237 lines
8.7 KiB
Python
237 lines
8.7 KiB
Python
# -*- coding: utf-8 -*-
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from odoo import models, fields, api
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class AiUserProfile(models.Model):
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"""用户 AI 画像 — 每个用户一个，存储 AI 对该用户的认知"""
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_name = 'ai.user.profile'
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_description = '用户 AI 画像'
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_rec_name = 'user_id'
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user_id = fields.Many2one(
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'res.users', string='用户', required=True,
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ondelete='cascade', index=True,
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default=lambda self: self.env.uid,
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)
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employee_id = fields.Many2one(
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'hr.employee', string='关联员工',
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help='关联员工后自动带出职位、部门等信息',
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)
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# 基础信息（用户可手动维护，AI 也可通过工具更新）
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nickname = fields.Char(
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string='称呼',
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help='AI 如何称呼你，如"彭哥"、"张总"'
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)
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role = fields.Char(
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string='职位/角色',
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help='如：项目经理、产品总监、运维工程师'
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)
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department = fields.Char(
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string='部门',
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help='所属部门或团队'
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)
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work_scope = fields.Text(
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string='工作职责',
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help='简述你主要负责的工作内容，帮助 AI 更好理解你的需求'
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)
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style_preference = fields.Selection([
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('concise', '简洁直接'),
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('detailed', '详细深入'),
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('casual', '轻松口语'),
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('formal', '正式商务'),
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], string='沟通风格', default='concise',
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help='AI 回复时的风格偏好'
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)
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custom_instruction = fields.Text(
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string='自定义指令',
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help='给 AI 的额外指令，如"回答不要超过 200 字"、"优先用表格呈现数据"等'
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)
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# 记忆
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memory_ids = fields.One2many(
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'ai.user.memory', 'profile_id', string='AI 记忆'
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)
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memory_count = fields.Integer(
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string='记忆条数', compute='_compute_memory_count'
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)
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# Token 统计
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total_tokens_used = fields.Integer(
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string='总 Token', compute='_compute_token_stats'
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)
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token_call_count = fields.Integer(
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string='调用次数', compute='_compute_token_stats'
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)
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# 对话统计
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conversation_count = fields.Integer(
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string='对话数', compute='_compute_conversation_count'
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)
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_sql_constraints = [
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('user_unique', 'UNIQUE(user_id)', '每个用户只能有一个 AI 画像'),
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]
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@api.onchange('employee_id')
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def _onchange_employee_id(self):
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"""选择员工后自动填充称呼、职位、部门"""
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if self.employee_id:
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emp = self.employee_id
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if not self.nickname or self.nickname == self.user_id.name:
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self.nickname = emp.name
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self.role = emp.job_title or (emp.job_id.name if emp.job_id else '') or self.role
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self.department = emp.department_id.name if emp.department_id else self.department
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@api.onchange('user_id')
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def _onchange_user_id(self):
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"""切换用户后自动关联对应员工"""
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if self.user_id:
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emp = self.env['hr.employee'].search([
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('user_id', '=', self.user_id.id)
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], limit=1)
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if emp:
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self.employee_id = emp.id
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@api.depends('memory_ids')
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def _compute_memory_count(self):
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for rec in self:
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rec.memory_count = len(rec.memory_ids)
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def _compute_token_stats(self):
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"""统计该用户的 Token 总量和调用次数"""
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for rec in self:
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usage_data = self.env['ai.token.usage'].sudo().read_group(
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[('user_id', '=', rec.user_id.id)],
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['total_tokens:sum'],
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[],
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)
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rec.total_tokens_used = usage_data[0]['total_tokens'] if usage_data else 0
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rec.token_call_count = self.env['ai.token.usage'].sudo().search_count(
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[('user_id', '=', rec.user_id.id)]
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)
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def _compute_conversation_count(self):
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"""统计该用户的对话数"""
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for rec in self:
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rec.conversation_count = self.env['ai.conversation'].search_count(
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[('create_uid', '=', rec.user_id.id)]
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)
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def action_view_token_usage(self):
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"""打开当前用户的 Token 用量明细"""
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self.ensure_one()
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return {
