++

rpc_alpha_workflow/02_generate_alpha_idea.py

@@ -1,5 +1,7 @@
 # -*- coding: utf-8 -*-
-from xmlrpc.client import ServerProxy
+import time
+from datetime import datetime
+from xmlrpc.client import ServerProxy, Fault
 
 is_local = 0
 if is_local:
@@ -14,4 +16,101 @@ db = "quantify"
 common = ServerProxy(f"{url}/xmlrpc/2/common")
 uid = common.authenticate(db, username, password, {})
 
-print(uid)
+models = ServerProxy(f"{url}/xmlrpc/2/object")
+
+# 超时时间（秒）
+TIMEOUT_SECONDS = 10 * 60  # 10分钟
+
+# 1. 搜索 alpha.idea 模型中当天创建且状态为 generated_prompt 的数据
+today = datetime.now().strftime('%Y-%m-%d')
+print(f"搜索日期: {today}")
+
+# 构建搜索条件: 当天创建且状态为 generated_prompt
+# Odoo 中日期字段通常存储为 datetime，我们使用 date 函数比较
+search_domain = [
+    ['status', '=', 'generated_prompt'],
+    ['create_date', '>=', f"{today} 00:00:00"],
+    ['create_date', '<=', f"{today} 23:59:59"]
+]
+
+idea_ids = models.execute_kw(db, uid, password, 'alpha.idea', 'search', [search_domain])
+
+if not idea_ids:
+    print(f"没有找到当天状态为 'generated_prompt' 的 alpha.idea 记录")
+    exit(0)
+
+print(f"找到 {len(idea_ids)} 条待处理记录: {idea_ids}")
+
+# 2. 遍历这些数据，单线程处理
+for idx, idea_id in enumerate(idea_ids, 1):
+    print(f"\n{'='*60}")
+    print(f"处理第 {idx}/{len(idea_ids)} 条记录，ID: {idea_id}")
+    print(f"{'='*60}")
+
+    # 点击 post_to_ms 按钮
+    print("调用 post_to_ms 方法...")
+    try:
+        result = models.execute_kw(db, uid, password, 'alpha.idea', 'post_to_ms', [[idea_id]])
+        print(f"post_to_ms 执行结果: {result}")
+    except Fault as e:
+        # 忽略 "cannot marshal None" 错误
+        if "cannot marshal None" in str(e):
+            print("post_to_ms 已执行（返回 None，这是正常的）")
+        else:
+            print(f"post_to_ms 执行出错: {e}")
+            continue
+
+    # 死循环等待状态变为 llm_received，带10分钟超时
+    print("等待 LLM 返回结果...")
+    start_time = time.time()
+    current_status = None
+    while True:
+        # 检查是否超时
+        elapsed_time = time.time() - start_time
+        if elapsed_time > TIMEOUT_SECONDS:
+            print(f"超时！已等待 {elapsed_time/60:.1f} 分钟，强制退出等待...")
+            # 在 result_message 字段回写 timeout
+            try:
+                models.execute_kw(db, uid, password, 'alpha.idea', 'write', [[idea_id], {'result_message': 'timeout'}])
+                print(f"已在记录 {idea_id} 的 result_message 字段写入 'timeout'")
+            except Exception as write_e:
+                print(f"写入 result_message 失败: {write_e}")
+            break
+
+        time.sleep(5)  # 每5秒检查一次
+        record_data = models.execute_kw(db, uid, password, 'alpha.idea', 'read', [[idea_id], ['status', 'name']])
+        if not record_data:
+            print("记录不存在")
+            break
+
+        current_status = record_data[0].get('status')
+        record_name = record_data[0].get('name', 'Unknown')
+        print(f"当前状态: {current_status} (已等待 {elapsed_time:.0f} 秒)")
+
+        if current_status == 'llm_received':
+            print(f"LLM 已返回结果，共等待 {elapsed_time:.0f} 秒（{elapsed_time/60:.1f} 分钟），开始解码模板...")
