Node References

This page provides comprehensive documentation for all available node types

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Database nodes

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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.

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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.

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Core nodes

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Merge (Merge fields)

Merges all incoming JSON data into one. Parameters: None Use Case: Combine data from multiple sources or workflow branches.

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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.

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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.

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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.

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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.

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AI & Language models

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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.

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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.

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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.

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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.

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File storage

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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.

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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.

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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.

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API Integrations

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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.

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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.

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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.

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Graph AI Tools

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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.

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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.

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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

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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.