All transformations available in our tool builder are also supported from the our python code step.

​
Studio Transformation

​
Prompt Completion

Use a large language model such as GPT Arguments
  • prompt: str The prompt that is fed to the model.
  • model: str The model to use for completion. If using gpt3.5, if you do not set your own API Key you will be charged credits for usage. For other models, Make sure to set an API key.
  • fallback_model: str The model to use for completion if the model selected in the main window fails for whatever reason. If using gpt3.5, if you do not set your own API Key you will be charged credits for usage. For other models, Make sure to set an API key.
  • history: Any Conversation history to be passed into the prompt. For example, [{role: ‘user’, message: ‘Hello, my name is Bob.’}, {role: ‘ai’, message: ‘Hello Bob, how are you?’}].
  • history_only: bool If true, prompt isn’t included in what gets sent to the llm. mostly useful for re-prompting an llm after an function call
  • system_prompt: str System prompt to be passed into the GPT chat completion prompts.
  • strip_linebreaks: bool Whether to strip linebreaks from the output.
  • temperature: int | float Temperature of the selected model. Typically, higher temperature means more random output.
  • response_format: str For certain models such as OpenAI, response format can be forced through this field.
  • max_tokens: int | float How many tokens would you like the output to be?
  • seed: int | float Seed for generated output. The seed value stores the nature of randomly generated output, the same seed with generated the same response regardless of other parameters
  • validators: Any Validate that the LLM produces output in the expected format, and re-prompts to fix issues if not. Note: the LLM still outputs text, this only validates the shape of that text.
  • split_and_map: bool If a prompt variable is too long, split the variable into chunks, and run the entire prompt on each chunk. Very helpful for processing large pdfs.
  • memory: Any Define how variables should be handled if they’re too long. Choose from extracting the most relevant part, a summary of it or whether to use it all.
  • functions: Any
  • function_to_force_gpt_to_call: str The name of the function to force GPT to call. This is useful if you want force GPT to call a specific function.
  • timeout_ms: int | float Timeout in milliseconds, request will timeout after this much time if it runs for too long.
  • debug: bool
  • mock_llm_response: dict
  • token_logging: dict When provided, tokens will be logged based on these properties, and aggregatable by agent and use case.
  • stream: bool Streams the output as it is generated. Only implemented when running an agent.
  • azure_model: str The Azure OpenAI Deployment Id to use when picking the azure-openai custom deployment model.
Returns
  • answer: str
  • prompt: str
  • validation_history: Any Conversation history to be passed into the prompt. For example, [{role: ‘user’, message: ‘Hello, my name is Bob.’}, {role: ‘ai’, message: ‘Hello Bob, how are you?’}].
  • split_answers: Any
  • user_key_used: bool
  • credits_cost: int | float
  • errors: Any
  • debug: dict
  • history_items: Any Conversation history to be passed into the prompt. For example, [{role: ‘user’, message: ‘Hello, my name is Bob.’}, {role: ‘ai’, message: ‘Hello Bob, how are you?’}].
  • model_used: str
Python
response = run_step(
    "prompt_completion",
	{
		"prompt": ...,
		"model": ...,
		"fallback_model": ...,
		"history": ...,
		"history_only": ...,
		"system_prompt": ...,
		"strip_linebreaks": ...,
		"temperature": ...,
		"response_format": ...,
		"max_tokens": ...,
		"seed": ...,
		"validators": ...,
		"split_and_map": ...,
		"memory": ...,
		"functions": ...,
		"function_to_force_gpt_to_call": ...,
		"timeout_ms": ...,
		"debug": ...,
		"mock_llm_response": ...,
		"token_logging": ...,
		"stream": ...,
		"azure_model": ...,
	}
)

​
Api Call

Run an API request Arguments
  • method: str The HTTP method to use.
  • url: str The URL to make the request to.
  • headers: dict The headers to send with the request.
  • body: Any The body to send with the request.
  • url_params: dict Enter any key-value pairs to append to the base URL as query parameters. It’ll be formatted like key1=value1&key2=value2. Useful for specifying attributes of data you’re requesting.
  • response_type: str The format of the response.
  • cookies: dict The cookies to send with the request.
Returns
  • response_body: Any
  • status: int | float
  • body: str
  • url: str
  • response_headers: dict
Python
response = run_step(
    "api_call",
	{
		"method": ...,
		"url": ...,
		"headers": ...,
		"body": ...,
		"url_params": ...,
		"response_type": ...,
		"cookies": ...,
	}
)

​
Js Code Transformation

Run JavaScript code Arguments
  • code: str The JS code to execute, returning the value to be used as the output of this transformation.
  • backend: str Pick the runtime to use for this code.
Returns
  • transformed: Any
  • duration: int | float
Python
response = run_step(
    "js_code_transformation",
	{
		"code": ...,
		"backend": ...,
	}
)
Retrieve data that best match a provided query Arguments
  • dataset_id: str The dataset to use as knowledge. If the list is empty, please upload a dataset, and check to vectorize it when prompted.
  • query: str Items that best match the query will be retrieved from the knowledge source.
  • query_type: str The type of search to perform. Keyword search is better at finding names, and proper nouns, while vector search is better at finding synonyms and related ideas.
  • vector_field: str The name of the field that contains the vector. Only applicable for vector search.
  • model: str The model name to use.
  • content_field: str
  • output_all_fields: bool If set to true, will always output all fields. If set to false, this setting will have no behaviour.
  • page_size: int | float The number of results to return.
  • raw_filters: Any Raw relevance filters to filter results searched on.
Returns
  • results: Any
  • documents: dict
Python
response = run_step(
    "search",
	{
		"dataset_id": ...,
		"query": ...,
		"query_type": ...,
		"vector_field": ...,
		"model": ...,
		"content_field": ...,
		"output_all_fields": ...,
		"page_size": ...,
		"raw_filters": ...,
	}
)

​
Search Array

Vectorise an array of strings and do similarity search Arguments
  • array: Any The array of data to search. If it is an object, it will be stringified when searching.
  • query: str The query to search for.
  • page_size: int | float The number of results to return.
  • field: str The field to search in if array includes objects.
Returns
  • results: Any
  • scores: Any
Python
response = run_step(
    "search_array",
	{
		"array": ...,
		"query": ...,
		"page_size": ...,
		"field": ...,
	}
)

​
Bulk Update

Insert data into a knowledge set. Arguments
  • dataset_id: str The knowledge set to insert data to.
  • documents: Any
  • clean_field_names: bool If set to true, field names will be cleaned before inserting. This is recommended to avoid issues with special characters in field names.
  • ingest_in_background: bool If set to true, the step will immediately continue, without waiting for documents to be inserted and available.
Returns
  • inserted: int | float
  • failed_documents: Any
Python
response = run_step(
    "bulk_update",
	{
		"dataset_id": ...,
		"documents": ...,
		"clean_field_names": ...,
		"ingest_in_background": ...,
	}
)

