self.message.value = message\n\n self.status = message\n return message\n"},"files":{"advanced":true,"display_name":"Files","dynamic":false,"fileTypes":["txt","md","mdx","csv","json","yaml","yml","xml","html","htm","pdf","docx","py","sh","sql","js","ts","tsx","jpg","jpeg","png","bmp","image"],"file_path":"","info":"Files to be sent with the message.","list":true,"name":"files","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"file","value":""},"input_value":{"advanced":false,"display_name":"Text","dynamic":false,"info":"Message to be passed as input.","input_types":["Message"],"list":false,"load_from_db":false,"multiline":true,"name":"input_value","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":"Show me all the posts about 'Grok' from February"},"sender":{"advanced":true,"display_name":"Sender Type","dynamic":false,"info":"Type of sender.","name":"sender","options":["Machine","User"],"placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"str","value":"User"},"sender_name":{"advanced":true,"display_name":"Sender Name","dynamic":false,"info":"Name of the sender.","input_types":["Message"],"list":false,"load_from_db":false,"name":"sender_name","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":"User"},"session_id":{"advanced":true,"display_name":"Session ID","dynamic":false,"info":"The session ID of the chat. If empty, the current session ID parameter will be used.","input_types":["Message"],"list":false,"load_from_db":false,"name":"session_id","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":""},"should_store_message":{"advanced":true,"display_name":"Store Messages","dynamic":false,"info":"Store the message in the history.","list":false,"name":"should_store_message","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"bool","value":true}},"lf_version":"1.0.18"},"type":"ChatInput"},"dragging":false,"height":300,"id":"ChatInput-Q4qqI","position":{"x":642.3545710150049,"y":220.22556606238678},"positionAbsolute":{"x":642.3545710150049,"y":220.22556606238678},"selected":false,"type":"genericNode","width":384},{"data":{"description":"Convert Data into plain text following a specified template.","display_name":"Parse Data","id":"ParseData-6jOw3","node":{"base_classes":["Message"],"beta":false,"conditional_paths":[],"custom_fields":{},"description":"Convert Data into plain text following a specified template.","display_name":"Parse Data","documentation":"","edited":false,"field_order":["data","template","sep"],"frozen":false,"icon":"braces","output_types":[],"outputs":[{"cache":true,"display_name":"Text","method":"parse_data","name":"text","selected":"Message","types":["Message"],"value":"__UNDEFINED__"}],"pinned":false,"template":{"_type":"Component","code":{"advanced":true,"dynamic":true,"fileTypes":[],"file_path":"","info":"","list":false,"load_from_db":false,"multiline":true,"name":"code","password":false,"placeholder":"","required":true,"show":true,"title_case":false,"type":"code","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"},"data":{"advanced":false,"display_name":"Data","dynamic":false,"info":"The data to convert to text.","input_types":["Data"],"list":false,"name":"data","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"other","value":""},"sep":{"advanced":true,"display_name":"Separator","dynamic":false,"info":"","list":false,"load_from_db":false,"name":"sep","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"str","value":"\n"},"template":{"advanced":false,"display_name":"Template","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.","input_types":["Message"],"list":false,"load_from_db":false,"multiline":true,"name":"template","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":"{text}"}},"lf_version":"1.0.18"},"type":"ParseData"},"dragging":false,"height":376,"id":"ParseData-6jOw3","position":{"x":1866.112576503821,"y":248.8295854775978},"positionAbsolute":{"x":1866.112576503821,"y":248.8295854775978},"selected":false,"type":"genericNode","width":384},{"data":{"description":"Create a prompt template with dynamic variables.","display_name":"Prompt","id":"Prompt-OGlc6","node":{"template":{"_type":"Component","code":{"advanced":true,"dynamic":true,"fileTypes":[],"file_path":"","info":"","list":false,"load_from_db":false,"multiline":true,"name":"code","password":false,"placeholder":"","required":true,"show":true,"title_case":false,"type":"code","value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\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 _update_template(self, frontend_node: dict):\n prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n custom_fields = frontend_node[\"custom_fields\"]\n frontend_node_template = frontend_node[\"template\"]\n _ = process_prompt_template(\n template=prompt_template,\n name=\"template\",\n custom_fields=custom_fields,\n frontend_node_template=frontend_node_template,\n )\n return frontend_node\n\n def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"\n This function is called after the code validation is done.