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 Chatling 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.
List chatbot templates
Get a list of all the available chatbot templates.
Create chatbot
Create a new chatbot.
List chatbots
Get a list of all existing chatbots.
Retrieve chatbot
Retrieve details of a specific chatbot.
Update chatbot settings
Update settings for a specific chatbot.
List contacts
Get a list of all contacts.
Retrieve contact
Retrieve details of a specific contact.
Delete contact
Delete a specific contact.
Delete contact
Delete a specific contact from the project.
List conversations
Get a list of all conversations.
Retrieve conversation
Retrieve details of a specific conversation.
List data sources
Get a list of all data sources in the knowledge base.
List settings
Retrieve a list of project settings.
Update settings
Update the project settings.
List members
Retrieve a list of project members.
List AI models
Retrieve a list of available AI models.
List AI languages
Retrieve a list of supported AI languages.
Chat
Send a chat request to the AI service.
Add link
Add a link to the knowledge base.
Add text
Add text to the knowledge base.
To set up Chatling 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 Chatling to query the data you need using the API endpoint URLs you provide.
These 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 Chatling 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.
These 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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