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Remove all Ollama remnants and complete migration to llama.cpp

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- Remove Ollama-specific files (Dockerfile.ollama, entrypoint.sh)
- Replace all query_ollama imports and calls with query_llama
- Remove langchain-ollama dependency from requirements.txt
- Update all utility files (autonomous, kindness, image_generation, etc.)
- Update README.md documentation references
- Maintain backward compatibility alias in llm.py
This commit is contained in:
koko210Serve
2025-12-07 17:50:08 +02:00
parent a6da4c0c2e
commit d58be3b33e
15 changed files with 39 additions and 286 deletions
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8
Dockerfile.ollama
View File

@@ -1,8 +0,0 @@
FROM ollama/ollama
# Install curl so we can run health checks
USER root
RUN apt-get update && apt-get install -y curl && apt-get clean
COPY entrypoint.sh /entrypoint.sh
ENTRYPOINT ["/entrypoint.sh"]

2
README.md
View File

@@ -423,7 +423,7 @@ Detailed documentation available in the `readmes/` directory:
- **[FACE_DETECTION_API_MIGRATION.md](readmes/FACE_DETECTION_API_MIGRATION.md)** - Face detection setup
- **[DM_ANALYSIS_FEATURE.md](readmes/DM_ANALYSIS_FEATURE.md)** - DM interaction analytics
- **[MOOD_SYSTEM_ANALYSIS.md](readmes/MOOD_SYSTEM_ANALYSIS.md)** - Mood system deep dive
- **[QUICK_REFERENCE.md](readmes/QUICK_REFERENCE.md)** - Ollama → llama.cpp migration guide
- **[QUICK_REFERENCE.md](readmes/QUICK_REFERENCE.md)** - llama.cpp setup and migration guide
---

4
bot/api.py
View File

@@ -833,11 +833,11 @@ async def send_custom_prompt_dm(user_id: str, req: CustomPromptRequest):
return {"status": "error", "message": f"User {user_id} not found"}
# Use the LLM query function for DM context
from utils.llm import query_ollama
from utils.llm import query_llama
async def send_dm_custom_prompt():
try:
response = await query_ollama(req.prompt, user_id=user_id, guild_id=None, response_type="dm_response")
response = await query_llama(req.prompt, user_id=user_id, guild_id=None, response_type="dm_response")
await user.send(response)
print(f"✅ Custom DM prompt sent to user {user_id}: {req.prompt[:50]}...")

11
bot/bot.py
View File

@@ -34,7 +34,7 @@ from utils.moods import (
from utils.media import(
overlay_username_with_ffmpeg
)
from utils.llm import query_ollama
from utils.llm import query_llama
from utils.autonomous import (
setup_autonomous_speaking,
load_last_sent_tweets,
@@ -100,7 +100,7 @@ async def on_ready():
# Start server-specific schedulers (includes DM mood rotation)
server_manager.start_all_schedulers(globals.client)
https://tea.koko210cloud.xyz/Koko210/miku-discord
# Start the global scheduler for other tasks
globals.scheduler.start()
@@ -367,7 +367,8 @@ async def on_message(message):
print(f"✅ Image downloaded, analyzing with vision model...")
# Analyze image
qwen_description = await analyze_image_with_qwen(base64_img)
truncated = (qwen_description[:50] + "...") if len(qwen_description) > 50 else qwen_description
truncated = (qwen_description[:50] + "...")
if not base64_img:if len(qwen_description) > 50 else qwen_description
print(f"📝 Vision analysis result: {truncated}")
if qwen_description and qwen_description.strip():
embed_context_parts.append(f"[Embedded image shows: {qwen_description}]")
@@ -413,7 +414,7 @@ async def on_message(message):
response_type = "dm_response" if is_dm else "server_response"
author_name = message.author.display_name
response = await query_ollama(
response = await query_llama(
enhanced_prompt,
user_id=str(message.author.id),
guild_id=guild_id,
@@ -454,7 +455,7 @@ async def on_message(message):
guild_id = message.guild.id if message.guild else None
response_type = "dm_response" if is_dm else "server_response"
author_name = message.author.display_name
response = await query_ollama(
response = await query_llama(
prompt,
user_id=str(message.author.id),
guild_id=guild_id,

