++

rpc_alpha_workflow/01_generate_direction.py

@@ -0,0 +1,191 @@
+# -*- coding: utf-8 -*-
+import random
+import time
+from xmlrpc.client import ServerProxy, Fault
+
+is_local = 0
+if is_local:
+    url = 'http://127.0.0.1:28888'
+else:
+    url = 'http://192.168.31.41:32000'
+
+username = 'rpc'
+password = 'aaaAAA111'
+db = "quantify"
+
+common = ServerProxy(f"{url}/xmlrpc/2/common")
+uid = common.authenticate(db, username, password, {})
+
+models = ServerProxy(f"{url}/xmlrpc/2/object")
+
+# 1. 在 alpha.generate.research.direction 表中创建一条数据
+# 需要准备: region_id 和 universe_id
+
+region = 'USA'
+universe = 'TOP3000'
+
+# 根据名称搜索 region 和 universe 的ID
+region_ids = models.execute_kw(db, uid, password, 'alpha.region.settings', 'search', [[['name', '=', region]]], {'limit': 1})
+universe_ids = models.execute_kw(db, uid, password, 'alpha.universe.settings', 'search', [[['name', '=', universe]]], {'limit': 1})
+
+if not region_ids:
+    print(f"没有找到 region: '{region}'，请检查名称是否正确")
+    exit(1)
+if not universe_ids:
+    print(f"没有找到 universe: '{universe}'，请检查名称是否正确")
+    exit(1)
+
+region_id = region_ids[0]
+universe_id = universe_ids[0]
+
+# 获取 region 和 universe 的 name
+region_data = models.execute_kw(db, uid, password, 'alpha.region.settings', 'read', [[region_id], ['name']])
+universe_data = models.execute_kw(db, uid, password, 'alpha.universe.settings', 'read', [[universe_id], ['name']])
+region_name = region_data[0]['name'] if region_data else 'Unknown'
+universe_name = universe_data[0]['name'] if universe_data else 'Unknown'
+
+print(f"使用 region_id: {region_id} (name: {region_name}), universe_id: {universe_id} (name: {universe_name})")
+
+# 随机选择一个 llm_settings_line_id
+llm_line_ids = models.execute_kw(db, uid, password, 'llm.settings.line', 'search', [[['id', '!=', 0]]])
+if not llm_line_ids:
+    print("没有找到 llm.settings.line 记录")
+    exit(1)
+
+llm_settings_line_id = random.choice(llm_line_ids)
+llm_line_data = models.execute_kw(db, uid, password, 'llm.settings.line', 'read', [[llm_settings_line_id], ['display_name', 'model_name']])
+llm_display_name = llm_line_data[0].get('display_name', 'Unknown') if llm_line_data else 'Unknown'
+print(f"随机选择 llm_settings_line_id: {llm_settings_line_id} (name: {llm_display_name})")
+
+# 搜索 Research Direction Prompt
+rd_prompt_ids = models.execute_kw(db, uid, password, 'alpha.prompt.settings', 'search', [[['prompt_type', '=', 'Research Direction']]], {'limit': 1})
+if not rd_prompt_ids:
+    print("没有找到 prompt_type='Research Direction' 的 alpha.prompt.settings 记录")
+    exit(1)
+research_direction_prompt_id = rd_prompt_ids[0]
+print(f"使用 Research Direction Prompt ID: {research_direction_prompt_id}")
+
+# 搜索 Feature Engineering Prompt
+fe_prompt_ids = models.execute_kw(db, uid, password, 'alpha.prompt.settings', 'search', [[['prompt_type', '=', 'Feature Engineering']]], {'limit': 1})
+if not fe_prompt_ids:
+    print("没有找到 prompt_type='Feature Engineering' 的 alpha.prompt.settings 记录")
+    exit(1)
+feature_engineering_prompt_id = fe_prompt_ids[0]
+print(f"使用 Feature Engineering Prompt ID: {feature_engineering_prompt_id}")
+
+# 创建研究方向记录
+record_vals = {
+    'region': region_id,
+    'universe': universe_id,
+    'llm_settings_line_id': llm_settings_line_id,
