Knowledge Retrieval Tool Node

The Knowledge Retrieval Tool node provides AI Agents with the ability to retrieve relevant information from knowledge bases. Unlike the Knowledge Retrieval Action node, the Tool version is called autonomously by AI based on conversation needs.

Use Cases

Typical Applications

Intelligent Q&A Assistant - AI autonomously retrieves knowledge base content based on user questions
Document Search - AI automatically searches product manuals and technical documents when needed
Customer Support - AI autonomously retrieves relevant solutions based on question types
RAG Systems - Provide context information for AI Agents to enhance answer quality
Multi-knowledge Base Switching - AI selects different knowledge bases based on question domains
Dynamic Information Supplement - AI retrieves additional information on demand during conversations

Tool vs Action Differences

Feature	Knowledge Retrieval Action	Knowledge Retrieval Tool
Execution Method	Direct execution	AI calls on demand
Use Case	Fixed retrieval flow	AI Agent retrieves when needed
Call Timing	Always executes	AI decides when to call
Query Source	Pre-configured	AI extracts from conversation

Example Comparison:

Action Method (Fixed Flow):
User Input → Knowledge Retrieval → Use Results

Tool Method (Intelligent Interaction):
User: "What are the features of your product?"
AI: (Identifies need to retrieve product info) → Calls knowledge retrieval tool → Returns results
User: "What's the price?"
AI: (Identifies need to retrieve price info) → Calls knowledge retrieval tool again → Returns results

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: "searchKnowledgeBase"

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

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

Naming Recommendations:

Use camelCase or underscore: searchKnowledgeBase or search_knowledge_base
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 and use cases. AI decides when to call based on description.

Field Properties:

Required field
Supports expressions
Supports multi-line text

Configuration Examples:

javascript

// 1. Clearly describe tool functionality
"Retrieve content related to query from knowledge base. Input parameters: query (query text). Returns: results (retrieval result array), query (original query). Use when answering questions that require knowledge base reference."

// 2. Explain use cases
"Query product-related information.
Input parameters:
- query: Query text (product name, features, specifications, etc.)
Returns:
- results: Array of matching knowledge snippets
- query: Original query text

Call searchProductInfo tool when user asks about product information."

// 3. Specify knowledge base scope
"Retrieve FAQ answers from company FAQ knowledge base.
Input parameters: query (user question)
Returns: results (matching FAQ entries)
Use case: Call companyFAQSearch tool when user asks common questions."

Query Text (query)

Query content to retrieve. AI will extract and fill this field from conversation.

Field Properties:

Required field
Supports expressions
Supports multi-line text
AI automatically extracts query content from conversation

Configuration Examples:

javascript

// 1. AI extracts from conversation
query: ""  // AI will automatically fill with user question

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

// 3. Combine multiple information sources
query: `Product: ${$('Entity Recognition').productName}
Query: ${$('Chat Trigger').message}`

Knowledge Base (knowledgeBaseId)

Select the knowledge base to retrieve from.

Field Properties:

Required field
Select from created knowledge bases
Supports expressions (dynamic knowledge base selection)

Configuration Examples:

javascript

// 1. Static selection
knowledgeBaseId: "kb_product_manual_2024"

// 2. Dynamic selection based on classification
knowledgeBaseId: $('AI Classifier').class === "technical"
  ? "kb_technical_docs"
  : "kb_general_faq"

// 3. Read from config
knowledgeBaseId: $('Config').knowledgeBaseId

Advanced Settings (Settings Panel)

Node Description (nodeDescription)

Add custom description for the node.

yaml

nodeDescription: "Product information retrieval tool"

Output Data

Returns array of retrieved knowledge snippets.

Output Structure:

javascript

{
  results: [
    {
      content: "Knowledge snippet content",
      score: 0.85,           // Relevance score (0-1)
      metadata: {            // Metadata information
        source: "Document source",
        title: "Document title",
        page: 12,
        url: "https://..."
      }
    },
    // ... more results
  ],
  query: "Original query text"
}

Access Output:

javascript

// Get all results
$('Knowledge Retrieval Tool').results

// Get first result content
$('Knowledge Retrieval Tool').results[0].content

// Get first result relevance score
$('Knowledge Retrieval Tool').results[0].score

// Get metadata
$('Knowledge Retrieval Tool').results[0].metadata.source

Workflow Examples

Example 1: Intelligent Q&A Assistant

Chat Trigger
  → AI Agent Node
    System Prompt: "You are an intelligent customer service assistant that can answer various user questions about products."
    
    Tools: [Knowledge Retrieval Tool]
      Tool Name: "searchProductInfo"
      Tool Description: "Retrieve product-related information from product knowledge base. Input: query (query text). Returns: results (retrieval results). Call this tool when user asks about product information."
      Knowledge Base: "kb_product_manual"
      Query: ""  // AI extracts from conversation
  → Answer Node

Conversation example:
User: "What are the features of your product?"
AI: (Calls searchProductInfo tool, query="product features")
Retrieval results: [Knowledge snippets containing product features]
AI: "According to our product manual, our products have the following features:..."