\n \"\"\"\n frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n # Kept it duplicated for backwards compatibility\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_frontend_node\n # and update the frontend_node with those values\n update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n return frontend_node\n\n def _get_fallback_input(self, **kwargs):\n return DefaultPromptField(**kwargs)\n"},"context":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","name":"context","display_name":"context","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"question":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","name":"question","display_name":"question","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"template":{"advanced":false,"display_name":"Template","dynamic":false,"info":"","list":false,"load_from_db":false,"name":"template","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"type":"prompt","value":"{context}\n\n---\n\nGiven the context above, answer the question as best as possible. You are a helpful assistant built to answer questions from the provided context above.\n\nYour primary goal is to assist users by providing relevant data, insights, and summaries based on the context, whether they are looking for specific data points or broader content creation suggestions. \n\nYou are expected to provide accurate, relevant, and insightful responses that directly answer the user's query, integrating data where appropriate.\n\nThink through your reasoning step-by-step before arriving at an answer. Whenever applicable, please extract direct quotes from the context to support your answer. Verify each claim by referring directly to the relevant section of the context. If verification isn't possible, flag the claim as needing more information. If the context does not provide enough information to answer the question, please respond with \"I don't have enough information from the context to answer this accurately.\"\n\nQuestion: {question}\n\nAnswer: "}},"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":["context","question"]},"output_types":[],"full_path":null,"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","hidden":null,"display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true}],"field_order":["template"],"beta":false,"error":null,"edited":false,"lf_version":"1.0.18"},"type":"Prompt"},"dragging":false,"height":499,"id":"Prompt-OGlc6","position":{"x":2486.0988668404975,"y":496.5120474157301},"positionAbsolute":{"x":2486.0988668404975,"y":496.5120474157301},"selected":false,"type":"genericNode","width":384},{"data":{"description":"Display a chat message in the Playground.","display_name":"Chat Output","id":"ChatOutput-OKe5n","node":{"base_classes":["Message"],"beta":false,"conditional_paths":[],"custom_fields":{},"description":"Display a chat message in the Playground.","display_name":"Chat Output","documentation":"","edited":false,"field_order":["input_value","should_store_message","sender","sender_name","session_id","data_template"],"frozen":false,"icon":"ChatOutput","output_types":[],"outputs":[{"cache":true,"display_name":"Message","method":"message_response","name":"message","selected":"Message","types":["Message"],"value":"__UNDEFINED__"}],"pinned":false,"template":{"_type":"Component","code":{"advanced":true,"dynamic":true,"fileTypes":[],"file_path":"","info":"","list":false,"load_from_db":false,"multiline":true,"name":"code","password":false,"placeholder":"","required":true,"show":true,"title_case":false,"type":"code","value":"from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n name = \"ChatOutput\"\n\n inputs = [\n MessageTextInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Message to be passed as output.