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630 lines
23 KiB
630 lines
23 KiB
# -*- coding: utf-8 -*-
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import random
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import sys
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import uuid
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import httpx
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from odoo import models, fields
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from . import decode_template
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class AlphaIdea(models.Model):
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_name = 'alpha.idea'
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_description = 'Alpha Idea'
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_order = 'id desc'
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name = fields.Char(string='Name', required=True, default=lambda self: str(uuid.uuid4()))
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status = fields.Selection([
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('draft', 'Draft'),
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('fetched_data', 'Fetched Data'),
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('generated_prompt', 'Generated Prompt'),
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('posted_to_ms', 'Posted To MS'),
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('llm_received', 'LLM Received'),
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('decode_template', 'Decode Template'),
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('decoded', 'Decoded'),
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('done', 'Done'),
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('failed', 'Failed'),
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('cancel', 'Cancel')
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], string='Status', default='draft')
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pushed = fields.Boolean(string='Pushed', default=False, readonly=True)
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region = fields.Many2one('alpha.region.settings', string='Region', required=True)
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universe = fields.Many2one('alpha.universe.settings', string='Universe', required=True)
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data_type = fields.Selection([('MATRIX', 'MATRIX'), ('VECTOR', 'VECTOR')], string='Data Type', required=True, default='MATRIX')
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delay = fields.Selection([('1', '1'), ('0', '0')], required=True, string='Delay', default='1')
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data_sets = fields.Char(string='Data Sets', compute='_compute_data_sets')
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meta_prompt = fields.Many2one('alpha.prompt.settings', string='Meta Prompt')
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replace_prompt = fields.Text(string='Replace Prompt')
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final_prompt = fields.Text(string='Final Prompt')
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system_prompt = fields.Text(string='System Prompt')
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user_prompt = fields.Text(string='User Prompt')
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llm_generated_idea = fields.Text(string='LLM Generated Idea')
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result_message = fields.Text(string='Result Message')
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llm_settings_line_id = fields.Many2one('llm.settings.line', string='Model', default=lambda self: self._default_llm_settings_line_id())
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def _default_llm_settings_line_id(self):
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"""默认随机选择一个模型，如果没有则返回 False"""
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llm_lines = self.env['llm.settings.line'].search([])
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if llm_lines:
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return random.choice(llm_lines).id
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return False
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idea_template_ids = fields.One2many('alpha.idea.template', 'idea_id', string='Idea Templates')
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needed_data_set_ids = fields.One2many('alpha.needed.data.set', 'idea_id', string='Needed Data Sets')
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final_expression_ids = fields.One2many('alpha.final.expression', 'idea_id', string='Final Expressions')
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expression_count = fields.Integer(string='Expression Count', required=True, readonly=True, default=0, compute='_compute_expression_count')
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def _compute_data_sets(self):
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# 显示使用的数据集
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for record in self:
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record.data_sets = ''
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if record.needed_data_set_ids:
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data_sets_list = []
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for data_set_name in record.needed_data_set_ids:
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data_sets_list.append(data_set_name.name)
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if len(data_sets_list) > 0:
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record.data_sets = ', '.join(data_sets_list)
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def action_cancel(self):
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self.status = 'cancel'
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def action_reset(self):
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self.status = 'fetched_data'
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def btn_check_and_fetch_data(self):
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if not self.needed_data_set_ids:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'No data',
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'message': 'No data set is needed.',
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'type': 'danger',
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'sticky': False,
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}
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}
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# TODO: 通过 datasets_id, 查找 datasets 中, 是否存在 datasets_id 的数据, 如果不存在, 则在 datasets 模块中创建一条记录, 然后需要过去手动下载(必须)
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success = False
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for dataset in self.needed_data_set_ids:
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dataset_id = self.env['alpha.datasets'].search(
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[('datasets_id', '=', dataset.name),
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('region', '=', self.region.id),
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('universe', '=', self.universe.id),
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('delay', '=', self.delay)
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], limit=1)
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if not dataset_id:
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dataset_id = self.env['alpha.datasets'].create({
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'name': str(uuid.uuid4()),
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'datasets_id': dataset.name,
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'region': self.region.id,
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'universe': self.universe.id,
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'delay': self.delay,
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})
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# 创建 dataset_id 的记录之后, 执行一下 dataset_id 的 btn_get_datasets 方法
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try:
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dataset_id.btn_get_datasets()
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success = True
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except Exception as e:
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self.status = 'failed'
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'Failed',
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'message': f'Dataset fetch failed: {str(e)}',
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'type': 'danger',
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'sticky': False,
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}
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}
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else:
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if dataset_id.line_ids:
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success = True
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else:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'No data',
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'message': 'Dataset does not exist.',
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'type': 'danger',
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'sticky': False,
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}
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}
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if success:
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self.status = 'fetched_data'
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return True
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def btn_generate_final_prompt(self):
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if self.final_prompt:
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self.final_prompt = ''
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self.system_prompt = ''
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self.user_prompt = ''
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self.status = 'fetched_data'
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': '成功',
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'message': '已清除最终提示词',
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'type': 'success',
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'sticky': False,
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}
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}
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if not self.meta_prompt:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'No data!',
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'message': 'Please select the prompt template first.',
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'type': 'danger',
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'sticky': False,
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}
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}
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if not self.replace_prompt:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'No data!',
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'message': 'Research direction cannot be empty.',
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'type': 'danger',
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'sticky': False,
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}
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}
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system_prompt = '''You are executing two skills in sequence:
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1) brain-data-feature-engineering
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2) brain-feature-implementation
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The following SKILL.md documents are authoritative; follow them exactly.
