# Long-Term Memory System

Stores persistent facts about the user, their preferences, routines, and important information.

## Features

- **Persistent Storage**: SQLite database for long-term storage
- **Categories**: Personal, family, preferences, routines, facts
- **Confidence Scoring**: Track certainty of each fact
- **Source Tracking**: Know where facts came from (explicit, inferred, confirmed)
- **Fast Retrieval**: Indexed lookups and search
- **Prompt Integration**: Format memory for LLM prompts

## Usage

```python
from memory.manager import get_memory_manager
from memory.schema import MemoryCategory, MemorySource

manager = get_memory_manager()

# Store explicit fact
manager.store_fact(
    category=MemoryCategory.PREFERENCES,
    key="favorite_color",
    value="blue",
    confidence=1.0,
    source=MemorySource.EXPLICIT
)

# Store inferred fact
manager.store_fact(
    category=MemoryCategory.ROUTINES,
    key="morning_routine",
    value="coffee at 7am",
    confidence=0.8,
    source=MemorySource.INFERRED,
    context="Mentioned in conversation on 2024-01-06"
)

# Get fact
fact = manager.get_fact(MemoryCategory.PREFERENCES, "favorite_color")
if fact:
    print(f"Favorite color: {fact.value}")

# Search facts
facts = manager.search_facts("coffee", category=MemoryCategory.ROUTINES)

# Format for LLM prompt
memory_text = manager.format_for_prompt()
# Use in system prompt: "## User Memory\n{memory_text}"
```

## Categories

- **PERSONAL**: Personal facts (name, age, location)
- **FAMILY**: Family member information
- **PREFERENCES**: User preferences (favorite foods, colors)
- **ROUTINES**: Daily/weekly routines
- **FACTS**: General facts about the user

## Memory Write Policy

### Explicit Facts (confidence: 1.0)
- User explicitly states: "My favorite color is blue"
- Source: `MemorySource.EXPLICIT`

### Inferred Facts (confidence: 0.7-0.9)
- Inferred from conversation: "I always have coffee at 7am"
- Source: `MemorySource.INFERRED`

### Confirmed Facts (confidence: 0.9-1.0)
- User confirms inferred fact
- Source: `MemorySource.CONFIRMED`

## Integration with LLM

Memory is formatted and injected into system prompts:

```python
memory_text = manager.format_for_prompt(limit=20)
system_prompt = f"""
You are a helpful assistant.

## User Memory

{memory_text}

Use this information to provide personalized responses.
"""
```

## Storage

- **Database**: `data/memory.db` (SQLite)
- **Schema**: See `memory/schema.py`
- **Indexes**: Category, key, last_accessed for fast queries

## Future Enhancements

- Semantic search using embeddings
- Memory summarization
- Confidence decay over time
- Conflict resolution for conflicting facts
- Memory validation and cleanup