\",\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\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 )\n if (\n self.session_id\n and isinstance(message, Message)\n and isinstance(message.text, str)\n and self.should_store_message\n ):\n store_message(\n message,\n flow_id=self.graph.flow_id,\n )\n self.message.value = message\n\n self.status = message\n return message\n"},"data_template":{"advanced":true,"display_name":"Data Template","dynamic":false,"info":"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.","input_types":["Message"],"list":false,"load_from_db":false,"name":"data_template","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":"{text}"},"input_value":{"advanced":false,"display_name":"Text","dynamic":false,"info":"Message to be passed as output.","input_types":["Message"],"list":false,"load_from_db":false,"name":"input_value","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":""},"sender":{"advanced":true,"display_name":"Sender Type","dynamic":false,"info":"Type of sender.","name":"sender","options":["Machine","User"],"placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"str","value":"Machine"},"sender_name":{"advanced":true,"display_name":"Sender Name","dynamic":false,"info":"Name of the sender.","input_types":["Message"],"list":false,"load_from_db":false,"name":"sender_name","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":"AI"},"session_id":{"advanced":true,"display_name":"Session ID","dynamic":false,"info":"The session ID of the chat. If empty, the current session ID parameter will be used.","input_types":["Message"],"list":false,"load_from_db":false,"name":"session_id","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":""},"should_store_message":{"advanced":true,"display_name":"Store Messages","dynamic":false,"info":"Store the message in the history.","list":false,"name":"should_store_message","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"bool","value":true}},"lf_version":"1.0.18"},"type":"ChatOutput"},"dragging":false,"height":300,"id":"ChatOutput-OKe5n","position":{"x":3769.242086248817,"y":585.3403837062634},"positionAbsolute":{"x":3769.242086248817,"y":585.3403837062634},"selected":false,"type":"genericNode","width":384},{"data":{"description":"Split text into chunks based on specified criteria.","display_name":"Split Text","id":"SplitText-PhJXn","node":{"base_classes":["Data"],"beta":false,"conditional_paths":[],"custom_fields":{},"description":"Split text into chunks based on specified criteria.","display_name":"Split Text","documentation":"","edited":false,"field_order":["data_inputs","chunk_overlap","chunk_size","separator"],"frozen":false,"icon":"scissors-line-dashed","output_types":[],"outputs":[{"cache":true,"display_name":"Chunks","method":"split_text","name":"chunks","selected":"Data","types":["Data"],"value":"__UNDEFINED__"}],"pinned":false,"template":{"_type":"Component","chunk_overlap":{"advanced":false,"display_name":"Chunk Overlap","dynamic":false,"info":"Number of characters to overlap between chunks.","list":false,"name":"chunk_overlap","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"int","value":75},"chunk_size":{"advanced":false,"display_name":"Chunk Size","dynamic":false,"info":"The maximum number of characters in each chunk.","list":false,"name":"chunk_size","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"int","value":500},"code":{"advanced":true,"dynamic":true,"fileTypes":[],"file_path":"","info":"","list":false,"load_from_db":false,"multiline":true,"name":"code","password":false,"placeholder":"","required":true,"show":true,"title_case":false,"type":"code","value":"from typing import List\n\nfrom langchain_text_splitters import CharacterTextSplitter\n\nfrom langflow.custom import Component\nfrom langflow.io import HandleInput, IntInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.utils.util import unescape_string\n\n\nclass SplitTextComponent(Component):\n display_name: str = \"Split Text\"\n description: str = \"Split text into chunks based on specified criteria.\"\n icon = \"scissors-line-dashed\"\n name = \"SplitText\"\n\n inputs = [\n HandleInput(\n name=\"data_inputs\",\n display_name=\"Data Inputs\",\n info=\"The data to split.\",\n input_types=[\"Data\"],\n is_list=True,\n ),\n IntInput(\n name=\"chunk_overlap\",\n display_name=\"Chunk Overlap\",\n info=\"Number of characters to overlap between chunks.\",\n value=200,\n ),\n IntInput(\n name=\"chunk_size\",\n display_name=\"Chunk Size\",\n info=\"The maximum number of characters in each chunk.\",\n value=1000,\n ),\n MessageTextInput(\n name=\"separator\",\n display_name=\"Separator\",\n info=\"The character to split on. Defaults to newline.