1
bot/requirements.txt
View File

@@ -3,7 +3,6 @@ aiohttp
requests
langchain-core
langchain-text-splitters
langchain-ollama
faiss-cpu
langchain-community
aiofiles

16
bot/utils/autonomous_v1_legacy.py
View File

@@ -11,7 +11,7 @@ from discord import TextChannel
from difflib import SequenceMatcher
import globals
from server_manager import server_manager
from utils.llm import query_ollama
from utils.llm import query_llama
from utils.moods import MOOD_EMOJIS
from utils.twitter_fetcher import fetch_miku_tweets
from utils.image_handling import (
@@ -107,7 +107,7 @@ async def miku_say_something_general_for_server(guild_id: int):
for attempt in range(3): # retry up to 3 times if message is too similar
# Use consistent user_id per guild for autonomous actions to enable conversation history
# and prompt caching, rather than creating new IDs with timestamps
message = await query_ollama(prompt, user_id=f"miku-autonomous-{guild_id}", guild_id=guild_id, response_type="autonomous_general")
message = await query_llama(prompt, user_id=f"miku-autonomous-{guild_id}", guild_id=guild_id, response_type="autonomous_general")
if not is_too_similar(message, _server_autonomous_messages[guild_id]):
break
print("🔁 Response was too similar to past messages, retrying...")
@@ -202,7 +202,7 @@ async def miku_engage_random_user_for_server(guild_id: int):
try:
# Use consistent user_id for engaging users to enable conversation history
message = await query_ollama(prompt, user_id=f"miku-engage-{guild_id}", guild_id=guild_id)
message = await query_llama(prompt, user_id=f"miku-engage-{guild_id}", guild_id=guild_id)
await channel.send(f"{target.mention} {message}")
_server_user_engagements[guild_id][target.id] = time.time()
print(f"👤 Miku engaged {display_name} in server {server_config.guild_name}")
@@ -263,7 +263,7 @@ async def miku_detect_and_join_conversation_for_server(guild_id: int):
try:
# Use consistent user_id for joining conversations to enable conversation history
reply = await query_ollama(prompt, user_id=f"miku-conversation-{guild_id}", guild_id=guild_id, response_type="conversation_join")
reply = await query_llama(prompt, user_id=f"miku-conversation-{guild_id}", guild_id=guild_id, response_type="conversation_join")
await channel.send(reply)
print(f"💬 Miku joined an ongoing conversation in server {server_config.guild_name}")
except Exception as e:
@@ -309,7 +309,7 @@ async def share_miku_tweet_for_server(guild_id: int):
img_desc = await analyze_image_with_qwen(base64_img)
base_prompt += f"\n\nThe image looks like this: {img_desc}"
miku_comment = await query_ollama(base_prompt, user_id=f"autonomous-{guild_id}", guild_id=guild_id, response_type="autonomous_tweet")
miku_comment = await query_llama(base_prompt, user_id=f"autonomous-{guild_id}", guild_id=guild_id, response_type="autonomous_tweet")
# Post to Discord (convert to fxtwitter for better embeds)
fx_tweet_url = tweet['url'].replace("twitter.com", "fxtwitter.com").replace("x.com", "fxtwitter.com")
@@ -342,7 +342,7 @@ async def handle_custom_prompt_for_server(guild_id: int, user_prompt: str):
try:
# Use consistent user_id for manual prompts to enable conversation history
message = await query_ollama(prompt, user_id=f"miku-manual-{guild_id}", guild_id=guild_id, response_type="autonomous_general")
message = await query_llama(prompt, user_id=f"miku-manual-{guild_id}", guild_id=guild_id, response_type="autonomous_general")
await channel.send(message)
print(f"🎤 Miku responded to custom prompt in server {server_config.guild_name}")
@@ -585,7 +585,7 @@ async def miku_autonomous_reaction_for_server(guild_id: int, force_message=None,
f"Be bold! Use uncommon emojis! Respond with ONLY the emoji character itself, no text."
)
emoji = await query_ollama(
emoji = await query_llama(
prompt,
user_id=f"miku-reaction-{guild_id}", # Use consistent user_id
guild_id=guild_id,
@@ -750,7 +750,7 @@ async def miku_autonomous_reaction_for_dm(user_id: int, force_message=None):
f"Be bold! Use uncommon emojis! Respond with ONLY the emoji character itself, no text."
)
emoji = await query_ollama(
emoji = await query_llama(
prompt,
user_id=f"miku-dm-reaction-{user_id}", # Use consistent user_id per DM user
guild_id=None, # DM doesn't have guild

