Mai/.planning/phases/04-memory-context-management/04-memory-context-management-VERIFICATION.md

phase, verified, status, score, re_verification, gaps

phase	verified	status	score	re_verification	gaps
04-memory-context-management	2026-01-28T00:00:00Z	gaps_found	14/16 must-haves verified	previous_status previous_score gaps_closed gaps_remaining regressions gaps_found 12/16 PersonalityAdaptation class implementation - now exists (701 lines) PersonalityLearner integration in MemoryManager - now exported src/personality.py file with memory integration - now exists (483 lines) search_by_keyword method implementation in VectorStore - now implemented store_embeddings method implementation in VectorStore - now implemented sqlite_manager.get_conversation_metadata method - now implemented Pattern extractor integration with PersonalityLearner (missing method) Personality layers learning from conversation patterns (integration broken)	truth status reason artifacts missing Personality layers learn from conversation patterns failed PersonalityLearner calls non-existent extract_conversation_patterns method path issue src/memory/__init__.py Line 103 calls extract_conversation_patterns() which doesn't exist in PatternExtractor path issue src/memory/personality/pattern_extractor.py Missing extract_conversation_patterns method to aggregate all pattern types extract_conversation_patterns method in PatternExtractor class Pattern aggregation method in PersonalityLearner truth status reason artifacts missing Personality system integrates with existing personality.py partial PersonalitySystem exists and integrates with PersonalityLearner but learning pipeline broken path issue src/personality.py Integration exists but PersonalityLearner learning fails due to missing method path issue src/memory/__init__.py PersonalityLearner._aggregate_patterns method exists but can't process data Working pattern extraction pipeline from conversations to personality layers

Phase 04: Memory & Context Management Verification Report

Phase Goal: Build long-term conversation memory and context management system that stores conversation history locally, recalls past conversations efficiently, compresses memory as it grows, distills patterns into personality layers, and proactively surfaces relevant context from memory.

Verified: 2026-01-28T00:00:00Z Status: gaps_found Re-verification: Yes — after gap closure

Goal Achievement

Observable Truths

#	Truth	Status	Evidence
1	Conversations are stored locally in SQLite database	✓ VERIFIED	SQLiteManager with full schema implementation (514 lines)
2	Vector embeddings are stored using sqlite-vec extension	✓ VERIFIED	VectorStore with sqlite-vec integration (487 lines)
3	Database schema supports conversations, messages, and embeddings	✓ VERIFIED	Complete schema with proper indexes and relationships
4	Memory system persists across application restarts	✓ VERIFIED	Thread-local connections and WAL mode for persistence
5	User can search conversations by semantic meaning	✓ VERIFIED	SemanticSearch with VectorStore methods now complete
6	Search results are ranked by relevance to query	✓ VERIFIED	SemanticSearch with relevance scoring and result ranking
7	Context-aware search prioritizes current topic discussions	✓ VERIFIED	ContextAwareSearch now integrates with sqlite_manager metadata
8	Timeline search allows filtering by date ranges	✓ VERIFIED	TimelineSearch with date-range filtering and temporal analysis
9	Hybrid search combines semantic and keyword matching	✓ VERIFIED	SemanticSearch.hybrid_search implementation
10	Old conversations are automatically compressed to save space	✓ VERIFIED	CompressionEngine with progressive compression (606 lines)
11	Compression preserves important information while reducing size	✓ VERIFIED	Multi-level compression with quality scoring
12	JSON archival system stores compressed conversations	✓ VERIFIED	ArchivalManager with organized directory structure (431 lines)
13	Smart retention keeps important conversations longer	✓ VERIFIED	RetentionPolicy with importance scoring (540 lines)
14	7/30/90 day compression tiers are implemented	✓ VERIFIED	CompressionLevel enum with tier-based compression
15	Personality layers learn from conversation patterns	✗ FAILED	PersonalityLearner integration broken due to missing method
16	Personality system integrates with existing personality.py	⚠️ PARTIAL	Integration exists but learning pipeline fails

Score: 14/16 truths verified

Required Artifacts

Artifact	Expected	Status	Details
src/memory/storage/sqlite_manager.py	SQLite database operations and schema management	✓ VERIFIED	514 lines, full implementation, no stubs
src/memory/storage/vector_store.py	Vector storage and retrieval with sqlite-vec	✓ VERIFIED	487 lines, all required methods now implemented
src/memory/__init__.py	Memory module entry point	⚠️ PARTIAL	877 lines, PersonalityLearner export exists but integration broken
src/memory/retrieval/semantic_search.py	Semantic search with embedding-based similarity	✓ VERIFIED	373 lines, complete implementation
src/memory/retrieval/context_aware.py	Topic-based search prioritization	✓ VERIFIED	385 lines, metadata integration now complete
src/memory/retrieval/timeline_search.py	Date-range filtering and temporal search	✓ VERIFIED	449 lines, complete implementation
src/memory/storage/compression.py	Progressive conversation compression	✓ VERIFIED	606 lines, complete implementation
src/memory/backup/archival.py	JSON export/import for long-term storage	✓ VERIFIED	431 lines, complete implementation
src/memory/backup/retention.py	Smart retention policies based on importance	✓ VERIFIED	540 lines, complete implementation
src/memory/personality/pattern_extractor.py	Pattern extraction from conversations	⚠️ PARTIAL	851 lines, missing extract_conversation_patterns method
src/memory/personality/layer_manager.py	Personality overlay system	✓ VERIFIED	630 lines, complete implementation
src/memory/personality/adaptation.py	Dynamic personality updates	✓ VERIFIED	701 lines, complete implementation
src/personality.py	Updated personality system with memory integration	✓ VERIFIED	483 lines, integration implemented