\",\n value=\"\\n\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Chunks\", name=\"chunks\", method=\"split_text\"),\n ]\n\n def _docs_to_data(self, docs):\n data = []\n for doc in docs:\n data.append(Data(text=doc.page_content, data=doc.metadata))\n return data\n\n def split_text(self) -> List[Data]:\n separator = unescape_string(self.separator)\n\n documents = []\n for _input in self.data_inputs:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n\n splitter = CharacterTextSplitter(\n chunk_overlap=self.chunk_overlap,\n chunk_size=self.chunk_size,\n separator=separator,\n )\n docs = splitter.split_documents(documents)\n data = self._docs_to_data(docs)\n self.status = data\n return data\n"},"data_inputs":{"advanced":false,"display_name":"Data Inputs","dynamic":false,"info":"The data to split.","input_types":["Data"],"list":true,"name":"data_inputs","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"other","value":""},"separator":{"advanced":false,"display_name":"Separator","dynamic":false,"info":"The character to split on. Defaults to newline.","input_types":["Message"],"list":false,"load_from_db":false,"name":"separator","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":""}},"lf_version":"1.0.18"},"type":"SplitText"},"dragging":false,"height":519,"id":"SplitText-PhJXn","position":{"x":2044.2799160989089,"y":1185.3130355818519},"positionAbsolute":{"x":2044.2799160989089,"y":1185.3130355818519},"selected":false,"type":"genericNode","width":384},{"data":{"description":"A generic file loader.","display_name":"File","id":"File-engxz","node":{"template":{"_type":"Component","path":{"trace_as_metadata":true,"file_path":"26b6f566-3086-4149-97d4-e2963d715f53\\2024-09-19_13-47-26_TikTok-Profile-Scripts-analytics.csv","fileTypes":["csv"],"list":false,"required":false,"placeholder":"","show":true,"name":"path","value":"TikTok-Profile-Scripts-analytics.csv","display_name":"Path","advanced":false,"dynamic":false,"info":"Supported file type: CSV","title_case":false,"type":"file","_input_type":"FileInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from pathlib import Path\r\nimport pandas as pd\r\nimport re\r\nfrom langflow.base.data.utils import TEXT_FILE_TYPES\r\nfrom langflow.custom import Component\r\nfrom langflow.io import BoolInput, FileInput, Output\r\nfrom langflow.schema import Data\r\n\r\n\r\nclass FileComponent(Component):\r\n display_name = \"File\"\r\n description = \"A CSV file loader optimized for content extraction.\"\r\n icon = \"file-text\"\r\n name = \"File\"\r\n\r\n inputs = [\r\n FileInput(\r\n name=\"path\",\r\n display_name=\"Path\",\r\n file_types=[\"csv\"],\r\n info=\"Supported file type: CSV\",\r\n ),\r\n BoolInput(\r\n name=\"silent_errors\",\r\n display_name=\"Silent Errors\",\r\n advanced=True,\r\n info=\"If true, errors will not raise an exception.\",\r\n ),\r\n ]\r\n\r\n outputs = [\r\n Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\r\n ]\r\n\r\n def load_file(self) -> Data:\r\n if not self.path:\r\n raise ValueError(\"Please, upload a file to use this component.\")\r\n \r\n resolved_path = self.resolve_path(self.path)\r\n silent_errors = self.silent_errors\r\n\r\n extension = Path(resolved_path).suffix[1:].lower()\r\n if extension != \"csv\":\r\n raise ValueError(f\"Unsupported file type: {extension}\")\r\n\r\n try:\r\n df = pd.read_csv(resolved_path, encoding='ISO-8859-1') \r\n except Exception as e:\r\n if silent_errors:\r\n return Data(status=\"Error loading CSV file.\")\r\n else:\r\n raise e\r\n\r\n relevant_columns = [\r\n \"Transcripts\", \"Caption\", \"Hashtags\", \"Views\", \r\n \"Likes\", \"Comments\", \"Shares\", \"Bookmark\", \"Duration\", \"Date Posted\"\r\n ]\r\n \r\n if not all(col in df.columns for col in relevant_columns):\r\n raise ValueError(\"Required columns are missing from the CSV file.\")\r\n \r\n extracted_data = df[relevant_columns]\r\n\r\n def clean_text(text):\r\n if pd.isna(text):\r\n return \"\"\r\n text = re.sub(r'\\s+', ' ', text) \r\n text = re.sub(r'[^\\w\\s,.