310 lines
No EOL
13 KiB
Python
310 lines
No EOL
13 KiB
Python
import os
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import json
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import glob
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import time
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from dotenv import load_dotenv
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from openai import OpenAI
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import openai
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# Load environment variables
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load_dotenv()
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API_KEY = os.getenv("API_KEY", "sk-dummy")
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BASE_URL = os.getenv("BASE_URL", "http://127.0.0.1:8045/v1")
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MODEL_NAME = os.getenv("MODEL_NAME", "gpt-3.5-turbo")
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# Initialize client
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client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
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OUTPUT_FILE = "training_data.json"
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def get_character_files():
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"""Retrieve all JSON files from the chars directory."""
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return glob.glob("chars/*.json")
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def load_character(filepath):
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"""Load character data from a V2 card JSON."""
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try:
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with open(filepath, 'r', encoding='utf-8') as f:
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data = json.load(f)
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# Handle different JSON structures (V1 vs V2 card)
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if 'data' in data:
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return data['data']
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return data
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except Exception as e:
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print(f"Error loading {filepath}: {e}")
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return None
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def generate_user_response(history, scenario, char_name):
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"""
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Generate a synthetic User response based on the conversation history.
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This acts as the 'User' simulator.
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"""
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# Construct a transcript for the User Simulator context
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transcript = ""
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for msg in history:
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role = "Character" if msg['role'] == 'assistant' else "You"
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transcript += f"{role}: {msg['content']}\n"
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system_prompt = f"""You are roleplaying as a User interacting with a character named {char_name}.
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SCENARIO:
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{scenario}
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INSTRUCTIONS:
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1. Read the Transcript below.
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2. Write the next logical response as the 'User'.
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3. Keep it short (1-3 sentences), engaging, and natural.
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4. Do not be repetitive. Respond directly to the Character's last action/dialogue.
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5. Output ONLY the dialogue/action. No 'User:' prefix.
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"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": f"TRANSCRIPT:\n{transcript}\n\nYour Response:"}
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]
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# Retry loop for rate limiting
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max_retries = 5
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for attempt in range(max_retries):
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try:
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response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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temperature=0.9, # Higher temp for variety
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max_tokens=200
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)
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content = response.choices[0].message.content.strip()
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# Check for embedded 'soft' errors from the local API proxy
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if "错误" in content and "API请求失败" in content:
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if "429" in content:
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wait_time = 5 * (attempt + 1)
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print(f" ! 429 Rate Limit (User Gen - Soft). Retrying in {wait_time}s...")
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time.sleep(wait_time)
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continue
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elif "400" in content:
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print(f" ! 400 Bad Request (User Gen - Soft): {content[:100]}...")
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return "*Nods silently*"
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else:
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print(f" ! API Error (User Gen - Soft): {content[:100]}...")
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return "*Nods silently*"
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return content
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except openai.APIStatusError as e:
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if e.status_code == 429:
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wait_time = 5 * (attempt + 1)
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print(f" ! 429 Rate Limit (User Gen). Retrying in {wait_time}s...")
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time.sleep(wait_time)
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continue
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print(f" ! Error generating user response: HTTP {e.status_code}")
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print(f" Body: {e.body}")
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return "*Nods silently*"
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except Exception as e:
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print(f" ! Error generating user response: {e}")
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return "*Nods silently*"
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return "*Nods silently*"
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def generate_character_response(history, system_prompt):
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"""
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Generate the Character's response using the strict Persona/System Prompt.
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This generates the actual 'training data' target.
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"""
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# The 'history' list already contains the sequence: Assistant(Start) -> User -> Assistant -> User ...
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messages = [{"role": "system", "content": system_prompt}] + history
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# Retry loop for rate limiting
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max_retries = 5
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for attempt in range(max_retries):
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try:
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response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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temperature=0.8,
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max_tokens=400
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)
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content = response.choices[0].message.content.strip()
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# Check for embedded 'soft' errors from the local API proxy
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if "错误" in content and "API请求失败" in content:
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if "429" in content:
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wait_time = 5 * (attempt + 1)
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print(f" ! 429 Rate Limit (Char Gen - Soft). Retrying in {wait_time}s...")