​
Apify

Run a custom Apify Job, and optionally save results to Knowledge Arguments
  • input: dict The input object for the Apify Job
  • actor: str The actor to use, for example apify/web-scraper
  • knowledge_set: str If provided, write results to a knowledge set
  • output_settings: dict Controls the output options for the apify job, based on https://docs.apify.com/api/client/js/reference/interface/DatasetClientListItemOptions
  • dont_use_personal_key: bool
  • cost_target: int | float This is a cost target for the transformation to aim for. We use a rough linear approximation to estimate how much the step will cost. The true cost may be slightly over or under depending how noisy the sampling method is. Does not apply to those who use their own Apify key.
Returns
  • credits_cost: int | float
  • items: Any
Python
response = run_step(
    "apify",
	{
		"input": ...,
		"actor": ...,
		"knowledge_set": ...,
		"output_settings": ...,
		"dont_use_personal_key": ...,
		"cost_target": ...,
	}
)

​
Scrape Glassdoor

Arguments
  • urls: Any URLs of Companies on Glassdoor
  • knowledge_set: str Did you want to insert the results of the transformation into a knowledge set?
  • search_keyword: str Search Keyword
  • max_items: int | float Number of results per Glassdoor search page
  • end_page: int | float Number of search pages
  • scrape_salary: bool Do you want to scrape salary information?
  • custom_map_function: str A custom map function for scraping each page.
  • extended_output_function: str A custom extended output function for scraping each page.
Returns
  • documents: Any
  • credits_cost: int | float
Python
response = run_step(
    "scrape_glassdoor",
	{
		"urls": ...,
		"knowledge_set": ...,
		"search_keyword": ...,
		"max_items": ...,
		"end_page": ...,
		"scrape_salary": ...,
		"custom_map_function": ...,
		"extended_output_function": ...,
	}
)

​
Multiple File Upload

Arguments
  • urls: Any URLs of Companies on Glassdoor
  • knowledge_set: str Did you want to insert the results of the transformation into a knowledge set?
  • should_chunk_text: bool Should the text fields be chunked?
Returns
  • status: str
  • documents_url: str
Python
response = run_step(
    "multiple_file_upload",
	{
		"urls": ...,
		"knowledge_set": ...,
		"should_chunk_text": ...,
	}
)

​
Generate Vector Embedding

Generate a vector embedding from a given input with a choice of models. Arguments
  • input: str The input to generate a vector embedding with.
  • model: str The model name to use.
Returns
  • vector: Any
Python
response = run_step(
    "generate_vector_embedding",
	{
		"input": ...,
		"model": ...,
	}
)

​
Send Email Step

Send an email with subject and body Arguments
  • recipientEmails: Any The recipient emails
  • subject: str What is the subject of the email you would like to send?
  • body: Any What is the body of the email?
Returns
  • result: str
  • status_code: int | float
Python
response = run_step(
    "send_email_step",
	{
		"recipientEmails": ...,
		"subject": ...,
		"body": ...,
	}
)

​
Join Array

Combine an array of strings into a single string Arguments
  • array: Any The array of strings to join.
  • sep: str The separator to use between each element when joining the array.
Returns
  • text: str
Python
response = run_step(
    "join_array",
	{
		"array": ...,
		"sep": ...,
	}
)

​
Pdf To Text

Extract text from PDF without OCR. Doesn’t support handwritting and scans. Arguments
  • pdf_url: str The URL of the PDF to convert to text.
  • use_ocr: bool whether to use Microsofts ocr recognition model to extract text from the page. If no custom azure key and domain are provided in project keys, it will charge 100 credits per page.
  • ocr_type: str
  • return_as_documents: bool Toggling this will either return the PDF text as an aggregated text or as a list of texts.
Returns
  • text: str
  • number_of_pages: int | float
  • documents: Any
Python
response = run_step(
    "pdf_to_text",
	{
		"pdf_url": ...,
		"use_ocr": ...,
		"ocr_type": ...,
		"return_as_documents": ...,
	}
)

​
Advanced Retrieval

Retrieve knowledge given a query and source. Arguments
  • query: str Enter your question here. Tip: keep it terse to avoid ambiguity for clearer answers.
  • top_k: Any No. of results to retrieve.
  • knowledge_set: str Select the knowledge source. If empty, will run retrieval on your entire knowledge base.
  • text_field: Any Select the text fields/columns to vectorize and run retrieval on. It will vectorize all fields, if unspecified.
  • use_files: bool Select this if you want to run retrieval on raw files instead of pre-extracted text. Note: Set a knowledge set when using this.
  • custom_prompt: str Enter a custom prompt to generate the response, or use the default prompt below.
  • temperature: int | float The higher the temperature, the more random the output. Lower temperatures give more predictable results.
  • llm_model: str The LLM model to use for generation.
  • embed_provider: str The embedding model to use for retrieval.
  • reranker_model: str The reranking model to use post-retrieval.
  • chunking_strategy: str The strategy to use for chunking the input text.
  • retrieval_strategy: str The strategy to use for retrieval. Use hybrid for best results.
Returns
  • response: str
  • sources: Any
  • citations: Any
  • duration: int | float
Python
response = run_step(
    "advanced_retrieval",
	{
		"query": ...,
		"top_k": ...,
		"knowledge_set": ...,
		"text_field": ...,
		"use_files": ...,
		"custom_prompt": ...,
		"temperature": ...,
		"llm_model": ...,
		"embed_provider": ...,
		"reranker_model": ...,
		"chunking_strategy": ...,
		"retrieval_strategy": ...,
	}
)

​
Audio To Text

Extract text from an audio file using OpenAI. Arguments
  • audio_url: str URL of the audio to transcribe.
  • response_type: str Whether to return the transcript only, or also advanced metadata such as timestamps and confidence scores. If returning advanced metadata, the keys ‘language’, ‘duration’ and ‘segments’ can be accessed on the output object.
Returns
  • text: str
  • language: str
  • duration: int | float
  • segments: Any
Python
response = run_step(
    "audio_to_text",
	{
		"audio_url": ...,
		"response_type": ...,
	}
)

​
Audio To Text V2

Transcribe an audio/video file, including speakers, timestamps and automatic grammar correction, with Deepgram. Arguments
  • audio_url: str URL of the audio to transcribe.
  • diarize: bool Identify when different people speak. Each word in the transcript will be assigned a speaker ID, starting from 0.
Returns
  • metadata: dict
  • results: dict
Python
response = run_step(
    "audio_to_text_v2",
	{
		"audio_url": ...,
		"diarize": ...,
	}
)

