Tool Builder Transformations in the Python Code Step

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": ...,
	}
)

Search

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

input: str The text you would like to process
usecase: str The usecase for which you want to moderate the output of you LLM
documents: Any If you have selected Topic Control, these are the texts that will be used.
colang_content: str For examples, see the sample_rails folder in any of these examples: https://github.com/NVIDIA/NeMo-Guardrails/tree/main/examples
yaml_content: str For examples, see the sample_rails folder in any of these examples: https://github.com/NVIDIA/NeMo-Guardrails/tree/main/examples

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": ...,
	}
)

Serper Google Search

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": ...,
	}
)

Apollo People Search

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": ...,
	}
)

Fuzzy Search

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

phone_number_id: str
assistant_id: str
customer_phone_number: str See https://docs.vapi.ai/api-reference/calls/create-phone-call for total list of parameters
phone_number: dict See https://docs.vapi.ai/api-reference/calls/create-phone-call for total list of parameters
assistant: dict See https://docs.vapi.ai/api-reference/calls/create-phone-call for total list of parameters
customer: 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_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

qrContent: str
prompt: str
workflow: str
qrStrength: int | float
qrImage: str
qrSteps: int | float
qrCodePattern: str https://docs.quickqr.art/user-guide/qr-pattern-list
seed: int | float
negativePrompt: str

Returns

image_url: str
response_body: dict

Python
response = run_step(
    "qr_code_art",
	{
		"qrContent": ...,
		"prompt": ...,
		"workflow": ...,
		"qrStrength": ...,
		"qrImage": ...,
		"qrSteps": ...,
		"qrCodePattern": ...,
		"seed": ...,
		"negativePrompt": ...,
	}
)

Linkedin People Search

Search linkedin profiles with rapid API

Arguments

geo_codes: Any https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references
geo_codes_exclude: Any https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references
title_keywords: Any https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references
title_keywords_exclude: Any https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references
current_company_ids: Any https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references
past_company_ids: Any https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references
functions: Any https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references
keywords: str https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references
limit: int | float https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references

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": ...,
	}
)

Linkedin Company Search

Search linkedin companies with rapid API

Arguments

company_headcounts: str
headquarters_location: Any
industry_codes: Any
hiring_on_linkedin: bool
recent_activities: Any https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references
keywords: str https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references
limit: int | float https://rapidapi.com/freshdata-freshdata-default/api/fresh-linkedin-profile-data/tutorials/search-employees-endpoint-payload-references

Returns

data: Any

Python
response = run_step(
    "linkedin_company_search",
	{
		"company_headcounts": ...,
		"headquarters_location": ...,
		"industry_codes": ...,
		"hiring_on_linkedin": ...,
		"recent_activities": ...,
		"keywords": ...,
		"limit": ...,
	}
)

Linkedin Job Search

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": ...,
	}
)

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On this page

Studio Transformation
Prompt Completion
Api Call
Js Code Transformation
Search
Search Array
Bulk Update
Apify
Scrape Glassdoor
Multiple File Upload
Generate Vector Embedding
Send Email Step
Join Array
Pdf To Text
Advanced Retrieval
Audio To Text
Audio To Text V2
Audio To Text V3
Anonymize Text
Nemo Guardrails
Markdown
Relevance Api Call
Get Linkedin Profile
Summarize Knowledge
Prompt Completion Vision
To Json
Export To File
Object Key Filter
Regex
Run Transformation
Run Chain
Split Text
Combine Array
Python Code Transformation
Upload File S3
Serper Google Search
Analyse Image
Text To Image
Text To Speech
Elevenlabs
Blandai Send Call
Blandai Call Details
Heygen
D Id
Stable Diffusion Xl
Truncate Text
Browserless Scrape
Slack Message
Whatsapp Message
Whatsapp Template Message
Zendesk Ticket
Create Linear Ticket
Apollo Api Call
Apollo People Search
Postgres Sql
Hubspot Api Call
Freshdesk Api Call
Translate
Excel To Text
Word To Text
File To Text Llm Friendly
Echo
Spreadsheet To Json
Fuzzy Search
Google Api Call
Salesforce Api Call
Microsoft Api Call
Send Gmail Email
Message Agent
Check Google Calendar
Create Google Calendar Event
Send Outlook Email
Check Outlook Calendar
Create Outlook Calendar Event
Delay
Retrieve Data
Zoom Api Call
Vapiai Send Call Managed
Vapiai Send Call Custom
Vapiai Call Details
Qr Code Art
Linkedin People Search
Linkedin Company Search
Linkedin Job Search
Webflow List Collection Items
Webflow Create Collection Item
Webflow Get Collection Item
Webflow Delete Collection Item
Jforce Convert Lead