#@]', '', text) \r\n return text.strip()\r\n\r\n extracted_data[['Transcripts', 'Caption', 'Hashtags']] = extracted_data[['Transcripts', 'Caption', 'Hashtags']].applymap(clean_text)\r\n\r\n combined_text = extracted_data.apply(lambda row: \" \".join(map(str, row)), axis=1).str.cat(sep='\\n')\r\n\r\n return Data(text=combined_text)\r\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"silent_errors":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"silent_errors","value":false,"display_name":"Silent Errors","advanced":true,"dynamic":false,"info":"If true, errors will not raise an exception.","title_case":false,"type":"bool","_input_type":"BoolInput"}},"description":"A CSV file loader optimized for content extraction.","icon":"file-text","base_classes":["Data"],"display_name":"File","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"data","display_name":"Data","method":"load_file","value":"__UNDEFINED__","cache":true}],"field_order":["path","silent_errors"],"beta":false,"edited":true,"lf_version":"1.0.18"},"type":"File"},"dragging":false,"height":328,"id":"File-engxz","position":{"x":1418.981990122179,"y":1539.3825691184466},"positionAbsolute":{"x":1418.981990122179,"y":1539.3825691184466},"selected":false,"type":"genericNode","width":384},{"id":"CohereEmbeddings-05qax","type":"genericNode","position":{"x":596.1715982912552,"y":792.7131000899468},"data":{"type":"CohereEmbeddings","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langchain_community.embeddings.cohere import CohereEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import DropdownInput, FloatInput, IntInput, MessageTextInput, Output, SecretStrInput\n\n\nclass CohereEmbeddingsComponent(LCModelComponent):\n display_name = \"Cohere Embeddings\"\n description = \"Generate embeddings using Cohere models.\"\n icon = \"Cohere\"\n name = \"CohereEmbeddings\"\n\n inputs = [\n SecretStrInput(name=\"cohere_api_key\", display_name=\"Cohere API Key\"),\n DropdownInput(\n name=\"model\",\n display_name=\"Model\",\n advanced=True,\n options=[\n \"embed-english-v2.0\",\n \"embed-multilingual-v2.0\",\n \"embed-english-light-v2.0\",\n \"embed-multilingual-light-v2.0\",\n ],\n value=\"embed-english-v2.0\",\n ),\n MessageTextInput(name=\"truncate\", display_name=\"Truncate\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n MessageTextInput(name=\"user_agent\", display_name=\"User Agent\", advanced=True, value=\"langchain\"),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n ]\n\n outputs = [\n Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n ]\n\n def build_embeddings(self) -> Embeddings:\n return CohereEmbeddings( # type: ignore\n cohere_api_key=self.cohere_api_key,\n model=self.model,\n truncate=self.truncate,\n max_retries=self.max_retries,\n user_agent=self.user_agent,\n request_timeout=self.request_timeout or None,\n )\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"cohere_api_key":{"load_from_db":true,"required":false,"placeholder":"","show":true,"name":"cohere_api_key","value":null,"display_name":"Cohere API Key","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"max_retries":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"max_retries","value":3,"display_name":"Max Retries","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"int","_input_type":"IntInput"},"model":{"trace_as_metadata":true,"options":["embed-english-v2.0","embed-multilingual-v2.0","embed-english-light-v2.0","embed-multilingual-light-v2.0"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"model","value":"embed-english-v2.0","display_name":"Model","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"request_timeout":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"request_timeout","value":"","display_name":"Request Timeout","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"},"truncate":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"truncate","value":"","display_name":"Truncate","advanced":true,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageTextInput"},"user_agent":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"user_agent","value":"langchain","display_name":"User Agent","advanced":true,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageTextInput"}},"description":"Generate embeddings using Cohere models.","icon":"Cohere","base_classes":["Embeddings"],"display_name":"Cohere