+            break
+        elif current_status == 'failed':
+            print(f"处理失败，跳过此记录")
+            break
+
+    # 如果状态是 llm_received，调用 decode_template
+    if current_status == 'llm_received':
+        print("调用 decode_template 方法...")
+        try:
+            result = models.execute_kw(db, uid, password, 'alpha.idea', 'decode_template', [[idea_id]])
+            print(f"decode_template 执行结果: {result}")
+        except Fault as e:
+            # 忽略 "cannot marshal None" 错误
+            if "cannot marshal None" in str(e):
+                print("decode_template 已执行（返回 None，这是正常的）")
+            else:
+                print(f"decode_template 执行出错: {e}")
+
+        print(f"记录 {idea_id} 处理完成")
+
+    print(f"第 {idx}/{len(idea_ids)} 条记录处理完毕，继续下一条...")
+
+print(f"\n{'='*60}")
+print("全部流程执行完成！收工！")
+print(f"{'='*60}")

rpc_alpha_workflow/03_decode_template.py

@@ -1,17 +0,0 @@
-# -*- 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/migration_plan.md

@@ -0,0 +1,404 @@
+# 02_generate_alpha_idea.py 微服务迁移方案
+
+## 一、功能总结
+
+### 1.1 核心功能
+`02_generate_alpha_idea.py` 是一个 Odoo 工作流自动化脚本，主要功能：
+
+1. **搜索数据**：在 `alpha.idea` 模型中查找当天创建且状态为 `generated_prompt` 的记录
+2. **遍历处理**：单线程依次处理每条记录
+3. **调用 post_to_ms**：触发 `alpha.idea` 模型的 `post_to_ms()` 方法，将数据发送到微服务
+4. **等待 LLM 返回**：死循环轮询状态，每5秒检查一次
+5. **超时处理**：10分钟超时，超时后在 `result_message` 字段写入 "timeout"
+6. **调用 decode_template**：状态变为 `llm_received` 后，调用 `decode_template()` 方法解码模板
+
+### 1.2 关键特性
+- **单线程处理**：不并发，避免 LLM 压力过大
+- **10分钟硬编码超时**：正常2-3分钟，超时即放弃
+- **状态机驱动**：依赖 Odoo 模型的状态字段流转
+
+---
+
+## 二、微服务架构设计
+
+### 2.1 接口设计
+
+```
+POST /api/alpha-idea/process
+```
+
+**请求参数**：
+```json
+{
+    "idea_id": 123  // alpha.idea 记录的 ID
+}
+```
+
+**响应**：
+```json
+{
+    "success": true,
+    "message": "处理完成",
+    "idea_id": 123,
+    "wait_time": 45,  // 等待秒数
+    "status": "llm_received"
+}
+```
+
+### 2.2 处理流程
+
+```
+Odoo 按钮点击
+    ↓
+httpx 请求 FastAPI 微服务
+    ↓
+POST /api/alpha-idea/process (携带 idea_id)
+    ↓
+微服务调用 Odoo XML-RPC
+    ↓
+1. 调用 post_to_ms()
+2. 轮询等待状态 (每5秒)
+3. 超时10分钟或成功
+4. 调用 decode_template()
+    ↓
+返回结果给 Odoo
+```
+
+---
+
+## 三、代码实现
+
+### 3.1 目录结构
+
+```
+rpc_alpha_workflow/
+├── 01_generate_direction.py
+├── 02_generate_alpha_idea.py      # 原脚本（保留备份）
+├── reference_01.py
+├── reference_02.py
+├── migration_plan.md              # 本文档
+└── alpha_idea_service/            # 微服务目录
+    ├── main.py                    # FastAPI 入口
+    ├── config.py                  # 配置
+    ├── odoo_client.py             # Odoo XML-RPC 客户端
+    └── requirements.txt           # 依赖
+```
+
+### 3.2 核心代码
+
+#### config.py
+```python
+# -*- coding: utf-8 -*-
+"""配置文件"""
+
+# Odoo 连接配置
+ODOO_URL = "http://192.168.31.41:32000"
+ODOO_DB = "quantify"
+ODOO_USERNAME = "rpc"
+ODOO_PASSWORD = "aaaAAA111"
+
+# 超时配置（硬编码，不对外暴露）
+TIMEOUT_SECONDS = 10 * 60  # 10分钟
+POLL_INTERVAL = 5  # 轮询间隔秒数
+```
+
+#### odoo_client.py
+```python
+# -*- coding: utf-8 -*-
+"""Odoo XML-RPC 客户端"""
+from xmlrpc.client import ServerProxy, Fault
+from . import config
+
+
+class OdooClient:
+    def __init__(self):
+        self.url = config.ODOO_URL
+        self.db = config.ODOO_DB
+        self.username = config.ODOO_USERNAME
+        self.password = config.ODOO_PASSWORD
+        self.uid = None
+        self.models = None
+        self._authenticate()
+    
+    def _authenticate(self):
+        """认证并获取 uid"""
+        common = ServerProxy(f"{self.url}/xmlrpc/2/common")
+        self.uid = common.authenticate(self.db, self.username, self.password, {})
