Dani
a7f091aa45
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>
135 lines
3.3 KiB
Python
135 lines
3.3 KiB
Python
"""
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REST API for NOVA chat
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"""
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from typing import Optional, List
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import uvicorn
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from .agent import ChatAgent
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from .persona import Persona, PersonaLoader
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app = FastAPI(
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title="NOVA Chat API",
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description="REST API for NOVA - Neuro-Optimizing Versatile Agent",
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version="0.1.0"
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)
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# Request/Response models
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class ChatRequest(BaseModel):
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message: str
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conversation_id: Optional[str] = None
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persona: Optional[str] = None # Persona name or path
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class ChatResponse(BaseModel):
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response: str
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conversation_id: str
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class PersonaInfo(BaseModel):
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name: str
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pronouns: str
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description: str
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always_disclose: bool
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# Global state (in production, use proper state management)
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agents = {}
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default_persona = PersonaLoader.create_girlfriend_supportive()
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@app.get("/")
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async def root():
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"""API info"""
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return {
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"name": "NOVA Chat API",
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"version": "0.1.0",
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"description": "Local-first transformer LLM with persona support"
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}
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@app.post("/chat", response_model=ChatResponse)
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async def chat(request: ChatRequest):
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"""
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Send a message and get response
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Args:
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request: Chat request with message and optional conversation ID
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Returns:
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Chat response with NOVA's reply
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"""
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# Get or create agent for conversation
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conv_id = request.conversation_id or "default"
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if conv_id not in agents:
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# TODO: Load actual model and tokenizer
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# For now, this is a placeholder
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raise HTTPException(
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status_code=501,
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detail="Chat requires trained model. Please train a model first."
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)
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agent = agents[conv_id]
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# Get response
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response = agent.chat(request.message)
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return ChatResponse(
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response=response,
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conversation_id=conv_id
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)
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@app.get("/personas", response_model=List[str])
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async def list_personas():
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"""List available personas"""
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return [
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"girlfriend_gentle",
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"girlfriend_playful",
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"girlfriend_supportive",
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]
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@app.get("/personas/{persona_name}", response_model=PersonaInfo)
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async def get_persona(persona_name: str):
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"""Get persona details"""
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# Load persona
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if persona_name == "girlfriend_gentle":
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persona = PersonaLoader.create_girlfriend_gentle()
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elif persona_name == "girlfriend_playful":
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persona = PersonaLoader.create_girlfriend_playful()
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elif persona_name == "girlfriend_supportive":
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persona = PersonaLoader.create_girlfriend_supportive()
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else:
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raise HTTPException(status_code=404, detail="Persona not found")
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return PersonaInfo(
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name=persona.name,
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pronouns=persona.pronouns,
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description=persona.description,
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always_disclose=persona.always_disclose
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)
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@app.delete("/conversations/{conversation_id}")
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async def delete_conversation(conversation_id: str):
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"""Delete a conversation"""
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if conversation_id in agents:
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del agents[conversation_id]
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return {"status": "deleted"}
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raise HTTPException(status_code=404, detail="Conversation not found")
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def serve(host: str = "0.0.0.0", port: int = 8000):
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"""Start the API server"""
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uvicorn.run(app, host=host, port=port)
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if __name__ == "__main__":
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serve()
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