Knowledge Locator

Table of Contents

• What It Is
• Quick Start
• Search Palaces
• List All Palaces
• When to Use
• Search Modalities
• Core Workflow
• Target Metrics
• Detailed Resources
• PR Review Search
• Quick Commands
• Review Chamber Rooms
• Context-Aware Surfacing
• Integration

Knowledge Locator

A spatial indexing and retrieval system for finding information within and across memory palaces. Enables multi-modal search using spatial, semantic, sensory, and associative queries.

What It Is

The Knowledge Locator provides efficient information retrieval across your memory palace network by:

• Building and maintaining spatial indices for fast lookup
• Supporting multiple search modalities (spatial, semantic, sensory)
• Mapping cross-references between palaces
• Tracking access patterns for optimization

Quick Start

Search Palaces

python scripts/palace_manager.py search "authentication" --type semantic

Verification: Run python --version to verify Python environment.

List All Palaces

python scripts/palace_manager.py list

Verification: Run python --version to verify Python environment.

When To Use

• Finding specific concepts within one or more memory palaces
• Cross-referencing information across different palaces
• Discovering connections between stored information
• Finding information using partial or contextual queries
• Analyzing access patterns for palace optimization

When NOT To Use

• Creating new

palace structures - use memory-palace-architect

• Processing new

external resources - use knowledge-intake

• Creating new

palace structures - use memory-palace-architect

• Processing new

external resources - use knowledge-intake

Search Modalities

Core Workflow

1. Build Index - Create spatial index of all palaces 2. Optimize Search - Configure search strategies and heuristics 3. Map Cross-References - Identify inter-palace connections 4. Test Retrieval - Validate search accuracy and speed 5. Analyze Patterns - Track and optimize based on usage

Target Metrics

• Retrieval latency: ≤ 150ms cached, ≤ 500ms cold
• Top-3 accuracy: ≥ 90% for semantic queries
• Robustness: ≥ 80% success with incomplete queries

Detailed Resources

• Index Structure: See modules/index-structure.md
• Search Strategies: See modules/search-strategies.md
• Cross-Reference Mapping: See modules/index-structure.md

PR Review Search

Search the review chamber within project palaces for past decisions and patterns.

Quick Commands

# Search review chamber by query
python scripts/palace_manager.py search "authentication" \
  --palace <project_id> \
  --room review-chamber

# List entries in specific room
python scripts/palace_manager.py list-reviews \
  --palace <project_id> \
  --room decisions

# Find by tags
python scripts/palace_manager.py search-reviews \
  --tags security,api \
  --since 2025-01-01

Verification: Run python --version to verify Python environment.

Review Chamber Rooms

Context-Aware Surfacing

When starting work in a code area, surface relevant review knowledge:

# When in auth/ directory
python scripts/palace_manager.py context-search auth/

# Returns:
# - Past decisions about authentication
# - Known patterns in this area
# - Relevant standards to follow

Verification: Run python --version to verify Python environment.

Integration

Works with:

• memory-palace-architect - Indexes palaces created by architect
• session-palace-builder - Searches session-specific palaces
• digital-garden-cultivator - Finds garden content and links
• review-chamber - Searches PR review knowledge in project palaces

Index Structure

The Knowledge Locator maintains multiple indices for efficient multi-modal search.

Primary Index Schema

{
  "spatial_index": {
    "coordinates": {
      "palace": "string",
      "district": "string",
      "building": "string",
      "room": "string",
      "area": "string"
    },
    "concept_data": {
      "primary_concept": "string",
      "keywords": ["list"],
      "sensory_signature": "object",
      "associations": ["related_concepts"],
      "access_frequency": "number",
      "last_accessed": "timestamp"
    }
  },
  "semantic_index": {
    "concept_clusters": {},
    "relationship_graph": {},
    "context_mappings": {}
  },
  "temporal_index": {
    "creation_timeline": {},
    "access_patterns": {},
    "decay_rates": {}
  }
}

Index Types

Spatial Index

• Hierarchical path-based lookup
• O(log n) traversal for location queries
• Supports wildcard matching at any level

Semantic Index

• Keyword-to-concept mapping
• Concept clustering for similarity search
• Relationship graph for association traversal

Temporal Index

• Creation timeline for history queries
• Access frequency for hot/cold optimization
• Decay rates for staleness detection

Index Maintenance

• Rebuild: Full reindex when structure changes significantly
• Incremental: Update affected entries on concept changes
• Compact: Remove deleted entries and optimize storage

Search Strategies

Effective search combines multiple strategies based on query type and context.

Strategy Selection

Strategy Implementations

Spatial Lookup

1. Parse path components 2. Traverse hierarchy 3. Return exact match or nearest ancestors

Semantic Search

1. Extract query keywords 2. Match against semantic index 3. Rank by relevance score 4. Return top-k results

Fuzzy Matching

1. Apply edit distance tolerance 2. Check phonetic similarity 3. Expand query with synonyms 4. Merge and rank results

Associative Walk

1. Start from known concept 2. Follow association edges 3. Score by path relevance 4. Return connected concepts

Optimization Techniques

• Query caching - Cache frequent queries
• Index partitioning - Split by palace for parallelism
• Hot path optimization - Preload frequently accessed paths
• Lazy loading - Load deep indices on demand