FindSimilarConcepts

Find [[concept|concepts]] semantically similar to a given concept using [[vector-embeddings]] and cosine similarity. This tool discovers related concepts based on semantic meaning rather than [[graph]] co-occurrence patterns, enabling knowledge exploration through conceptual relationships beyond explicit [[WikiLink]] connections.

When to Use This Tool

Key Features

Parameters

ParameterTypeRequiredDescriptionExample
conceptNamestringYesConcept name to find similar concepts for”machine learning”, “testing”, “agent”
maxResultsintNoMaximum number of similar concepts to return (default: 10)20

Usage Examples

{
  "conceptName": "machine learning"
}

Finds 10 concepts most semantically similar to “machine learning” (e.g., [[neural-networks]], [[deep-learning]], [[AI]]).

Extended Results

{
  "conceptName": "testing",
  "maxResults": 20
}

Returns 20 similar concepts to discover broader semantic neighborhood.

Exploring Agent Concepts

{
  "conceptName": "agent"
}

Discovers agent-related concepts: [[agent-orchestration]], [[multi-agent]], [[autonomous-agent]], etc.

{
  "conceptName": "MCP"
}

Identifies MCP ecosystem concepts: [[MCP-server]], [[Model-Context-Protocol]], [[tool]], [[resource]], etc.

Synonym Detection

{
  "conceptName": "knowledge-graph"
}

May reveal: [[concept-graph]], [[semantic-network]], [[ontology]] - potential duplicates to consolidate.

How It Works

Embedding Generation

  1. Normalizes input concept name (lowercase, hyphens)
  2. Generates 384-dimensional [[vector-embedding]] using [[ONNX]] model
  3. Converts embedding to SQLite-compatible blob format

Similarity Calculation

  1. Loads SQLite [[vector-extension]] for cosine distance calculation
  2. Queries vec_concepts table with vector similarity search
  3. Uses vec_distance_cosine() function to compute distances
  4. Converts distance to similarity score: similarity = 1.0 / (1.0 + distance)
  5. Ranks concepts by similarity score (higher = more similar)

Result Filtering

Output Structure

Similar concepts to 'machine-learning' (by semantic similarity):

  • neural-network (similarity: 0.923)
  • deep-learning (similarity: 0.891)
  • supervised-learning (similarity: 0.854)
  • artificial-intelligence (similarity: 0.832)
  • training (similarity: 0.798)
  • model (similarity: 0.776)
  • prediction (similarity: 0.743)
  • algorithm (similarity: 0.721)
  • data-science (similarity: 0.698)
  • classification (similarity: 0.673)

Suggested next steps: BuildContext, SearchMemories

No Results Found

No similar concepts found for 'obscure-concept'. Run Sync to ensure concept embeddings are generated.

Similarity Score Interpretation

Score Ranges

Use Cases by Score

FindSimilarConcepts (Semantic)

BuildContext (Graph)

Example Difference

Query: “testing”

FindSimilarConcepts might return:

BuildContext might return:

Both are valuable - semantic for discovery, graph for structure.

Common Patterns

Concept Quality Audit

# Find similar concepts to check for duplicates
FindSimilarConcepts conceptName="agent"

# If high-similarity concepts exist, analyze with AnalyzeConceptCorruption
AnalyzeConceptCorruption conceptFamily="agent"

# Consolidate true duplicates with RepairConcepts
RepairConcepts conceptsToReplace="agents" canonicalConcept="agent" dryRun=true

Knowledge Exploration Workflow

# Step 1: Find semantically similar concepts
FindSimilarConcepts conceptName="machine-learning" maxResults=20

# Step 2: Build graph context for most similar
BuildContext conceptName="neural-network" depth=2

# Step 3: Search files using both concepts
SearchMemories query="machine learning neural network"

Research Support Pattern

# Exploring a new research area
FindSimilarConcepts conceptName="graph-neural-networks"

# Each similar concept becomes a research thread
# Use SearchMemories to find existing knowledge on each

# Find concepts that should be linked but aren't
FindSimilarConcepts conceptName="testing"

# For high-similarity concepts without graph connections,
# consider adding WikiLinks to create explicit relationships

Troubleshooting

Error: “No similar concepts found. Run Sync to ensure concept embeddings are generated”

Cause: vec_concepts table doesn’t have embeddings for concepts Solution: Run Sync tool to scan memory files and generate concept embeddings

Error: “Unable to generate embedding for ‘{concept}’”

Cause: ONNX embedding model failed to generate vector for input Solution: Check concept name is valid text, verify ONNX model is loaded correctly

Error: “Ensure vector extension is available”

Cause: SQLite vector extension not loaded Solution: Verify vector extension installation, check Config.DatabasePath

Result: Only low-similarity matches

Cause: Concept is unique or very specific, no semantically close concepts exist Solution: This is valid - not all concepts have close semantic neighbors

Results Seem Wrong or Unexpected

Cause: Vector embeddings capture semantic meaning, not domain-specific relationships Solution: Remember this is general language semantics, not your specific knowledge structure - use BuildContext for your intentional connections

Same Concepts with Different Casing

Cause: Concepts not normalized consistently in source files Solution: Use RepairConcepts to standardize casing after identifying duplicates

Integration Workflows

With AnalyzeConceptCorruption

# Discover potential duplicates
FindSimilarConcepts conceptName="tool" maxResults=50

# Analyze the concept family
AnalyzeConceptCorruption conceptFamily="tool"

# Consolidate true duplicates
RepairConcepts conceptsToReplace="tools,Tools" canonicalConcept="tool"

With BuildContext

# Find semantically similar concepts
FindSimilarConcepts conceptName="agent"

# For interesting similar concepts, explore graph relationships
BuildContext conceptName="autonomous-agent" depth=2

# Compare semantic similarity vs explicit graph connections

With SearchMemories

# Find similar concepts
FindSimilarConcepts conceptName="testing"

# Search for files using top similar concepts
SearchMemories query="testing quality-assurance validation"

# Discover knowledge using semantic neighborhood

Performance Considerations

First Run Performance

Database Requirements

Optimization Tips

Example Exploration Session

Step 1: Start with Known Concept

{
  "conceptName": "sequential-thinking",
  "maxResults": 10
}

Step 2: Review Results

  • chain-of-thought (similarity: 0.912)
  • reasoning (similarity: 0.867)
  • workflow (similarity: 0.834)
  • thinking (similarity: 0.801)
  • analysis (similarity: 0.776)

Step 3: Explore Interesting Similar Concept

{
  "conceptName": "chain-of-thought",
  "maxResults": 10
}

Step 4: Build Graph Context

{
  "conceptName": "chain-of-thought",
  "depth": 2
}

Step 5: Search Files

{
  "query": "chain of thought reasoning"
}

Ma Protocol Compliance

FindSimilarConcepts follows Maenifold’s Ma Protocol principles:

This tool embodies Ma Protocol’s principle of space for semantic discovery - revealing conceptual relationships that emerge from meaning itself, not just explicit connections, while maintaining transparency about how similarity is calculated.