+    'research_direction_prompt': research_direction_prompt_id,
+    'feature_engineering_prompt': feature_engineering_prompt_id,
+}
+
+record_id = models.execute_kw(db, uid, password, 'alpha.generate.research.direction', 'create', [record_vals])
+print(f"成功创建研究方向记录，ID: {record_id}")
+
+# 2. 调用 btn_quick_selection_datasets 按钮方法
+result = models.execute_kw(db, uid, password, 'alpha.generate.research.direction', 'btn_quick_selection_datasets', [[record_id]])
+print(f"btn_quick_selection_datasets 执行结果: {result}")
+
+# 验证数据集行是否已创建
+datasets_line_ids = models.execute_kw(db, uid, password, 'alpha.research.datasets.line', 'search', [[['research_direction_id', '=', record_id]]])
+print(f"已创建的数据集行数量: {len(datasets_line_ids)}")
+
+# 获取数据集行的 dataset_code（数据集名字）
+if datasets_line_ids:
+    datasets_lines = models.execute_kw(db, uid, password, 'alpha.research.datasets.line', 'read', [datasets_line_ids, ['datasets_code']])
+    dataset_codes = [line.get('datasets_code') for line in datasets_lines if line.get('datasets_code')]
+    print(f"关联的数据集名字: {dataset_codes}")
+    print(f"数据集总数: {len(dataset_codes)}")
+
+
+# 3. 点击 btn_generate_research 按钮
+print("\n开始生成研究方向...")
+try:
+    result = models.execute_kw(db, uid, password, 'alpha.generate.research.direction', 'btn_generate_research', [[record_id]])
+    print(f"btn_generate_research 执行结果: {result}")
+except Fault as e:
+    # 忽略 "cannot marshal None" 错误，因为方法返回 None 是正常的
+    if "cannot marshal None" in str(e):
+        print("btn_generate_research 已执行（返回 None，这是正常的）")
+    else:
+        raise
+
+# 死循环等待 status = 'generated_research'
+print("等待研究方向生成完成...")
+while True:
+    time.sleep(5)  # 每5秒检查一次
+    record_data = models.execute_kw(db, uid, password, 'alpha.generate.research.direction', 'read', [[record_id], ['status', 'message']])
+    if not record_data:
+        print("记录不存在")
+        exit(1)
+    current_status = record_data[0].get('status')
+    message = record_data[0].get('message', '')
+    print(f"当前状态: {current_status}, 消息: {message}")
+    
+    if current_status == 'generated_research':
+        print("研究方向生成完成！")
+        break
+    elif current_status == 'failed':
+        print(f"研究方向生成失败: {message}")
+        exit(1)
+
+# 4. 点击 btn_generate_prepare_feature_engineering_prompt 按钮
+print("\n开始生成特征工程提示词...")
+try:
+    result = models.execute_kw(db, uid, password, 'alpha.generate.research.direction', 'btn_generate_prepare_feature_engineering_prompt', [[record_id]])
+    print(f"btn_generate_prepare_feature_engineering_prompt 执行结果: {result}")
+except Fault as e:
+    # 忽略 "cannot marshal None" 错误，因为方法返回 None 是正常的
+    if "cannot marshal None" in str(e):
+        print("btn_generate_prepare_feature_engineering_prompt 已执行（返回 None，这是正常的）")
+    else:
+        raise
+
+# 5. 点击 btn_generate_feature 按钮
+print("\n开始生成特征工程...")
+try:
+    result = models.execute_kw(db, uid, password, 'alpha.generate.research.direction', 'btn_generate_feature', [[record_id]])
+    print(f"btn_generate_feature 执行结果: {result}")
+except Fault as e:
+    # 忽略 "cannot marshal None" 错误，因为方法返回 None 是正常的
+    if "cannot marshal None" in str(e):
+        print("btn_generate_feature 已执行（返回 None，这是正常的）")
+    else:
+        raise
+
+# 死循环等待 status = 'done'
+print("等待特征工程生成完成...")