+        self.models = ServerProxy(f"{self.url}/xmlrpc/2/object")
+    
+    def call(self, model, method, args=None, kwargs=None):
+        """调用 Odoo 方法"""
+        args = args or []
+        kwargs = kwargs or {}
+        return self.models.execute_kw(self.db, self.uid, self.password, model, method, args, kwargs)
+    
+    def post_to_ms(self, idea_id: int):
+        """调用 post_to_ms 方法"""
+        try:
+            result = self.call('alpha.idea', 'post_to_ms', [[idea_id]])
+            return {"success": True, "result": result}
+        except Fault as e:
+            if "cannot marshal None" in str(e):
+                return {"success": True, "result": None}
+            return {"success": False, "error": str(e)}
+    
+    def read_record(self, idea_id: int, fields: list):
+        """读取记录"""
+        result = self.call('alpha.idea', 'read', [[idea_id], fields])
+        return result[0] if result else None
+    
+    def write_record(self, idea_id: int, values: dict):
+        """写入记录"""
+        return self.call('alpha.idea', 'write', [[idea_id], values])
+    
+    def decode_template(self, idea_id: int):
+        """调用 decode_template 方法"""
+        try:
+            result = self.call('alpha.idea', 'decode_template', [[idea_id]])
+            return {"success": True, "result": result}
+        except Fault as e:
+            if "cannot marshal None" in str(e):
+                return {"success": True, "result": None}
+            return {"success": False, "error": str(e)}
+```
+
+#### main.py
+```python
+# -*- coding: utf-8 -*-
+"""FastAPI 微服务入口"""
+import time
+from fastapi import FastAPI
+from pydantic import BaseModel
+from .odoo_client import OdooClient
+from . import config
+
+app = FastAPI(title="Alpha Idea Service")
+
+
+class ProcessRequest(BaseModel):
+    idea_id: int
+
+
+class ProcessResponse(BaseModel):
+    success: bool
+    message: str
+    idea_id: int
+    wait_time: float = 0
+    status: str = ""
+
+
+@app.post("/api/alpha-idea/process", response_model=ProcessResponse)
+async def process_idea(request: ProcessRequest):
+    """
+    处理单个 alpha.idea 记录
+    1. 调用 post_to_ms
+    2. 等待状态变为 llm_received（10分钟超时）
+    3. 调用 decode_template
+    """
+    idea_id = request.idea_id
+    client = OdooClient()
+    
+    # 1. 调用 post_to_ms
+    post_result = client.post_to_ms(idea_id)
+    if not post_result["success"]:
+        return ProcessResponse(
+            success=False,
+            message=f"post_to_ms 失败: {post_result.get('error')}",
+            idea_id=idea_id,
+            status="failed"
+        )
+    
+    # 2. 等待状态变为 llm_received
+    start_time = time.time()
+    current_status = None
+    
+    while True:
+        elapsed_time = time.time() - start_time
+        
+        # 检查超时
+        if elapsed_time > config.TIMEOUT_SECONDS:
+            # 写入 timeout
+            try:
+                client.write_record(idea_id, {"result_message": "timeout"})
+            except Exception:
+                pass
+            return ProcessResponse(
+                success=False,
+                message=f"超时！已等待 {elapsed_time/60:.1f} 分钟",
+                idea_id=idea_id,
+                wait_time=elapsed_time,
+                status="timeout"
+            )
+        
+        time.sleep(config.POLL_INTERVAL)
+        
+        # 读取状态
+        record = client.read_record(idea_id, ["status", "name"])
+        if not record:
+            return ProcessResponse(
+                success=False,
+                message="记录不存在",