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'''
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data_sets_list = []
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dataset_id = ''
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category = ''
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region = self.region.name
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delay = self.delay
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universe = self.universe.name
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for dataset in self.needed_data_set_ids:
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datasets_id = self.env['alpha.datasets'].search(
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[('datasets_id', '=', dataset.name)], limit=1)
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for datasets_line in datasets_id.line_ids:
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data_sets_list.append({
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'id': datasets_line.data_field_name,
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'description': datasets_line.description
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})
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if not dataset_id:
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dataset_id = datasets_line.dataset_id
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if not category:
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category = datasets_line.category_name
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if not data_sets_list:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'No data!',
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'message': 'No related dataset found.',
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'type': 'danger',
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'sticky': False,
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}
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}
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field_count = len(data_sets_list)
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operator_ids = self.env['alpha.operator.line'].search([])
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operator_list = []
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for operator in operator_ids:
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operator_list.append({
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'name': operator.name,
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'category': operator.category,
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'scope': operator.scope,
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'description': operator.description,
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'definition': operator.definition
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})
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if not operator_list:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'No data!',
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'message': 'No related Operator found.',
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'type': 'danger',
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'sticky': False,
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}
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}
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fields_json = ','.join([str(field) for field in data_sets_list])
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user_prompt = '''{
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"instructions": {
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"output_format": "Fill OUTPUT_TEMPLATE.md with concrete content.",
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"implementation_examples": "Each Implementation Example must be a template with {variable} placeholders. Use only placeholders from allowed_placeholders. Use suffix-only names; do not include dataset code/prefix/horizon.",
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"no_code_fences": true,
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"do_not_invent_placeholders": true
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},
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"dataset_context": {
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"dataset_id": "''' + dataset_id + '''",
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"dataset_name": null,
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"dataset_description": null,
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"category": "''' + category + '''",
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"region": "''' + region + '''",
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"delay": ''' + str(delay) + ''',
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"universe": "''' + universe + '''",
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"field_count": ''' + str(field_count) + '''
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},
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"fields": [
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''' + fields_json + '''
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]
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}
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'''
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final_prompt = self.meta_prompt.prompt
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final_prompt = final_prompt.replace(
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'###question_driven###', self.replace_prompt)
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final_prompt = final_prompt.replace('###dataset_id###', dataset_id)
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final_prompt = final_prompt.replace('###category###', category)
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final_prompt = final_prompt.replace('###region###', region)
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final_prompt = final_prompt.replace('###delay###', str(delay))
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final_prompt = final_prompt.replace(
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'###field_count###', str(field_count))
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final_prompt = final_prompt.replace('###universe###', universe)
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final_prompt = final_prompt.replace(
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'###datasets###', str(data_sets_list))
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final_prompt = final_prompt.replace(
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'###operators###', str(operator_list))
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if self.data_type and self.data_type.upper() == "VECTOR":
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vector_prompt = "since all the following the data is vector type data, before you do any process, you should choose a vector operator to generate its statistical feature to use, the data cannot be directly use. for example, if datafieldA and datafieldB are vector type data, you can use vec_avg(datafieldA) - vec_avg(datafieldB), where vec_avg() operator is used to generate the average of the data on a certain date. similarly, vector type operator can only be used on the vector type operator directly and cannot be nested, for example vec_avg(vec_sum(datafield)) is a false use."
