Build lightweight AI agent admin
This commit is contained in:
Codex
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"""
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智能体服务
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"""
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import logging
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import time
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from typing import Any, Dict, List, Optional
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from ai_platform.models import (
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Agent, AgentConversation, AgentMessage, LLMModel,
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)
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from .llm_service import LLMService
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logger = logging.getLogger(__name__)
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class AgentService:
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"""
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智能体服务
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管理智能体的对话和推理
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"""
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def __init__(self, db: Optional[AsyncSession] = None):
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self._db = db
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self.llm_service = LLMService(db)
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async def chat(
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self,
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agent: Agent,
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conversation: AgentConversation,
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user_message: str,
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application_id: str = None,
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form_code: str = None,
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attachments: List[Dict] = None,
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):
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"""
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与智能体对话（流式）
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Args:
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agent: 智能体
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conversation: 对话
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user_message: 用户消息
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application_id: 子应用 ID（用于表单创建等场景）
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form_code: 表单编码（从表单列表调用时传入）
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attachments: 附件列表 [{id, type, name, url, mime_type, size}]
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Yields:
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事件流
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"""
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if agent.mode == 'autonomous':
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async for event in self._chat_autonomous(agent, conversation, user_message, attachments):
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yield event
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elif agent.mode == 'dialog_flow':
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async for event in self._chat_dialog_flow(agent, conversation, user_message, application_id, form_code, attachments):
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yield event
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else:
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yield {'type': 'error', 'content': f'不支持的模式: {agent.mode}'}
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async def _chat_autonomous(
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self,
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agent: Agent,
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conversation: AgentConversation,
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user_message: str,
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attachments: List[Dict] = None,
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):
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"""
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自主规划模式对话
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优先使用原生 Function Calling，如果模型不支持则回退到 ReAct 模式
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Yields:
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流式事件
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"""
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# 检查模型是否支持 Function Calling
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supports_function_call = await self._check_model_supports_function_call(agent.model_id)
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if supports_function_call:
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async for event in self._chat_autonomous_function_calling(agent, conversation, user_message, attachments):
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yield event
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else:
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async for event in self._chat_autonomous_react(agent, conversation, user_message, attachments):
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yield event
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async def _check_model_supports_function_call(self, model_id) -> bool:
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"""检查模型是否支持 Function Calling"""
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if not self._db or not model_id:
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return False
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result = await self._db.execute(
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select(LLMModel).where(LLMModel.id == model_id, LLMModel.is_deleted == False)
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)
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model = result.scalar_one_or_none()
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return model.supports_function_call if model else False
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async def _chat_autonomous_function_calling(
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self,