User: "What's the price?"
AI: (Calls searchProductInfo tool, query="price")
Retrieval results: [Knowledge snippets containing price information]
AI: "Product prices vary by configuration, price range is..."

Example 2: Multi-knowledge Base Intelligent Retrieval

Chat Trigger
  → AI Agent Node
    System Prompt: "You are an intelligent assistant that can answer technical questions and common questions."
    
    Tools: [
      Tool 1 - Knowledge Retrieval Tool (Technical Docs)
        Tool Name: "searchTechnicalDocs"
        Tool Description: "Retrieve technical question answers from technical documentation library. Input: query. Returns: results. Call when user asks technical questions."
        Knowledge Base: "kb_technical_docs"
      
      Tool 2 - Knowledge Retrieval Tool (FAQ)
        Tool Name: "searchFAQ"
        Tool Description: "Retrieve common question answers from FAQ knowledge base. Input: query. Returns: results. Call when user asks common questions."
        Knowledge Base: "kb_faq"
    ]
  → Answer Node

Conversation example:
User: "How to install?"
AI: (Identifies as technical question) → Calls searchTechnicalDocs
Retrieval results: [Installation steps documentation]
AI: "According to technical documentation, installation steps are:..."

User: "Do you support refunds?"
AI: (Identifies as common question) → Calls searchFAQ
Retrieval results: [Refund policy FAQ]
AI: "According to our FAQ, refund policy is..."

Example 3: Conversational Information Collection

Chat Trigger
  → AI Agent Node
    System Prompt: "You are a product consultant helping users understand product information."
    
    Tools: [Knowledge Retrieval Tool]
      Tool Name: "queryProductDetails"
      Tool Description: "Retrieve detailed product information. Input: query (product name or features). Returns: results (product information)."
      Knowledge Base: "kb_products"
  → Answer Node

Conversation example:
User: "I want to learn about laptops"
AI: (Calls queryProductDetails, query="laptops")
Retrieval results: [Multiple laptop product information]
AI: "We have multiple laptop products, including..."

User: "Any lightweight ones?"
AI: (Calls queryProductDetails, query="lightweight laptops")
Retrieval results: [Lightweight model information]
AI: "Yes, we have the following lightweight models:..."

Best Practices

1. Write Clear Tool Descriptions

Good tool description:

"Retrieve product information from product knowledge base.
Input parameters:
- query: Query text (product name, features, specifications, etc.)
Returns:
- results: Array of matching knowledge snippets, each containing content, relevance score, and metadata
- query: Original query text

Use cases:
- User asks about product features
- User asks about product specifications
- User asks about product pricing
- User needs product comparison

Example: Call searchProductInfo tool when user says 'what features does this product have'."

2. Reasonably Configure Knowledge Bases

javascript

// 1. Select knowledge base based on domain
knowledgeBaseId: "kb_product_info"  // Product-related

// 2. Dynamic knowledge base selection
knowledgeBaseId: $('AI Classifier').category === "technical"
  ? "kb_technical"
  : "kb_general"

// 3. Multiple tools use different knowledge bases
Tool 1: knowledgeBaseId: "kb_products"      // Product library
Tool 2: knowledgeBaseId: "kb_support"       // Support library
Tool 3: knowledgeBaseId: "kb_pricing"       // Pricing library

3. Handle Retrieval Results

javascript

// AI automatically handles retrieval results, but can guide in System Prompt
System Prompt: `After calling knowledge retrieval tool:
1. Check relevance scores of retrieval results
2. If highest score >= 0.7, use that result to answer
3. If highest score < 0.7, tell user information may not be accurate
4. If results are empty, explain no relevant information in knowledge base`

4. Optimize Query Extraction

javascript

// Help AI understand how to extract queries in tool description
toolDescription: `Retrieve product information.
Input parameters: query (keywords extracted from user questions, such as product name, features, specifications, etc.)
Examples:
- User says "what features does this product have" → query="product features"
- User says "what's the price" → query="price"
- User says "what systems are supported" → query="system requirements"`

FAQ

Q1: How does AI know when to call knowledge retrieval tool?

A: AI decides based on:

Tool description: Whether it matches user question type
Conversation context: Whether knowledge base content reference is needed
Question complexity: Whether additional information is needed to answer

Optimization advice:

Clearly state use cases in tool description
Provide call examples
Explain knowledge base scope being retrieved

Q2: How to handle empty retrieval results?

A: AI automatically handles:

If retrieval results are empty:
1. AI will identify no relevant information found
2. Can tell user no relevant content in knowledge base
3. Suggest user try different wording or contact human support

Q3: Can multiple knowledge bases be used simultaneously?