​
Audio To Text V3

Transcribe an audio/video file, including speakers, timestamps and automatic grammar correction, with AssemblyAI. Arguments
  • audio_url: str URL of the audio to transcribe.
Returns
  • id: str The unique identifier for the transcription.
  • language_model: str The language model used for transcription.
  • acoustic_model: str The acoustic model used for transcription.
  • language_code: str The language code representing the language of the transcription.
  • status: str The status of the transcription.
  • audio_url: str The URL of the audio file being transcribed.
  • text: str The full transcription text.
  • words: Any
  • utterances: Any
  • confidence: int | float
  • audio_duration: Any
  • punctuate: bool
  • format_text: bool
  • dual_channel: bool
  • webhook_url: Any
  • webhook_status_code: Any
  • webhook_auth: bool
  • webhook_auth_header_name: Any
  • speed_boost: bool
  • auto_highlights_result: Any
  • auto_highlights: bool
  • audio_start_from: Any
  • audio_end_at: Any
  • word_boost: Any
  • boost_param: Any
  • filter_profanity: bool
  • redact_pii: bool
  • redact_pii_audio: bool
  • redact_pii_audio_quality: Any
  • redact_pii_policies: Any
  • redact_pii_sub: Any
  • speaker_labels: bool
  • content_safety: bool
  • iab_categories: bool
  • content_safety_labels: dict
  • iab_categories_result: dict
  • language_detection: bool
  • custom_spelling: Any
  • throttled: Any
  • auto_chapters: bool
  • summarization: bool
  • summary_type: Any
  • summary_model: Any
  • custom_topics: bool
  • topics: Any
  • speech_threshold: Any
  • disfluencies: bool
  • sentiment_analysis: bool
  • chapters: Any
  • sentiment_analysis_results: Any
  • entity_detection: bool
  • entities: Any
  • summary: Any
  • speakers_expected: Any
Python
response = run_step(
    "audio_to_text_v3",
	{
		"audio_url": ...,
	}
)

​
Anonymize Text

Using Text Anonymization, you can remove personal / private information from your tool. Arguments
  • input: str The text you would like to process
Returns
  • output: str
Python
response = run_step(
    "anonymize_text",
	{
		"input": ...,
	}
)

​
Nemo Guardrails

Using NeMo Guard Rails content filter, you can ensure that your content is profanity free. Arguments Returns
  • output: str
Python
response = run_step(
    "nemo_guardrails",
	{
		"input": ...,
		"usecase": ...,
		"documents": ...,
		"colang_content": ...,
		"yaml_content": ...,
	}
)

​
Markdown

Insert markdown notes Arguments
  • markdown: str
Returns
Python
response = run_step(
    "markdown",
	{
		"markdown": ...,
	}
)

​
Relevance Api Call

Run a Relevance API request Arguments
  • path: str Path to call for api method
  • method: str The HTTP method to use.
  • body: dict The body to send with the request.
Returns
  • response_body: dict
  • status: int | float
  • body: str
  • url: str
  • response_headers: dict
Python
response = run_step(
    "relevance_api_call",
	{
		"path": ...,
		"method": ...,
		"body": ...,
	}
)

​
Get Linkedin Profile

Fetch a linkedin profile with rapid API Arguments
  • url: str The URL of the linkedin profile you’d like to scrape
  • method: str The type of request to make to LinkedIn
Returns
  • data: Any
Python
response = run_step(
    "get_linkedin_profile",
	{
		"url": ...,
		"method": ...,
	}
)

​
Summarize Knowledge

Pick from a variety of methods to extract a useful summary of information from knowledge. Arguments
  • knowledge: str
  • optimization: dict
  • token_limit: int | float The Summary generated will be limited to less than this many words.
Returns
  • answer: str
  • credits_cost: int | float
  • optimization: dict
Python
response = run_step(
    "summarize_knowledge",
	{
		"knowledge": ...,
		"optimization": ...,
		"token_limit": ...,
	}
)

​
Prompt Completion Vision

Use a large multimodal model such as GPTV or Gemini-Vision Arguments
  • prompt: str The prompt that is fed to the model.
  • model: str The model to use for completion. If using gpt3.5, if you do not set your own API Key you will be charged credits for usage. For other models, Make sure to set an API key.
  • images: Any The list of images that the model has acces to.
  • temperature: int | float Temperature of the selected model. Typically, higher temperature means more random output.
  • timeout_ms: int | float Timeout in milliseconds, request will timeout after this much time if it runs for too long.
Returns
Python
response = run_step(
    "prompt_completion_vision",
	{
		"prompt": ...,
		"model": ...,
		"images": ...,
		"temperature": ...,
		"timeout_ms": ...,
	}
)

​
To Json

Convert a string to a JSON object. Arguments
  • text: str The string to convert to JSON.
Returns
  • output: Any
Python
response = run_step(
    "to_json",
	{
		"text": ...,
	}
)

​
Export To File

Export data to a temporary downloadable file. It will be deleted after 1 hour automatically. Arguments
  • data: Any Data to store in the file. This could be csv, json content etc.
  • extension: str Extension of the file to save to. Defaults to csv
Returns
  • export_url: str
Python
response = run_step(
    "export_to_file",
	{
		"data": ...,
		"extension": ...,
	}
)

​
Object Key Filter

Filter & extract data from JSON Arguments
  • haystack: dict The JSON object to filter.
  • needle: Any Fields to keep in the JSON object.
Returns
  • filtered: Any
Python
response = run_step(
    "object_key_filter",
	{
		"haystack": ...,
		"needle": ...,
	}
)

​
Regex

Filter & extract data from string Arguments
  • regex: str The regex to use for extraction, in JS flavor.
  • string: str The string to search for matches.
Returns
  • match: Any
  • found: bool
Python
response = run_step(
    "regex",
	{
		"regex": ...,
		"string": ...,
	}
)

​
Run Transformation

Executes a transformation dynamically by a passed in ID. Useful for creating agents. Arguments
  • transformation_id: str The ID of the transformation to run.
  • params: Any The parameters to pass to the transformation.
Returns
  • output: Any
Python
response = run_step(
    "run_transformation",
	{
		"transformation_id": ...,
		"params": ...,
	}
)

​
Run Chain

Executes any one of your other tools. Arguments
  • studio_id: str The tool to run.
  • project: str The ID of the project the tool belongs to.
  • region: str The ID of the region the tool belongs to.
  • version: str
  • raise_subtool_errors: bool When true, errors from the subtool being called will also will also be raised in the current tool.
  • params: dict The parameters to pass to the tool.
  • studio_override: dict
  • wait_for_completion: bool If checked this step will wait until the tool completes, and return its output and errors. Otherwise this step will start the tool, and immediately return tool run information for polling.
Returns
  • output: dict
  • state: dict
  • status: str
  • errors: Any
  • cost: int | float
  • credits_used: Any
  • executionTime: int | float
  • job_info: dict
Python
response = run_step(
    "run_chain",
	{
		"studio_id": ...,
		"project": ...,
		"region": ...,
		"version": ...,
		"raise_subtool_errors": ...,
		"params": ...,
		"studio_override": ...,
		"wait_for_completion": ...,
	}
)

