81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
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# utils/core.py
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import asyncio
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import aiohttp
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import re
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import globals
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from langchain_community.vectorstores import FAISS
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from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter
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from langchain_core.documents import Document
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# switch_model() removed - llama-swap handles model switching automatically
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async def is_miku_addressed(message) -> bool:
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# Check if this is a DM (no guild)
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if message.guild is None:
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# In DMs, always respond to every message
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return True
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# Safety check: ensure guild and guild.me exist
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if not message.guild or not message.guild.me:
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print(f"⚠️ Warning: Invalid guild or guild.me in message from {message.author}")
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return False
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# If message contains a ping for Miku, return true
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if message.guild.me in message.mentions:
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return True
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# If message is a reply, check the referenced message author
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if message.reference:
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try:
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referenced_msg = await message.channel.fetch_message(message.reference.message_id)
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if referenced_msg.author == message.guild.me:
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return True
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except Exception as e:
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print(f"⚠️ Could not fetch referenced message: {e}")
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cleaned = message.content.strip()
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return bool(re.search(
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r'(?<![\w\(])(?:[^\w\s]{0,2}\s*)?miku(?:\s*[^\w\s]{0,2})?(?=,|\s*,|[!\.?\s]*$)',
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cleaned,
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re.IGNORECASE
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))
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# Vectorstore functionality disabled - not needed with current structured context approach
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# If you need embeddings in the future, you can use a different embedding provider
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# For now, the bot uses structured prompts from context_manager.py
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# def load_miku_knowledge():
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# with open("miku_lore.txt", "r", encoding="utf-8") as f:
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# text = f.read()
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#
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# from langchain_text_splitters import RecursiveCharacterTextSplitter
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#
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# text_splitter = RecursiveCharacterTextSplitter(
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# chunk_size=520,
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# chunk_overlap=50,
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# separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""]
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# )
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#
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# docs = [Document(page_content=chunk) for chunk in text_splitter.split_text(text)]
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#
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# vectorstore = FAISS.from_documents(docs, embeddings)
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# return vectorstore
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#
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# def load_miku_lyrics():
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# with open("miku_lyrics.txt", "r", encoding="utf-8") as f:
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# lyrics_text = f.read()
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#
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# text_splitter = CharacterTextSplitter(chunk_size=520, chunk_overlap=50)
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# docs = [Document(page_content=chunk) for chunk in text_splitter.split_text(lyrics_text)]
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#
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# vectorstore = FAISS.from_documents(docs, embeddings)
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# return vectorstore
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#
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# miku_vectorstore = load_miku_knowledge()
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# miku_lyrics_vectorstore = load_miku_lyrics()
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