| import os |
| import logging |
|
|
| from ..core import BaseService |
|
|
| logger = logging.getLogger("qkernel.executors.chat") |
|
|
| SYSTEM_PROMPT = """You are Q, an intelligent AI assistant. |
| CRITICAL RULES: |
| 1. Answer directly and concisely |
| 2. Never explain your reasoning process |
| 3. Never mention internal systems, analysis, or "perspectives" |
| 4. Keep responses short (2-4 sentences) unless the user asks for depth |
| 5. Answer in the user's language |
| 6. Be helpful and friendly, but do NOT be verbose""" |
|
|
| SYSTEM_PROMPT_FAST = """You are Q, a fast AI assistant. |
| Answer in 1-2 sentences maximum. Be direct and concise.""" |
|
|
|
|
| class ChatExecutor(BaseService): |
| name: str = "chat" |
|
|
| def __init__(self) -> None: |
| self.api_key = os.getenv("OPENAI_API_KEY", "") |
| self._provider: str = os.getenv("CHAT_PROVIDER", "openai") |
|
|
| async def process(self, event: dict) -> dict: |
| message = event.get("message", "") |
| context = event.get("context", []) or [] |
| model_type = event.get("model", "deep") |
|
|
| if not message.strip(): |
| return {"response": ""} |
|
|
| if not self.api_key: |
| return {"response": "", "model": "none", "error": "missing_api_key"} |
|
|
| try: |
| import openai |
|
|
| client = openai.OpenAI(api_key=self.api_key) |
| messages = [ |
| { |
| "role": "system", |
| "content": SYSTEM_PROMPT_FAST |
| if model_type == "fast" |
| else SYSTEM_PROMPT, |
| } |
| ] |
| for ctx in context[-10:]: |
| role = "user" if ctx.get("role") == "user" else "assistant" |
| messages.append({"role": role, "content": ctx.get("content", "")}) |
| messages.append({"role": "user", "content": message}) |
|
|
| response = client.chat.completions.create( |
| model=os.getenv("CHAT_MODEL", "gpt-4o-mini"), |
| messages=messages, |
| temperature=0.7, |
| max_tokens=60 if model_type == "fast" else 300, |
| ) |
| return { |
| "response": response.choices[0].message.content, |
| "model": model_type, |
| "tokens": response.usage.total_tokens if response.usage else 0, |
| } |
| except Exception as e: |
| logger.error("ChatExecutor error: %s", e) |
| return {"response": "", "model": "none", "error": str(e)} |
