gb/ALWAYS.md

ALWAYS.md - THINK KB Keyword Documentation

Overview

The THINK KB keyword provides explicit knowledge base reasoning capabilities, allowing bots to perform structured semantic searches across active knowledge bases and return detailed results for analysis and decision-making.

Syntax

results = THINK KB "query_text"
results = THINK KB query_variable

Parameters

Parameter	Type	Description
query_text	String	The question or search query to execute against active KBs
query_variable	Variable	Variable containing the search query

Return Value

The THINK KB keyword returns a structured object containing:

{
  "results": [
    {
      "content": "Relevant text content from document",
      "source": "path/to/source/document.pdf", 
      "kb_name": "knowledge_base_name",
      "relevance": 0.85,
      "tokens": 150
    }
  ],
  "summary": "Brief summary of findings",
  "confidence": 0.78,
  "total_results": 5,
  "sources": ["doc1.pdf", "doc2.md"],
  "query": "original search query",
  "kb_count": 2
}

Result Fields

Field	Type	Description
results	Array	Array of search results with content and metadata
summary	String	Human-readable summary of the search findings
confidence	Number	Overall confidence score (0.0 to 1.0)
total_results	Number	Total number of results found
sources	Array	List of unique source documents
query	String	The original search query
kb_count	Number	Number of knowledge bases searched

Individual Result Fields

Field	Type	Description
content	String	The relevant text content from the document
source	String	Path to the source document
kb_name	String	Name of the knowledge base containing this result
relevance	Number	Relevance score (0.0 to 1.0)
tokens	Number	Estimated token count for this content

Examples

Basic Knowledge Base Query

' Activate knowledge bases first
USE KB "company_policies"
USE KB "hr_handbook"

' Perform structured search
results = THINK KB "What is the remote work policy?"

' Access results
TALK results.summary
PRINT "Confidence: " + results.confidence
PRINT "Found " + results.total_results + " results"

' Process individual results
FOR i = 0 TO results.results.length - 1
  result = results.results[i]
  PRINT "Source: " + result.source
  PRINT "Relevance: " + result.relevance
  PRINT "Content: " + result.content
  PRINT "---"
NEXT i

Decision Making with Confidence Thresholds

USE KB "technical_docs"
USE KB "troubleshooting"

query = "How to fix database connection errors?"
results = THINK KB query

IF results.confidence > 0.8 THEN
  TALK "I found reliable information: " + results.summary
  ' Show top result
  IF results.total_results > 0 THEN
    top_result = results.results[0]
    TALK "Best match from: " + top_result.source
    TALK top_result.content
  END IF
ELSE IF results.confidence > 0.5 THEN
  TALK "I found some relevant information, but I'm not completely certain: " + results.summary
ELSE
  TALK "I couldn't find reliable information about: " + query
  TALK "You might want to consult additional resources."
END IF

Comparative Analysis

USE KB "product_specs"
USE KB "competitor_analysis"

' Compare multiple queries
queries = ["pricing strategy", "feature comparison", "market positioning"]

FOR i = 0 TO queries.length - 1
  query = queries[i]
  results = THINK KB query
  
  PRINT "=== Analysis: " + query + " ==="
  PRINT "Confidence: " + results.confidence
  PRINT "Sources: " + results.sources.length
  PRINT "Summary: " + results.summary
  PRINT ""
NEXT i

Source-Based Filtering

USE KB "legal_documents"

results = THINK KB "contract termination clauses"

' Filter by source type
pdf_results = []
FOR i = 0 TO results.results.length - 1
  result = results.results[i]
  IF result.source CONTAINS ".pdf" THEN
    pdf_results.push(result)
  END IF
NEXT i

TALK "Found " + pdf_results.length + " results from PDF documents"

Dynamic Query Building

USE KB "customer_support"

customer_issue = HEAR "What's your issue?"
priority = HEAR "What's the priority level?"