+while True:
+    time.sleep(5)  # 每5秒检查一次
+    record_data = models.execute_kw(db, uid, password, 'alpha.generate.research.direction', 'read', [[record_id], ['status', 'message']])
+    if not record_data:
+        print("记录不存在")
+        exit(1)
+    current_status = record_data[0].get('status')
+    message = record_data[0].get('message', '')
+    print(f"当前状态: {current_status}")
+    
+    if current_status == 'done':
+        print("特征工程生成完成！")
+        break
+    elif current_status == 'failed':
+        print(f"特征工程生成失败: {message}")
+        exit(1)
+
+# 6. 点击 btn_generate_idea 按钮
+print("\n开始生成 Idea...")
+try:
+    result = models.execute_kw(db, uid, password, 'alpha.generate.research.direction', 'btn_generate_idea', [[record_id]])
+    print(f"btn_generate_idea 执行结果: {result}")
+except Fault as e:
+    # 忽略 "cannot marshal None" 错误，因为方法返回 None 是正常的
+    if "cannot marshal None" in str(e):
+        print("btn_generate_idea 已执行（返回 None，这是正常的）")
+    else:
+        raise
+
+print("\n全部流程执行完成！收工！")
+

rpc_alpha_workflow/02_generate_alpha_idea.py

@@ -0,0 +1,17 @@
+# -*- coding: utf-8 -*-
+from xmlrpc.client import ServerProxy
+
+is_local = 0
+if is_local:
+    url = 'http://127.0.0.1:28888'
+else:
+    url = 'http://192.168.31.41:32000'
+
+username = 'rpc'
+password = 'aaaAAA111'
+db = "quantify"
+
+common = ServerProxy(f"{url}/xmlrpc/2/common")
+uid = common.authenticate(db, username, password, {})
+
+print(uid)

rpc_alpha_workflow/03_decode_template.py

@@ -0,0 +1,17 @@
+# -*- coding: utf-8 -*-
+from xmlrpc.client import ServerProxy
+
+is_local = 0
+if is_local:
+    url = 'http://127.0.0.1:28888'
+else:
+    url = 'http://192.168.31.41:32000'
+
+username = 'rpc'
+password = 'aaaAAA111'
+db = "quantify"
+
+common = ServerProxy(f"{url}/xmlrpc/2/common")
+uid = common.authenticate(db, username, password, {})
+
+print(uid)

rpc_alpha_workflow/reference_01.py

@@ -0,0 +1,519 @@
+# -*- coding: utf-8 -*-
+import uuid
+import sys
+import httpx
+from odoo import models, fields
+
+
+class AlphaGenerateResearchDirection(models.Model):
+    _name = 'alpha.generate.research.direction'
+    _description = 'Alpha Generate Research Direction'
+    _order = 'id desc'
+
+    name = fields.Char(string='Name', required=True, default=lambda self: str(uuid.uuid4()))
+
+    status = fields.Selection([
+        ('draft', 'Draft'),
+        ('generated_research', 'Generated Research'),
+        ('generated_feature', 'Generated Feature'),
+        ('done', 'Done'),
+        ('failed', 'Failed'),
+        ('cancel', 'Cancel')
+    ], string='Status', default='draft')
+
+    region = fields.Many2one('alpha.region.settings', string='Region', required=True)
+
+    universe = fields.Many2one('alpha.universe.settings', string='Universe', required=True)
+
+    research_direction_prompt = fields.Many2one('alpha.prompt.settings', string='Research Direction Prompt')
+
+    feature_engineering_prompt = fields.Many2one('alpha.prompt.settings', string='Feature Engineering Prompt')  # 用于加上替换的研究方向, 组合新的工程特征提示词
+
+    results = fields.Text(string='Results')
+
+    message = fields.Char(string='Message')
+
+    llm_settings_line_id = fields.Many2one('llm.settings.line', string='Model')
+
+    models_name = fields.Char(string='Model Name', related='llm_settings_line_id.model_name')
+