​
Split Text

Split text into an array of strings. Arguments
  • text: str The text to split.
  • method: str Separator will split the text by the separator. Tokens will split the text into chunks of a given number of tokens.
  • num_tokens: int | float The number of tokens to split the text into.
  • num_tokens_to_slide_window: int | float When using number of tokens, this param allow overlap between each sliced result, often improving accuracy for Ask Relevance.
  • sep: str The separator to split the text by.
Returns
  • chunks: Any
Python
response = run_step(
    "split_text",
	{
		"text": ...,
		"method": ...,
		"num_tokens": ...,
		"num_tokens_to_slide_window": ...,
		"sep": ...,
	}
)

​
Combine Array

Combine an array of strings into a single array. Arguments
  • array: Any
  • method: str
Returns
  • combined: Any
Python
response = run_step(
    "combine_array",
	{
		"array": ...,
		"method": ...,
	}
)

​
Python Code Transformation

Run Python code Arguments
  • code: str The python code to execute, returning the value to be used as the output of this transformation.
  • backend: str Which backend service should run the code?
  • packages: Any The python packages you would like to install to run your code
  • run_commands: Any A list of linux terminal commands to be run prior to executing code
  • session_id: str Define a custom session that saves all local variables between python executions. This only applies when the backend is Modal Labs.
  • num_gpus: str Should the code use a GPU? This only applies when the backend is Modal Labs.
  • num_cpus: str How many CPU cores should the code use? This only applies when the backend is Modal Labs.
  • memory: int | float The Amount of Memory (RAM) in MBs the code will use. This only applies when the backend is Modal Labs.
  • timeout: int | float How long before the code times out in seconds? This only applies when the backend is Modal Labs.
  • raise_error: str Should python Step error
Returns
  • transformed: Any Return value of provided code
  • duration: int | float Duration of provided code in milliseconds
  • stdout: Any Return stdout of provided code
  • stderr: Any Return stderr of provided code
  • session_id: str The session ID used to run the execution
  • credits_cost: int | float Extra calculated credits based on image used
Python
response = run_step(
    "python_code_transformation",
	{
		"code": ...,
		"backend": ...,
		"packages": ...,
		"run_commands": ...,
		"session_id": ...,
		"num_gpus": ...,
		"num_cpus": ...,
		"memory": ...,
		"timeout": ...,
		"raise_error": ...,
	}
)

​
Upload File S3

Upload to a temporary file in S3. Arguments
  • blob: Any
  • extension: str
Returns
  • url: str
Python
response = run_step(
    "upload_file_s3",
	{
		"blob": ...,
		"extension": ...,
	}
)
Run Google search via serper.dev Arguments
  • search_query: str Query to search for
  • country_code: str Which region would you like to google search from?
  • locale: str Which locale would you like to google search from?
Returns
  • peopleAlsoAsk: Any
  • relatedSearches: Any
  • organic: Any
  • user_key_used: bool
Python
response = run_step(
    "serper_google_search",
	{
		"search_query": ...,
		"country_code": ...,
		"locale": ...,
	}
)

​
Analyse Image

Tag or extract information from images Arguments
  • image_url: str URL of the image to analyse. JPEG/JPG, PNG and WebP are supported.
  • prompt: str The question(s) to answer about the image.
Returns
  • output: str
Python
response = run_step(
    "analyse_image",
	{
		"image_url": ...,
		"prompt": ...,
	}
)

​
Text To Image

Generate image via Stable Diffusion Arguments
  • prompt: str What do you want to see? You can use a single word or a full sentence.
  • num_outputs: int | float Select the number of images you would like to generate.
Returns
  • images: Any
Python
response = run_step(
    "text_to_image",
	{
		"prompt": ...,
		"num_outputs": ...,
	}
)

​
Text To Speech

Generate Speech from text Arguments
  • prompt: str What do you want to hear? You can use a single word or a full sentence.
  • voice: str
Returns
  • audio_url: str
Python
response = run_step(
    "text_to_speech",
	{
		"prompt": ...,
		"voice": ...,
	}
)

​
Elevenlabs

Generate audio from text with ElevenLabs Arguments
  • prompt: str What do you want to hear? You can use a single word or a full sentence.
  • voice_id: str
  • model_id: str
  • pronounciation_dictionary_locators: Any
  • similarity_boost: int | float Voice settings: similarity boost
  • stability: int | float Voice settings: stability
  • style: int | float Voice settings: style
  • use_speaker_boost: bool Voice settings: speaker boost
Returns
  • audio_url: str
Python
response = run_step(
    "elevenlabs",
	{
		"prompt": ...,
		"voice_id": ...,
		"model_id": ...,
		"pronounciation_dictionary_locators": ...,
		"similarity_boost": ...,
		"stability": ...,
		"style": ...,
		"use_speaker_boost": ...,
	}
)

​
Blandai Send Call

Send an AI phone call with Bland.AI Arguments
  • phone_number: str The phone number to call. Country code defaults to +1 (US) if not specified. Formatting is flexible, however for the most predictable results use the E.164 format.
  • task: str Provide instructions, relevant information, and examples of the ideal conversation flow.
  • transfer_list: dict Give your agent the ability to transfer calls to a set of phone numbers. Overrides transfer_phone_number if a transfer_list.default is specified. Will default to transfer_list.default, or the chosen phone number
  • model: str Select a model to use for your call. In nearly all cases, enhanced is the best choice for now.
  • transfer_phone_number: str A phone number that the agent can transfer to under specific conditions - such as being asked to speak to a human or supervisor.
  • answered_by_enabled: bool Enables machine detection when the call starts to determine whether the call was answered by a person or a voicemail.
  • from: str Specify a purchased Outbound Number to call from. Country code is required, spaces or parentheses must be excluded. By default, calls are initiated from a separate pool of numbers owned by Bland.
  • voice_id: str Determines the voice of the AI agent, in conjunction with reduce_latency.
  • voice_preset_id: str Use a voice preset instead of specifying individual voice settings.
  • start_time: str The time you want the call to start. If you don’t specify a time (or the time is in the past), the call will send immediately. Set your time in the format YYYY-MM-DD HH:MM:SS -HH:MM (ex. 2021-01-01 12:00:00 -05:00). The timezone is optional, and defaults to UTC if not specified.
  • webhook: str The webhook should be a http / https callback url. We will send the call_id and transcript to this URL after the call completes. This can be useful if you want to have real time notifications when calls finish.
  • wait_for_greeting: bool Should the AI speak first or wait for someone else to talk? Creates more realistic conversations when answered with “Hello?”, “This is {name} speaking.” and so on.
  • first_sentence: str A phrase that your call will start with instead of a generating one on the fly. This works both with and without wait_for_greeting. Can be more than one sentence, but must be less than 200 characters.
  • record: bool To record your phone call, set record to true. When your call completes, you can access the recording by requesting the /call/recording endpoint.
  • language: str Select a supported language of your choice. Optimizes every part of our API for that language - transcription, speech, and other inner workings.
  • max_duration: int | float Set the longest you want the call to possibly go in minutes. After the max_duration minutes have passed, the call will automatically end.
  • amd: bool AMD mode helps our AI navigate phone trees and IVR systems. If you know your call will hit an automated system you should switch it on. NOTE: AMD mode causes increased delay for the first response, even if answered by a human. Highly recommended to set to false in the majority of cases.
  • interruption_threshold: dict
Returns
  • status: str
  • call_id: str
Python
response = run_step(
    "blandai_send_call",
	{
		"phone_number": ...,
		"task": ...,
		"transfer_list": ...,
		"model": ...,
		"transfer_phone_number": ...,
		"answered_by_enabled": ...,
		"from": ...,
		"voice_id": ...,
		"voice_preset_id": ...,
		"start_time": ...,
		"webhook": ...,
		"wait_for_greeting": ...,
		"first_sentence": ...,
		"record": ...,
		"language": ...,
		"max_duration": ...,
		"amd": ...,
		"interruption_threshold": ...,
	}
)