+                idea_id=idea_id,
+                wait_time=elapsed_time,
+                status="not_found"
+            )
+        
+        current_status = record.get("status")
+        
+        if current_status == "llm_received":
+            break
+        elif current_status == "failed":
+            return ProcessResponse(
+                success=False,
+                message="处理失败",
+                idea_id=idea_id,
+                wait_time=elapsed_time,
+                status="failed"
+            )
+    
+    # 3. 调用 decode_template
+    decode_result = client.decode_template(idea_id)
+    
+    return ProcessResponse(
+        success=decode_result["success"],
+        message="处理完成" if decode_result["success"] else f"decode_template 失败: {decode_result.get('error')}",
+        idea_id=idea_id,
+        wait_time=time.time() - start_time,
+        status=current_status
+    )
+
+
+@app.get("/health")
+async def health_check():
+    """健康检查"""
+    return {"status": "ok"}
+```
+
+#### requirements.txt
+```
+fastapi
+uvicorn
+pydantic
+```
+
+---
+
+## 四、Odoo 端改造
+
+### 4.1 修改 post_to_ms 方法
+
+在 `reference_02.py` 的 `post_to_ms` 方法中，移除直接调用 httpx 发送请求到微服务的逻辑，改为：
+
+```python
+def post_to_ms(self):
+    """
+    修改为：只准备数据，不直接发送请求
+    真正的发送由外部微服务触发
+    """
+    # ... 原有校验逻辑不变 ...
+    
+    # 组装请求数据
+    payload = {
+        'record_id': self.id,
+        'prompt': full_prompt,
+        'model_name': model_name,
+        'base_url': base_url,
+        'api_key': api_key,
+        'callback_url': callback_url,
+        'system_prompt': self.system_prompt,
+        'user_prompt': self.user_prompt,
+        'final_prompt': self.final_prompt,
+    }
+    
+    # 将 payload 保存到某个字段，或者返回给调用方
+    # 这里我们返回 payload，让调用方（微服务）来处理
+    return payload
+```
+
+**或者更简单的方式**：Odoo 按钮点击时，直接 httpx 请求微服务：
+
+```python
+def btn_trigger_alpha_idea_service(self):
+    """按钮方法：触发微服务处理"""
+    import httpx
+    
+    # 获取微服务地址（从配置读取）
+    ms_url = self.env['ir.config_parameter'].sudo().get_param('alpha.idea.service.url')
+    
+    try:
+        response = httpx.post(
+            f"{ms_url}/api/alpha-idea/process",
+            json={"idea_id": self.id},
+            timeout=600  # 10分钟超时
+        )
+        result = response.json()
+        
+        if result["success"]:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': '成功',
+                    'message': f'处理完成，等待时间: {result["wait_time"]:.0f}秒',
+                    'type': 'success',
+                    'sticky': False,
+                }
+            }
+        else:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': '失败',
+                    'message': result["message"],
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+    except Exception as e:
+        return {
+            'type': 'ir.actions.client',
+            'tag': 'display_notification',
+            'params': {
+                'title': '错误',
+                'message': str(e),
+                'type': 'danger',
+                'sticky': False,
+            }
+        }
+```
+
+---
+
+## 五、部署步骤
+
+### 5.1 微服务部署
+
+1. 创建目录结构
+2. 复制代码文件
+3. 安装依赖：`pip install -r requirements.txt`
+4. 启动服务：`uvicorn alpha_idea_service.main:app --host 0.0.0.0 --port 32005`
+
+### 5.2 Odoo 配置
+
+1. 添加系统参数：`alpha.idea.service.url` = `http://微服务地址:32005`
+2. 在 `alpha.idea` 模型添加按钮方法 `btn_trigger_alpha_idea_service`
+3. 在视图添加按钮
+
+---
+
+## 六、注意事项
+
+1. **单线程保证**：微服务内部不开启多线程/异步并发处理单个请求
+2. **超时处理**：HTTP 客户端（Odoo 端）和微服务端都要设置 10 分钟超时
+3. **状态回写**：超时后必须回写 `result_message = 'timeout'`