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agent: Agent,
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conversation: AgentConversation,
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user_message: str,
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attachments: List[Dict] = None,
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):
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"""
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自主规划模式对话（支持多模态附件）
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Yields:
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流式事件
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"""
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from ai_platform.providers.base import LLMMessage
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start_time = time.time()
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if not self._db:
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yield {'type': 'error', 'content': '数据库会话未初始化'}
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return
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# 创建用户消息（包含附件）
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# 附件格式: [{file_id, type, name, mime_type, size}]
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stored_attachments = []
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if attachments:
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for att in attachments:
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stored_attachments.append({
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'file_id': att.get('file_id'),
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'type': att.get('type', 'file'),
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'name': att.get('name', ''),
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'mime_type': att.get('mime_type', ''),
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'size': att.get('size', 0),
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})
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user_msg = AgentMessage(
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conversation_id=conversation.id,
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role='user',
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content=user_message,
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attachments=stored_attachments,
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status='completed',
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)
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self._db.add(user_msg)
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await self._db.flush()
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# 创建助手消息（pending 状态）
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assistant_msg = AgentMessage(
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conversation_id=conversation.id,
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role='assistant',
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content='',
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status='pending',
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)
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self._db.add(assistant_msg)
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await self._db.flush()
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yield {
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'type': 'start',
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'conversation_id': str(conversation.id),
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'message_id': str(assistant_msg.id),
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}
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try:
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# 构建系统提示词
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system_prompt = self._build_system_prompt_simple(agent)
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# 标注直接回复检查（高相似度时跳过 LLM，直接返回标注答案）
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annotation_reply = await self._check_annotation_direct_reply(agent, user_message)
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if annotation_reply:
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final_answer = annotation_reply['answer']
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elapsed_time = int((time.time() - start_time) * 1000)
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assistant_msg.content = final_answer
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assistant_msg.status = 'completed'
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assistant_msg.elapsed_time = elapsed_time
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yield {
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'type': 'annotation_reply',
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'content': f"标注直接回复 (相似度: {annotation_reply['score']:.2f})",
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}
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yield {
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'type': 'answer',
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'content': final_answer,
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}
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conversation.message_count = (conversation.message_count or 0) + 2
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if conversation.message_count == 2 and (not conversation.title or conversation.title == "新建对话"):
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self._generate_title(conversation, user_message)
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agent.message_count = (agent.message_count or 0) + 2
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await self._db.commit()
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yield {
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'type': 'complete',
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'message_id': str(assistant_msg.id),
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'conversation_id': str(conversation.id),
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'tokens_used': 0,
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'elapsed_time': elapsed_time,
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}
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return
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# 知识库检索增强（RAG）
