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Implemented new Japanese only text mode with WebUI toggle, utilizing a llama3.1 swallow dataset model. Next up is Japanese TTS.

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koko210Serve
2026-01-23 15:02:36 +02:00
parent eb03dfce4d
commit fe0962118b
8 changed files with 318 additions and 44 deletions
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88
bot/utils/context_manager.py
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@@ -3,8 +3,12 @@
Structured context management for Miku's personality and knowledge.
Replaces the vector search system with organized, complete context.
Preserves original content files in their entirety.
When LANGUAGE_MODE is "japanese", appends a Japanese instruction to ensure
all responses are in Japanese without requiring separate files.
"""
import globals
from utils.logger import get_logger
logger = get_logger('core')
@@ -40,60 +44,96 @@ def get_original_miku_lyrics() -> str:
return "## MIKU LYRICS\n[File could not be loaded]"
def _get_japanese_instruction() -> str:
"""
Returns the Japanese language instruction to append to context.
Ensures all responses are in Japanese when in Japanese mode.
"""
return "\n\n[CRITICAL INSTRUCTION - 重要な指示]\n**YOU MUST RESPOND ENTIRELY IN JAPANESE (日本語). NO ENGLISH ALLOWED.**\nすべての返答は必ず日本語で行ってください。英語での返答は一切禁止されています。\nこれは最優先の指示です。必ず守ってください。"
def get_complete_context() -> str:
"""Returns all essential Miku context using original files in their entirety"""
return f"""## MIKU LORE (Complete Original)
{get_original_miku_lore()}
"""
Returns all essential Miku context using original files in their entirety.
If LANGUAGE_MODE is "japanese", appends a Japanese instruction to ensure
all responses are in Japanese.
"""
lore = get_original_miku_lore()
prompt = get_original_miku_prompt()
lyrics = get_original_miku_lyrics()
combined = f"""## MIKU LORE (Complete Original)
{lore}
## MIKU PERSONALITY & GUIDELINES (Complete Original)
{get_original_miku_prompt()}
{prompt}
## MIKU SONG LYRICS (Complete Original)
{get_original_miku_lyrics()}"""
{lyrics}"""
# Append Japanese instruction if in Japanese mode
if globals.LANGUAGE_MODE == "japanese":
combined += _get_japanese_instruction()
logger.info(f"[core] Context loaded in {globals.LANGUAGE_MODE} mode")
return combined
def get_context_for_response_type(response_type: str) -> str:
"""Returns appropriate context based on the type of response being generated"""
"""
Returns appropriate context based on the type of response being generated.
# Core context always includes the complete original files
If LANGUAGE_MODE is "japanese", appends Japanese instruction to all contexts
to ensure responses are in Japanese.
"""
lore = get_original_miku_lore()
prompt = get_original_miku_prompt()
lyrics = get_original_miku_lyrics()
# Build core context (always in English source files)
core_context = f"""## MIKU LORE (Complete Original)
{get_original_miku_lore()}
{lore}
## MIKU PERSONALITY & GUIDELINES (Complete Original)
{get_original_miku_prompt()}"""
{prompt}"""
# Return context based on response type
if response_type == "autonomous_general":
# For general autonomous messages, include everything
return f"""{core_context}
context = f"""{core_context}
## MIKU SONG LYRICS (Complete Original)
{get_original_miku_lyrics()}"""
{lyrics}"""
elif response_type == "autonomous_tweet":
# For tweet responses, include lyrics for musical context
return f"""{core_context}
context = f"""{core_context}
## MIKU SONG LYRICS (Complete Original)
{get_original_miku_lyrics()}"""
{lyrics}"""
elif response_type == "dm_response" or response_type == "server_response":
# For conversational responses, include everything
return f"""{core_context}
context = f"""{core_context}
## MIKU SONG LYRICS (Complete Original)
{get_original_miku_lyrics()}"""
{lyrics}"""
elif response_type == "conversation_join":
# For joining conversations, include everything
return f"""{core_context}
context = f"""{core_context}
## MIKU SONG LYRICS (Complete Original)
{get_original_miku_lyrics()}"""
{lyrics}"""
elif response_type == "emoji_selection":
# For emoji reactions, no context needed - the prompt has everything
return ""
# For emoji reactions, minimal context needed
context = ""
else:
# Default: comprehensive context
return get_complete_context()
context = get_complete_context()
# Append Japanese instruction if in Japanese mode
if globals.LANGUAGE_MODE == "japanese" and context:
context += _get_japanese_instruction()
return context

