Save yourself the work of writing custom integrations for Chatling and Databricks and use n8n instead. Build adaptable and scalable Communication, workflows that work with your technology stack. All within a building experience you will love.

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.
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.
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 Chatling official documentation to get a full list of all API endpoints and verify the scraped ones!
Execute Query
Execute a SQL query and wait for results
Create Directory
Create a directory in volume
Delete Directory
Delete a directory in volume
Delete File
Delete a file from a volume
Download File
Download file content from a volume
Get File Metadata
Get file metadata from a volume
List Directory
List directory contents in volume
Upload File
Upload a file (up to 5 GiB) to Databricks volumes
Create Conversation Message
Create a new conversation message
Execute Message SQL Query
Execute a SQL query from a message attachment
Get Conversation Message
Get a conversation message by ID
Get Genie Space
Get details of a Genie space
Get Query Results
Get results of a SQL query execution
Start Conversation
Start a new conversation
Query Endpoint
Query a serving endpoint. The input format is automatically detected from the endpoint schema.
Create Catalog
Create a new catalog
Create Function
Create a new function
Create Table
Register a new table
Create Volume
Create a new volume
Delete Catalog
Delete a catalog
Delete Function
Delete a function
Delete Table
Delete a table
Delete Volume
Delete a volume
Get Catalog
Get catalog information
Get Function
Get function information
Get Table
Get table information
Get Volume
Get volume information
List Catalogs
List all catalogs
List Functions
List functions in schema
List Tables
List tables in schema
List Volumes
List volumes in schema
Update Catalog
Update catalog information
Create Index
Create a new vector search index
Get Index
Get details of a vector search index
List Indexes
List all vector search indexes
Query Index
Query a vector search index with text or vectors
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