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knowledge_context = await self._retrieve_knowledge_context(agent, user_message)
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if knowledge_context:
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system_prompt = self._inject_knowledge_context(system_prompt, knowledge_context)
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yield {
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'type': 'knowledge_retrieval',
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'content': knowledge_context['summary'],
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'result_count': knowledge_context['result_count'],
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}
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# 获取对话历史
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history = await self._get_conversation_history(conversation, limit=10)
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# 构建当前用户消息内容（支持多模态）
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user_content = LLMMessage.create_multimodal_content(user_message, attachments)
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history.append({'role': 'user', 'content': user_content})
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# 构建消息
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messages = [{'role': 'system', 'content': system_prompt}] + history
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# 检查是否启用流式输出
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enable_streaming = getattr(agent, 'enable_streaming', True)
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total_tokens = 0
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final_answer = ''
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if enable_streaming:
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# 流式输出
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accumulated_content = ''
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async for chunk in self.llm_service.chat_stream(
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model_id=str(agent.model_id),
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messages=messages,
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temperature=agent.temperature or 0.7,
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max_tokens=agent.max_tokens or 2048,
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):
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if chunk.content:
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accumulated_content += chunk.content
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yield {
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'type': 'llm_chunk',
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'content': chunk.content,
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'accumulated_content': accumulated_content,
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}
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if chunk.is_finished:
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total_tokens = chunk.total_tokens
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final_answer = accumulated_content
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else:
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# 非流式输出
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response = await self.llm_service.chat_async(
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model_id=str(agent.model_id),
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messages=messages,
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temperature=agent.temperature or 0.7,
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max_tokens=agent.max_tokens or 2048,
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)
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total_tokens = response.total_tokens
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final_answer = response.content
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yield {
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'type': 'answer',
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'content': final_answer,
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}
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# 更新助手消息
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elapsed_time = int((time.time() - start_time) * 1000)
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assistant_msg.content = final_answer
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assistant_msg.status = 'completed'
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assistant_msg.total_tokens = total_tokens
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assistant_msg.elapsed_time = elapsed_time
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# 更新对话统计
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conversation.message_count = (conversation.message_count or 0) + 2
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conversation.total_tokens = (conversation.total_tokens or 0) + total_tokens
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# 自动生成标题（第一次对话时，且标题为默认值）
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if conversation.message_count == 2 and (not conversation.title or conversation.title == "新建对话"):
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self._generate_title(conversation, user_message)
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# 更新智能体统计
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agent.message_count = (agent.message_count or 0) + 2
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agent.total_tokens = (agent.total_tokens or 0) + total_tokens
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await self._db.commit()
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yield {
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'type': 'complete',
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'message_id': str(assistant_msg.id),
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'conversation_id': str(conversation.id),
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'tokens_used': total_tokens,
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'elapsed_time': elapsed_time,
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}
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except Exception as e:
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logger.exception(f'Agent chat error: {e}')
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assistant_msg.status = 'failed'
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assistant_msg.error_message = str(e)
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await self._db.commit()
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yield {
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'type': 'error',
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'content': str(e),
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}
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async def _chat_autonomous_react(
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self,
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agent: Agent,
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conversation: AgentConversation,
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user_message: str,
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attachments: List[Dict] = None,
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):
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"""
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ReAct 模式（简化版，不使用工具，直接复用 function calling 逻辑）
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Yields:
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流式事件
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"""
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# 直接复用简化后的 function calling 逻辑
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async for event in self._chat_autonomous_function_calling(agent, conversation, user_message, attachments):
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yield event
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async def _chat_dialog_flow(
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self,
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agent: Agent,
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conversation: AgentConversation,
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user_message: str,
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application_id: str = None,
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form_code: str = None,
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attachments: List[Dict] = None,
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):
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"""
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对话流模式对话
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直接复用工作流服务的执行逻辑，透传工作流事件
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Args:
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agent: 智能体
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conversation: 对话
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user_message: 用户消息
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application_id: 子应用 ID（用于表单创建等场景）
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form_code: 表单编码（从表单列表调用时传入）
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attachments: 附件列表
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Yields:
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流式事件
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"""
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start_time = time.time()
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if not self._db:
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yield {'type': 'error', 'content': '数据库会话未初始化'}
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return
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# 创建用户消息（包含附件）
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# 附件格式: [{file_id, type, name, mime_type, size}]
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stored_attachments = []
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if attachments:
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for att in attachments:
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stored_attachments.append({
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'file_id': att.get('file_id'),
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'type': att.get('type', 'file'),
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'name': att.get('name', ''),
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'mime_type': att.get('mime_type', ''),
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'size': att.get('size', 0),
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})
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user_msg = AgentMessage(
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conversation_id=conversation.id,
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role='user',
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content=user_message,
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attachments=stored_attachments,
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status='completed',
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)
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self._db.add(user_msg)
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await self._db.flush()
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# 创建助手消息
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assistant_msg = AgentMessage(
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conversation_id=conversation.id,
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role='assistant',
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content='',
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status='pending',
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)
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self._db.add(assistant_msg)
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await self._db.flush()
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yield {
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'type': 'start',
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'conversation_id': str(conversation.id),
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'message_id': str(assistant_msg.id),
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}
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try:
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from ai_platform.services.workflow_service import AIWorkflowService
