import os import logging import requests from time import perf_counter, sleep from memory_manager import embed_and_store, retrieve_relevant # Agent prompts PROMPTS = { "Initiator": "You are the Discussion Initiator...", "Responder": "You are the Critical Responder...", "Guardian": "You are the Depth Guardian...", "Provocateur": "You are the Cross-Disciplinary Provocateur...", "Cultural": "You are the Cultural Perspective...", "Judge": "You are the Impartial Judge..." } CHAT_MODEL = os.environ.get("CHAT_MODEL", "HuggingFaceH4/zephyr-7b-beta") HF_API_TOKEN = os.environ.get("HF_API_TOKEN", "") logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") def safe_chat(system_prompt: str, history: list, temperature: float = 0.7) -> str: """Call HF inference API with fallback formatting.""" start = perf_counter() chat_history = "\n".join([f"{msg['role'].capitalize()}: {msg['content']}" for msg in history]) full_prompt = f"{system_prompt}\n\n{chat_history}\n\nAssistant:" payload = { "inputs": full_prompt, "parameters": {"max_new_tokens": 300, "temperature": temperature} } headers = {"Authorization": f"Bearer {HF_API_TOKEN}"} if HF_API_TOKEN else {} try: resp = requests.post( f"https://api-inference.huggingface.co/models/{CHAT_MODEL}", json=payload, headers=headers, timeout=60 ) if resp.status_code == 200: data = resp.json() text = data[0].get("generated_text", "").strip() if isinstance(data, list) else data.get("generated_text", "").strip() elif resp.status_code == 503: logging.warning("Model loading… retrying after 15s.") sleep(15) return safe_chat(system_prompt, history, temperature) else: logging.error(f"HF error {resp.status_code}: {resp.text}") text = f"⚠️ API Error {resp.status_code}" except Exception as e: logging.error(f"safe_chat exception: {e}") text = f"⚠️ System Error: {e}" elapsed = perf_counter() - start logging.info(f"safe_chat: {elapsed:.3f}s for prompt '{system_prompt[:30]}…'") return text def step_turn(conversation: list, turn: int, topic: str, params: dict) -> list: """Advance one turn of the multi-agent conversation.""" sequence = ["Initiator", "Responder", "Guardian", "Provocateur", "Cultural"] agent = sequence[turn % len(sequence)] prompt = PROMPTS.get(agent, "") history = [{"role": "user", "content": msg['text']} for msg in conversation[-5:] if msg['agent'] != "System"] response = safe_chat(prompt, history, temperature=params[agent]['creativity']) embed_and_store(response, agent, topic) conversation.append({"agent": agent, "text": response, "turn": turn + 1}) return conversation