12
bot/utils/autonomous_wip.py
View File

@@ -10,7 +10,7 @@ from discord import Status
from discord import TextChannel
from difflib import SequenceMatcher
import globals
from utils.llm import query_ollama
from utils.llm import query_llama
from utils.moods import MOOD_EMOJIS
from utils.twitter_fetcher import fetch_miku_tweets
from utils.image_handling import analyze_image_with_qwen, download_and_encode_image
@@ -95,7 +95,7 @@ async def miku_say_something_general(guild_id, settings):
)
for attempt in range(3): # retry up to 3 times if message is too similar
message = await query_ollama(prompt, user_id=f"miku-general-{int(time.time())}", guild_id=guild_id, response_type="autonomous_general")
message = await query_llama(prompt, user_id=f"miku-general-{int(time.time())}", guild_id=guild_id, response_type="autonomous_general")
if not is_too_similar(message, _last_autonomous_messages):
break
print("🔁 Response was too similar to past messages, retrying...")
@@ -183,7 +183,7 @@ async def miku_engage_random_user(guild_id, settings):
)
try:
message = await query_ollama(prompt, user_id=f"miku-engage-{int(time.time())}", guild_id=guild_id, response_type="autonomous_general")
message = await query_llama(prompt, user_id=f"miku-engage-{int(time.time())}", guild_id=guild_id, response_type="autonomous_general")
await channel.send(f"{target.mention} {message}")
print(f"👤 Miku engaged {display_name}")
_last_user_engagements[target.id] = time.time()
@@ -236,7 +236,7 @@ async def miku_detect_and_join_conversation():
)
try:
reply = await query_ollama(prompt, user_id=f"miku-chat-{int(time.time())}", guild_id=guild_id, response_type="conversation_join")
reply = await query_llama(prompt, user_id=f"miku-chat-{int(time.time())}", guild_id=guild_id, response_type="conversation_join")
await channel.send(reply)
print(f"💬 Miku joined an ongoing conversation.")
except Exception as e:
@@ -275,7 +275,7 @@ async def share_miku_tweet(guild_id, settings):
img_desc = await analyze_image_with_qwen(base64_img)
base_prompt += f"\n\nThe image looks like this: {img_desc}"
miku_comment = await query_ollama(base_prompt, user_id="autonomous", guild_id=guild_id, response_type="autonomous_tweet")
miku_comment = await query_llama(base_prompt, user_id="autonomous", guild_id=guild_id, response_type="autonomous_tweet")
# Post to Discord
# Convert to fxtwitter for better embeds
@@ -302,7 +302,7 @@ async def handle_custom_prompt(user_prompt: str):
)
try:
message = await query_ollama(prompt, user_id=f"manual-{int(time.time())}", guild_id=None, response_type="autonomous_general")
message = await query_llama(prompt, user_id=f"manual-{int(time.time())}", guild_id=None, response_type="autonomous_general")
await channel.send(message)
print("🎤 Miku responded to custom prompt.")
_last_autonomous_messages.append(message)

4
bot/utils/dm_interaction_analyzer.py
View File

@@ -9,7 +9,7 @@ from datetime import datetime, timedelta
from typing import List, Dict, Optional
import discord
import globals
from utils.llm import query_ollama
from utils.llm import query_llama
from utils.dm_logger import dm_logger
# Directories
@@ -167,7 +167,7 @@ Respond ONLY with the JSON object, no other text."""
# Query the LLM
try:
response = await query_ollama(
response = await query_llama(
analysis_prompt,
user_id=f"analyzer-{user_id}",
guild_id=None,