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workflow_service = AIWorkflowService(self._db)
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# 检查是否有进行中的工作流运行
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workflow_run_id = None
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if conversation.extra_data:
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workflow_run_id = conversation.extra_data.get('workflow_run_id')
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final_content = ''
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is_waiting = False
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total_tokens = 0
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current_run_id = workflow_run_id
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# 获取对话历史（如果启用了记忆功能）
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conversation_history = []
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if agent.enable_memory:
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conversation_history = await self._get_conversation_history(
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conversation,
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limit=agent.memory_window or 10
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)
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if workflow_run_id:
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# 恢复工作流执行 - 使用异步方法
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# 将附件内容（文件解析内容或图片OCR）整合到 user_input 中
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enhanced_user_input = user_message
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if attachments:
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attachment_texts = []
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for att in attachments:
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text_content = att.get('text_content', '')
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if text_content:
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file_name = att.get('name', 'unknown')
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attachment_texts.append(f"\n[附件: {file_name}]\n{text_content}")
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if attachment_texts:
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enhanced_user_input = user_message + '\n' + '\n'.join(attachment_texts)
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async for event in workflow_service.resume_workflow_stream_async(
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run_id=workflow_run_id,
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user_input=enhanced_user_input,
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conversation_history=conversation_history,
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):
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event_type = event.get('type')
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if event_type == 'answer':
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final_content = event.get('content', '')
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elif event_type == 'llm_chunk':
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accumulated = event.get('accumulated_content', '')
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if accumulated:
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final_content = accumulated
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elif event_type == 'node_complete':
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node_type = event.get('node_type')
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if node_type == 'llm':
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outputs = event.get('outputs', {})
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output = outputs.get('output', '')
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if output:
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final_content = output
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elif event_type == 'waiting_input':
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is_waiting = True
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elif event_type == 'complete':
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total_tokens = event.get('total_tokens', 0)
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yield event
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else:
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# 新对话，启动工作流 - 使用异步方法
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if not agent.workflow_id:
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yield {'type': 'error', 'content': '智能体未配置工作流'}
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return
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# 构建工作流输入变量
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# 将附件内容（文件解析内容或图片OCR）整合到 user_input 中
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enhanced_user_input = user_message
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if attachments:
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attachment_texts = []
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for att in attachments:
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text_content = att.get('text_content', '')
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if text_content:
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file_name = att.get('name', 'unknown')
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attachment_texts.append(f"\n[附件: {file_name}]\n{text_content}")
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if attachment_texts:
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enhanced_user_input = user_message + '\n' + '\n'.join(attachment_texts)
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workflow_inputs = {'user_input': enhanced_user_input}
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if application_id:
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workflow_inputs['application_id'] = application_id
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if form_code:
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workflow_inputs['form_code'] = form_code
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# 调试日志
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import logging
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logger = logging.getLogger(__name__)