+4. **错误处理**：任何异常都不能阻塞流程，要返回给调用方
+
+---
+
+## 七、待确认事项
+
+1. 微服务端口是否固定为 32005？
+2. Odoo 端是否需要批量处理接口（一次性传入多个 idea_id）？
+3. 是否需要添加日志记录（loguru 等）？
+4. 是否需要限制同时处理的请求数（比如只处理一个，其他排队）？

rpc_alpha_workflow/reference_02.py

@@ -0,0 +1,630 @@
+# -*- coding: utf-8 -*-
+import random
+import sys
+import uuid
+
+import httpx
+
+from odoo import models, fields
+from . import decode_template
+
+
+class AlphaIdea(models.Model):
+    _name = 'alpha.idea'
+    _description = 'Alpha Idea'
+    _order = 'id desc'
+
+    name = fields.Char(string='Name', required=True, default=lambda self: str(uuid.uuid4()))
+
+    status = fields.Selection([
+        ('draft', 'Draft'),
+        ('fetched_data', 'Fetched Data'),
+        ('generated_prompt', 'Generated Prompt'),
+        ('posted_to_ms', 'Posted To MS'),
+        ('llm_received', 'LLM Received'),
+        ('decode_template', 'Decode Template'),
+        ('decoded', 'Decoded'),
+        ('done', 'Done'),
+        ('failed', 'Failed'),
+        ('cancel', 'Cancel')
+    ], string='Status', default='draft')
+
+    pushed = fields.Boolean(string='Pushed', default=False, readonly=True)
+
+    region = fields.Many2one('alpha.region.settings', string='Region', required=True)
+
+    universe = fields.Many2one('alpha.universe.settings', string='Universe', required=True)
+
+    data_type = fields.Selection([('MATRIX', 'MATRIX'), ('VECTOR', 'VECTOR')], string='Data Type', required=True, default='MATRIX')
+
+    delay = fields.Selection([('1', '1'), ('0', '0')], required=True, string='Delay', default='1')
+
+    data_sets = fields.Char(string='Data Sets', compute='_compute_data_sets')
+
+    meta_prompt = fields.Many2one('alpha.prompt.settings', string='Meta Prompt')
+
+    replace_prompt = fields.Text(string='Replace Prompt')
+
+    final_prompt = fields.Text(string='Final Prompt')
+
+    system_prompt = fields.Text(string='System Prompt')
+
+    user_prompt = fields.Text(string='User Prompt')
+
+    llm_generated_idea = fields.Text(string='LLM Generated Idea')
+
+    result_message = fields.Text(string='Result Message')
+
+    llm_settings_line_id = fields.Many2one('llm.settings.line', string='Model', default=lambda self: self._default_llm_settings_line_id())
+
+    def _default_llm_settings_line_id(self):
+        """默认随机选择一个模型，如果没有则返回 False"""
+        llm_lines = self.env['llm.settings.line'].search([])
+        if llm_lines:
+            return random.choice(llm_lines).id
+        return False
+
+    idea_template_ids = fields.One2many('alpha.idea.template', 'idea_id', string='Idea Templates')
+    needed_data_set_ids = fields.One2many('alpha.needed.data.set', 'idea_id', string='Needed Data Sets')
+    final_expression_ids = fields.One2many('alpha.final.expression', 'idea_id', string='Final Expressions')
+
+    expression_count = fields.Integer(string='Expression Count', required=True, readonly=True, default=0, compute='_compute_expression_count')
+
+    def _compute_data_sets(self):
+        # 显示使用的数据集
+        for record in self:
+            record.data_sets = ''
+            if record.needed_data_set_ids:
+                data_sets_list = []
+                for data_set_name in record.needed_data_set_ids:
+                    data_sets_list.append(data_set_name.name)
+
+                if len(data_sets_list) > 0:
+                    record.data_sets = ', '.join(data_sets_list)
+
+    def action_cancel(self):
+        self.status = 'cancel'
+
+    def action_reset(self):
+        self.status = 'fetched_data'
+