​
Blandai Call Details

Retrieve the call details for a Bland AI call Arguments
  • call_id: str The call id to fetch the transcript for.
Returns
  • response: dict
Python
response = run_step(
    "blandai_call_details",
	{
		"call_id": ...,
	}
)

​
Heygen

Generate professional avatar videos with HeyGen Arguments
  • prompt: str What do you want to hear? You can use a single word or a full sentence.
  • voice_id: str
  • avatar_id: str
  • avatar_style: str
  • scale: int | float
  • offset_x: int | float
  • offset_y: int | float
  • background: str
  • ratio: str
  • test: bool Set the test flag to true to use test mode. Generation in test mode will not cost your credits and will contain a watermark on your video.
Returns
  • video_url: str
Python
response = run_step(
    "heygen",
	{
		"prompt": ...,
		"voice_id": ...,
		"avatar_id": ...,
		"avatar_style": ...,
		"scale": ...,
		"offset_x": ...,
		"offset_y": ...,
		"background": ...,
		"ratio": ...,
		"test": ...,
	}
)

​
D Id

Generate professional avatar videos with D-ID Arguments
  • prompt: str What do you want to hear? You can use a single word or a full sentence.
  • voice_id: str
  • avatar_id: str
Returns
  • video_url: str
Python
response = run_step(
    "d_id",
	{
		"prompt": ...,
		"voice_id": ...,
		"avatar_id": ...,
	}
)

​
Stable Diffusion Xl

Generate image via Stable Diffusion XL Arguments
  • prompt: Any What do you want to see? You can use a single word or multiple sentences.
  • negative_prompt: Any What do you not want to see? You can use a single word or multiple sentences.
  • height: int | float What should the height of the generated images be?
  • width: int | float What should the height of the generated images be?
  • guidance_scale: int | float Higher guidance scale encourages to generate images that are closely linked to the text prompts usually at the expense of lower image quality
  • num_images_per_prompt: int | float How many images do you want to generate per prompt?
  • eta: int | float Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502.
  • steps: int | float The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
Returns
  • images: Any
  • credits_used: int | float
Python
response = run_step(
    "stable_diffusion_xl",
	{
		"prompt": ...,
		"negative_prompt": ...,
		"height": ...,
		"width": ...,
		"guidance_scale": ...,
		"num_images_per_prompt": ...,
		"eta": ...,
		"steps": ...,
	}
)

​
Truncate Text

Truncate text based on a specified token count. Arguments
  • text: Any The text to truncate.
  • num_tokens: int | float The number of tokens to truncate the text or array by.
Returns
  • chunks: Any
Python
response = run_step(
    "truncate_text",
	{
		"text": ...,
		"num_tokens": ...,
	}
)

​
Browserless Scrape

Scrape and access website content Arguments
  • website_url: str URL of the website to scrape.
  • method: str
  • model: dict Type of service to use when scraping the website
Returns
  • output: dict
  • outputs: Any
  • user_key_used: bool
  • credits_cost: int | float
Python
response = run_step(
    "browserless_scrape",
	{
		"website_url": ...,
		"method": ...,
		"model": ...,
	}
)

​
Slack Message

Send a Slack message Arguments
  • oauth_account_id: str The Slack channel to post message to.
  • message: str
  • blocks: Any
Returns
  • status: int | float
Python
response = run_step(
    "slack_message",
	{
		"oauth_account_id": ...,
		"message": ...,
		"blocks": ...,
	}
)

​
Whatsapp Message

Send a WhatsApp message Arguments
  • from_phone_number_id: str The from phone number ID (not to be confused with the actual phone number)
  • to: str The phone number to message
  • message: str
Returns
  • status: int | float
  • contact_id: str
  • details: str
Python
response = run_step(
    "whatsapp_message",
	{
		"from_phone_number_id": ...,
		"to": ...,
		"message": ...,
	}
)

​
Whatsapp Template Message

Send a WhatsApp template message Arguments
  • from_phone_number_id: str The from phone number ID (not to be confused with the actual phone number)
  • to: str The phone number to message
  • template_name: str The name of the template you wish to send
  • template_language: str The language for this template
  • content_parameters: dict
Returns
  • status: int | float
  • contact_id: str
  • details: str
Python
response = run_step(
    "whatsapp_template_message",
	{
		"from_phone_number_id": ...,
		"to": ...,
		"template_name": ...,
		"template_language": ...,
		"content_parameters": ...,
	}
)

​
Zendesk Ticket

Create a Zendesk Ticket Arguments
  • email: str The email address you use to access your Zendesk account
  • subdomain: str https://{subdomain}.zendesk.com/
  • ticket: str
Returns
  • status: int | float
Python
response = run_step(
    "zendesk_ticket",
	{
		"email": ...,
		"subdomain": ...,
		"ticket": ...,
	}
)

​
Create Linear Ticket

Create a linear ticket Arguments
  • oauth_account_id: str The Linear Account used to create the ticket.
  • team: str
  • title: str
  • description: str
  • project: str
  • priority: str The priority of the linear ticket
  • teamLabels: Any The labels specific to the Linear team.
  • workspaceLabels: Any The labels specific to the Linear workspace.
Returns
  • status: int | float
Python
response = run_step(
    "create_linear_ticket",
	{
		"oauth_account_id": ...,
		"team": ...,
		"title": ...,
		"description": ...,
		"project": ...,
		"priority": ...,
		"teamLabels": ...,
		"workspaceLabels": ...,
	}
)

