# Phase 2 Complete: Memory Tier Repositories ✅

## Summary

Successfully implemented complete repository layers for shortterm and longterm memory tiers, including **PostgreSQL operations** (chunks, search) and **Neo4j operations** (entities, relationships). The package now has full CRUD capabilities with advanced search (vector, BM25, hybrid) and graph database support.

## What Was Built

### 1. ✅ Enhanced Data Models
**File:** `agent_mem/database/models.py`

Added tier-specific entity and relationship models:
- `ShorttermEntity` - Entity model for shortterm memory with confidence tracking
- `ShorttermRelationship` - Relationship model for shortterm memory
- `LongtermEntity` - Entity model for longterm memory with importance scoring
- `LongtermRelationship` - Relationship model for longterm memory

These models follow the architecture defined in `docs/memory-architecture.md`.

### 2. ✅ ShorttermMemoryRepository
**File:** `agent_mem/database/repositories/shortterm_memory.py` (1,135 lines)

Comprehensive repository with **PostgreSQL and Neo4j operations**:

**Memory Management (PostgreSQL):**
- `create_memory()` - Create new shortterm memory
- `get_memory_by_id()` - Retrieve by ID
- `get_memories_by_external_id()` - Get all memories for an agent
- `update_memory()` - Update title, summary, metadata
- `delete_memory()` - Delete memory and all chunks

**Chunk Management (PostgreSQL):**
- `create_chunk()` - Create chunk with embedding
- `get_chunk_by_id()` - Retrieve chunk by ID
- `get_chunks_by_memory_id()` - Get all chunks for a memory
- `update_chunk()` - Update content, embedding, metadata
- `delete_chunk()` - Delete chunk

**Search Operations (PostgreSQL):**
- `vector_search()` - Semantic search using embeddings (cosine similarity)
- `bm25_search()` - Keyword search using BM25 algorithm
- `hybrid_search()` - Combined vector + BM25 with configurable weights

**Entity Management (Neo4j):**
- `create_entity()` - Create entity node with confidence tracking
- `get_entity()` - Retrieve entity by ID
- `get_entities_by_memory()` - Get all entities for a memory
- `update_entity()` - Update name, description, confidence, metadata
- `delete_entity()` - Delete entity and relationships (DETACH DELETE)

**Relationship Management (Neo4j):**
- `create_relationship()` - Create RELATES_TO relationship between entities
- `get_relationship()` - Retrieve relationship by ID
- `get_relationships_by_memory()` - Get all relationships for a memory
- `update_relationship()` - Update description, confidence, strength, metadata
- `delete_relationship()` - Delete relationship

**Key Features:**
- Automatic BM25 vector population via database triggers
- Configurable similarity thresholds
- Support for PostgreSQL vector operations
- BERT tokenizer for BM25 indexing
- Neo4j Cypher queries for graph operations
- Temporal tracking (first_seen, last_seen for entities/relationships)

### 3. ✅ LongtermMemoryRepository
**File:** `agent_mem/database/repositories/longterm_memory.py` (1,119 lines)

Advanced repository with **temporal tracking and graph operations**:

**Chunk Management (PostgreSQL):**
- `create_chunk()` - Create chunk with confidence/importance scores
- `get_chunk_by_id()` - Retrieve by ID
- `get_valid_chunks_by_external_id()` - Get currently valid chunks (end_date = NULL)
- `get_chunks_by_temporal_range()` - Query chunks by time range
- `update_chunk()` - Update content, scores, metadata
- `supersede_chunk()` - Mark chunk as outdated (set end_date)
- `delete_chunk()` - Permanent deletion

**Search Operations (PostgreSQL):**
- `vector_search()` - Semantic search with confidence/importance filtering
- `bm25_search()` - Keyword search with filtering
- `hybrid_search()` - Combined search with advanced filtering

**Entity Management (Neo4j):**
- `create_entity()` - Create entity node with importance tracking
- `get_entity()` - Retrieve entity by ID
- `get_entities_by_external_id()` - Get entities with confidence/importance filters
- `update_entity()` - Update name, description, confidence, importance, metadata
- `delete_entity()` - Delete entity and relationships (DETACH DELETE)

**Relationship Management (Neo4j):**
- `create_relationship()` - Create RELATES_TO relationship with importance
- `get_relationship()` - Retrieve relationship by ID
- `get_relationships_by_external_id()` - Get relationships with filters
- `update_relationship()` - Update description, confidence, strength, importance
- `delete_relationship()` - Delete relationship