Embeddings","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Embeddings"],"selected":"Embeddings","name":"embeddings","display_name":"Embeddings","method":"build_embeddings","value":"__UNDEFINED__","cache":true}],"field_order":["cohere_api_key","model","truncate","max_retries","user_agent","request_timeout"],"beta":false,"edited":false,"lf_version":"1.0.18"},"id":"CohereEmbeddings-05qax"},"selected":false,"width":384,"height":300,"positionAbsolute":{"x":596.1715982912552,"y":792.7131000899468},"dragging":false},{"id":"Pinecone-Uaxnk","type":"genericNode","position":{"x":1259.086276272934,"y":342.5538969347815},"data":{"type":"Pinecone","node":{"template":{"_type":"Component","embedding":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"embedding","value":"","display_name":"Embedding","advanced":false,"input_types":["Embeddings"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"HandleInput"},"ingest_data":{"trace_as_metadata":true,"list":true,"trace_as_input":true,"required":false,"placeholder":"","show":true,"name":"ingest_data","value":"","display_name":"Ingest Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"DataInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from typing import List\n\nfrom langchain_pinecone import Pinecone\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers.data import docs_to_data\nfrom langflow.io import (\n DropdownInput,\n HandleInput,\n IntInput,\n StrInput,\n SecretStrInput,\n DataInput,\n MultilineInput,\n)\nfrom langflow.schema import Data\n\n\nclass PineconeVectorStoreComponent(LCVectorStoreComponent):\n display_name = \"Pinecone\"\n description = \"Pinecone Vector Store with search capabilities\"\n documentation = \"https://python.langchain.com/v0.2/docs/integrations/vectorstores/pinecone/\"\n name = \"Pinecone\"\n icon = \"Pinecone\"\n\n inputs = [\n StrInput(name=\"index_name\", display_name=\"Index Name\", required=True),\n StrInput(name=\"namespace\", display_name=\"Namespace\", info=\"Namespace for the index.\"),\n DropdownInput(\n name=\"distance_strategy\",\n display_name=\"Distance Strategy\",\n options=[\"Cosine\", \"Euclidean\", \"Dot Product\"],\n value=\"Cosine\",\n advanced=True,\n ),\n SecretStrInput(name=\"pinecone_api_key\", display_name=\"Pinecone API Key\", required=True),\n StrInput(\n name=\"text_key\",\n display_name=\"Text Key\",\n info=\"Key in the record to use as text.\",\n value=\"text\",\n advanced=True,\n ),\n MultilineInput(name=\"search_query\", display_name=\"Search Query\"),\n DataInput(\n name=\"ingest_data\",\n display_name=\"Ingest Data\",\n is_list=True,\n ),\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n value=4,\n advanced=True,\n ),\n ]\n\n @check_cached_vector_store\n def build_vector_store(self) -> Pinecone:\n from langchain_pinecone._utilities import DistanceStrategy\n from langchain_pinecone.vectorstores import Pinecone\n\n distance_strategy = self.distance_strategy.replace(\" \", \"_\").upper()\n _distance_strategy = DistanceStrategy[distance_strategy]\n\n pinecone = Pinecone(\n index_name=self.index_name,\n embedding=self.embedding,\n text_key=self.text_key,\n namespace=self.namespace,\n distance_strategy=_distance_strategy,\n pinecone_api_key=self.pinecone_api_key,\n )\n\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n\n if documents:\n pinecone.add_documents(documents)\n return pinecone\n\n def search_documents(self) -> List[Data]:\n vector_store = self.build_vector_store()\n\n if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():\n docs = vector_store.similarity_search(\n query=self.search_query,\n k=self.number_of_results,\n )\n\n data = docs_to_data(docs)\n self.status = data\n return data\n else:\n return []\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"distance_strategy":{"trace_as_metadata":true,"options":["Cosine","Euclidean","Dot Product"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"distance_strategy","value":"Cosine","display_name":"Distance Strategy","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"index_name":{"trace_as_metadata":true,"load_from_db":true,"list":false,"required":true,"placeholder":"","show":true,"name":"index_name","value":"PINECONE_INDEX_NAME","display_name":"Index Name","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"StrInput"},"namespace":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"namespace","value":"","display_name":"Namespace","advanced":false,"dynamic":false,"info":"Namespace for the index.","title_case":false,"type":"str","_input_type":"StrInput"},"number_of_results":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"number_of_results","value":4,"display_name":"Number of Results","advanced":true,"dynamic":false,"info":"Number of results to return.","title_case":false,"type":"int","_input_type":"IntInput"},"pinecone_api_key":{"load_from_db":true,"required":true,"placeholder":"","show":true,"name":"pinecone_api_key","value":null,"display_name":"Pinecone API