Entity Recognition Tool Node

The Entity Recognition Tool node provides AI Agents with the ability to extract structured information from unstructured text. Unlike the Entity Recognition Action node, the Tool version is called autonomously by AI based on needs.

Use Cases

Typical Applications

Form Information Extraction - Extract form fields from natural language user input
Order Information Recognition - Extract order-related information from conversations
User Profile Collection - Collect user information from dialogue
Product Information Extraction - Extract specifications from product descriptions
Event Information Extraction - Extract time, location, people from text
Multi-step Information Collection - AI can call multiple times to collect different information

Tool vs Action Differences

Feature	Entity Recognition Action	Entity Recognition Tool
Execution Method	Direct execution	AI calls on demand
Use Case	Fixed information extraction flow	AI Agent extracts when needed
Call Timing	Always executes	AI decides when to call
Multiple Calls	Needs multiple nodes	Same tool can be called multiple times

Example Comparison:

Action Method (Fixed Flow):
User Input → Entity Recognition → Process Result

Tool Method (Intelligent Interaction):
User: "I want to order food delivery"
AI: "Sure, what would you like?" (Tool not called)
User: "Kung Pao Chicken, address xxx, phone 138xxx"
AI: (Calls entity recognition tool to extract: dish, address, phone)

Node Configuration

Basic Settings (Parameters Panel)

Tool Name (toolName)

Unique identifier used by AI Agent to call the tool.

Field Properties:

Required field
Must be unique within the workflow
Does not support expressions
Format requirements:
- Only letters, numbers, underscores, and hyphens allowed
- Must start with a letter
- Cannot duplicate tool names of other tool nodes in the workflow

Configuration Examples:

javascript

// 1. Concise and clear tool name
toolName: "extractOrderInfo"

// 2. Descriptive tool name
toolName: "parseUserProfile"

// 3. Tool name with prefix
toolName: "orderEntityRecognition"

Naming Recommendations:

Use camelCase or underscore: extractOrderInfo or extract_order_info
Self-explanatory: Name should clearly express tool functionality
Avoid too long: Recommended within 20 characters
Avoid special characters: Only use letters, numbers, underscores, and hyphens

Important Notes:

AI Agent identifies and calls tools by tool name, not node name
Tool name must be unique in the workflow; duplicate names will automatically get a suffix
English naming is recommended for consistency with AI calls

Tool Description (toolDescription)

Describes the tool's functionality. AI decides when to call based on description.

Field Properties:

Required field
Supports expressions

Configuration Examples:

javascript

// 1. Clearly describe extraction target
"Extract order information from user messages including: product name, quantity, delivery address, contact phone. Returns structured order object."

// 2. Explain use cases
"Extract product requirement information from product consultation dialogue.
Extract fields:
- productType: Product type
- budget: Budget range
- requirements: Special requirements
- deliveryDate: Expected delivery date

Call extractProductRequirements tool when user describes product requirements."

// 3. Provide examples
"Extract user personal information for registration.
Fields: name, email, phone, city
Example: User says 'My name is John, email john@example.com, phone 13800138000, in Beijing'
Returns: {name: 'John', email: 'john@example.com', phone: '13800138000', city: 'Beijing'}"

Model (model)

Select AI model for entity recognition.

Field Properties:

Required field
Supports mainstream LLM providers

Recommended Models:

yaml

GPT-4: Highest accuracy, suitable for complex structure extraction
GPT-3.5-turbo: Cost-effective, suitable for most scenarios
Claude 3 Sonnet: Balanced performance, suitable for medium complexity

Input Text (input)

Text content to extract information from.

Field Properties:

Required field
Supports expressions

Configuration Examples:

javascript

// 1. AI passes from conversation
input: ""  // AI will automatically fill with user message

// 2. Get from context
input: $('Chat Trigger').message

// 3. Combine multiple information sources
input: `Current conversation: ${$('Chat Trigger').message}
Historical info: ${$('Context').history}`

Instructions (instructions)

Provide additional extraction guidance for AI.

Field Properties:

Optional field
Supports expressions

Configuration Examples:

javascript

// 1. Specify extraction rules
"Extract all mentioned product information from text. If a field is not mentioned, return null instead of guessing."

// 2. Handle ambiguity
"If address information is incomplete, only extract explicitly mentioned parts. If multiple phone numbers appear, extract the first one."

// 3. Format requirements
"Convert dates to YYYY-MM-DD format uniformly, remove all spaces and dashes from phone numbers."

JSON Schema (jsonSchema)

Define data structure to extract.