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'type': 'ir.actions.act_window',
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'name': f'{self.user_id.name} - Token 用量',
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'res_model': 'ai.token.usage',
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'view_mode': 'tree',
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'domain': [('user_id', '=', self.user_id.id)],
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'context': {'search_default_filter_month': 1},
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}
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def action_view_conversations(self):
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"""打开当前用户的对话列表"""
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self.ensure_one()
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return {
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'type': 'ir.actions.act_window',
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'name': f'{self.user_id.name} - 对话',
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'res_model': 'ai.conversation',
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'view_mode': 'tree,form',
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'domain': [('create_uid', '=', self.user_id.id)],
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}
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def action_start_chat(self):
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"""发起新对话 → 跳转到聊天界面"""
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self.ensure_one()
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# 获取或创建个人对话
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conversation = self.env['ai.conversation'].search([
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('create_uid', '=', self.env.uid),
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], limit=1, order='create_date desc')
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if not conversation:
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provider = self.env['ai.provider'].search([('active', '=', True)], limit=1)
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conversation = self.env['ai.conversation'].create({
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'name': '新对话',
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'provider_id': provider.id if provider else False,
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})
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return conversation.open_chat()
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@api.model
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def get_or_create(self, user_id=None):
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"""获取或创建当前用户的 AI 画像"""
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uid = user_id or self.env.uid
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profile = self.search([('user_id', '=', uid)], limit=1)
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if not profile:
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user = self.env['res.users'].browse(uid)
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# 自动查找关联员工
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emp = self.env['hr.employee'].search([('user_id', '=', uid)], limit=1)
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vals = {
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'user_id': uid,
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'nickname': emp.name if emp else user.name,
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'role': (emp.job_title or (emp.job_id.name if emp and emp.job_id else '')) or user.job_title or '',
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'department': emp.department_id.name if emp and emp.department_id else '',
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}
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if emp:
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vals['employee_id'] = emp.id
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profile = self.create(vals)
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return profile
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def get_system_context(self):
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"""生成注入到 system prompt 的用户上下文段落"""
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self.ensure_one()
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parts = []
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if self.nickname:
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parts.append(f'用户称呼：{self.nickname}')
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if self.role:
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parts.append(f'职位：{self.role}')
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if self.department:
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parts.append(f'部门：{self.department}')
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if self.work_scope:
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parts.append(f'工作职责：{self.work_scope}')
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if self.style_preference:
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style_map = {
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'concise': '请简洁直接回答，不要冗余',
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'detailed': '请详细深入回答，提供充分背景',
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'casual': '请用轻松口语化风格回答',
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'formal': '请用正式商务风格回答',
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}
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parts.append(f'沟通风格要求：{style_map.get(self.style_preference, "")}')
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if self.custom_instruction:
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parts.append(f'用户自定义指令：{self.custom_instruction}')
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# 加入最近的活跃记忆（最多 20 条）
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memories = self.memory_ids.filtered('active').sorted('write_date', reverse=True)[:20]
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if memories:
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mem_lines = []
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for m in memories:
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cat_label = dict(m._fields['category'].selection).get(m.category, '')
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mem_lines.append(f' [{cat_label}] {m.content}')
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parts.append('关于此用户的记忆：\n' + '\n'.join(mem_lines))
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return '\n'.join(parts) if parts else ''
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class AiUserMemory(models.Model):
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"""AI 对用户的长期记忆"""
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_name = 'ai.user.memory'
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_description = 'AI 用户记忆'
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_order = 'write_date desc'
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profile_id = fields.Many2one(
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'ai.user.profile', string='用户画像',
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required=True, ondelete='cascade', index=True,
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)
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category = fields.Selection([
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('habit', '习惯偏好'),
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('work', '工作内容'),
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('knowledge', '专业知识'),
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('style', '沟通风格'),
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('personal', '个人信息'),
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('other', '其他'),
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], string='类别', default='other', required=True)
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content = fields.Text(string='记忆内容', required=True)
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source = fields.Selection([
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('auto', 'AI 自动提取'),
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('manual', '用户手动添加'),
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], string='来源', default='manual')
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active = fields.Boolean(string='有效', default=True)
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