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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nova_chat/agent.py
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190
nova_chat/agent.py
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"""
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Chat agent for NOVA with persona support
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"""
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import torch
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from typing import Optional, List, Dict
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from .persona import Persona, PersonaLoader
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from .memory import ConversationMemory
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from nova_core import NovaTransformer
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from nova_tokenizer import NovaTokenizer
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class ChatAgent:
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"""
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Chat agent that combines NOVA model with persona and memory
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"""
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def __init__(
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self,
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model: NovaTransformer,
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tokenizer: NovaTokenizer,
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persona: Optional[Persona] = None,
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use_memory: bool = True,
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memory_db_path: Optional[str] = None,
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):
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"""
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Args:
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model: NOVA transformer model
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tokenizer: NOVA tokenizer
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persona: Persona configuration (defaults to supportive girlfriend)
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use_memory: Whether to use conversation memory
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memory_db_path: Path to memory database
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"""
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self.model = model
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self.tokenizer = tokenizer
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self.persona = persona or PersonaLoader.create_girlfriend_supportive()
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# Conversation memory
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self.use_memory = use_memory
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if use_memory:
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self.memory = ConversationMemory(db_path=memory_db_path)
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else:
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self.memory = None
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# Current conversation context
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self.conversation_id = None
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self.context = []
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def start_conversation(self, conversation_id: Optional[str] = None):
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"""Start a new conversation"""
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if conversation_id and self.memory:
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# Load existing conversation
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self.conversation_id = conversation_id
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self.context = self.memory.load_conversation(conversation_id)
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else:
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# Start fresh
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import uuid
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self.conversation_id = conversation_id or str(uuid.uuid4())
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self.context = []
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# Add system prompt if configured
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system_prompt = self.persona.format_system_prompt()
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if system_prompt:
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self.context.append({
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'role': 'system',
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'content': system_prompt
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})
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def chat(self, message: str) -> str:
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"""
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Send a message and get response
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Args:
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message: User message
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Returns:
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NOVA's response
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"""
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# Add user message to context
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self.context.append({
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'role': 'user',
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'content': message
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})
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# Format prompt from conversation context
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prompt = self._format_prompt()
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# Get generation parameters from persona
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gen_params = self.persona.get_generation_params()
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# Generate response
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response = self._generate(prompt, **gen_params)
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# Add to context
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self.context.append({
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'role': 'assistant',
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'content': response
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})
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# Save to memory
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if self.memory:
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self.memory.add_message(
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conversation_id=self.conversation_id,
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role='user',
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content=message
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)
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self.memory.add_message(
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conversation_id=self.conversation_id,
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role='assistant',
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content=response
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)
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return response
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def _format_prompt(self) -> str:
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"""Format conversation context into prompt string"""
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parts = []
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for msg in self.context:
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role = msg['role']
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content = msg['content']
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if role == 'system':
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parts.append(f"{content}")
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elif role == 'user':
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parts.append(f"User: {content}")
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elif role == 'assistant':
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parts.append(f"{self.persona.name}: {content}")
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# Add prefix for assistant response
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parts.append(f"{self.persona.name}:")
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return "\n".join(parts)
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def _generate(
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self,
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prompt: str,
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temperature: float = 0.8,
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top_p: float = 0.9,
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top_k: Optional[int] = 50,
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repetition_penalty: float = 1.1,
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max_new_tokens: int = 200,
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) -> str:
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"""Generate response using model"""
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# Tokenize prompt
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input_ids = self.tokenizer.encode(prompt, add_bos=True, add_eos=False)
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input_ids = torch.tensor([input_ids], dtype=torch.long)
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# Move to model device
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device = next(self.model.parameters()).device
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input_ids = input_ids.to(device)
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# Generate
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with torch.no_grad():
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output_ids = self.model.generate(
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input_ids=input_ids,
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max_new_tokens=max_new_tokens,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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repetition_penalty=repetition_penalty,
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do_sample=True,
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eos_token_id=self.tokenizer.eos_id,
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)
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# Decode response (skip the prompt part)
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response_ids = output_ids[0][input_ids.shape[1]:].tolist()
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response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
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# Clean up response
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response = response.strip()
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# Remove any accidental continuation of prompt
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if response.startswith(f"{self.persona.name}:"):
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response = response[len(f"{self.persona.name}:"):].strip()
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return response
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def clear_context(self):
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"""Clear conversation context (but keep system prompt)"""
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system_messages = [msg for msg in self.context if msg['role'] == 'system']
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self.context = system_messages
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def get_context(self) -> List[Dict[str, str]]:
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"""Get current conversation context"""
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return self.context.copy()
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def set_persona(self, persona: Persona):
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"""Change persona mid-conversation"""
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self.persona = persona
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