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Agents

Tools

Data

Use cases

Integrations

SDK

Datasets

​Studio Transformation

​Prompt Completion

​Api Call

​Js Code Transformation

​Search

​Search Array

​Bulk Update

​Apify

​Scrape Glassdoor

​Multiple File Upload

​Generate Vector Embedding

​Send Email Step

​Join Array

​Pdf To Text

​Advanced Retrieval

​Audio To Text

​Audio To Text V2

​Audio To Text V3

​Anonymize Text

​Nemo Guardrails

​Markdown

​Relevance Api Call

​Get Linkedin Profile

​Summarize Knowledge

​Prompt Completion Vision

​To Json

​Export To File

​Object Key Filter

​Regex

​Run Transformation

​Run Chain

​Split Text

​Combine Array

​Python Code Transformation

​Upload File S3

​Serper Google Search

​Analyse Image

​Text To Image

​Text To Speech

​Elevenlabs

​Blandai Send Call

​Blandai Call Details

​Heygen

​D Id

​Stable Diffusion Xl

​Truncate Text

​Browserless Scrape

​Slack Message

​Whatsapp Message

​Whatsapp Template Message

​Zendesk Ticket

​Create Linear Ticket

​Apollo Api Call

​Apollo People Search

​Postgres Sql

​Hubspot Api Call

​Freshdesk Api Call

​Translate

​Excel To Text

​Word To Text

​File To Text Llm Friendly

​Echo

​Spreadsheet To Json

​Fuzzy Search

​Google Api Call

​Salesforce Api Call

​Microsoft Api Call

​Send Gmail Email

​Message Agent

​Check Google Calendar

​Create Google Calendar Event

​Send Outlook Email

Studio Transformation

Prompt Completion

Api Call

Js Code Transformation

Search

Search Array

Bulk Update

Apify

Scrape Glassdoor

Multiple File Upload

Generate Vector Embedding

Send Email Step

Join Array

Pdf To Text

Advanced Retrieval

Audio To Text

Audio To Text V2

Audio To Text V3

Anonymize Text

Nemo Guardrails

Markdown

Relevance Api Call

Get Linkedin Profile

Summarize Knowledge

Prompt Completion Vision

To Json

Export To File

Object Key Filter

Regex

Run Transformation

Run Chain

Split Text

Combine Array

Python Code Transformation

Upload File S3

Serper Google Search

Analyse Image

Text To Image

Text To Speech

Elevenlabs

Blandai Send Call

Blandai Call Details

Heygen

D Id

Stable Diffusion Xl

Truncate Text

Browserless Scrape

Slack Message

Whatsapp Message

Whatsapp Template Message

Zendesk Ticket

Create Linear Ticket

Apollo Api Call

Apollo People Search

Postgres Sql

Hubspot Api Call

Freshdesk Api Call

Translate

Excel To Text

Word To Text

File To Text Llm Friendly

Echo

Spreadsheet To Json

Fuzzy Search

Google Api Call

Salesforce Api Call

Microsoft Api Call

Send Gmail Email

Message Agent

Check Google Calendar

Create Google Calendar Event

Send Outlook Email

Check Outlook Calendar

Create Outlook Calendar Event

Delay

Retrieve Data

Zoom Api Call

Vapiai Send Call Managed

Vapiai Send Call Custom

Vapiai Call Details