+    provider_name = fields.Char(string='Provider Name', related='llm_settings_line_id.llm_setting_id.name')
+
+    research_direction = fields.Text(string='Research Direction')
+
+    prepare_feature_engineering_prompt = fields.Text(string='Feature Engineering Prompt')  # 用于已组合完成, 准备生成工程特征的提示词
+
+    feature_engineering = fields.Text(string='Feature Engineering')
+
+    use_datasets = fields.Many2one('alpha.datasets', string='Use Datasets')
+
+    datasets_line_ids = fields.One2many('alpha.research.datasets.line', 'research_direction_id', string='Datasets Lines')
+
+    def _get_callback_url(self):
+        """根据平台获取回调 URL"""
+        platform_info = sys.platform
+        if platform_info == "darwin":
+            # Mac 开发环境使用本地地址
+            base_url_odoo = self.env['ir.config_parameter'].sudo().get_param('web.base.url.local', 'http://127.0.0.1:8069')
+        else:
+            base_url_odoo = self.env['ir.config_parameter'].sudo().get_param('web.base.url')
+        return f"{base_url_odoo}/api/alpha-generate-research-direction/result"
+
+    def btn_generate_research(self):
+        """
+        生成研究方向按钮
+        通过选择的基础提示词，推送到 LLM 微服务生成研究方向
+        post 无需等待，LLM 会回写结果到 research_direction 字段
+        成功后状态变为 generated_research，失败不修改状态，失败信息写到 message
+        """
+        self.ensure_one()
+
+        # 如果research_direction字段已存在, 则跳过不生成
+        if self.research_direction:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Research direction already exists, please delete it first.',
+                    'sticky': False,
+                },
+            }
+
+        # 校验必填字段
+        if not self.research_direction_prompt:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Please select a research direction prompt template.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+        if not self.llm_settings_line_id:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Please select a model.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        model_name = self.llm_settings_line_id.model_name
+        base_url = self.llm_settings_line_id.llm_setting_id.base_url
+        api_key = self.llm_settings_line_id.llm_setting_id.api_key
+
+        final_prompt = self.research_direction_prompt.prompt
+
+        ms_config = self.get_ms_config()
+        ms_url = ms_config.get('url')
+
+        callback_url = self._get_callback_url()
+
+        post_data = {
+            "record_id": self.id,
+            "odoo_model": self._name,
+            "action_type": "research_direction",
+            "prompt": final_prompt,
+            "model_name": model_name,
+            "base_url": base_url,
+            "api_key": api_key,
+            "callback_url": callback_url
+        }
+
+        if not ms_url:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Microservice URL is not available.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        # 发送请求，超短超时，不等待响应
+        try:
+            httpx.post(f'{ms_url}:32004/api_llm_generate', json=post_data, timeout=0.001)
+        except httpx.TimeoutException:
+            pass
+        except Exception as e:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': f'Failed to send microservices: {e}',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        self.write({
+            'status': 'draft',
+            'message': 'Request sent to microservice, waiting for research direction...'