Field Properties:

Optional field
Supports full JSON Schema specification

Configuration Examples:

json

{
  "type": "object",
  "properties": {
    "orderInfo": {
      "type": "object",
      "properties": {
        "productName": {
          "type": "string",
          "description": "Product name"
        },
        "quantity": {
          "type": "number",
          "description": "Purchase quantity"
        },
        "address": {
          "type": "string",
          "description": "Delivery address"
        },
        "phone": {
          "type": "string",
          "description": "Contact phone"
        }
      },
      "required": ["productName", "quantity"]
    }
  }
}

Complex Schema Example:

json

{
  "type": "object",
  "properties": {
    "userProfile": {
      "type": "object",
      "properties": {
        "name": {"type": "string"},
        "age": {"type": "number"},
        "interests": {
          "type": "array",
          "items": {"type": "string"}
        },
        "contact": {
          "type": "object",
          "properties": {
            "email": {"type": "string"},
            "phone": {"type": "string"}
          }
        }
      }
    }
  }
}

Custom Labels (customLabels)

Add friendly labels for Schema fields.

Field Properties:

Optional field
Key-value pair format

javascript

customLabels: {
  "productName": "Product Name",
  "quantity": "Quantity",
  "address": "Delivery Address",
  "phone": "Contact Phone"
}

Advanced Settings (Settings Panel)

Always Output (alwaysOutput)

Whether to output empty item on execution failure. Default: false

Execute Once (executeOnce)

Whether to execute only once using first input item. Default: false

Retry on Fail (retryOnFail)

Whether to automatically retry on execution failure. Default: false

Max Tries (maxTries)

Maximum retries after failure. Default: 3

Wait Between Tries (waitBetweenTries)

Wait time between retries (milliseconds). Default: 1000

Output Data

Outputs structured data conforming to JSON Schema definition.

javascript

// Access extracted data
$('Entity Recognition Tool').orderInfo.productName
$('Entity Recognition Tool').orderInfo.quantity
$('Entity Recognition Tool').orderInfo.address

Workflow Examples

Example 1: Smart Order Collection

Chat Trigger
  → AI Agent Node
    System Prompt: "You are an ordering assistant helping users place orders."

    Tools: [Entity Recognition Tool]
      Tool Description: "Extract order info from user messages: dish, quantity, address, phone."
      JSON Schema: {
        "properties": {
          "dish": {"type": "string"},
          "quantity": {"type": "number"},
          "address": {"type": "string"},
          "phone": {"type": "string"}
        }
      }
  → Answer Node

Conversation example:
User: "I want to order Kung Pao Chicken"
AI: "Sure, how many servings? Where to deliver?" (Tool not called, info incomplete)

User: "Two servings, deliver to xx road xx number, phone 138xxxx"
AI: (Calls entity recognition tool)
Extraction result: {
  dish: "Kung Pao Chicken",
  quantity: 2,
  address: "xx road xx number",
  phone: "138xxxx"
}
AI: "Got it, order recorded: 2 servings of Kung Pao Chicken, deliver to xx road..."

Example 2: Multi-step Information Collection

Chat Trigger
  → AI Agent Node
    System Prompt: "You are a customer service assistant collecting user feedback."

    Tools: [Entity Recognition Tool]
      Tool Description: "Extract user feedback from conversation: issueType, product, description, severity."
      Instructions: "If information incomplete, return already extracted partial fields."
      JSON Schema: {...}
  → Answer Node

Conversation example:
User: "My phone has a problem"
AI: (Calls tool) → Extracts: {product: "phone"}
AI: "What specific issue?"

User: "Can't charge, very serious"
AI: (Calls tool again) → Extracts: {
  product: "phone",
  description: "Can't charge",
  severity: "serious"
}
AI: "Issue recorded, we'll handle it ASAP..."

Example 3: Product Consultation Information Extraction

Chat Trigger
  → AI Agent Node
    System Prompt: "You are a product consultant helping users choose suitable products."

    Tools: [
      Tool 1 - Entity Recognition Tool
        Tool Description: "Extract product requirement info: budget, size, color, requirements."

      Tool 2 - Code Tool
        Tool Description: "Search matching products based on requirements"
    ]
  → Answer Node

Conversation flow:
User: "I want to buy a laptop, budget around 8000, needs to be lightweight"
AI: (Calls entity recognition tool)
Extracts: {
  productType: "laptop",
  budget: 8000,
  requirements: "lightweight"
}
AI: (Calls product search tool)
AI: "Based on your requirements, I recommend..."

Example 4: Event Information Extraction

Chat Trigger
  → AI Agent Node
    System Prompt: "You are a schedule management assistant helping users create schedules."