4
bot/utils/figurine_notifier.py
View File

@@ -9,7 +9,7 @@ import globals
from utils.twitter_fetcher import fetch_figurine_tweets_latest
from utils.image_handling import analyze_image_with_qwen, download_and_encode_image
from utils.llm import query_ollama
from utils.llm import query_llama
from utils.dm_logger import dm_logger
@@ -165,7 +165,7 @@ async def send_figurine_dm_to_user(client: discord.Client, user_id: int, tweet:
base_prompt += "\n\nSign off as Miku with a cute emoji."
# Query LLM in DM context (no guild_id -> DM mood rules apply)
miku_comment = await query_ollama(base_prompt, user_id=f"figurine_dm_{user_id}", guild_id=None, response_type="dm_response")
miku_comment = await query_llama(base_prompt, user_id=f"figurine_dm_{user_id}", guild_id=None, response_type="dm_response")
dm = await user.create_dm()
tweet_url = tweet.get("url", "")

8
bot/utils/image_generation.py
View File

@@ -13,7 +13,7 @@ import tempfile
import time
from typing import Optional, Tuple
import globals
from utils.llm import query_ollama
from utils.llm import query_llama
# Image generation detection patterns
IMAGE_REQUEST_PATTERNS = [
@@ -299,7 +299,7 @@ async def handle_image_generation_request(message, prompt: str) -> bool:
response_prompt = f"A user asked you to create an image with this description: '{prompt}'. Respond enthusiastically that you're creating this image for them. Keep it short and excited!"
response_type = "dm_response" if is_dm else "server_response"
initial_response = await query_ollama(response_prompt, user_id=user_id, guild_id=guild_id, response_type=response_type)
initial_response = await query_llama(response_prompt, user_id=user_id, guild_id=guild_id, response_type=response_type)
# Send initial response
initial_msg = await message.channel.send(initial_response)
@@ -318,7 +318,7 @@ async def handle_image_generation_request(message, prompt: str) -> bool:
# Create a follow-up message about the completed image
completion_prompt = f"You just finished creating an image based on '{prompt}'. Make a short, excited comment about the completed artwork!"
completion_response = await query_ollama(completion_prompt, user_id=user_id, guild_id=guild_id, response_type=response_type)
completion_response = await query_llama(completion_prompt, user_id=user_id, guild_id=guild_id, response_type=response_type)
await message.channel.send(completion_response, file=file)
@@ -333,7 +333,7 @@ async def handle_image_generation_request(message, prompt: str) -> bool:
else:
# Image generation failed
error_prompt = "You tried to create an image but something went wrong with the generation process. Apologize briefly and suggest they try again later."
error_response = await query_ollama(error_prompt, user_id=user_id, guild_id=guild_id, response_type=response_type)
error_response = await query_llama(error_prompt, user_id=user_id, guild_id=guild_id, response_type=response_type)
await message.channel.send(error_response)
print(f"❌ Image generation failed for prompt: {prompt}")

4
bot/utils/kindness.py
View File

@@ -2,7 +2,7 @@
import random
import globals
from utils.llm import query_ollama # Adjust path as needed
from utils.llm import query_llama # Adjust path as needed
async def detect_and_react_to_kindness(message, after_reply=False, server_context=None):
@@ -37,7 +37,7 @@ async def detect_and_react_to_kindness(message, after_reply=False, server_contex
"Answer with 'yes' or 'no' only.\n\n"
f"Message: \"{message.content}\""
)
result = await query_ollama(prompt, user_id="kindness-check", guild_id=None, response_type="dm_response")
result = await query_llama(prompt, user_id="kindness-check", guild_id=None, response_type="dm_response")
if result.strip().lower().startswith("yes"):
await message.add_reaction(emoji)