A: Yes! Create multiple knowledge retrieval tool nodes, each using different knowledge base:

javascript

Tools: [
  Tool 1: knowledgeBaseId: "kb_products"      // Product library
  Tool 2: knowledgeBaseId: "kb_support"       // Support library
  Tool 3: knowledgeBaseId: "kb_pricing"       // Pricing library
]

AI will select appropriate tool to call based on question type.

Q4: How to improve retrieval accuracy?

1. Optimize knowledge base content

Ensure knowledge base content is complete and accurate
Use clear document structure
Add appropriate metadata

2. Optimize tool description

javascript

toolDescription: `Retrieve technical documentation.
Input parameters: query (technical keywords, such as "installation", "configuration", "troubleshooting", etc.)
Returns: results (relevant technical documentation snippets)
Note: Query should include specific technical keywords`

3. Use relevance scores

javascript

System Prompt: `When using knowledge retrieval results:
- Prioritize results with relevance score >= 0.7
- If all results score < 0.7, inform user may need more precise question`

Q5: Can the tool be called multiple times?

A: Yes! AI can call multiple times in same conversation:

User: "What features does this product have?"
AI: (1st call) → Retrieves product feature information

User: "What's the price?"
AI: (2nd call) → Retrieves price information

User: "What colors are available?"
AI: (3rd call) → Retrieves color option information

Next Steps

AI Agent Node - Learn how to use AI Agents
Knowledge Retrieval Action Node - Learn about Action version
HTTP Request Tool Node - Combine with API calls

Entity Recognition Tool Node - Extract query keywords from conversation
Expression Syntax - Learn how to use expressions in configuration

Trigger Nodes

Chat Trigger

Action Nodes

Control Nodes

Tool Nodes

Group Nodes

Knowledge Retrieval Tool Node

Use Cases

Typical Applications

Tool vs Action Differences

Node Configuration

Basic Settings (Parameters Panel)

Tool Name (toolName)

Tool Description (toolDescription)

Query Text (query)

Knowledge Base (knowledgeBaseId)

Advanced Settings (Settings Panel)

Node Description (nodeDescription)

Output Data

Workflow Examples

Example 1: Intelligent Q&A Assistant

Example 2: Multi-knowledge Base Intelligent Retrieval

Example 3: Conversational Information Collection

Best Practices

1. Write Clear Tool Descriptions

2. Reasonably Configure Knowledge Bases

3. Handle Retrieval Results

4. Optimize Query Extraction

FAQ

Q1: How does AI know when to call knowledge retrieval tool?

Q2: How to handle empty retrieval results?

Q3: Can multiple knowledge bases be used simultaneously?

Q4: How to improve retrieval accuracy?

Q5: Can the tool be called multiple times?

Next Steps

Chat Trigger

Knowledge Retrieval Tool Node ​

Use Cases ​

Typical Applications ​

Tool vs Action Differences ​

Node Configuration ​

Basic Settings (Parameters Panel) ​

Tool Name (toolName) ​

Tool Description (toolDescription) ​

Query Text (query) ​

Knowledge Base (knowledgeBaseId) ​

Advanced Settings (Settings Panel) ​

Node Description (nodeDescription) ​

Output Data ​

Workflow Examples ​

Example 1: Intelligent Q&A Assistant ​

Example 2: Multi-knowledge Base Intelligent Retrieval ​

Example 3: Conversational Information Collection ​

Best Practices ​

1. Write Clear Tool Descriptions ​

2. Reasonably Configure Knowledge Bases ​

3. Handle Retrieval Results ​

4. Optimize Query Extraction ​

FAQ ​

Q1: How does AI know when to call knowledge retrieval tool? ​

Q2: How to handle empty retrieval results? ​

Q3: Can multiple knowledge bases be used simultaneously? ​

Q4: How to improve retrieval accuracy? ​

Q5: Can the tool be called multiple times? ​

Next Steps ​

Related Resources ​

Knowledge Retrieval Tool Node

Use Cases

Typical Applications

Tool vs Action Differences

Node Configuration

Basic Settings (Parameters Panel)

Tool Name (toolName)

Tool Description (toolDescription)

Query Text (query)

Knowledge Base (knowledgeBaseId)

Advanced Settings (Settings Panel)

Node Description (nodeDescription)

Output Data

Workflow Examples

Example 1: Intelligent Q&A Assistant

Example 2: Multi-knowledge Base Intelligent Retrieval

Example 3: Conversational Information Collection

Best Practices

1. Write Clear Tool Descriptions

2. Reasonably Configure Knowledge Bases

3. Handle Retrieval Results

4. Optimize Query Extraction

FAQ

Q1: How does AI know when to call knowledge retrieval tool?

Q2: How to handle empty retrieval results?

Q3: Can multiple knowledge bases be used simultaneously?

Q4: How to improve retrieval accuracy?

Q5: Can the tool be called multiple times?

Next Steps

Related Resources