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logger.info(f"[AgentService] form_code parameter: {form_code}")
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||||
logger.info(f"[AgentService] workflow_inputs: {workflow_inputs}")
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async for event in workflow_service.run_workflow_stream_async(
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workflow_id=str(agent.workflow_id),
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inputs=workflow_inputs,
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conversation_id=str(conversation.id),
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conversation_history=conversation_history,
|
||||
trigger_type='agent',
|
||||
):
|
||||
event_type = event.get('type')
|
||||
|
||||
if event_type == 'start':
|
||||
current_run_id = event.get('run_id')
|
||||
elif event_type == 'answer':
|
||||
final_content = event.get('content', '')
|
||||
elif event_type == 'llm_chunk':
|
||||
accumulated = event.get('accumulated_content', '')
|
||||
if accumulated:
|
||||
final_content = accumulated
|
||||
elif event_type == 'node_complete':
|
||||
node_type = event.get('node_type')
|
||||
if node_type == 'llm':
|
||||
outputs = event.get('outputs', {})
|
||||
output = outputs.get('output', '')
|
||||
if output:
|
||||
final_content = output
|
||||
elif event_type == 'waiting_input':
|
||||
is_waiting = True
|
||||
# 保存工作流运行 ID 到对话元数据
|
||||
if current_run_id:
|
||||
conversation.extra_data = conversation.extra_data or {}
|
||||
conversation.extra_data['workflow_run_id'] = current_run_id
|
||||
elif event_type == 'complete':
|
||||
total_tokens = event.get('total_tokens', 0)
|
||||
|
||||
yield event
|
||||
|
||||
# 更新助手消息
|
||||
elapsed_time = int((time.time() - start_time) * 1000)
|
||||
|
||||
if final_content:
|
||||
assistant_msg.content = final_content
|
||||
|
||||
assistant_msg.status = 'completed'
|
||||
assistant_msg.elapsed_time = elapsed_time
|
||||
assistant_msg.total_tokens = total_tokens
|
||||
|
||||
# 更新对话统计
|
||||
conversation.message_count = (conversation.message_count or 0) + 2
|
||||
conversation.total_tokens = (conversation.total_tokens or 0) + total_tokens
|
||||
|
||||
# 自动生成标题（第一次对话时，且标题为默认值）
|
||||
if conversation.message_count == 2 and (not conversation.title or conversation.title == "新建对话"):
|
||||
self._generate_title(conversation, user_message)
|
||||
|
||||
# 如果工作流完成（非等待状态），清除对话元数据
|
||||
if not is_waiting:
|
||||
if conversation.extra_data:
|
||||
conversation.extra_data.pop('workflow_run_id', None)
|
||||
|
||||
await self._db.commit()
|
||||
|
||||
yield {
|
||||
'type': 'complete',
|
||||
'elapsed_time': elapsed_time,
|
||||
'tokens_used': total_tokens,
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.exception(f'Dialog flow error: {e}')
|
||||
assistant_msg.status = 'failed'
|
||||
assistant_msg.error_message = str(e)
|
||||
await self._db.commit()
|
||||
yield {
|
||||
'type': 'error',
|
||||
'content': str(e),
|
||||
}
|
||||
|
||||
def _build_system_prompt_simple(self, agent: Agent) -> str:
|
||||
"""构建简化的系统提示词"""
|
||||
# 基础提示词
|
||||
base_prompt = agent.system_prompt
|
||||
|
||||
# 如果没有系统提示词，从人设生成
|
||||
if not base_prompt and agent.persona:
|
||||
base_prompt = self._generate_prompt_from_persona(agent.persona)
|
||||
|
||||
if not base_prompt:
|
||||
base_prompt = "你是一个智能助手，可以帮助用户完成各种任务。回答要简洁明了，使用中文。"
|
||||
|
||||
return base_prompt
|
||||
|
||||
def _generate_prompt_from_persona(self, persona: Dict[str, Any]) -> str:
|
||||
"""从人设配置生成提示词"""
|
||||
parts = []
|
||||
|
||||
if persona.get('role'):
|
||||
parts.append(persona['role'])
|
||||
|
||||
if persona.get('personality'):
|
||||
personalities = persona['personality']
|
||||
if isinstance(personalities, list):
|
||||
parts.append(f"你的性格特点是：{', '.join(personalities)}。")
|
||||
|
||||
if persona.get('skills'):
|
||||
skills = persona['skills']
|
||||
if isinstance(skills, list):
|
||||
parts.append(f"你擅长：{', '.join(skills)}。")
|
||||
|
||||
if persona.get('background'):
|
||||
parts.append(persona['background'])
|
||||
|
||||
if persona.get('constraints'):
|
||||
constraints = persona['constraints']
|
||||
if isinstance(constraints, list):
|
||||
parts.append("注意事项：\n" + "\n".join(f"- {c}" for c in constraints))
|
||||
|
||||
return "\n\n".join(parts)
|
||||
|
||||
async def _get_conversation_history(
|
||||
self,
|
||||
conversation: AgentConversation,
|
||||
limit: int = 10,
|
||||
) -> List[Dict[str, str]]:
|
||||
"""获取对话历史"""
|
||||
if not self._db:
|
||||
return []
|
||||
|
||||
result = await self._db.execute(
|
||||
select(AgentMessage).where(
|
||||
AgentMessage.conversation_id == conversation.id,
|
||||
AgentMessage.is_deleted == False,
|
||||
AgentMessage.role.in_(['user', 'assistant']),
|
||||
AgentMessage.status == 'completed'
|
||||
).order_by(AgentMessage.sys_create_datetime.desc()).limit(limit)
|
||||
)
|
||||
messages = result.scalars().all()
|
||||
|
||||
history = []
|
||||
for msg in reversed(list(messages)):
|
||||
history.append({
|
||||
'role': msg.role,
|
||||
'content': msg.content,
|
||||
})
|
||||
|
||||
return history
|
||||
|
||||
def _generate_title(self, conversation, first_message: str):
|
||||
"""自动生成对话标题"""
|
||||
# 简单截取前 20 个字符作为标题
|
||||
title = first_message[:20]
|
||||
if len(first_message) > 20:
|
||||
title += '...'
|
||||
conversation.title = title
|
||||
|
||||
async def _check_annotation_direct_reply(
|
||||
self,
|
||||
agent: Agent,
|
||||
query: str,
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
检查标注直接回复（Dify 风格）
|
||||
|
||||
当启用标注回复且匹配到高相似度标注时，直接返回标注答案，跳过 LLM 调用。
|
||||
|
||||
Returns:
|
||||
{'answer': str, 'score': float, 'question': str} 或 None
|
||||
"""
|
||||
knowledge_base_ids = getattr(agent, 'knowledge_base_ids', None) or []
|
||||
if not knowledge_base_ids or not self._db:
|
||||
return None
|
||||
|
||||
knowledge_config = getattr(agent, 'knowledge_config', None) or {}
|
||||
annotation_reply_enabled = knowledge_config.get('annotation_reply_enabled', False)
|
||||
if not annotation_reply_enabled:
|
||||
return None
|
||||
|
||||
annotation_threshold = knowledge_config.get('annotation_threshold', 0.9)
|
||||
|
||||
try:
|
||||
from ai_platform.knowledge.services.retrieval_service import RetrievalService
|
||||
from ai_platform.knowledge.models import KnowledgeBase
|
||||
from sqlalchemy import select
|
||||
|
||||
# 获取第一个知识库的 embedding 配置
|
||||
kb_result = await self._db.execute(
|
||||