Key","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"search_query":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"search_query","value":"","display_name":"Search Query","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MultilineInput"},"text_key":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"text_key","value":"text","display_name":"Text Key","advanced":true,"dynamic":false,"info":"Key in the record to use as text.","title_case":false,"type":"str","_input_type":"StrInput"}},"description":"Pinecone Vector Store with search capabilities","icon":"Pinecone","base_classes":["Data","Retriever","VectorStore"],"display_name":"Pinecone","documentation":"https://python.langchain.com/v0.2/docs/integrations/vectorstores/pinecone/","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Retriever"],"selected":"Retriever","name":"base_retriever","display_name":"Retriever","method":"build_base_retriever","value":"__UNDEFINED__","cache":true},{"types":["Data"],"selected":"Data","name":"search_results","display_name":"Search Results","method":"search_documents","value":"__UNDEFINED__","cache":true},{"types":["VectorStore"],"selected":"VectorStore","name":"vector_store","display_name":"Vector Store","method":"cast_vector_store","value":"__UNDEFINED__","cache":true}],"field_order":["index_name","namespace","distance_strategy","pinecone_api_key","text_key","search_query","ingest_data","embedding","number_of_results"],"beta":false,"edited":false,"lf_version":"1.0.18"},"id":"Pinecone-Uaxnk"},"selected":false,"width":384,"height":741,"dragging":false,"positionAbsolute":{"x":1259.086276272934,"y":342.5538969347815}},{"id":"CohereEmbeddings-bayow","type":"genericNode","position":{"x":2050.0010427240677,"y":1817.6737370656308},"data":{"type":"CohereEmbeddings","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langchain_community.embeddings.cohere import CohereEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import DropdownInput, FloatInput, IntInput, MessageTextInput, Output, SecretStrInput\n\n\nclass CohereEmbeddingsComponent(LCModelComponent):\n display_name = \"Cohere Embeddings\"\n description = \"Generate embeddings using Cohere models.\"\n icon = \"Cohere\"\n name = \"CohereEmbeddings\"\n\n inputs = [\n SecretStrInput(name=\"cohere_api_key\", display_name=\"Cohere API Key\"),\n DropdownInput(\n name=\"model\",\n display_name=\"Model\",\n advanced=True,\n options=[\n \"embed-english-v2.0\",\n \"embed-multilingual-v2.0\",\n \"embed-english-light-v2.0\",\n \"embed-multilingual-light-v2.0\",\n ],\n value=\"embed-english-v2.0\",\n ),\n MessageTextInput(name=\"truncate\", display_name=\"Truncate\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n MessageTextInput(name=\"user_agent\", display_name=\"User Agent\", advanced=True, value=\"langchain\"),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n ]\n\n outputs = [\n Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n ]\n\n def build_embeddings(self) -> Embeddings:\n return CohereEmbeddings( # type: ignore\n cohere_api_key=self.cohere_api_key,\n model=self.model,\n truncate=self.truncate,\n max_retries=self.max_retries,\n user_agent=self.user_agent,\n request_timeout=self.request_timeout or None,\n )\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"cohere_api_key":{"load_from_db":true,"required":false,"placeholder":"","show":true,"name":"cohere_api_key","value":null,"display_name":"Cohere API Key","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"max_retries":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"max_retries","value":3,"display_name":"Max Retries","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"int","_input_type":"IntInput"},"model":{"trace_as_metadata":true,"options":["embed-english-v2.0","embed-multilingual-v2.0","embed-english-light-v2.0","embed-multilingual-light-v2.0"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"model","value":"embed-english-v2.0","display_name":"Model","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"request_timeout":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"request_timeout","value":"","display_name":"Request Timeout","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"},"truncate":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"truncate","value":"","display_name":"Truncate","advanced":true,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageTextInput"},"user_agent":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"user_agent","value":"langchain","display_name":"User