+    def btn_check_and_fetch_data(self):
+        if not self.needed_data_set_ids:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'No data',
+                    'message': 'No data set is needed.',
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+
+        # 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)
+        success = False
+        for dataset in self.needed_data_set_ids:
+            dataset_id = self.env['alpha.datasets'].search(
+                [('datasets_id', '=', dataset.name),
+                 ('region', '=', self.region.id),
+                 ('universe', '=', self.universe.id),
+                 ('delay', '=', self.delay)
+                 ], limit=1)
+            if not dataset_id:
+                dataset_id = self.env['alpha.datasets'].create({
+                    'name': str(uuid.uuid4()),
+                    'datasets_id': dataset.name,
+                    'region': self.region.id,
+                    'universe': self.universe.id,
+                    'delay': self.delay,
+                })
+
+                # 创建 dataset_id 的记录之后, 执行一下 dataset_id 的 btn_get_datasets 方法
+                try:
+                    dataset_id.btn_get_datasets()
+                    success = True
+                except Exception as e:
+                    self.status = 'failed'
+                    return {
+                        'type': 'ir.actions.client',
+                        'tag': 'display_notification',
+                        'params': {
+                            'title': 'Failed',
+                            'message': f'Dataset fetch failed: {str(e)}',
+                            'type': 'danger',
+                            'sticky': False,
+                        }
+                    }
+            else:
+                if dataset_id.line_ids:
+                    success = True
+                else:
+                    return {
+                        'type': 'ir.actions.client',
+                        'tag': 'display_notification',
+                        'params': {
+                            'title': 'No data',
+                            'message': 'Dataset does not exist.',
+                            'type': 'danger',
+                            'sticky': False,
+                        }
+                    }
+
+        if success:
+            self.status = 'fetched_data'
+        return True
+
+    def btn_generate_final_prompt(self):
+        if self.final_prompt:
+            self.final_prompt = ''
+            self.system_prompt = ''
+            self.user_prompt = ''
+            self.status = 'fetched_data'
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': '成功',
+                    'message': '已清除最终提示词',
+                    'type': 'success',
+                    'sticky': False,
+                }
+            }
+
+        if not self.meta_prompt:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'No data!',
+                    'message': 'Please select the prompt template first.',
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+
+        if not self.replace_prompt:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'No data!',
+                    'message': 'Research direction cannot be empty.',
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+
+        system_prompt = '''You are executing two skills in sequence:
+    1) brain-data-feature-engineering
+    2) brain-feature-implementation
+    The following SKILL.md documents are authoritative; follow them exactly.
