Node References

Here we describe how to use nodes in the workflows and the types of nodes already pre-built and available to use

This page provides comprehensive documentation for all available node types

Database nodes

TuringDB Query

Sends a query to a running TuringDB instance.

Parameters:

• Host (string): TuringDB instance host
• Instance Id (string): TuringDB instance ID from Database instances section
• Graph Name (string): Name of the graph to query
• API Token (string): TuringDB API token from account settings
• Input Field (string): Field containing query string
• Output Field (string): Field where query result will be stored

Use Case: Execute queries against TuringDB graph database instances.

TuringDB Write

Submits new nodes/edges for creation in a TuringDB graph.

Parameters:

• Host (string): TuringDB instance host
• Instance Id (string): TuringDB instance ID from Database instances section
• Graph Name (string): Name of the graph to modify
• API Token (string): TuringDB API token from account settings
• Entities Field (string): Field containing entities to add

Use Case: Add new nodes and relationships to TuringDB graph databases.

Core nodes

Merge (Merge fields)

Merges all incoming JSON data into one.

Parameters: None

Use Case: Combine data from multiple sources or workflow branches.

Merge Entities

Merges multiple graph entities together using their labels as identifier.

Parameters:

• Input Field (string): Field containing entities to merge
• Output Field (string): Field for merged entities

Use Case: Consolidate duplicate entities in knowledge graphs.

Send email

Send emails using SendGrid API.

Parameters:

• Sendgrid Api Key (string): SendGrid API key from app.sendgrid.com
• From Email (string): Sender email address
• To Emails (array): List of recipient email addresses
• Subject (string): Email subject
• Cc Email (string, optional): Carbon copy recipient
• Bcc Email (string, optional): Blind carbon copy recipient
• Input Field (string): Field containing email content
• Output Field (string): Field to store response status

Use Case: Send automated emails and notifications from workflows.

Text Input

Entry-point node to input text into the data flow graph.

Parameters:

• Text Content (text): Text content you want to output
• Output Field Name (string): Name of the field where this text will be stored

Use Case: Starting point for workflows that need text input from users.

Text Output

Extract text from previous result and output it in standardized JSON object.

Parameters:

• Input Field Name (string): Name of the field containing the text to display
• Output Field Name (string): Name of the field where output text will be stored

Use Case: Final output node to display results from your workflow.

AI & Language models

DeepResearch

Use OpenAI’s deep research models for complex analysis and research tasks.

Parameters:

• Model (select): AI model to use. Options:

  • o3-deep-research
  • o4-mini-deep-research Default: “o4-mini-deep-research”

• Input Field (string): Field to use as prompt

• Output Field (string): Field to use as output

• OpenAI API Key (string): Your OpenAI API key

Use Case: Complex research tasks requiring deep analysis and comprehensive responses.

Generic LLM

Generic LLM node to query OpenAI, Mistral, or Anthropic models.

Parameters:

• Llm Provider (select): LLM provider. Options:

  • OpenAI
  • Mistral
  • Anthropic Default: “Anthropic”

• Model (string, optional): Specific model to use for the provider

• Prompt (string/DataField): The prompt or field to retrieve prompt from

• System Prompt (string, optional): System prompt for the LLM provider

• Api Key (string, optional): API key for the LLM provider

• Temperature (number): Temperature setting

• Output Format (select): Format of response. Options:

  • markdown
  • json
  • text
  • list Default: “markdown”

• Output Field (string): Field to store the LLM response

Use Case: General-purpose AI text generation, analysis, and transformation.

Extract Text from PDF

Description: Extract text and tables from PDF file.

Parameters:

• Input Field Name (string): Field containing PDF content or file path
• Output Field Name (string): Field where extracted text will be stored

Use Case: Process PDF documents to extract textual content for analysis.

Generate PDF

Generate PDF from input text.

Parameters:

• Input Type (select): Type of input text. Options:
  • markdown
  • plain_text
• Input Field (string): Field containing input text
• Output Field (string): Field to store generated PDF
• Page Size (string): Page size of PDF
• Margin (string): Margin around content

Use Case: Convert markdown or plain text into formatted PDF documents.

File storage

S3 List Files

List files in user’s S3 bucket.

Parameters:

• Filenames (array): Specific filenames to look for
• Extensions (array): File extensions to filter by
• Max Item Count (number): Maximum number of files to return
• Output Field (string): Field to store file list

Use Case: Discover and list files in S3 storage for further processing.

S3 Load File

Load content of a file from user’s S3 bucket and store it into JSON data.

Parameters:

• File Key (string/DataField): File path/key in S3 bucket

• File Type (select): Type of file. Options:

  • image
  • video
  • audio
  • text
  • pdf
  • markdown
  • html
  • csv
  • excel
  • other Default: “text”

• Output Field (string): Field to store loaded content

Use Case: Load various file types from S3 for processing in workflows.