+        })
+
+    def btn_generate_prepare_feature_engineering_prompt(self):
+        # 手动填入生成好的研究方向, 跳过第一步的 llm 生成
+        self.ensure_one()
+
+        # 如果没有研究方向, 需要生成
+        if not self.research_direction or not self.feature_engineering_prompt:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Need to generate research direction and need select feature_engineering_prompt',
+                    'sticky': False,
+                },
+            }
+
+        # 将已有的研究方向, 和基础的feature_engineering_prompt组合
+        # feature_engineering_prompt + research_direction
+        feature_engineering_prompt = self.feature_engineering_prompt.prompt
+        research_direction = self.research_direction
+
+        replaced_feature_engineering_prompt = feature_engineering_prompt.replace('[#replace#]', research_direction)
+
+        self.write({
+            'status': 'generated_research',
+            'prepare_feature_engineering_prompt': replaced_feature_engineering_prompt
+        })
+
+        return True
+
+    def btn_generate_feature(self):
+        """
+        生成特征工程按钮
+        通过 feature_engineering_prompt 作为提示词，推送到 LLM 微服务生成特征工程
+        post 无需等待，LLM 会回写结果到 feature_engineering 字段
+        成功后状态变为 done，失败状态变为 failed，失败信息写到 message
+        """
+        self.ensure_one()
+
+        # 如果未生成prepare_feature_engineering_prompt, 则不允许使用此按钮
+        if not self.prepare_feature_engineering_prompt:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Please generate prepare_feature_engineering_prompt first',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        # 如果feature_engineering字段不为空, 则提示需要清空才可以使用这个按钮, 避免重复生成
+        if self.feature_engineering:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Please clear feature_engineering field first',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        # 校验状态
+        if self.status != 'generated_research':
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Please generate research direction first.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        # 校验必填字段
+        if not self.research_direction:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Research direction is empty, please generate it first.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+        if not self.llm_settings_line_id:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Please select a model.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+        if not self.feature_engineering_prompt:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Please select a feature engineering prompt template.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        model_name = self.llm_settings_line_id.model_name
+        base_url = self.llm_settings_line_id.llm_setting_id.base_url
+        api_key = self.llm_settings_line_id.llm_setting_id.api_key
+
+        # 使用 feature_engineering_prompt 作为提示词
+        final_prompt = self.feature_engineering_prompt.prompt
+
+        ms_config = self.get_ms_config()
+        ms_url = ms_config.get('url')
+
+        callback_url = self._get_callback_url()
+
+        post_data = {
+            "record_id": self.id,
+            "odoo_model": self._name,
+            "action_type": "feature_engineering",
+            "prompt": final_prompt,
+            "model_name": model_name,
+            "base_url": base_url,
+            "api_key": api_key,
+            "callback_url": callback_url
+        }
+
+        if not ms_url:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Microservice URL is not available.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        # 发送请求，超短超时，不等待响应
+        try:
+            httpx.post(f'{ms_url}:32004/api_llm_generate', json=post_data, timeout=0.001)
+        except httpx.TimeoutException:
+            pass
+        except Exception as e:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Failed to send microservice.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        self.write({
+            'status': 'generated_research',
+            'message': 'Request sent to microservice, waiting for feature engineering...'
+        })
+
+    def get_ms_config(self):
+        # TODO 先从 nacos 获取微服务 url
+        nacos_url = ''
+
+        platform_info = sys.platform
+        if platform_info == "darwin":
+            nacos_url = 'http://192.168.31.41:30848/nacos/v1/cs/configs?dataId=microservices_dev&group=quantify'
+        elif platform_info.startswith("linux"):
+            nacos_url = 'http://192.168.31.41:30848/nacos/v1/cs/configs?dataId=microservices&group=quantify'
+
+        try:
+            ms_config_resp = httpx.get(nacos_url)
+            ms_config_resp.raise_for_status()
+        except Exception as e:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'Nacos request failed.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        ms_config = ms_config_resp.json()
+
+        return ms_config
+
+    def btn_generate_idea(self):
+        # 最后一步, 当工程特征已经生成, 并且已选择数据字段, 则生成idea
+        # 同一份特征工程, 多个数据字段, 生成与数据字段相同数量的 idea
+        self.ensure_one()
+
+        # if self.status != 'generated_feature':
+        #     return {
+        #         'type': 'ir.actions.client',