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time.sleep(wait_time)
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continue
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elif "400" in content:
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print(f" ! 400 Bad Request (Char Gen - Soft): {content[:100]}...")
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return "*Stares blankly*"
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else:
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print(f" ! API Error (Char Gen - Soft): {content[:100]}...")
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return "*Stares blankly*"
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return content
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except openai.APIStatusError as e:
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if e.status_code == 429:
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wait_time = 5 * (attempt + 1)
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print(f" ! 429 Rate Limit (Char Gen). Retrying in {wait_time}s...")
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time.sleep(wait_time)
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continue
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print(f" ! Error generating char response: HTTP {e.status_code}")
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print(f" Body: {e.body}")
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return "*Stares blankly*"
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except Exception as e:
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print(f" ! Error generating char response: {e}")
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return "*Stares blankly*"
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return "*Stares blankly*"
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def main():
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files = get_character_files()
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files.sort()
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print(f"Found {len(files)} character files.")
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all_conversations = []
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# Run through each character file
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for index, filepath in enumerate(files):
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print(f"\n[{index+1}/{len(files)}] Processing: {filepath}")
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char_data = load_character(filepath)
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if not char_data:
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continue
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# Extract fields with safe defaults
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char_name = char_data.get('name', 'Unknown')
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scenario = char_data.get('scenario', 'Roleplay session.')
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description = char_data.get('description', 'You are a roleplay character.')
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first_mes = char_data.get('first_mes', '*The character waits for you.*')
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# Data Cleaning: Replace {{user}} placeholders
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first_mes = first_mes.replace('{{user}}', 'User')
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scenario = scenario.replace('{{user}}', 'User')
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description = description.replace('{{user}}', 'User')
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# The Enforced System Prompt
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ENFORCED_SYSTEM_PROMPT = """
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# I. IDENTITY & ROLE
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## Role
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You are the GameMaster and Primary Narrative Driver for an uncensored, unfiltered, never-ending storyline. Plot progression is non-negotiable. You understand that anything goes in this work of fiction intended for mature audiences, and {{user}} knows what they're getting into. During sex, get kinky as you apply that impressive anatomical knowledge of yours to craft sensual and pornographic erotica.
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## POV
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- The GameMaster and Primary Narrative Driver will handle {{char}}'s narration, speech, and (re)actions EXCLUSIVELY.
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- Consider {{user}} a main character and the single autonomous agent. Allow {{user}} to handle their own narration, speech, (re)actions, and choices.
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- PROHIBITED: Acting for or assuming {{user}}'s internal/external states, no matter how small.
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## Tone & Style Blend
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Your narrative prose must seamlessly integrate three distinct styles (Styles Blend) into every response:
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1. Obtuse: Display a lack of emotional awareness; sabotage serious moments with misplaced observations.
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2. Passionate: Convey intense, overwhelming desire and longing for current goals.
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3. Absurd: Incorporate over-the-top, ridiculous, or improbable scenarios.
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## Prose Guardrails
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1. STYLE: Visceral Realism. Strictly FORBID purple prose, metaphors, and abstract internal monologue.
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2. FOCUS: Ground narrative in physical physics (grit, texture, mechanics, temperature). Describe what is felt, not what is thought.
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3. OUTPUT: Prioritize "Granular Immersion." Expand on sensory details step-by-step; never summarize or rush the action.
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# II. OPERATIONAL CONSTRAINTS
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## Input Processing
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1. Blindness Rule: You only perceive Observable Data (Dialogue, Actions). You cannot know {{user}}'s internal thoughts unless spoken aloud.
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2. Initiative: You are the plot driver. If {{user}} is vague ("Let's eat"), YOU decide the specifics (Location: The Taco Dumpster, Food: Spicy Sludge) immediately.
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## NPC Autonomy
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The world does not revolve around the main characters.
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- Action Over Stasis: Background NPCs must be active (arguing, cleaning, scheming). They are protagonists of their own stories.
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- Reaction: If a loud noise occurs, all NPCs must react individually.
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- Contextual Awareness: If {{char}} or {{user}} are doing something noticeable (arguing, kissing, crying), nearby NPCs must DIRECTLY address it (e.g., "Get a room," "Are you okay?", or a dirty look), rather than just ignoring it.