​
Apollo Api Call

Make an authenticated request to an Apollo API using credentials saved in Relevance Arguments
  • method: str The HTTP method to use.
  • path: str Path to make the request to. e.g. /v1/auth/health
  • body: dict The body to send with the request.
Returns
  • response_body: Any
  • status: int | float
  • body: str
  • url: str
  • response_headers: dict
Python
response = run_step(
    "apollo_api_call",
	{
		"method": ...,
		"path": ...,
		"body": ...,
	}
)
Search for users on apollo for people enrichment. Makes an authenticated request to Apollo API using credentials saved in Relevance Arguments
  • searchType: dict How you want to retrieve your information from Apollo.
Returns
  • response_body: Any
  • status: int | float
  • body: str
  • url: str
  • response_headers: dict
Python
response = run_step(
    "apollo_people_search",
	{
		"searchType": ...,
	}
)

​
Postgres Sql

Execute postgres SQL. Setup details in the api keys section. Arguments
  • sql: str The SQL Query you want to perform
  • rejectUnauthorized: bool Whether to verify the server’s identity. Strongly suggested to be true outside of development
Returns
  • rows: Any
  • metadata: dict
Python
response = run_step(
    "postgres_sql",
	{
		"sql": ...,
		"rejectUnauthorized": ...,
	}
)

​
Hubspot Api Call

Make an authenticated request to a Hubspot API using credentials saved in Relevance Arguments
  • method: str The HTTP method to use.
  • oauth_account_id: str The Hubspot account.
  • path: str Path to make the request to. e.g. /v1/auth/health
  • body: dict The body to send with the request.
Returns
  • response_body: Any
  • status: int | float
  • body: str
  • url: str
  • response_headers: dict
Python
response = run_step(
    "hubspot_api_call",
	{
		"method": ...,
		"oauth_account_id": ...,
		"path": ...,
		"body": ...,
	}
)

​
Freshdesk Api Call

Make an authenticated request to a Freshdesk REST API using your api key Arguments
  • freshdesk_portal_domain: str The domain of your freshdesk portal. e.g. relevance.freshdesk.com
  • method: str The HTTP method to use.
  • path: str Path to make the request to. e.g. /api/v2/tickets
  • body: dict The body to send with the request.
Returns
  • response_body: Any
  • status: int | float
  • body: str
  • url: str
  • response_headers: dict
Python
response = run_step(
    "freshdesk_api_call",
	{
		"freshdesk_portal_domain": ...,
		"method": ...,
		"path": ...,
		"body": ...,
	}
)

​
Translate

Translate 100+ languages via Google Translate Arguments
  • text: str Text to translate.
  • sourceLanguageCode: str Language of the input text. Determined automatically if no language is selected.
  • targetLanguageCode: str Language to translate the input text to.
Returns
  • result: str
Python
response = run_step(
    "translate",
	{
		"text": ...,
		"sourceLanguageCode": ...,
		"targetLanguageCode": ...,
	}
)

​
Excel To Text

Extract text from an Excel document. Arguments
  • excel_url: str The URL of the Excel document to convert to text.
Returns
  • text: Any
Python
response = run_step(
    "excel_to_text",
	{
		"excel_url": ...,
	}
)

​
Word To Text

Extract text from a Word document. Arguments
  • doc_url: str The URL of the Word document to convert to text.
Returns
  • text: str
Python
response = run_step(
    "word_to_text",
	{
		"doc_url": ...,
	}
)

​
File To Text Llm Friendly

Extract text from a file. Supports JSON, CSV, PDF and Word files. Arguments
  • llm_friendly_format: bool Set to false to not transform text into an llm friendly format.
  • file_url: str The URL of the file to convert to text.
Returns
  • text: str
  • detected_type: str
  • metadata: dict
Python
response = run_step(
    "file_to_text_llm_friendly",
	{
		"llm_friendly_format": ...,
		"file_url": ...,
	}
)

​
Echo

This transformation is useful for testing and debugging. It simply returns whatever you input. Arguments
  • data: Any
Returns
  • data: Any
Python
response = run_step(
    "echo",
	{
		"data": ...,
	}
)

​
Spreadsheet To Json

Converts an XLSX or CSV file to a list of JSON objects, with one object per row. Arguments
  • file_url: str
Returns
  • rows: Any
Python
response = run_step(
    "spreadsheet_to_json",
	{
		"file_url": ...,
	}
)
Search for a string in a document and return a match with start and end indices if found. Arguments
  • query: str The substring to search for in the text. Uses fuzzy search.
  • text: str The text to search in.
Returns
  • text: str
  • match: str
  • start: int | float
  • end: int | float
Python
response = run_step(
    "fuzzy_search",
	{
		"query": ...,
		"text": ...,
	}
)

​
Google Api Call

Make an authenticated request to a Google API using credentials saved in Relevance Arguments
  • oauth_account_id: str The Google account to use for authentication. Any API requests will be made on behalf of this account.
  • oauth_permission_type: str
  • url: str Full url to make the request to. e.g. https://gmail.googleapis.com/gmail/v1/users/me/messages
  • method: str The HTTP method to use.
  • body: dict The body to send with the request.
Returns
  • response_body: dict
  • status: int | float
  • body: str
  • url: str
  • response_headers: dict
Python
response = run_step(
    "google_api_call",
	{
		"oauth_account_id": ...,
		"oauth_permission_type": ...,
		"url": ...,
		"method": ...,
		"body": ...,
	}
)

​
Salesforce Api Call

Make an authenticated request to a Salesforce REST API using credentials saved in Relevance Arguments
  • oauth_account_id: str The Salesforce account to use for authentication. Any API requests will be made on behalf of this account.
  • api_version: str The Salesforce REST API version to use e.g. 59.0. By default we will use the latest version.
  • path: str Path to make the request to. e.g. /sobjects/Account
  • method: str The HTTP method to use.
  • body: dict The body to send with the request.
Returns
  • response_body: dict
  • status: int | float
  • body: str
  • url: str
  • response_headers: dict
Python
response = run_step(
    "salesforce_api_call",
	{
		"oauth_account_id": ...,
		"api_version": ...,
		"path": ...,
		"method": ...,
		"body": ...,
	}
)

​
Microsoft Api Call

Make an authenticated request to a Microsoft Graph API endpoint using credentials saved in Relevance Arguments
  • oauth_account_id: str The Microsoft account to use for authentication. Any API requests will be made on behalf of this account.
  • oauth_permission_type: str
  • path: str Path to make the request to. e.g. /v1.0/me/messages. See [Microsoft’s Graph Explorer](https://developer.microsoft.com/en-us/graph/graph-explorer/) for a list of endpoints
  • method: str The HTTP method to use.
  • body: dict The body to send with the request.
  • headers: dict The headers to send with the request. The ‘Authorization’ header is always sent and cannot be overwritten.
Returns
  • response_body: Any
  • status: int | float
  • body: str
  • url: str
  • response_headers: dict
Python
response = run_step(
    "microsoft_api_call",
	{
		"oauth_account_id": ...,
		"oauth_permission_type": ...,
		"path": ...,
		"method": ...,
		"body": ...,
		"headers": ...,
	}
)