+        #         'tag': 'display_notification',
+        #         'params': {
+        #             'title': 'ERROR',
+        #             'message': 'Feature engineering not completed.',
+        #             'type': 'danger',
+        #             'sticky': False
+        #         }
+        #     }
+
+        if not self.datasets_line_ids:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'No data fields selected.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        if not self.feature_engineering:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'ERROR',
+                    'message': 'No feature engineering selected.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        # 开始生成 idea, 先获取到数据集的 name 和 data_sets_type
+        for data_field in self.datasets_line_ids:
+            data_fields_name = data_field.datasets_id.datasets_id
+            data_sets_type = ''
+            region_id = self.region.id
+            universe_id = self.universe.id
+            for data_field_line in data_field.datasets_id.line_ids:
+                data_sets_type = data_field_line.data_sets_type
+                break
+
+            # 创建一个alpha.idea记录, 为了防止创建重复的 idea, 当前模型的 name 是 uuid4, 加上 region_id, universe_id, data_fields_name, data_sets_type,这几个字段, 先搜索是否存在相应的记录
+            # 如果不存在, 创建, 如果存在, 跳过
+
+            # 根据 data_sets_type 确定 data_type
+            data_type = 'MATRIX' if data_sets_type == 'MATRIX' else 'VECTOR'
+
+            # 检查是否已存在相同的 idea 记录（特征工程 + 数据集 都相同）
+            existing_idea = self.env['alpha.idea'].search([
+                ('region', '=', region_id),
+                ('universe', '=', universe_id),
+                ('data_type', '=', data_type),
+                ('replace_prompt', '=', self.feature_engineering),
+                ('needed_data_set_ids.name', '=', data_fields_name)
+            ], limit=1)
+
+            if existing_idea:
+                continue
+
+            # 创建新的 alpha.idea 记录
+            idea_vals = {
+                'region': region_id,
+                'universe': universe_id,
+                'data_type': data_type,
+                'delay': self.use_datasets.delay if self.use_datasets else '1',
+                'replace_prompt': self.feature_engineering,
+                'needed_data_set_ids': [(0, 0, {'name': data_fields_name})],
+            }
+
+            # 找到最终的 meta_prompt
+            meta_prompt = self.env['alpha.prompt.settings'].search([('prompt_type', '=', 'meta_prompt')], limit=1)
+
+            if meta_prompt:
+                idea_vals.update({'meta_prompt': meta_prompt.id})
+
+            new_record = self.env['alpha.idea'].create(idea_vals)
+
+            # 创建成功之后, 顺便点一下
+            try:
+                new_record.btn_check_and_fetch_data()
+                new_record.btn_generate_final_prompt()
+            except Exception as e:
+                print(f'error: {e}')
+
+        return {
+            'type': 'ir.actions.client',
+            'tag': 'display_notification',
+            'params': {
+                'title': 'Success',
+                'message': 'Ideas generated successfully.',
+                'type': 'success',
+                'sticky': False,
+            }
+        }
+
+    def btn_quick_selection_datasets(self):
+        self.ensure_one()
+
+        # 1, 检查是否存在已选择的数据集, 如果存在, 清空self.datasets_line_ids
+        if self.datasets_line_ids:
+            self.datasets_line_ids.unlink()
+
+        # 2, 查询数据集, 并创建关联行
+        datasets = self.env['alpha.datasets'].search([
+            ('region', '=', self.region.id),
+            ('universe', '=', self.universe.id),
+        ])
+
+        # 3, 遍历数据, 插入到 datasets_line_ids
+        for dataset in datasets:
+            self.datasets_line_ids.create({
+                'research_direction_id': self.id,
+                'datasets_id': dataset.id,
+            })
+
+        return True
+
+    def btn_action_cancel(self):
+        self.ensure_one()
+        self.write({
+            'status': 'cancel'
+        })
+
+
+class AlphaResearchDatasetsLine(models.Model):
+    """研究方向数据集关联行"""
+    _name = 'alpha.research.datasets.line'
+    _description = 'Alpha Research Datasets Line'
+    _order = 'id desc'
+
+    research_direction_id = fields.Many2one('alpha.generate.research.direction', string='Research Direction', required=True, ondelete='cascade')
+
+    datasets_id = fields.Many2one('alpha.datasets', string='Dataset', required=True)
+
+    data_field_count = fields.Integer(string='Data Field Count', related='datasets_id.data_field_count', readonly=True)
+
+    datasets_code = fields.Char(string='Dataset Code', related='datasets_id.datasets_id', readonly=True)
+
+    datasets_name = fields.Char(string='Dataset Name', related='datasets_id.name', readonly=True)
+
+    region = fields.Char(string='Region', related='datasets_id.region.name', readonly=True)
+
+    universe = fields.Char(string='Universe', related='datasets_id.universe.name', readonly=True)
+
+    delay = fields.Selection([
+        ('1', '1'),
+        ('0', '0')
+    ], string='Delay', related='datasets_id.delay', readonly=True)