    Tools: [Entity Recognition Tool]
      Tool Description: "Extract event info from user messages: title, date, time, location, participants."
      Instructions: "Convert dates to YYYY-MM-DD format. If only 'tomorrow' is mentioned, calculate based on today's date."
      JSON Schema: {
        "properties": {
          "title": {"type": "string"},
          "date": {"type": "string"},
          "time": {"type": "string"},
          "location": {"type": "string"},
          "participants": {
            "type": "array",
            "items": {"type": "string"}
          }
        }
      }
  → Answer Node

User: "Tomorrow 3 PM in Conference Room A, project meeting, invite John and Jane"
AI: (Calls tool)
Extracts: {
  title: "Project meeting",
  date: "2024-01-16",  // AI auto-calculates
  time: "15:00",
  location: "Conference Room A",
  participants: ["John", "Jane"]
}
AI: "Schedule created: Tomorrow 3 PM..."

Best Practices

1. Write Clear Tool Descriptions

Good description:

"Extract travel requirement information from user consultation.
Extract fields:
- destination: Destination city
- departureDate: Departure date (YYYY-MM-DD)
- duration: Trip days
- travelers: Number of travelers
- budget: Budget range

Call when user describes travel plans.
Example: 'Want to go to Sanya next month for 5 days, 2 people, budget around 10k'"

2. Design Reasonable Schema

json

// Good Schema design
{
  "type": "object",
  "properties": {
    "required_fields": {
      "type": "object",
      "properties": {
        "name": {"type": "string", "description": "Required: Name"}
      },
      "required": ["name"]
    },
    "optional_fields": {
      "type": "object",
      "properties": {
        "age": {"type": "number", "description": "Optional: Age"},
        "city": {"type": "string", "description": "Optional: City"}
      }
    }
  }
}

3. Provide Clear Extraction Instructions

javascript

instructions: `Extraction rules:
1. If field not explicitly mentioned, return null
2. Date format unified to YYYY-MM-DD
3. Phone numbers keep digits only
4. Extract numeric part from amounts, unify to yuan
5. If multiple values of same type, take first`

4. Handle Incomplete Information

javascript

// AI can call tool multiple times to gradually collect information
System Prompt: `You are an intelligent assistant.
If extracted information incomplete:
1. Call tool to extract available information
2. Identify missing fields
3. Ask user for missing information
4. Call tool again to supplement information`

5. Validate Extraction Results

Chat Trigger
  → AI Agent (Calls Entity Recognition Tool)
  → Code Node (Validate extraction result)
    Code: |
      const data = $('Entity Recognition Tool');

      // Validate required fields
      if (!data.name || !data.phone) {
        return {valid: false, missing: ['name', 'phone']};
      }

      // Validate format
      if (!/^1[3-9]\d{9}$/.test(data.phone)) {
        return {valid: false, error: 'Invalid phone format'};
      }

      return {valid: true, data: data};
  → Conditional Branch
    → [valid] → Continue processing
    → [invalid] → AI asks again

FAQ

Q1: Should I choose Tool or Action version?

Use Action: Fixed flow, always extract information
- Example: Immediately extract data after form submission
Use Tool: AI interaction scenario, extract on demand
- Example: Conversational information collection, AI decides when to extract

Q2: How does AI know when to call the tool?

A: AI decides based on:

Whether tool description matches current conversation needs
Whether user message contains extractable information
Whether conversation context needs structured information

Optimization advice:

Tool description should clearly state use cases
Provide call examples
Explain extracted fields and their purposes

Q3: How to handle missing partial information?

Option 1: Set Schema as optional

json

{
  "properties": {
    "name": {"type": "string"},  // Required
    "phone": {"type": "string"},  // Optional (not in required)
    "email": {"type": "string"}   // Optional
  },
  "required": ["name"]
}

Option 2: Instructions explain handling

javascript

instructions: "If a field is not mentioned, return null. Don't guess or use defaults."

Option 3: AI auto asks

javascript

System Prompt: "If extracted info missing required fields, ask user for missing information."

Q4: Can lists or arrays be extracted?

A: Yes, use array type:

json

{
  "properties": {
    "products": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {"type": "string"},
          "quantity": {"type": "number"}
        }
      }
    }
  }
}

Example:

User: "I want 3 apples, 2 oranges, 5 bananas"
Extraction result: {
  products: [
    {name: "apples", quantity: 3},
    {name: "oranges", quantity: 2},
    {name: "bananas", quantity: 5}
  ]
}

Q5: How to improve extraction accuracy?

1. Use more powerful model

javascript

model: "GPT-4"  // Instead of GPT-3.5

2. Provide detailed field descriptions

json

{
  "properties": {
    "date": {
      "type": "string",
      "description": "Date in YYYY-MM-DD format. If user says 'tomorrow', calculate specific date"
    }
  }
}

3. Use Instructions to clarify rules

javascript

instructions: "Strictly extract according to Schema, don't speculate unmentioned information."