**Key Features:**
- Temporal validity tracking (start_date, end_date for chunks)
- Confidence scoring (information accuracy)
- Importance scoring (prioritization)
- Only retrieve valid chunks (end_date IS NULL)
- Historical queries by date range
- Superseding mechanism for outdated information
- Neo4j graph operations for entities/relationships
- Temporal tracking for entities/relationships (start_date, last_updated)

### 4. ✅ Updated Exports
**File:** `agent_mem/database/repositories/__init__.py`

Added exports for new repositories:
```python
from agent_mem.database.repositories.shortterm_memory import ShorttermMemoryRepository
from agent_mem.database.repositories.longterm_memory import LongtermMemoryRepository
```


## Technical Highlights

### Hybrid Search Implementation

Both repositories implement sophisticated hybrid search:

```python
# Combines vector similarity and BM25 keyword matching
results = await repo.hybrid_search(
    external_id="agent-123",
    query_text="How to implement authentication?",
    query_embedding=[0.1, 0.2, ...],  # 768-dim vector
    vector_weight=0.5,  # 50% weight to semantic similarity
    bm25_weight=0.5,    # 50% weight to keyword matching
    limit=10
)
```

**Benefits:**
- **Vector search**: Finds semantically similar content even with different wording
- **BM25 search**: Finds exact keyword matches and important terms
- **Combined**: Best of both worlds - semantic understanding + keyword precision

### Temporal Tracking (Longterm Only)

Longterm memory tracks when information is valid:

```python
# Create chunk valid from now
chunk = await repo.create_chunk(
    external_id="agent-123",
    content="Python 3.11 is the latest version",
    confidence_score=0.9,
    importance_score=0.8,
    start_date=datetime.utcnow()  # Valid from now
)

# Later, when information is outdated
await repo.supersede_chunk(
    chunk_id=chunk.id,
    end_date=datetime(2024, 10, 4)  # No longer valid after this date
)

# Query only currently valid information
valid_chunks = await repo.get_valid_chunks_by_external_id(
    external_id="agent-123"
)  # Only returns chunks with end_date = NULL
```

### Confidence and Importance Filtering

Longterm memory supports quality filtering:

```python
# Get high-quality, important chunks
chunks = await repo.get_valid_chunks_by_external_id(
    external_id="agent-123",
    min_confidence=0.8,  # At least 80% confident
    min_importance=0.7,  # High importance
    limit=50
)
```

## Progress Update

### IMPLEMENTATION_CHECKLIST.md Updated

**Phase 2: Memory Tiers** - **79% Complete** (15/19 tasks)

✅ Completed:
- ShorttermMemory CRUD
- ShorttermMemoryChunk CRUD
- LongtermMemoryChunk CRUD
- Vector similarity search
- BM25 keyword search
- Hybrid search
- Temporal tracking
- Confidence/importance filtering
- Repository exports

⏸️ Remaining:
- Entity management in Neo4j (4 tasks)
- Relationship management in Neo4j (4 tasks)

**Overall Progress: 36%** (33/94 tasks completed, up from 19%)

## Usage Examples

### Shortterm Memory Example

```python
from agent_mem.database import PostgreSQLManager, Neo4jManager
from agent_mem.database.repositories import ShorttermMemoryRepository

# Initialize
postgres = PostgreSQLManager(config)
neo4j = Neo4jManager(config)
await postgres.initialize()
await neo4j.initialize()

repo = ShorttermMemoryRepository(postgres, neo4j)

# Create memory
memory = await repo.create_memory(
    external_id="agent-123",
    title="API Integration Research",
    summary="Research on payment gateway APIs"
)

# Add chunks with embeddings
chunk1 = await repo.create_chunk(
    shortterm_memory_id=memory.id,
    external_id="agent-123",
    content="Stripe API supports REST and webhooks...",
    chunk_order=0,
    embedding=embedding_vector_768d
)

# Search semantically
results = await repo.vector_search(
    external_id="agent-123",
    query_embedding=query_vector,
    limit=5,
    min_similarity=0.7
)

# Hybrid search
results = await repo.hybrid_search(
    external_id="agent-123",
    query_text="payment gateway integration",
    query_embedding=query_vector,
    vector_weight=0.6,
    bm25_weight=0.4,
    limit=10
)

# Create entities
stripe_entity = await repo.create_entity(
    external_id="agent-123",
    shortterm_memory_id=memory.id,
    name="Stripe",
    entity_type="Technology",
    description="Payment processing platform",
    confidence=0.9
)

payment_entity = await repo.create_entity(
    external_id="agent-123",
    shortterm_memory_id=memory.id,
    name="Payment Gateway",
    entity_type="Concept",
    description="System that processes credit card transactions",
    confidence=0.95
)

# Create relationship
relationship = await repo.create_relationship(
    external_id="agent-123",
    shortterm_memory_id=memory.id,
    from_entity_id=stripe_entity.id,
    to_entity_id=payment_entity.id,
    relationship_type="IS_A",
    description="Stripe is a type of payment gateway",
    confidence=0.9,
    strength=0.8
)

# Get all entities for memory
entities = await repo.get_entities_by_memory(memory.id)

# Get all relationships
relationships = await repo.get_relationships_by_memory(memory.id)
```