select(KnowledgeBase).where(
|
||||
KnowledgeBase.id.in_(knowledge_base_ids),
|
||||
KnowledgeBase.is_deleted == False,
|
||||
)
|
||||
)
|
||||
first_kb = kb_result.scalars().first()
|
||||
if not first_kb or not first_kb.embedding_model_id:
|
||||
return None
|
||||
|
||||
service = RetrievalService(self._db)
|
||||
annotation_results = await service._match_annotations(
|
||||
query=query,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
embedding_model_id=first_kb.embedding_model_id,
|
||||
score_threshold=annotation_threshold,
|
||||
dimensions=first_kb.embedding_dimensions,
|
||||
max_results=1,
|
||||
)
|
||||
|
||||
if annotation_results:
|
||||
best = annotation_results[0]
|
||||
metadata = getattr(best, 'metadata', {}) or {}
|
||||
logger.info(
|
||||
f'标注直接回复命中: score={best.score}, '
|
||||
f'question={metadata.get("question", "")}, answer={best.content[:50]}'
|
||||
)
|
||||
|
||||
# 记录检索日志
|
||||
try:
|
||||
from ai_platform.knowledge.models import KnowledgeRetrievalLog
|
||||
log = KnowledgeRetrievalLog(
|
||||
query=query,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
retrieval_mode='annotation',
|
||||
top_k=1,
|
||||
score_threshold=annotation_threshold,
|
||||
result_count=1,
|
||||
results=[{'segment_id': best.segment_id, 'score': best.score, 'kb_id': best.knowledge_base_id}],
|
||||
rerank_applied='false',
|
||||
elapsed_time=0,
|
||||
source='agent_annotation',
|
||||
)
|
||||
self._db.add(log)
|
||||
await self._db.flush()
|
||||
except Exception as e:
|
||||
logger.warning(f'记录标注回复日志失败: {e}')
|
||||
|
||||
return {
|
||||
'answer': best.content,
|
||||
'score': best.score,
|
||||
'question': metadata.get('question', ''),
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f'标注直接回复检查失败: {e}')
|
||||
|
||||
return None
|
||||
|
||||
async def _retrieve_knowledge_context(
|
||||
self,
|
||||
agent: Agent,
|
||||
query: str,
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
检索知识库上下文（RAG）
|
||||
|
||||
Args:
|
||||
agent: 智能体（包含 knowledge_base_ids 和 knowledge_config）
|
||||
query: 用户查询文本
|
||||
|
||||
Returns:
|
||||
知识库上下文字典，无结果时返回 None
|
||||
"""
|
||||
knowledge_base_ids = getattr(agent, 'knowledge_base_ids', None) or []
|
||||
if not knowledge_base_ids or not self._db:
|
||||
logger.info(f'RAG 跳过: knowledge_base_ids={knowledge_base_ids}, db={bool(self._db)}')
|
||||
return None
|
||||
|
||||
knowledge_config = getattr(agent, 'knowledge_config', None) or {}
|
||||
logger.info(f'RAG 开始: kb_ids={knowledge_base_ids}, config={knowledge_config}, query={query[:100]}')
|
||||
top_k = knowledge_config.get('top_k', 5)
|
||||
score_threshold = knowledge_config.get('score_threshold', 0.5)
|
||||
retrieval_mode = knowledge_config.get('retrieval_mode', None)
|
||||
rerank_enabled = knowledge_config.get('rerank_enabled', None)
|
||||
rerank_model_id = knowledge_config.get('rerank_model_id', None)
|
||||
|
||||
try:
|
||||
from ai_platform.knowledge.services.retrieval_service import RetrievalService
|
||||
|
||||
start_time = time.time()
|
||||
service = RetrievalService(self._db)
|
||||
results = await service.retrieve(
|
||||
query=query,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
top_k=top_k,
|
||||
score_threshold=score_threshold,
|
||||
retrieval_mode=retrieval_mode,
|
||||
rerank_enabled=rerank_enabled,
|
||||
rerank_model_id=rerank_model_id,
|
||||
)
|
||||
|
||||
elapsed = int((time.time() - start_time) * 1000)
|
||||
|
||||
logger.info(f'RAG 检索结果: {len(results)} 条')
|
||||
for r in results:
|
||||
logger.info(f' - segment_id={r.segment_id}, score={r.score}, doc={getattr(r, "document_name", "")}, type={getattr(r, "metadata", {}).get("type", "segment")}')
|
||||
|
||||
# 记录检索日志（与召回测试共用同一张日志表）
|
||||
try:
|
||||
from ai_platform.knowledge.models import KnowledgeRetrievalLog
|
||||
log = KnowledgeRetrievalLog(
|
||||
query=query,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
retrieval_mode=retrieval_mode or 'hybrid',
|
||||
top_k=top_k,
|
||||
score_threshold=score_threshold,
|
||||
result_count=len(results),
|
||||
results=[
|
||||
{'segment_id': r.segment_id, 'score': r.score, 'kb_id': r.knowledge_base_id}
|
||||
for r in results
|
||||
],
|
||||
rerank_applied='true' if rerank_enabled else 'false',
|
||||
elapsed_time=elapsed,
|
||||
source='agent',
|
||||
)
|
||||
self._db.add(log)
|
||||
await self._db.flush()
|
||||
except Exception as e:
|
||||
logger.warning(f'记录 Agent 检索日志失败: {e}')
|
||||
|
||||
if not results:
|
||||
return None
|
||||
|
||||
# 构建上下文文本
|
||||
context_parts = []
|
||||
for i, r in enumerate(results, 1):
|
||||
score = getattr(r, 'score', 0)
|
||||
content = getattr(r, 'content', '')
|
||||
metadata = getattr(r, 'metadata', {}) or {}
|
||||
|
||||
if metadata.get('type') == 'annotation':
|
||||
# 标注类型：显示为 Q&A 对，让 LLM 明确知道这是预设的标准答案
|
||||
question = metadata.get('question', '')
|
||||
context_parts.append(
|
||||
f"[{i}] [标准问答] (相似度: {score:.2f})\n"
|
||||
f"问题: {question}\n"
|
||||
f"标准答案: {content}"
|
||||
)
|
||||
else:
|
||||
source = getattr(r, 'document_name', '') or ''
|
||||
context_parts.append(
|
||||
f"[{i}] (来源: {source}, 相似度: {score:.2f})\n{content}"
|
||||
)
|
||||
|
||||
context_text = '\n\n'.join(context_parts)
|
||||
summary = f"检索到 {len(results)} 条相关结果"
|
||||
|
||||
return {
|
||||
'context': context_text,
|
||||
'result_count': len(results),
|
||||
'summary': summary,
|
||||
'results': [
|
||||
{
|
||||
'segment_id': getattr(r, 'segment_id', ''),
|
||||
'document_name': getattr(r, 'document_name', ''),
|
||||
'score': getattr(r, 'score', 0),
|
||||
}
|
||||
for r in results
|
||||
],
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f'知识库检索失败: {e}')
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _inject_knowledge_context(
|
||||
system_prompt: str,
|
||||
knowledge_context: Dict[str, Any],
|
||||
) -> str:
|
||||
"""
|
||||
将知识库检索结果注入系统提示词
|
||||
|
||||
参考 Dify 的 RAG 注入方式：在 system prompt 末尾追加参考资料段落，
|
||||
并指示 LLM 基于资料回答，资料中无相关信息时如实告知。
|
||||
"""
|
||||
context_text = knowledge_context.get('context', '')
|
||||
if not context_text:
|
||||
return system_prompt
|
||||
|
||||
rag_instruction = (
|
||||
"\n\n"
|
||||
"# 参考资料\n"
|
||||
"以下是从知识库中检索到的与用户问题相关的参考资料。\n"
|
||||
"- 标记为[标准问答]的条目是预设的权威问答对，当用户问题与其匹配时，请直接使用标准答案回答，不要自行发挥。\n"
|
||||
"- 其他条目为文档参考内容，请基于这些资料回答用户的问题。\n"
|
||||
"- 如果参考资料中没有相关信息，请基于你自身的知识回答，并说明该回答未基于知识库。\n\n"
|
||||
f"{context_text}"
|
||||
)
|
||||
|
||||
return system_prompt + rag_instruction
|
||||
Reference in New Issue
Block a user