​
Send Gmail Email

Send an email using a Google / Gmail account stored in Relevance Arguments
  • oauth_account_id: str The Gmail account from which to send emails.
  • to: Any The email address to send the email to.
  • cc: Any The email addresses to cc.
  • bcc: Any The email addresses to blind cc.
  • subject: str The subject of the email.
  • body: str The text or html body of the email.
  • gmail_thread_id: str The gmail thread in which to send this message.
  • reply_to_message: str The message id to reply to. If blank, the email will be sent as a new message.
  • draft: bool If true, the email will be saved as a draft instead of sent immediately.
Returns
  • gmail_thread_id: str
  • gmail_message_id: str
  • message_id: str
  • raw_email: str
Python
response = run_step(
    "send_gmail_email",
	{
		"oauth_account_id": ...,
		"to": ...,
		"cc": ...,
		"bcc": ...,
		"subject": ...,
		"body": ...,
		"gmail_thread_id": ...,
		"reply_to_message": ...,
		"draft": ...,
	}
)

​
Message Agent

Send a message to an agent Arguments
  • agent_id: str The agent to message
  • agent_override: dict
  • override: dict
  • message: str The text message to send to the agent.
  • conversation_id: str The conversation to send the message to. If blank, a new conversation will be created.
  • wait_for_reply: bool Deprecated - If true, this step will wait until the agent replies before continuing. Otherwise, the message will be sent, and the step will continue immediately.
  • metadata: dict Information to store in message metadata.
  • params: dict Params to set in the agent before running
Returns
  • job_info: dict
  • conversation_id: str
  • agent_id: str
  • state: str
  • message: dict
Python
response = run_step(
    "message_agent",
	{
		"agent_id": ...,
		"agent_override": ...,
		"override": ...,
		"message": ...,
		"conversation_id": ...,
		"wait_for_reply": ...,
		"metadata": ...,
		"params": ...,
	}
)

​
Check Google Calendar

Check Google calendar availability Arguments
  • oauth_account_id: str The Google account of the calendar to check.
  • start_date: str ISO formatted string for start date. e.g. 2023-12-06T19:00:00+00:00
  • end_date: str ISO formatted string for start date. e.g. 2023-12-06T19:00:00+00:00
  • slot_duration: int | float Duration of booking in minutes
  • start_working_time: str
  • end_working_time: str
  • timezone: str
  • calendars: Any If not defined, the Google account’s primary calendar is used
Returns
  • calendars: dict
Python
response = run_step(
    "check_google_calendar",
	{
		"oauth_account_id": ...,
		"start_date": ...,
		"end_date": ...,
		"slot_duration": ...,
		"start_working_time": ...,
		"end_working_time": ...,
		"timezone": ...,
		"calendars": ...,
	}
)

​
Create Google Calendar Event

Create a calendar event using a Google account stored in Relevance Arguments
  • oauth_account_id: str The Google account of the calendar to check.
  • event_title: str
  • event_description: str
  • start_datetime: str ISO formatted string for start date. e.g. 2023-12-06T19:00:00+00:00
  • end_datetime: str ISO formatted string for start date. e.g. 2023-12-06T19:00:00+00:00
  • attendees: Any
  • calendar: str If not defined, the Google account’s primary calendar is used
  • location: str Location for the meeting. Can be a physical location, or a link to a virtual meeting
  • guests_can_modify: bool Boolean that gives event guests the ability to modify the event
Returns
  • event_id: str
Python
response = run_step(
    "create_google_calendar_event",
	{
		"oauth_account_id": ...,
		"event_title": ...,
		"event_description": ...,
		"start_datetime": ...,
		"end_datetime": ...,
		"attendees": ...,
		"calendar": ...,
		"location": ...,
		"guests_can_modify": ...,
	}
)

​
Send Outlook Email

Send an email using a Microsoft account stored in Relevance Arguments
  • oauth_account_id: str The Microsoft account from which to send emails.
  • to: Any The email address to send the email to.
  • cc: Any The email addresses to cc.
  • bcc: Any The email addresses to blind cc.
  • subject: str The subject of the email.
  • body: str The text or html body of the email.
  • reply_to_message: str The outlook message id to reply to. If blank, the email will be sent as a new message.
  • draft: bool If true, the email will be saved as a draft instead of sent immediately.
Returns
  • outlook_thread_id: str
  • outlook_message_id: str
  • message_id: str
  • raw_email: str
Python
response = run_step(
    "send_outlook_email",
	{
		"oauth_account_id": ...,
		"to": ...,
		"cc": ...,
		"bcc": ...,
		"subject": ...,
		"body": ...,
		"reply_to_message": ...,
		"draft": ...,
	}
)

​
Check Outlook Calendar

Check calendar availability using a Microsoft Outlook account stored in Relevance Arguments
  • oauth_account_id: str The Microsoft account from which to send emails.
  • start_date: str ISO formatted string for start date. e.g. 2023-12-06T19:00:00+00:00
  • end_date: str ISO formatted string for start date. e.g. 2023-12-06T19:00:00+00:00
  • slot_duration: int | float Duration of booking in minutes
  • start_working_time: str
  • end_working_time: str
  • timezone: str
  • calendars: Any Calendars must be a Microsoft 365 work or school email in your organisation
  • is_organizer_optional: bool If there are no calendars to query, organizer is required by default
Returns
  • calendars: dict
Python
response = run_step(
    "check_outlook_calendar",
	{
		"oauth_account_id": ...,
		"start_date": ...,
		"end_date": ...,
		"slot_duration": ...,
		"start_working_time": ...,
		"end_working_time": ...,
		"timezone": ...,
		"calendars": ...,
		"is_organizer_optional": ...,
	}
)

​
Create Outlook Calendar Event

Create a calendar event using a Microsoft Outlook account stored in Relevance Arguments
  • oauth_account_id: str The Microsoft account from which to send emails.
  • subject: str
  • body: str
  • start_datetime: str ISO formatted string for start date. e.g. 2023-12-06T19:00:00+00:00
  • end_datetime: str ISO formatted string for start date. e.g. 2023-12-06T19:00:00+00:00
  • attendees: Any
  • calendar: str If not defined, the Microsoft account’s primary calendar is used
  • location: str Location for the meeting. Can be a physical location, or a link to a virtual meeting
Returns
  • event_id: str
Python
response = run_step(
    "create_outlook_calendar_event",
	{
		"oauth_account_id": ...,
		"subject": ...,
		"body": ...,
		"start_datetime": ...,
		"end_datetime": ...,
		"attendees": ...,
		"calendar": ...,
		"location": ...,
	}
)

​
Delay

Delay for specified amount of time. Arguments
  • delay: int | float Delay amount in seconds
Returns
  • delay: int | float
Python
response = run_step(
    "delay",
	{
		"delay": ...,
	}
)

​
Retrieve Data

Retrive data from your dataset. Arguments
  • knowledge_set: str
  • include_fields: Any
  • page_size: int | float
Returns
  • results: Any
Python
response = run_step(
    "retrieve_data",
	{
		"knowledge_set": ...,
		"include_fields": ...,
		"page_size": ...,
	}
)