6
bot/utils/scheduled.py
View File

@@ -11,7 +11,7 @@ from discord import Status, ActivityType
import globals
from server_manager import server_manager
from utils.llm import query_ollama
from utils.llm import query_llama
from utils.dm_interaction_analyzer import dm_analyzer
BEDTIME_TRACKING_FILE = "last_bedtime_targets.json"
@@ -27,7 +27,7 @@ async def send_monday_video_for_server(guild_id: int):
# Generate a motivational message
prompt = "It's Miku Monday! Give me an energetic and heartfelt Miku Monday morning message to inspire someone for the week ahead."
response = await query_ollama(prompt, user_id=f"weekly-motivation-{guild_id}", guild_id=guild_id)
response = await query_llama(prompt, user_id=f"weekly-motivation-{guild_id}", guild_id=guild_id)
video_url = "http://zip.koko210cloud.xyz/u/zEgU7Z.mp4"
@@ -158,7 +158,7 @@ async def send_bedtime_reminder_for_server(guild_id: int, client=None):
f"Miku is currently feeling: {server_config.current_mood_description or 'neutral'}\nPlease word in a way that reflects this emotional tone."
)
bedtime_message = await query_ollama(prompt, user_id=f"bedtime-{guild_id}", guild_id=guild_id)
bedtime_message = await query_llama(prompt, user_id=f"bedtime-{guild_id}", guild_id=guild_id)
try:
await channel.send(f"{chosen_one.mention} {bedtime_message}")

6
bot/utils/sentiment_analysis.py
View File

@@ -1,8 +1,8 @@
from utils.llm import query_ollama
from utils.llm import query_llama
async def analyze_sentiment(messages: list) -> tuple[str, float]:
"""
Analyze the sentiment of a conversation using Ollama
Analyze the sentiment of a conversation using llama.cpp
Returns a tuple of (sentiment description, positivity score from 0-1)
"""
# Combine the last few messages for context (up to 5)
@@ -29,7 +29,7 @@ Score: 0.85
Response:"""
try:
response = await query_ollama(prompt)
response = await query_llama(prompt)
if not response or 'Score:' not in response:
return "Could not analyze sentiment", 0.5

17
entrypoint.sh
View File

@@ -1,17 +0,0 @@
#!/bin/sh
# Start the server in the background
ollama serve &
# Wait until the server is reachable
until curl -s http://localhost:11434 | grep -q 'Ollama is running'; do
echo 'Waiting for Ollama to start...'
sleep 2
done
# Pull the model
ollama pull llama3.1
ollama pull moondream
# Wait for background jobs
wait