40
bot/utils/image_handling.py
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@@ -239,7 +239,13 @@ async def analyze_image_with_vision(base64_img):
Uses OpenAI-compatible chat completions API with image_url.
Always uses NVIDIA GPU for vision model.
"""
from utils.llm import get_vision_gpu_url
from utils.llm import get_vision_gpu_url, check_vision_endpoint_health
# Check if vision endpoint is healthy before attempting request
is_healthy, error = await check_vision_endpoint_health()
if not is_healthy:
logger.warning(f"Vision endpoint unhealthy: {error}")
return f"Vision service currently unavailable: {error}"
payload = {
"model": globals.VISION_MODEL,
@@ -269,17 +275,20 @@ async def analyze_image_with_vision(base64_img):
async with aiohttp.ClientSession() as session:
try:
vision_url = get_vision_gpu_url()
async with session.post(f"{vision_url}/v1/chat/completions", json=payload, headers=headers) as response:
logger.info(f"Sending vision request to {vision_url} using model: {globals.VISION_MODEL}")
async with session.post(f"{vision_url}/v1/chat/completions", json=payload, headers=headers, timeout=aiohttp.ClientTimeout(total=60)) as response:
if response.status == 200:
data = await response.json()
return data.get("choices", [{}])[0].get("message", {}).get("content", "No description.")
result = data.get("choices", [{}])[0].get("message", {}).get("content", "No description.")
logger.info(f"Vision analysis completed successfully")
return result
else:
error_text = await response.text()
logger.error(f"Vision API error: {response.status} - {error_text}")
logger.error(f"Vision API error: {response.status} - {error_text} (endpoint: {vision_url})")
return f"Error analyzing image: {response.status}"
except Exception as e:
logger.error(f"Error in analyze_image_with_vision: {e}")
return f"Error analyzing image: {str(e)}"
logger.error(f"Error in analyze_image_with_vision: {e}", exc_info=True)
async def analyze_video_with_vision(video_frames, media_type="video"):
@@ -288,6 +297,13 @@ async def analyze_video_with_vision(video_frames, media_type="video"):
video_frames: list of base64-encoded frames
media_type: "video", "gif", or "tenor_gif" to customize the analysis prompt
"""
from utils.llm import get_vision_gpu_url, check_vision_endpoint_health
# Check if vision endpoint is healthy before attempting request
is_healthy, error = await check_vision_endpoint_health()
if not is_healthy:
logger.warning(f"Vision endpoint unhealthy: {error}")
return f"Vision service currently unavailable: {error}"
# Customize prompt based on media type
if media_type == "gif":
@@ -331,16 +347,20 @@ async def analyze_video_with_vision(video_frames, media_type="video"):
async with aiohttp.ClientSession() as session:
try:
vision_url = get_vision_gpu_url()
async with session.post(f"{vision_url}/v1/chat/completions", json=payload, headers=headers) as response:
logger.info(f"Sending video analysis request to {vision_url} using model: {globals.VISION_MODEL} (media_type: {media_type}, frames: {len(video_frames)})")
async with session.post(f"{vision_url}/v1/chat/completions", json=payload, headers=headers, timeout=aiohttp.ClientTimeout(total=120)) as response:
if response.status == 200:
data = await response.json()
return data.get("choices", [{}])[0].get("message", {}).get("content", "No description.")
result = data.get("choices", [{}])[0].get("message", {}).get("content", "No description.")
logger.info(f"Video analysis completed successfully")
return result
else:
error_text = await response.text()
logger.error(f"Vision API error: {response.status} - {error_text}")
logger.error(f"Vision API error: {response.status} - {error_text} (endpoint: {vision_url})")
return f"Error analyzing video: {response.status}"
except Exception as e:
logger.error(f"Error in analyze_video_with_vision: {e}")
logger.error(f"Error in analyze_video_with_vision: {e}", exc_info=True)
return f"Error analyzing video: {str(e)}"

45
bot/utils/llm.py
View File

@@ -38,8 +38,47 @@ def get_vision_gpu_url():
Strategy: Always use NVIDIA GPU for vision to avoid unloading/reloading.
- When NVIDIA is primary: Use NVIDIA for both text and vision
- When AMD is primary: Use AMD for text, NVIDIA for vision (keeps vision loaded)
Important: Vision model (MiniCPM-V) is ONLY configured on NVIDIA GPU.
This ensures vision inference is always fast and doesn't interfere with
AMD text model inference.
"""
return globals.LLAMA_URL # Always use NVIDIA for vision
current_text_gpu = get_current_gpu_url()
nvidia_vision_url = globals.LLAMA_URL
# Vision ALWAYS uses NVIDIA, regardless of which GPU is primary for text
# Log this decision when GPU switching is active (primary text GPU is AMD)
if current_text_gpu == globals.LLAMA_AMD_URL:
logger.debug(f"Primary GPU is AMD for text, but using NVIDIA for vision model")
return nvidia_vision_url # Always use NVIDIA for vision
async def check_vision_endpoint_health():
"""
Check if NVIDIA GPU vision endpoint is healthy and responsive.
This is important when AMD is the primary GPU to ensure vision still works.
Returns:
Tuple of (is_healthy: bool, error_message: Optional[str])
"""
import aiohttp
vision_url = get_vision_gpu_url()
try:
async with aiohttp.ClientSession() as session:
async with session.get(f"{vision_url}/health", timeout=aiohttp.ClientTimeout(total=5)) as response:
is_healthy = response.status == 200
if is_healthy:
logger.info(f"Vision endpoint ({vision_url}) health check: OK")
else:
logger.warning(f"Vision endpoint ({vision_url}) health check failed: status {response.status}")
return is_healthy, None if is_healthy else f"Status {response.status}"
except asyncio.TimeoutError:
logger.error(f"Vision endpoint ({vision_url}) health check: timeout")
return False, "Endpoint timeout"
except Exception as e:
logger.error(f"Vision endpoint ({vision_url}) health check error: {e}")
return False, str(e)
def _strip_surrounding_quotes(text):
"""
@@ -108,8 +147,12 @@ async def query_llama(user_prompt, user_id, guild_id=None, response_type="dm_res
if evil_mode:
model = globals.EVIL_TEXT_MODEL # Use DarkIdol uncensored model
logger.info(f"Using evil model: {model}")
elif globals.LANGUAGE_MODE == "japanese":
model = globals.JAPANESE_TEXT_MODEL # Use Swallow for Japanese
logger.info(f"Using Japanese model: {model}")
else:
model = globals.TEXT_MODEL
logger.info(f"Using default model: {model}")
# Determine channel_id for conversation history
# For servers, use guild_id; for DMs, use user_id