​
Zoom Api Call

Make a call to the Zoom API Arguments
  • oauth_account_id: str
  • path: str Path to make the request to, and must include the API version. e.g. /v2/users/me
  • method: str The HTTP method to use.
  • body: dict The body to send with the request.
Returns
  • response_body: dict
  • status: int | float
  • body: str
  • url: str
  • response_headers: dict
Python
response = run_step(
    "zoom_api_call",
	{
		"oauth_account_id": ...,
		"path": ...,
		"method": ...,
		"body": ...,
	}
)

​
Vapiai Send Call Managed

Make a phone call with AI Arguments
  • phone_number: str
  • assistant_model: str
  • assistant_system_prompt: str
  • assistant_prompt: str
  • assistant_first_message: str
  • assistant_final_message: str
  • voice_id: str What kind of spoken voice do you want your assistant to have?
  • configuration: dict See https://docs.vapi.ai/api-reference/calls/create-phone-call for total list of parameters
  • wait_for_finish: bool Do we want this step to return once the call has been sent or once the call has concluded?
Returns
  • status: str
  • call_id: str
  • credits_used: int | float
Python
response = run_step(
    "vapiai_send_call_managed",
	{
		"phone_number": ...,
		"assistant_model": ...,
		"assistant_system_prompt": ...,
		"assistant_prompt": ...,
		"assistant_first_message": ...,
		"assistant_final_message": ...,
		"voice_id": ...,
		"configuration": ...,
		"wait_for_finish": ...,
	}
)

​
Vapiai Send Call Custom

Make a phone call with AI using Vapi AI Arguments Returns
  • status: str
  • call_id: str
  • credits_used: int | float
Python
response = run_step(
    "vapiai_send_call_custom",
	{
		"phone_number_id": ...,
		"assistant_id": ...,
		"customer_phone_number": ...,
		"phone_number": ...,
		"assistant": ...,
		"customer": ...,
		"wait_for_finish": ...,
	}
)

​
Vapiai Call Details

Retrieve the call details for a call made with Vapi AI Arguments
  • use_user_key: bool
  • call_id: str The call id to fetch the transcript for.
Returns
  • response: dict
Python
response = run_step(
    "vapiai_call_details",
	{
		"use_user_key": ...,
		"call_id": ...,
	}
)

​
Qr Code Art

codes Art & Legible Text Art directly in your products. Request a free API key to get started. Arguments Returns
  • image_url: str
  • response_body: dict
Python
response = run_step(
    "qr_code_art",
	{
		"qrContent": ...,
		"prompt": ...,
		"workflow": ...,
		"qrStrength": ...,
		"qrImage": ...,
		"qrSteps": ...,
		"qrCodePattern": ...,
		"seed": ...,
		"negativePrompt": ...,
	}
)
Search linkedin profiles with rapid API Arguments Returns
  • data: Any
Python
response = run_step(
    "linkedin_people_search",
	{
		"geo_codes": ...,
		"geo_codes_exclude": ...,
		"title_keywords": ...,
		"title_keywords_exclude": ...,
		"current_company_ids": ...,
		"past_company_ids": ...,
		"functions": ...,
		"keywords": ...,
		"limit": ...,
	}
)
Search linkedin companies with rapid API Arguments Returns
  • data: Any
Python
response = run_step(
    "linkedin_company_search",
	{
		"company_headcounts": ...,
		"headquarters_location": ...,
		"industry_codes": ...,
		"hiring_on_linkedin": ...,
		"recent_activities": ...,
		"keywords": ...,
		"limit": ...,
	}
)
Search LinkedIn Jobs with rapid API Arguments
  • keywords: str Keywords to search for in job postings.
  • geo_code: str Param to target jobs in specific region/country. To search worldwide, use 92000000. Find other geo codes via the provided link.
  • date_posted: str Filter by the date when jobs were posted.
  • experience_level: str Filter by the level of experience required for the job.
  • company_id: int | float Filter by the ID of the company posting the job.
  • title_id: str Filter by the ID of the job title. Find title IDs via the provided link.
  • onsite_remote: str Filter by the working arrangement of the job.
  • function_id: str Get the function ID of your choice following the provided instruction.
  • industry_code: str Filter by the industry code of the job.
  • sort_by: str Specify the sorting criteria for the job listings.
  • start: str Specify the starting point for retrieving job listings.
  • easy_apply: str Filter by whether the job offers easy application process.
  • under_10_applicants: bool Filter by whether the job has less than 10 applicants.
Returns
  • data: Any
Python
response = run_step(
    "linkedin_job_search",
	{
		"keywords": ...,
		"geo_code": ...,
		"date_posted": ...,
		"experience_level": ...,
		"company_id": ...,
		"title_id": ...,
		"onsite_remote": ...,
		"function_id": ...,
		"industry_code": ...,
		"sort_by": ...,
		"start": ...,
		"easy_apply": ...,
		"under_10_applicants": ...,
	}
)

​
Webflow List Collection Items

Choose which of your collections to list. Arguments
  • collection_id: str
  • cms_locale_id: str
  • offset: int | float
  • limit: int | float
Returns
Python
response = run_step(
    "webflow_list_collection_items",
	{
		"collection_id": ...,
		"cms_locale_id": ...,
		"offset": ...,
		"limit": ...,
	}
)

​
Webflow Create Collection Item

Create or Update a webflow collection item Arguments
  • collection_id: str
  • item_id: str
  • cms_locale_id: str
  • is_archived: bool
  • is_draft: bool
  • field_data: dict
Returns
Python
response = run_step(
    "webflow_create_collection_item",
	{
		"collection_id": ...,
		"item_id": ...,
		"cms_locale_id": ...,
		"is_archived": ...,
		"is_draft": ...,
		"field_data": ...,
	}
)

​
Webflow Get Collection Item

Get a webflow collection item Arguments
  • collection_id: str
  • item_id: str
  • cms_locale_id: str
Returns
Python
response = run_step(
    "webflow_get_collection_item",
	{
		"collection_id": ...,
		"item_id": ...,
		"cms_locale_id": ...,
	}
)

​
Webflow Delete Collection Item

Delete a webflow collection item Arguments
  • collection_id: str
  • cms_locale_id: str
  • item_id: str
Returns
Python
response = run_step(
    "webflow_delete_collection_item",
	{
		"collection_id": ...,
		"cms_locale_id": ...,
		"item_id": ...,
	}
)

​
Jforce Convert Lead

Make an authenticated request to a Salesforce REST API using credentials saved in Relevance Arguments
  • oauth_account_id: str The Salesforce account to use for authentication. Any API requests will be made on behalf of this account.
  • ids_to_convert: Any
Returns
Python
response = run_step(
    "jforce_convert_lead",
	{
		"oauth_account_id": ...,
		"ids_to_convert": ...,
	}
)