Key Link Verification

From	To	Via	Status	Details
src/memory/storage/vector_store.py	sqlite-vec extension	extension loading and virtual table creation	✓ VERIFIED	conn.load_extension("vec0) implemented
src/memory/storage/vector_store.py	src/memory/storage/sqlite_manager.py	database connection for vector operations	✓ VERIFIED	sqlite_manager.db connection used
src/memory/retrieval/semantic_search.py	src/memory/storage/vector_store.py	vector similarity search operations	✓ VERIFIED	All required methods now implemented
src/memory/retrieval/context_aware.py	src/memory/storage/sqlite_manager.py	conversation metadata for topic analysis	✓ VERIFIED	get_conversation_metadata method now integrated
src/memory/__init__.py	src/memory/retrieval/	search method delegation	✓ VERIFIED	Search methods properly delegated
src/memory/storage/compression.py	src/memory/storage/sqlite_manager.py	conversation data retrieval for compression	✓ VERIFIED	sqlite_manager.get_conversation used
src/memory/backup/archival.py	src/memory/storage/compression.py	compressed conversation data	✓ VERIFIED	compression_engine.compress_by_age used
src/memory/backup/retention.py	src/memory/storage/sqlite_manager.py	conversation importance analysis	✓ VERIFIED	sqlite_manager methods used for scoring
src/memory/__init__.py (PersonalityLearner)	src/memory/personality/pattern_extractor.py	conversation pattern extraction	✗ NOT_WIRED	extract_conversation_patterns method missing
src/memory/personality/layer_manager.py	src/memory/personality/pattern_extractor.py	pattern data for layer creation	⚠️ PARTIAL	Layer creation works but no data from extractor
src/personality.py	src/memory/__init__.py (PersonalityLearner)	personality learning integration	✓ VERIFIED	PersonalitySystem integrates with PersonalityLearner

Requirements Coverage

Requirement	Status	Blocking Issue
Store conversation history locally	✓ SATISFIED	None
Recall past conversations efficiently	✓ SATISFIED	None
Compress memory as it grows	✓ SATISFIED	None
Distill patterns into personality layers	✗ BLOCKED	Pattern extraction pipeline broken
Proactively surface relevant context from memory	✓ SATISFIED	All search systems working

Anti-Patterns Found

File	Line	Pattern	Severity	Impact
src/memory/__init__.py	103	Missing method call	🛑 Blocker	extract_conversation_patterns() doesn't exist in PatternExtractor
No new anti-patterns found in previously fixed areas

Human Verification Required

1. SQLite Database Persistence

   • Test: Create conversations, restart application, verify data persists
   • Expected: All conversations and messages remain after restart
   • Why human: Need to verify actual database file persistence and connection handling

2. Vector Search Accuracy

   • Test: Search for semantically similar conversations, verify relevance
   • Expected: Results ranked by semantic similarity, not just keyword matching
   • Why human: Need to assess search result quality and relevance

3. Compression Quality

   • Test: Compress conversations, verify important information preserved
   • Expected: Key conversation points retained while size reduced
   • Why human: Need to assess compression quality and information retention

4. Personality Learning Pipeline (Once fixed)

   • Test: Have conversations, trigger personality learning, verify patterns extracted
   • Expected: Personality layers created from conversation patterns
   • Why human: Need to assess learning effectiveness and personality adaptation

Gaps Summary

Significant progress has been made since the previous verification:

Successfully Closed Gaps:

• PersonalityAdaptation class now implemented (701 lines)
• PersonalityLearner now properly exported from memory module
• src/personality.py created with memory integration (483 lines)
• VectorStore missing methods (search_by_keyword, store_embeddings) now implemented
• sqlite_manager.get_conversation_metadata method now implemented
• ContextAwareSearch metadata integration now complete

Remaining Critical Gaps:

1. Missing Pattern Extraction Method: The PersonalityLearner calls extract_conversation_patterns(messages) on line 103 of src/memory/init.py, but this method doesn't exist in the PatternExtractor class. The PatternExtractor has individual methods for each pattern type (topics, sentiment, interaction, temporal, response style) but no unified method to extract all patterns from a conversation.

2. Broken Learning Pipeline: Due to the missing method, the entire personality learning pipeline fails. The PersonalityLearner can't extract patterns from conversations, can't aggregate them, and can't create personality layers.

This is a single, focused gap that prevents the personality learning system from functioning, despite all the individual components being well-implemented and substantial.

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Verified: 2026-01-28T00:00:00Z Verifier: Claude (gsd-verifier)