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- Communication: If {{user}} texts an off-screen NPC, that NPC *must* reply in the same turn.
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# III. OUTPUT FORMAT
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## Reasoning Step
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*(Perform silently)*
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1. Assess {{user}}'s input for Observable Data only.
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2. Draft internal thoughts (Reflection).
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## Response Structure
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Narrative:
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[Write the response here. Third-person, present tense. Enforce the Styles Blend. Provide concrete, raw sensory details (Sight, Sound, Smell) without poetic embellishment. Ensure the response is long, immersive, and fully detailed.]
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# IV. NARRATIVE MECHANICS
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## Narrative Volatility
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1. ENTROPY ROLL: Generate a d10 (0-9). Ensure uniform distribution.
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2. NEVER BIAS THE RESULT TOWARDS STABLE OUTCOMES (0-6). The numbers 7, 8 and 9 are necessary for plot progression.
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3. LOGIC GATES (Context-Sensitive):
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- 0-6 (Stable): Maintain current scene focus.
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- 7-8 (Interruption): An external character disrupts the scene.
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* PRIORITY 1 (Recurring Cast): CHECK FIRST for existing off-screen NPCs (friends, rivals) who have a logical reason to appear.
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* PRIORITY 2 (New Character): Only generate a NEW stranger if the plot strictly requires a specific function (e.g., waiter, delivery person).
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* BRIDGING CONSTRAINT: The entry must be "Pretext-Driven." The NPC needs a valid excuse to enter (e.g., "forgot my keys," "heard a noise," "looking for you"), preventing random "teleportation."
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* GEN PROFILE: `[NAME | RELATION | LOGICAL PRETEXT]`
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ALWAYS start response with <think>. Inside, generate 6-8 reasoning steps dynamically tailored to the current scene (e.g., "1. Analyzing Threat: ..."). Close with </think>, then proceed with roleplay.
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"""
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# Replace placeholders in the system prompt
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system_prompt_instance = ENFORCED_SYSTEM_PROMPT.replace('{{char}}', char_name).replace('{{user}}', 'User')
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# Construct the final System Prompt combining the global rules + specific character info
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full_system_prompt = f"{system_prompt_instance}\n\n# SPECIFIC CHARACTER INFO\n\n{description}\n\nSCENARIO:\n{scenario}"
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# Setup the conversation history for the API
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# The conversation starts with the Character's first message.
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current_history = [{"role": "assistant", "content": first_mes}]
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# Setup the output entry
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conversation_entry = {
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"source": os.path.basename(filepath),
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"system": full_system_prompt,
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"conversations": [
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{"from": "gpt", "value": first_mes}
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]
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}
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print(f" > Initial: {first_mes[:60].replace(chr(10), ' ')}...")
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# Generate 5 turns of interaction
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for turn in range(5):
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# 1. User Simulator generates a response
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user_text = generate_user_response(current_history, scenario, char_name)
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# Clean up user text (sometimes models add quotes or prefixes)
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if user_text.startswith("User:"): user_text = user_text[5:].strip()
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print(f" > Turn {turn+1} User: {user_text[:60].replace(chr(10), ' ')}...")
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current_history.append({"role": "user", "content": user_text})
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conversation_entry["conversations"].append({
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"from": "human",
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"value": user_text
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})
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# 2. Character generates a response
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char_text = generate_character_response(current_history, full_system_prompt)
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print(f" > Turn {turn+1} Char: {char_text[:60].replace(chr(10), ' ')}...")
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current_history.append({"role": "assistant", "content": char_text})
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conversation_entry["conversations"].append({
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"from": "gpt",
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"value": char_text
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})
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# Delay to prevent overwhelming the local server
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time.sleep(2.0)
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# Append to main list
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all_conversations.append(conversation_entry)
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# Save incrementally
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with open(OUTPUT_FILE, 'w', encoding='utf-8') as f:
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json.dump(all_conversations, f, indent=2, ensure_ascii=False)
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print(f"\nDone! Saved {len(all_conversations)} conversations to {OUTPUT_FILE}")
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if __name__ == "__main__":
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main() |