Initial commit: NOVA - Neuro-Optimizing Versatile Agent
Complete transformer LLM built from scratch with: Core Features: - Full transformer architecture (RoPE, RMSNorm, SwiGLU, KV-cache) - SentencePiece tokenizer (BPE/Unigram) - Training pipeline (AMP, gradient checkpointing, DDP) - Persona system with personality matrix (NO AI disclosure by default) - Genetic evolution (NOVA-EVO) for hyperparameter optimization - Legal-only data pipeline with license tracking - Chat interface (CLI + REST API) - Conversation memory (SQLite) Model Sizes: - 125M, 350M, 1.3B, 3B parameters - Local-first, runs on CPU or GPU - Python 3.10.6+, PyTorch 2.0+ Personas: - girlfriend_gentle (high warmth, high empathy) - girlfriend_playful (high humor, high playfulness) - girlfriend_supportive (balanced, default) Documentation: - Complete README with quickstart - Model card with ethical considerations - Privacy documentation (local-first, zero telemetry) - Data licenses and attribution - Contributing guide Infrastructure: - GitHub Actions CI/CD - Comprehensive test suite - Quickstart script - CLI tool License: Apache 2.0 🤖 Generated with Claude Code https://claude.com/claude-code Co-Authored-By: Claude <noreply@anthropic.com>
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169
nova_chat/memory.py
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169
nova_chat/memory.py
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"""
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Conversation memory system using SQLite
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"""
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import sqlite3
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from typing import List, Dict, Optional
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from pathlib import Path
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import json
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from datetime import datetime
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class ConversationMemory:
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"""
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Simple conversation memory using SQLite
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Stores conversation history for retrieval and continuity
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"""
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def __init__(self, db_path: Optional[str] = None):
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"""
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Args:
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db_path: Path to SQLite database (default: memory.db in current dir)
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"""
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self.db_path = db_path or "memory.db"
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self._init_db()
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def _init_db(self):
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"""Initialize database schema"""
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Path(self.db_path).parent.mkdir(parents=True, exist_ok=True)
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conn = sqlite3.connect(self.db_path)
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cursor = conn.cursor()
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# Conversations table
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS conversations (
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conversation_id TEXT PRIMARY KEY,
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created_at TEXT,
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last_message_at TEXT,
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metadata TEXT
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)
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''')
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# Messages table
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS messages (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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conversation_id TEXT,
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role TEXT,
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content TEXT,
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timestamp TEXT,
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FOREIGN KEY (conversation_id) REFERENCES conversations(conversation_id)
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)
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''')
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# Create indexes
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cursor.execute('''
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CREATE INDEX IF NOT EXISTS idx_messages_conversation
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ON messages(conversation_id)
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''')
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conn.commit()
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conn.close()
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def add_message(
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self,
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conversation_id: str,
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role: str,
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content: str,
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metadata: Optional[Dict] = None
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):
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"""Add a message to conversation history"""
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conn = sqlite3.connect(self.db_path)
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cursor = conn.cursor()
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timestamp = datetime.now().isoformat()
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# Ensure conversation exists
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cursor.execute('''
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INSERT OR IGNORE INTO conversations (conversation_id, created_at, last_message_at, metadata)
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VALUES (?, ?, ?, ?)
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''', (conversation_id, timestamp, timestamp, json.dumps(metadata or {})))
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# Update last message time
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cursor.execute('''
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UPDATE conversations
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SET last_message_at = ?
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WHERE conversation_id = ?
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''', (timestamp, conversation_id))
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# Add message
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cursor.execute('''
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INSERT INTO messages (conversation_id, role, content, timestamp)
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VALUES (?, ?, ?, ?)
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''', (conversation_id, role, content, timestamp))
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conn.commit()
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conn.close()
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def load_conversation(self, conversation_id: str) -> List[Dict[str, str]]:
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"""
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Load conversation history
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Returns:
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List of message dicts with 'role' and 'content'
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"""
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conn = sqlite3.connect(self.db_path)
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cursor = conn.cursor()
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cursor.execute('''
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SELECT role, content
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FROM messages
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WHERE conversation_id = ?
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ORDER BY id ASC
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''', (conversation_id,))
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messages = [
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{'role': row[0], 'content': row[1]}
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for row in cursor.fetchall()
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]
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conn.close()
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return messages
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def get_recent_conversations(self, limit: int = 10) -> List[Dict]:
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"""Get list of recent conversations"""
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conn = sqlite3.connect(self.db_path)
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cursor = conn.cursor()
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cursor.execute('''
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SELECT conversation_id, created_at, last_message_at
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FROM conversations
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ORDER BY last_message_at DESC
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LIMIT ?
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''', (limit,))
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conversations = [
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{
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'conversation_id': row[0],
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'created_at': row[1],
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'last_message_at': row[2]
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}
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for row in cursor.fetchall()
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]
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conn.close()
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return conversations
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def delete_conversation(self, conversation_id: str):
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"""Delete a conversation and all its messages"""
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conn = sqlite3.connect(self.db_path)
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cursor = conn.cursor()
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cursor.execute('DELETE FROM messages WHERE conversation_id = ?', (conversation_id,))
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cursor.execute('DELETE FROM conversations WHERE conversation_id = ?', (conversation_id,))
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conn.commit()
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conn.close()
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def clear_all(self):
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"""Clear all conversations (use with caution!)"""
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conn = sqlite3.connect(self.db_path)
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cursor = conn.cursor()
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cursor.execute('DELETE FROM messages')
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cursor.execute('DELETE FROM conversations')
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conn.commit()
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conn.close()
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