S3 Upload File

Create and upload a file to user’s S3 bucket.

Parameters:

• User ID (string): User identifier for S3 bucket access
• File Key/Name (string): Desired file path/key in S3
• Input Field Name (string): Field containing content to upload

Use Case: Store generated content or processed data in S3 storage.

API Integrations

Financial Data REST API

Call FinancialData Rest API.

Parameters:

• Endpoint (select): API endpoint. Options:

  • stock_symbols
  • international_stock_symbols
  • etf_symbols
  • mutual_fund_symbols
  • stock_prices
  • etf_prices Default: “stock_symbols”

• Api Key (string): API key

• Identifier (string/DataField): Company ticker or ETF

• Format (select): Output format. Options:

  • json
  • csv

• Offset (number): Record subset position.

• Output Field (string): Field to store response. Default: “response”

Use Case: Access financial data including stock symbols, prices, and ETF information.

Polygon REST API

Call the Polygon REST API for financial market data.

Parameters:

• Endpoint Type (select): Polygon endpoint. Options

  • tickers
  • market_status
  • aggregates
  • trades
  • quotes
  • options
  • indices
  • futures
  • currencies Default: “tickers”

• Polygon API Key (string): Polygon API key

• Ticker (string, optional): Ticker symbol

• Option Ticker (string, optional): Option ticker symbol

• Futures Ticker (string, optional): Futures ticker symbol

• Currency Ticker (string, optional): Currency ticker symbol

• Index Ticker (string, optional): Index ticker symbol

• Begin (string, optional): Start date (YYYY-MM-DD) or timestamp

• End (string, optional): End date (YYYY-MM-DD) or timestamp

• Timespan (select): Time window size. Options: second, minute, hour, day, week, month, quarter, year

• Multiplier (integer): Timespan multiplier

• Limit (number): Number of results (0-1000)

• Output Field (string): Field to store response

Use Case: Access real-time and historical financial market data.

Rest API

Call a REST API endpoint.

Parameters:

• Base Url (string): Base URL for the API

• Endpoint (string): API endpoint path

• Method (select): HTTP method. Options

  • GET
  • POST
  • PUT
  • DELETE Default: “GET”

• Headers (object): HTTP headers

• Http Params (object): HTTP query parameters

• Body (string, optional): Request body for POST/PUT requests

• Timeout (number): Timeout in seconds

• Output Field (string): Field to store response

Use Case: Integrate with external APIs and web services.

Graph AI Tools

GML Generator

Builds GML from Entities JSON schema.

Parameters:

• Input Field Name (Entities) (string): Field containing entities data
• Output Field Name (string): Field where generated GML will be stored

Use Case: Convert structured entity data into Graph Modeling Language format for visualization.

Mistral Entity Extractor

Call Mistral API to generate JSON containing entities and relationships extracted from input text.

Parameters:

• Input Field Name (string): Field containing text for entity extraction. Default: “content”
• Output Field Name (string): Field where extracted entities will be stored. Default: “entities”

Use Case: Extract structured entities and relationships from unstructured text for knowledge graph creation.

Mistral Graph Explainer

Call Mistral API to generate Markdown text explaining the overall structure of input GML-like text.

Parameters:

• Input Field Name (string): Field containing GML graph data to explain
• Output Field Name (string): Field where human-readable graph explanation will be stored

Use Case: Generate human-readable explanations of graph structures for documentation or analysis.

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Text to Cypher LLM

Text to Cypher conversion LLM node.

Parameters:

• Llm Provider (select): LLM service to use. Options

  • OpenAI
  • Anthropic
  • Mistral Default: “OpenAI”

• Model (string, optional): Specific model name

• Api Key (string): API key

• Input Field (string): Field containing natural language query

• Output Field (string): Field where generated Cypher query will be stored

• Node Labels (array): Available node types in the graph

• Relationship Types (array): Available relationship types in the graph

• Property Keys (array): Common node properties in the graph

• Database Description (string, optional): High-level description of your graph database

Use Case: Convert natural language queries into Cypher queries for graph databases.

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Experimental

For Each

Loops over elements in a list or a dict.

Parameters:

• Collection Field (string): Field containing collection to iterate over
• Current Field (string): Field name for current item in loop

Use Case: Process collections of data items in batch operations.

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If statement

Evaluates the condition and follows the true/false branch accordingly.

Parameters:

• Left-Hand Side Field (string): Field containing left-hand side operand
• Right-Hand Side Field (string): Field containing right-hand side operand
• Operator (select): Comparison operator. Options: $== != > >= < <=$
• Execution Count Limit (integer): Maximum execution count

Use Case: Implement conditional logic and branching in workflows.