Search Memory - Advanced Memory Retrieval

Search the AI Memory Module using semantic similarity with advanced filtering by collection, type, and intent detection.

Memory System V2.0

The memory system has 3 collections:

•code-patterns - HOW things are built (implementation, error_fix, refactor, file_pattern)
•conventions - WHAT rules to follow (rule, guideline, port, naming, structure)
•discussions - WHY things were decided (decision, session, blocker, preference, context)

Usage

bash

# Basic semantic search (searches code-patterns by default)
/search-memory "how do I implement authentication"

# Search specific collection
/search-memory "error handling patterns" --collection conventions

# Filter by memory type
/search-memory "recent bugs" --type error_fix

# Filter by multiple types
/search-memory "code patterns" --type implementation,refactor

# Use intent detection with cascading search
/search-memory "how do I implement auth" --intent how

# Limit results
/search-memory "database patterns" --limit 10

Options

•--collection <name> - Target specific collection (code-patterns, conventions, discussions)
•--type <type> - Filter by memory type (see types below)
•--intent <intent> - Use intent detection (how, what, why)
•--limit <n> - Maximum results to return (default: 5)
•--group-id <id> - Filter by project (default: auto-detect from cwd)

Memory Types by Collection

code-patterns

•implementation - How features/components were built
•error_fix - Errors encountered and solutions
•refactor - Refactoring patterns applied
•file_pattern - File or module-specific patterns

conventions

•rule - Hard rules that MUST be followed
•guideline - Soft guidelines (SHOULD follow)
•port - Port configuration rules
•naming - Naming conventions
•structure - File and folder structure conventions

discussions

•decision - Architectural/design decisions (DEC-xxx)
•session - Session summaries
•blocker - Blockers and resolutions (BLK-xxx)
•preference - User preferences and working style
•context - Important conversation context

Intent Detection

When using --intent, the system routes to the appropriate primary collection:

•how → code-patterns (implementation examples)
•what → conventions (rules and guidelines)
•why → discussions (decisions and context)

If primary collection has insufficient results, automatically expands to secondary collections.

Examples

bash

# Find implementation examples in current project
/search-memory "authentication implementation"

# Find shared conventions across all projects
/search-memory "naming conventions" --collection conventions

# Find specific error fixes
/search-memory "database connection" --type error_fix

# Use cascading search with intent
/search-memory "why did we choose postgres" --intent why

# Find architectural decisions
/search-memory "database choice" --type decision --collection discussions

# Search multiple types
/search-memory "auth patterns" --type implementation,error_fix --limit 10

Python Implementation Reference

This skill uses search_memories() from src/memory/search.py:

python

from src.memory.search import search_memories

results = search_memories(
    query="your search query",
    collection="code-patterns",  # Optional
    memory_type="implementation",  # Optional, can be list
    use_cascading=True,  # Enable cascading search
    intent="how",  # Optional: auto-detects from query
    limit=5
)

Technical Details

•Semantic Search: Uses jina-embeddings-v2-base-en for vector similarity
•Project Scoping: Automatically detects project from current working directory
•Cascading: Searches primary collection first, expands only if insufficient results
•Attribution: All results include collection and type attribution
•Performance: < 2s for typical searches (NFR-P1)

Notes

•Results sorted by relevance score (highest first)
•Score threshold defaults to 0.7 (configurable in .env)
•Project auto-detection uses git repository root
•code-patterns filtered by project, conventions/discussions are cross-project