Agent","advanced":true,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageTextInput"}},"description":"Generate embeddings using Cohere models.","icon":"Cohere","base_classes":["Embeddings"],"display_name":"Cohere Embeddings","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Embeddings"],"selected":"Embeddings","name":"embeddings","display_name":"Embeddings","method":"build_embeddings","value":"__UNDEFINED__","cache":true}],"field_order":["cohere_api_key","model","truncate","max_retries","user_agent","request_timeout"],"beta":false,"edited":false,"lf_version":"1.0.18"},"id":"CohereEmbeddings-bayow"},"selected":false,"width":384,"height":300,"positionAbsolute":{"x":2050.0010427240677,"y":1817.6737370656308},"dragging":false},{"id":"Pinecone-wMQtk","type":"genericNode","position":{"x":2831.2247574169614,"y":1383.6605622362454},"data":{"type":"Pinecone","node":{"template":{"_type":"Component","embedding":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"embedding","value":"","display_name":"Embedding","advanced":false,"input_types":["Embeddings"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"HandleInput"},"ingest_data":{"trace_as_metadata":true,"list":true,"trace_as_input":true,"required":false,"placeholder":"","show":true,"name":"ingest_data","value":"","display_name":"Ingest Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"DataInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from typing import List\n\nfrom langchain_pinecone import Pinecone\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers.data import docs_to_data\nfrom langflow.io import (\n DropdownInput,\n HandleInput,\n IntInput,\n StrInput,\n SecretStrInput,\n DataInput,\n MultilineInput,\n)\nfrom langflow.schema import Data\n\n\nclass PineconeVectorStoreComponent(LCVectorStoreComponent):\n display_name = \"Pinecone\"\n description = \"Pinecone Vector Store with search capabilities\"\n documentation = \"https://python.langchain.com/v0.2/docs/integrations/vectorstores/pinecone/\"\n name = \"Pinecone\"\n icon = \"Pinecone\"\n\n inputs = [\n StrInput(name=\"index_name\", display_name=\"Index Name\", required=True),\n StrInput(name=\"namespace\", display_name=\"Namespace\", info=\"Namespace for the index.\"),\n DropdownInput(\n name=\"distance_strategy\",\n display_name=\"Distance Strategy\",\n options=[\"Cosine\", \"Euclidean\", \"Dot Product\"],\n value=\"Cosine\",\n advanced=True,\n ),\n SecretStrInput(name=\"pinecone_api_key\", display_name=\"Pinecone API Key\", required=True),\n StrInput(\n name=\"text_key\",\n display_name=\"Text Key\",\n info=\"Key in the record to use as text.\",\n value=\"text\",\n advanced=True,\n ),\n MultilineInput(name=\"search_query\", display_name=\"Search Query\"),\n DataInput(\n name=\"ingest_data\",\n display_name=\"Ingest Data\",\n is_list=True,\n ),\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n value=4,\n advanced=True,\n ),\n ]\n\n @check_cached_vector_store\n def build_vector_store(self) -> Pinecone:\n from langchain_pinecone._utilities import DistanceStrategy\n from langchain_pinecone.vectorstores import Pinecone\n\n distance_strategy = self.distance_strategy.replace(\" \", \"_\").upper()\n _distance_strategy = DistanceStrategy[distance_strategy]\n\n pinecone = Pinecone(\n index_name=self.index_name,\n embedding=self.embedding,\n text_key=self.text_key,\n namespace=self.namespace,\n distance_strategy=_distance_strategy,\n pinecone_api_key=self.pinecone_api_key,\n )\n\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n\n if documents:\n pinecone.add_documents(documents)\n return pinecone\n\n def search_documents(self) -> List[Data]:\n vector_store = self.build_vector_store()\n\n if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():\n docs = vector_store.similarity_search(\n query=self.search_query,\n k=self.number_of_results,\n )\n\n data = docs_to_data(docs)\n self.status = data\n return data\n else:\n return []\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"distance_strategy":{"trace_as_metadata":true,"options":["Cosine","Euclidean","Dot Product"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"distance_strategy","value":"Cosine","display_name":"Distance Strategy","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"index_name":{"trace_as_metadata":true,"load_from_db":true,"list":false,"required":true,"placeholder":"","show":true,"name":"index_name","value":"PINECONE_INDEX_NAME","display_name":"Index 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