91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
from __future__ import annotations
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from django.db.models import Q, QuerySet
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from django.utils import timezone
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from .llm import LLMConfigurationError, LLMRequestError, generate_reply
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from .models import Conversation, Message
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def list_conversations(user, search: str = "") -> QuerySet[Conversation]:
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"""Returns a user's conversations, optionally filtered by title or content."""
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conversations = Conversation.objects.filter(user=user)
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if not search:
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return conversations
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return conversations.filter(
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Q(title__icontains=search) | Q(messages__content__icontains=search)
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).distinct()
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def get_conversation_for_user(user, conversation_id: int | None) -> Conversation | None:
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"""Loads a conversation only when it belongs to the current user."""
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if not conversation_id:
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return None
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return Conversation.objects.filter(user=user, pk=conversation_id).first()
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def create_conversation(user) -> Conversation:
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"""Creates an empty conversation that can immediately accept messages."""
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now = timezone.localtime()
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return Conversation.objects.create(
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user=user,
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title=f"新对话 {now.strftime('%m-%d %H:%M')}",
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)
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def append_user_message(conversation: Conversation, content: str) -> Message:
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"""Appends a user message and updates the conversation title from the first prompt."""
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message = Message.objects.create(
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conversation=conversation,
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role=Message.Role.USER,
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content=content.strip(),
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)
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if conversation.messages.filter(role=Message.Role.USER).count() == 1:
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conversation.title = build_conversation_title(content)
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conversation.save(update_fields=["title", "updated_at"])
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return message
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def append_assistant_message(conversation: Conversation, content: str) -> Message:
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"""Appends the deterministic assistant reply."""
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return Message.objects.create(
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conversation=conversation,
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role=Message.Role.ASSISTANT,
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content=content,
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)
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def send_message(conversation: Conversation, content: str) -> tuple[Message, Message]:
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"""Stores one user message and one provider-backed assistant reply."""
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user_message = append_user_message(conversation, content)
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try:
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reply_content = generate_reply(conversation, content)
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except (LLMConfigurationError, LLMRequestError) as exc:
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reply_content = f"模型调用失败：{exc}"
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assistant_message = append_assistant_message(conversation, reply_content)
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if conversation.title.startswith("新对话"):
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conversation.title = build_conversation_title(content)
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conversation.save(update_fields=["title", "updated_at"])
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return user_message, assistant_message
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def build_conversation_title(content: str) -> str:
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"""Creates a concise title from the first user message."""
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normalized = " ".join(content.strip().split())
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if not normalized:
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return "新对话"
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return normalized[:24]
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