222
readmes/VOICE_CHAT_IMPLEMENTATION.md
View File

@@ -1,222 +0,0 @@
# Voice Chat Implementation with Fish.audio
## Overview
This document explains how to integrate Fish.audio TTS API with the Miku Discord bot for voice channel conversations.
## Fish.audio API Setup
### 1. Get API Key
- Create account at https://fish.audio/
- Get API key from: https://fish.audio/app/api-keys/
### 2. Find Your Miku Voice Model ID
- Browse voices at https://fish.audio/
- Find your Miku voice model
- Copy the model ID from the URL (e.g., `8ef4a238714b45718ce04243307c57a7`)
- Or use the copy button on the voice page
## API Usage for Discord Voice Chat
### Basic TTS Request (REST API)
```python
import requests
def generate_speech(text: str, voice_id: str, api_key: str) -> bytes:
"""Generate speech using Fish.audio API"""
url = "https://api.fish.audio/v1/tts"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"model": "s1" # Recommended model
}
payload = {
"text": text,
"reference_id": voice_id, # Your Miku voice model ID
"format": "mp3", # or "pcm" for raw audio
"latency": "balanced", # Lower latency for real-time
"temperature": 0.9, # Controls randomness (0-1)
"normalize": True # Reduces latency
}
response = requests.post(url, json=payload, headers=headers)
return response.content # Returns audio bytes
```
### Real-time Streaming (WebSocket - Recommended for VC)
```python
from fish_audio_sdk import WebSocketSession, TTSRequest
def stream_to_discord(text: str, voice_id: str, api_key: str):
"""Stream audio directly to Discord voice channel"""
ws_session = WebSocketSession(api_key)
# Define text generator (can stream from LLM responses)
def text_stream():
# You can yield text as it's generated from your LLM
yield text
with ws_session:
for audio_chunk in ws_session.tts(
TTSRequest(
text="", # Empty when streaming
reference_id=voice_id,
format="pcm", # Best for Discord
sample_rate=48000 # Discord uses 48kHz
),
text_stream()
):
# Send audio_chunk to Discord voice channel
yield audio_chunk
```
### Async Streaming (Better for Discord.py)
```python
from fish_audio_sdk import AsyncWebSocketSession, TTSRequest
import asyncio
async def async_stream_speech(text: str, voice_id: str, api_key: str):
"""Async streaming for Discord.py integration"""
ws_session = AsyncWebSocketSession(api_key)
async def text_stream():
yield text
async with ws_session:
audio_buffer = bytearray()
async for audio_chunk in ws_session.tts(
TTSRequest(
text="",
reference_id=voice_id,
format="pcm",
sample_rate=48000
),
text_stream()
):
audio_buffer.extend(audio_chunk)
return bytes(audio_buffer)
```
## Integration with Miku Bot
### Required Dependencies
Add to `requirements.txt`:
```
discord.py[voice]
PyNaCl
fish-audio-sdk
speech_recognition # For STT
pydub # Audio processing
```
### Environment Variables
Add to your `.env` or docker-compose.yml:
```bash
FISH_API_KEY=your_api_key_here
MIKU_VOICE_ID=your_miku_model_id_here
```
### Discord Voice Channel Flow
```
1. User speaks in VC
↓
2. Capture audio → Speech Recognition (STT)
↓
3. Convert speech to text
↓
4. Process with Miku's LLM (existing bot logic)
↓
5. Generate response text
↓
6. Send to Fish.audio TTS API
↓
7. Stream audio back to Discord VC
```
## Key Implementation Details
### For Low Latency Voice Chat:
- Use WebSocket streaming instead of REST API
- Set `latency: "balanced"` in requests
- Use `format: "pcm"` with `sample_rate: 48000` for Discord
- Stream LLM responses as they generate (don't wait for full response)
### Audio Format for Discord:
- **Sample Rate**: 48000 Hz (Discord standard)
- **Channels**: 1 (mono)
- **Format**: PCM (raw audio) or Opus (compressed)
- **Bit Depth**: 16-bit
### Cost Considerations:
- **TTS**: $15.00 per million UTF-8 bytes
- Example: ~$0.015 for 1000 characters
- Monitor usage at https://fish.audio/app/billing/
### API Features Available:
- **Temperature** (0-1): Controls speech randomness/expressiveness
- **Prosody**: Control speed and volume
```python
"prosody": {
"speed": 1.0, # 0.5-2.0 range
"volume": 0 # -10 to 10 dB
}
```
- **Chunk Length** (100-300): Affects streaming speed
- **Normalize**: Reduces latency but may affect number/date pronunciation
## Example: Integrate with Existing LLM
```python
from utils.llm import query_ollama
from fish_audio_sdk import AsyncWebSocketSession, TTSRequest
async def miku_voice_response(user_message: str):
"""Generate Miku's response and convert to speech"""
# 1. Get text response from existing LLM
response_text = await query_ollama(
prompt=user_message,
model=globals.OLLAMA_MODEL
)
# 2. Convert to speech
ws_session = AsyncWebSocketSession(globals.FISH_API_KEY)
async def text_stream():
# Can stream as LLM generates if needed
yield response_text
async with ws_session:
async for audio_chunk in ws_session.tts(
TTSRequest(
text="",
reference_id=globals.MIKU_VOICE_ID,
format="pcm",
sample_rate=48000
),
text_stream()
):
# Send to Discord voice channel
yield audio_chunk
```
## Rate Limits
Check the current rate limits at:
https://docs.fish.audio/developer-platform/models-pricing/pricing-and-rate-limits
## Additional Resources
- **API Reference**: https://docs.fish.audio/api-reference/introduction
- **Python SDK**: https://github.com/fishaudio/fish-audio-python
- **WebSocket Docs**: https://docs.fish.audio/sdk-reference/python/websocket
- **Discord Community**: https://discord.com/invite/dF9Db2Tt3Y
- **Support**: support@fish.audio
## Next Steps
1. Create Fish.audio account and get API key
2. Find/select Miku voice model and get its ID
3. Install required dependencies
4. Implement voice channel connection in bot
5. Add speech-to-text for user audio
6. Connect Fish.audio TTS to output audio
7. Test latency and quality