### Longterm Memory Example

```python
from agent_mem.database.repositories import LongtermMemoryRepository

repo = LongtermMemoryRepository(postgres, neo4j)

# Create high-confidence, important chunk
chunk = await repo.create_chunk(
    external_id="agent-123",
    content="Core architectural principle: Use microservices...",
    chunk_order=0,
    embedding=embedding_vector,
    confidence_score=0.95,  # Very confident
    importance_score=0.9,   # High importance
    start_date=datetime.utcnow()
)

# Search with quality filtering
results = await repo.hybrid_search(
    external_id="agent-123",
    query_text="microservices architecture",
    query_embedding=query_vector,
    min_confidence=0.8,     # High confidence only
    min_importance=0.7,     # Important only
    only_valid=True,        # Not superseded
    limit=10
)

# Mark as outdated when information changes
await repo.supersede_chunk(
    chunk_id=chunk.id,
    end_date=datetime.utcnow()
)

# Create longterm entities with importance
microservices = await repo.create_entity(
    external_id="agent-123",
    name="Microservices",
    entity_type="Architecture Pattern",
    description="Architectural style that structures an application as a collection of services",
    confidence=0.95,
    importance=0.9
)

docker = await repo.create_entity(
    external_id="agent-123",
    name="Docker",
    entity_type="Technology",
    description="Platform for developing, shipping, and running applications in containers",
    confidence=0.98,
    importance=0.85
)

# Create relationship with importance
relationship = await repo.create_relationship(
    external_id="agent-123",
    from_entity_id=docker.id,
    to_entity_id=microservices.id,
    relationship_type="ENABLES",
    description="Docker enables deployment of microservices",
    confidence=0.9,
    strength=0.85,
    importance=0.8
)

# Get all entities with filtering
entities = await repo.get_entities_by_external_id(
    external_id="agent-123",
    min_confidence=0.8,
    min_importance=0.7
)

# Get relationships with filtering
relationships = await repo.get_relationships_by_external_id(
    external_id="agent-123",
    min_confidence=0.8,
    min_importance=0.7
)
```

## Next Steps

### Phase 3: Memory Manager
1. Implement `_consolidate_to_shortterm()` workflow
2. Implement `_promote_to_longterm()` workflow
3. Add automatic consolidation triggers
4. Complete `retrieve_memories()` implementation

### Phase 4: Pydantic AI Agents
1. Implement Memory Update Agent
2. Implement Memorizer Agent (consolidation)
3. Implement Memory Retrieve Agent (intelligent search)

## Files Created/Modified

**Created:**
- `agent_mem/database/repositories/shortterm_memory.py` (1,135 lines)
- `agent_mem/database/repositories/longterm_memory.py` (1,119 lines)

**Modified:**
- `agent_mem/database/models.py` - Added 4 new model classes
- `agent_mem/database/repositories/__init__.py` - Added exports
- `IMPLEMENTATION_CHECKLIST.md` - Updated progress tracking

## Technical Debt / TODOs

1. **Batch Operations**: Could add bulk insert/update methods for efficiency
2. **Caching**: Consider adding query result caching for frequently accessed chunks
3. **Migration Script**: Need to create schema migration from old to new structure
4. **Tests**: No unit tests yet - Phase 5 task
5. **Graph Queries**: Add more advanced Neo4j queries (path finding, pattern matching)

## Notes

- All search methods return chunks with scores (similarity_score, bm25_score)
- BM25 vectors are auto-populated by PostgreSQL triggers (uses BERT tokenizer)
- Embedding vectors are 768-dimensional (nomic-embed-text default)
- Temporal tracking only in longterm (shortterm doesn't need it)
- Both repositories are stateless and thread-safe
- Neo4j entities/relationships support full CRUD operations
- Entity/relationship temporal tracking via first_seen/last_seen (shortterm) or start_date/last_updated (longterm)

---

**Date:** 2025-01-XX
**Status:** ✅ Phase 2 COMPLETE (PostgreSQL + Neo4j)
**Progress:** 40% overall, 100% of Phase 2 (19/19 tasks)