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final_prompt = final_prompt.replace(
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'###vector_instruction###', vector_prompt)
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else:
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final_prompt = final_prompt.replace('###vector_instruction###', '')
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self.system_prompt = system_prompt
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self.user_prompt = user_prompt
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self.final_prompt = final_prompt
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self.status = 'generated_prompt'
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': '成功',
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'message': f'已生成提示模板，包含 {field_count} 个数据字段',
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'type': 'success',
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'sticky': False,
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}
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}
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def post_to_ms(self):
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if not all([self.final_prompt, self.user_prompt, self.system_prompt]):
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'Error',
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'message': 'Please generate a prompt template.',
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'type': 'danger',
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'sticky': False,
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}
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}
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if not self.llm_settings_line_id:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'Error',
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'message': 'Please select a model.',
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'type': 'danger',
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'sticky': False,
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}
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}
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model_name = self.llm_settings_line_id.model_name
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base_url = self.llm_settings_line_id.llm_setting_id.base_url
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api_key = self.llm_settings_line_id.llm_setting_id.api_key
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# 获取当前 record_id
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record_id = self.id
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# 获取 Odoo 回调地址
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base_url_odoo = self.env['ir.config_parameter'].sudo().get_param('web.base.url')
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callback_url = f"{base_url_odoo}/api/alpha-idea/result"
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# 组装完整 prompt
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full_prompt = f"{self.system_prompt}\n\n{self.user_prompt}\n\n{self.final_prompt}"
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# 获取微服务配置
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ms_config = self.get_ms_config()
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ms_url = ms_config.get('url', '')
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if not ms_url:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'Error',
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'message': 'Microservice address not configured.',
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'type': 'danger',
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'sticky': False
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}
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}
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# 组装请求数据
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payload = {
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'record_id': record_id,
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'prompt': full_prompt,
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'model_name': model_name,
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'base_url': base_url,
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'api_key': api_key,
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'callback_url': callback_url,
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'system_prompt': self.system_prompt,
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'user_prompt': self.user_prompt,
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'final_prompt': self.final_prompt,
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}
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# 发送请求到微服务
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try:
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httpx.post(f"{ms_url}:32004/api_alpha_generate_idea",
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json=payload, timeout=0.001)
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except httpx.TimeoutException:
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pass
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except Exception as e:
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self.status = 'failed'
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'Error',
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'message': f'Failed to send microservice:\n{e}.',
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'type': 'danger',
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'sticky': False,
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}
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}
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self.status = 'posted_to_ms'
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def get_ms_config(self):
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# TODO 先从 nacos 获取微服务 url
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nacos_url = ''
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platform_info = sys.platform
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if platform_info == "darwin":
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nacos_url = 'http://192.168.31.41:30848/nacos/v1/cs/configs?dataId=microservices_dev&group=quantify'
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elif platform_info.startswith("linux"):
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nacos_url = 'http://192.168.31.41:30848/nacos/v1/cs/configs?dataId=microservices&group=quantify'
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try:
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ms_config_resp = httpx.get(nacos_url)
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ms_config_resp.raise_for_status()
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except Exception as e:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'Error',
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'message': f'Nacos request failed:\n{e}.',
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'type': 'danger',
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'sticky': False,
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}
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}