4. Provide extraction examples

javascript

toolDescription: `Extract user address information.
Example input: "I live at xx road xx number, Chaoyang District, Beijing"
Example output: {
  province: "Beijing",
  district: "Chaoyang District",
  street: "xx road xx number"
}`

Q6: Can the tool be called multiple times?

A: Yes! This is a Tool advantage:

Conversation flow:
User: "I want to order food delivery"
AI: "Sure, what would you like?" (Don't call tool)

User: "Kung Pao Chicken"
AI: (1st call) → Extracts: {dish: "Kung Pao Chicken"}
AI: "Where to deliver?"

User: "xx road xx number, phone 138xxx"
AI: (2nd call) → Extracts: {dish: "Kung Pao Chicken", address: "xx road", phone: "138xxx"}
AI: "Order confirmed..."

AI autonomously decides whether to call again based on information completeness.

Q7: How to handle format conversion?

A: Explain in Instructions:

javascript

instructions: `Format conversion rules:
- Date: Unify to YYYY-MM-DD (e.g., "tomorrow" → "2024-01-16")
- Phone: Remove spaces, dashes (e.g., "138-0000-0000" → "13800000000")
- Amount: Extract number, unify to yuan (e.g., "99.9 yuan" → 99.9)
- Boolean: "yes/ok/want" → true, "no/don't want" → false`

AI will automatically convert formats according to rules.

Next Steps

AI Agent Node - Learn how to use AI Agents
Code Tool Node - Process extracted data
HTTP Request Tool Node - Combine with API calls

Entity Recognition Action Node - Learn about Action version
JSON Schema Specification - Learn JSON Schema syntax

Chat Trigger

Entity Recognition Tool Node ​

Use Cases ​

Typical Applications ​

Tool vs Action Differences ​

Node Configuration ​

Basic Settings (Parameters Panel) ​

Tool Name (toolName) ​

Tool Description (toolDescription) ​

Model (model) ​

Input Text (input) ​

Instructions (instructions) ​

JSON Schema (jsonSchema) ​

Custom Labels (customLabels) ​

Advanced Settings (Settings Panel) ​

Always Output (alwaysOutput) ​

Execute Once (executeOnce) ​

Retry on Fail (retryOnFail) ​

Max Tries (maxTries) ​

Wait Between Tries (waitBetweenTries) ​

Output Data ​

Workflow Examples ​

Example 1: Smart Order Collection ​

Example 2: Multi-step Information Collection ​

Example 3: Product Consultation Information Extraction ​

Example 4: Event Information Extraction ​

Best Practices ​

1. Write Clear Tool Descriptions ​

2. Design Reasonable Schema ​

3. Provide Clear Extraction Instructions ​

4. Handle Incomplete Information ​

5. Validate Extraction Results ​

FAQ ​

Q1: Should I choose Tool or Action version? ​

Q2: How does AI know when to call the tool? ​

Q3: How to handle missing partial information? ​

Q4: Can lists or arrays be extracted? ​

Q5: How to improve extraction accuracy? ​

Q6: Can the tool be called multiple times? ​

Q7: How to handle format conversion? ​

Next Steps ​

Related Resources ​

Entity Recognition Tool Node

Use Cases

Typical Applications

Tool vs Action Differences

Node Configuration

Basic Settings (Parameters Panel)

Tool Name (toolName)

Tool Description (toolDescription)

Model (model)

Input Text (input)

Instructions (instructions)

JSON Schema (jsonSchema)

Custom Labels (customLabels)

Advanced Settings (Settings Panel)

Always Output (alwaysOutput)

Execute Once (executeOnce)

Retry on Fail (retryOnFail)

Max Tries (maxTries)

Wait Between Tries (waitBetweenTries)

Output Data

Workflow Examples

Example 1: Smart Order Collection

Example 2: Multi-step Information Collection

Example 3: Product Consultation Information Extraction

Example 4: Event Information Extraction

Best Practices

1. Write Clear Tool Descriptions

2. Design Reasonable Schema

3. Provide Clear Extraction Instructions

4. Handle Incomplete Information

5. Validate Extraction Results

FAQ

Q1: Should I choose Tool or Action version?

Q2: How does AI know when to call the tool?

Q3: How to handle missing partial information?

Q4: Can lists or arrays be extracted?

Q5: How to improve extraction accuracy?

Q6: Can the tool be called multiple times?

Q7: How to handle format conversion?

Next Steps

Related Resources