' Build contextual query
query = customer_issue + " priority:" + priority + " resolution steps"
results = THINK KB query

IF results.confidence > 0.7 THEN
  TALK "Here's what I found for your " + priority + " priority issue:"
  TALK results.summary
  
  ' Show most relevant result
  IF results.total_results > 0 THEN
    best_result = results.results[0]
    TALK "From " + best_result.source + ":"
    TALK best_result.content
  END IF
ELSE
  TALK "I need to escalate this issue. Let me connect you with a human agent."
END IF

Advanced Usage Patterns

Multi-Stage Reasoning

USE KB "research_papers"
USE KB "case_studies"

' Stage 1: Find general information
general_results = THINK KB "machine learning applications"

' Stage 2: Drill down based on initial findings
IF general_results.confidence > 0.6 THEN
  ' Extract key terms from top results for refined search
  specific_query = "deep learning " + general_results.results[0].content.substring(0, 50)
  specific_results = THINK KB specific_query
  
  TALK "General overview: " + general_results.summary
  TALK "Specific details: " + specific_results.summary
END IF

Quality Assessment

USE KB "quality_standards"

results = THINK KB "ISO certification requirements"

' Assess result quality
high_quality_results = []
FOR i = 0 TO results.results.length - 1
  result = results.results[i]
  IF result.relevance > 0.8 AND result.tokens > 100 THEN
    high_quality_results.push(result)
  END IF
NEXT i

IF high_quality_results.length > 0 THEN
  TALK "Found " + high_quality_results.length + " high-quality matches"
ELSE
  TALK "Results may need verification from additional sources"
END IF

Differences from Automatic KB Search

Feature	Automatic (USE KB)	Explicit (THINK KB)
Trigger	User questions automatically search	Explicit keyword execution
Control	Automatic, behind-the-scenes	Full programmatic control
Results	Injected into LLM context	Structured data for processing
Analysis	LLM interprets automatically	Bot can analyze before responding
Confidence	Not exposed	Explicit confidence scoring
Filtering	Not available	Full result filtering and processing

Performance Considerations

• Search Time: 100-500ms depending on KB size and query complexity
• Memory Usage: Results cached for session duration
• Token Limits: Automatically respects token limits (default: 2000 tokens)
• Concurrent Searches: Searches all active KBs in parallel

Error Handling

TRY
  results = THINK KB user_query
  IF results.total_results = 0 THEN
    TALK "No information found for: " + user_query
  END IF
CATCH error
  TALK "Search failed: " + error.message
  TALK "Please try a different query or check if knowledge bases are active"
END TRY

Best Practices

1. Activate Relevant KBs First: Use USE KB to activate appropriate knowledge bases
2. Check Confidence Scores: Use confidence thresholds for decision making
3. Handle Empty Results: Always check total_results before accessing results array
4. Filter by Relevance: Consider filtering results below 0.5 relevance
5. Limit Result Processing: Process only top N results to avoid performance issues
6. Cache Results: Store results in variables for multiple uses

Integration with Other Keywords

With LLM Keyword

results = THINK KB "technical specifications"
IF results.confidence > 0.7 THEN
  context = "Based on: " + results.summary
  response = LLM "Explain this in simple terms" WITH CONTEXT context
  TALK response
END IF

With Decision Making

policy_results = THINK KB "expense policy limits"
IF policy_results.confidence > 0.8 THEN
  ' Use structured data for automated decisions
  FOR i = 0 TO policy_results.results.length - 1
    result = policy_results.results[i]
    IF result.content CONTAINS "maximum $500" THEN
      SET expense_limit = 500
      BREAK
    END IF
  NEXT i
END IF

Troubleshooting

No Results Returned

results = THINK KB query
IF results.total_results = 0 THEN
  PRINT "No active KBs: " + results.kb_count
  PRINT "Try: USE KB 'collection_name' first"
END IF

Low Confidence Scores

• Refine query terms to be more specific
• Check if relevant documents are in active KBs
• Consider expanding search to additional knowledge bases
• Verify document quality and indexing

Performance Issues

• Limit concurrent THINK KB calls
• Use more specific queries to reduce result sets
• Consider caching results for repeated queries
• Monitor token usage in results

See Also

• USE KB - Activate knowledge bases for automatic search
• CLEAR KB - Deactivate knowledge bases
• KB Statistics - Knowledge base metrics
• Knowledge Base System - Technical architecture
• Semantic Search - Search algorithms