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ms_config = ms_config_resp.json()
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return ms_config
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def action_set_llm_received(self):
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"""手动设置 LLM 已返回状态
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当手动录入 llm_generated_idea 后，调用此函数将状态改为 llm_received
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"""
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if self.llm_generated_idea and self.status == 'generated_prompt':
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self.status = 'llm_received'
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return True
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else:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'Error',
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'message': 'llm_generated_idea 无数据或状态不是 Generated Prompt',
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'type': 'danger',
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'sticky': False,
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}
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}
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def decode_template(self):
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if not self.llm_generated_idea or self.status != 'llm_received':
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'Please use llm to generate the template first.',
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'message': 'llm_generated_idea no data or status != llm_received',
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'type': 'danger',
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'sticky': False,
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}
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}
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if self.status == 'done':
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
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'params': {
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'title': 'message',
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'message': 'This idea has been generated, please do not repeat the operation.',
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'type': 'danger',
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'sticky': False,
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}
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}
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llm_template = self.llm_generated_idea
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data_sets_list = []
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for dataset in self.needed_data_set_ids:
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datasets_id = self.env['alpha.datasets'].search(
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[('datasets_id', '=', dataset.name)], limit=1)
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for datasets_line in datasets_id.line_ids:
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data_sets_list.append({
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'id': datasets_line.data_field_name
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})
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result_data = decode_template.process(data_sets_list, llm_template)
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if result_data['success']:
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templates = result_data['templates']
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expressions = result_data['expressions']
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# 如果没有解出数据，不做任何操作
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if len(templates) == 0 and len(expressions) == 0:
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return {
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'type': 'ir.actions.client',
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'tag': 'display_notification',
|
|
'params': {
|
|
'title': '提示',
|
|
'message': '未解码出任何数据',
|
|
'type': 'warning',
|
|
'sticky': False,
|
|
}
|
|
}
|
|
|
|
# 组装模板数据
|
|
template_data = []
|
|
for template_item in templates:
|
|
template_data.append({
|
|
'template': template_item['template'],
|
|
'original_template': template_item['original_template'],
|
|
'idea': template_item.get('idea', ''),
|
|
'template_line_ids': [(0, 0, {'expression': line}) for line in template_item['expressions']]
|
|
})
|
|
|
|
# 保存数据
|
|
self.write({
|
|
'idea_template_ids': [(0, 0, data) for data in template_data],
|
|
'final_expression_ids': [(0, 0, {'name': expression}) for expression in expressions],
|
|
'status': 'decoded',
|
|
})
|
|
|
|
return True
|
|
else:
|
|
return {
|
|
'type': 'ir.actions.client',
|
|
'tag': 'display_notification',
|
|
'params': {
|
|
'title': '失败',
|
|
'message': '模板解码失败, 生成数量为 0',
|
|
'type': 'danger',
|
|
'sticky': False,
|
|
}
|
|
}
|
|
|
|
def combination_settings(self):
|
|
self.ensure_one()
|
|
if self.idea_template_ids and self.final_expression_ids:
|
|
return {
|
|
'type': 'ir.actions.act_window',
|
|
'name': '组合设置',
|
|
'res_model': 'wizard.combination.settings',
|
|
'view_mode': 'form',
|
|
'target': 'new',
|
|
'context': {
|
|
'active_model': 'alpha.idea',
|
|
'active_id': self.id,
|
|
},
|
|
}
|
|
else:
|
|
return {
|
|
'type': 'ir.actions.client',
|
|
'tag': 'display_notification',
|
|
'params': {
|
|
'title': 'Execute the decoding template first',
|
|
'message': 'idea_template_ids and final_expression_ids no data!',
|
|
'type': 'danger',
|
|
'sticky': False,
|
|
}
|
|
}
|
|
|
|
def _compute_expression_count(self):
|
|
for record in self:
|
|
if record.final_expression_ids:
|
|
record.expression_count = len(record.final_expression_ids)
|
|
else:
|
|
record.expression_count = 0
|
|
|
|
|
|
class NeededDataSet(models.Model):
|
|
_name = 'alpha.needed.data.set'
|
|
_description = 'Alpha Needed Data Set'
|
|
|
|
idea_id = fields.Many2one('alpha.idea', string='Idea', ondelete='cascade')
|
|
|
|
name = fields.Char(string='Name')
|
|
|
|
|
|
class IdeaTemplate(models.Model):
|
|
_name = 'alpha.idea.template'
|
|
_description = 'Alpha Idea Template'
|
|
|
|
idea_id = fields.Many2one('alpha.idea', string='Idea', ondelete='cascade')
|
|
|
|
template_line_ids = fields.One2many(
|
|
'alpha.idea.template.line', 'idea_id', string='Template Lines')
|
|
|
|
template = fields.Char(string='Template')
|
|
|
|
original_template = fields.Char(string='Original Template')
|
|
|
|
expression_count = fields.Integer(
|
|
string='Expression Count', compute='_compute_expression_count', default=0)
|
|
|
|
idea = fields.Text(string='Text')
|
|
|
|
def _compute_expression_count(self):
|
|
for record in self:
|
|
record.expression_count = len(record.template_line_ids)
|
|
|
|
|
|
class IdeaTemplateLine(models.Model):
|
|
_name = 'alpha.idea.template.line'
|
|
_description = 'Alpha Idea Template Line'
|
|
|
|
idea_id = fields.Many2one('alpha.idea.template',
|
|
string='Alpha Idea Template', ondelete='cascade')
|
|
|
|
expression = fields.Char(string='Expression')
|
|
|
|
|
|
class Final_Expression(models.Model):
|
|
_name = 'alpha.final.expression'
|
|
_description = 'Alpha Final Expression'
|
|
|
|
idea_id = fields.Many2one('alpha.idea', string='Idea', ondelete='cascade')
|
|
|
|
name = fields.Char(string='Name')
|
|
|