In n8n, click the "Add workflow" button in the Workflows tab to create a new workflow. Add the starting point – a trigger on when your workflow should run: an app event, a schedule, a webhook call, another workflow, an AI chat, or a manual trigger. Sometimes, the HTTP Request node might already serve as your starting point.
Create custom Breezy HR and Google Vertex AI workflows by choosing triggers and actions. Nodes come with global operations and settings, as well as app-specific parameters that can be configured. You can also use the HTTP Request node to query data from any app or service with a REST API.
Update position state
Update the state of a specific position.
Get position by ID
Retrieves the details of a specified position.
Update position
Modify the details of an existing position by its ID.
Sign in user
Authenticates a user and starts a session.
Sign out user
Ends the session for a user.
Get user details
Retrieves the information of the authenticated user.
Get user
Retrieve the authenticated user or another user by id
Get user details
Retrieve the details of a specific user.
Get user details
Retrieve the details for the configurations of each company the authenticated user belongs to.
Get company by ID
Fetches details of a specified company.
Get custom attributes for candidate
Fetches custom attributes associated with candidates for a company.
Get pipeline by ID
Retrieves the details of a specified pipeline within a company.
Get all pipelines
Fetches all pipelines associated with a company.
Get questionnaire by ID
Retrieves a specific questionnaire for a company identified by ID.
Get all questionnaires
Retrieves all questionnaires associated with a specific company identified by ID.
Add a new company position
Create a new position under the specified company.
Get template by ID
Retrieve a specific template using its ID.
List all positions
Fetches a list of all positions within a company or context.
Get candidate by ID
Retrieves the details of a specified candidate.
Update candidate
Updates the details of a specified candidate.
Add candidate assessment
Adds an assessment for a specified candidate.
Add candidate document
Upload a document for a specific candidate.
Move candidate
Change the position of a specific candidate within the pipeline.
Send candidate questionnaire
Send a questionnaire to a specific candidate.
Update candidate scorecard
Update the scorecard information for a candidate.
List all candidates
Fetches a list of all candidates associated with a company or position.
Add a new candidate
Submits a new candidate to the system.
Get user details
Fetch comprehensive information about the authenticated user.
Get position custom attributes
Fetch custom attributes related to positions for a specific company.
Get all companies
Fetch a list of all companies.
To set up Breezy HR integration, add the HTTP Request node to your workflow canvas and authenticate it using a generic authentication method. The HTTP Request node makes custom API calls to Breezy HR to query the data you need using the API endpoint URLs you provide.
See the example hereThese API endpoints were generated using n8n
n8n AI workflow transforms web scraping into an intelligent, AI-powered knowledge extraction system that uses vector embeddings to semantically analyze, chunk, store, and retrieve the most relevant API documentation from web pages. Remember to check the Breezy HR official documentation to get a full list of all API endpoints and verify the scraped ones!
Generate content
This endpoint generates content based on the input provided.
Call function
This endpoint allows for calling functions as part of the generative tasks.
Ground content
This endpoint grounds the content to ensure relevance and context.
List API errors
This endpoint retrieves potential API errors that can occur during requests.
Generate text embeddings
This endpoint generates embeddings for given text inputs.
Generate multimodal embeddings
This endpoint generates embeddings that leverage multiple modalities.
Generate and edit images
This endpoint is used for generating and editing images based on input specifications.
Use code completions
API for generating code completion suggestions.
Perform batch predictions
API for executing batch predictions on data.
Batch prediction
API for performing batch prediction.
Tune models
API for tuning machine learning models.
Tuning model parameters
API for tuning model parameters.
Rapid evaluation
API for quickly evaluating model performance.
Evaluate model performance
API for rapid evaluation of models.
Use LlamaIndex
API for accessing the LlamaIndex for retrieval-augmented generations.
Manage extensions
API for managing custom extensions.
Manage extensions
API for handling extensions.
Count tokens
API for counting tokens in text inputs.
Use reasoning engine
API for performing reasoning tasks.
Utilize reasoning engine
API for using the reasoning engine capabilities.
Use MedLM API
API for accessing medical language models.
Access MedLM
API for accessing MedLM functionality.
Generate and edit images
API for generating and editing images.
Access LlamaIndex
API for accessing LlamaIndex functionality.
Count tokens
API for counting tokens in text.
To set up Google Vertex AI integration, add the HTTP Request node to your workflow canvas and authenticate it using a generic authentication method. The HTTP Request node makes custom API calls to Google Vertex AI to query the data you need using the API endpoint URLs you provide.
See the example hereThese API endpoints were generated using n8n
n8n AI workflow transforms web scraping into an intelligent, AI-powered knowledge extraction system that uses vector embeddings to semantically analyze, chunk, store, and retrieve the most relevant API documentation from web pages. Remember to check the Google Vertex AI official documentation to get a full list of all API endpoints and verify the scraped ones!
Google Vertex AI is a unified machine learning platform that enables developers to build, deploy, and manage models efficiently. It provides a wide range of tools and services, such as AutoML and datasets, to accelerate the deployment of AI solutions.
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