+            '''
+
+        data_sets_list = []
+        dataset_id = ''
+        category = ''
+        region = self.region.name
+        delay = self.delay
+        universe = self.universe.name
+
+        for dataset in self.needed_data_set_ids:
+            datasets_id = self.env['alpha.datasets'].search(
+                [('datasets_id', '=', dataset.name)], limit=1)
+            for datasets_line in datasets_id.line_ids:
+                data_sets_list.append({
+                    'id': datasets_line.data_field_name,
+                    'description': datasets_line.description
+                })
+
+                if not dataset_id:
+                    dataset_id = datasets_line.dataset_id
+
+                if not category:
+                    category = datasets_line.category_name
+
+        if not data_sets_list:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'No data!',
+                    'message': 'No related dataset found.',
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+
+        field_count = len(data_sets_list)
+
+        operator_ids = self.env['alpha.operator.line'].search([])
+        operator_list = []
+        for operator in operator_ids:
+            operator_list.append({
+                'name': operator.name,
+                'category': operator.category,
+                'scope': operator.scope,
+                'description': operator.description,
+                'definition': operator.definition
+            })
+
+        if not operator_list:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'No data!',
+                    'message': 'No related Operator found.',
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+
+        fields_json = ','.join([str(field) for field in data_sets_list])
+
+        user_prompt = '''{
+      "instructions": {
+        "output_format": "Fill OUTPUT_TEMPLATE.md with concrete content.",
+        "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.",
+        "no_code_fences": true,
+        "do_not_invent_placeholders": true
+      },
+      "dataset_context": {
+        "dataset_id": "''' + dataset_id + '''",
+        "dataset_name": null,
+        "dataset_description": null,
+        "category": "''' + category + '''",
+        "region": "''' + region + '''",
+        "delay": ''' + str(delay) + ''',
+        "universe": "''' + universe + '''",
+        "field_count": ''' + str(field_count) + '''
+      },
+      "fields": [
+      ''' + fields_json + '''
+      ]
+      }
+            '''
+
+        final_prompt = self.meta_prompt.prompt
+        final_prompt = final_prompt.replace(
+            '###question_driven###', self.replace_prompt)
+        final_prompt = final_prompt.replace('###dataset_id###', dataset_id)
+        final_prompt = final_prompt.replace('###category###', category)
+        final_prompt = final_prompt.replace('###region###', region)
+        final_prompt = final_prompt.replace('###delay###', str(delay))
+        final_prompt = final_prompt.replace(
+            '###field_count###', str(field_count))
+        final_prompt = final_prompt.replace('###universe###', universe)
+        final_prompt = final_prompt.replace(
+            '###datasets###', str(data_sets_list))
+        final_prompt = final_prompt.replace(
+            '###operators###', str(operator_list))
+
+        if self.data_type and self.data_type.upper() == "VECTOR":
+            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."
+            final_prompt = final_prompt.replace(
+                '###vector_instruction###', vector_prompt)
+        else:
+            final_prompt = final_prompt.replace('###vector_instruction###', '')
+
+        self.system_prompt = system_prompt
+        self.user_prompt = user_prompt
+        self.final_prompt = final_prompt
+
+        self.status = 'generated_prompt'
+
+        return {
+            'type': 'ir.actions.client',
+            'tag': 'display_notification',
+            'params': {
+                'title': '成功',
+                'message': f'已生成提示模板，包含 {field_count} 个数据字段',
+                'type': 'success',
+                'sticky': False,
+            }
+        }
+
+    def post_to_ms(self):
+        if not all([self.final_prompt, self.user_prompt, self.system_prompt]):
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'Error',
+                    'message': 'Please generate a 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
+
+        # 获取当前 record_id
+        record_id = self.id
+
+        # 获取 Odoo 回调地址
+        base_url_odoo = self.env['ir.config_parameter'].sudo().get_param('web.base.url')
+        callback_url = f"{base_url_odoo}/api/alpha-idea/result"
+
+        # 组装完整 prompt
+        full_prompt = f"{self.system_prompt}\n\n{self.user_prompt}\n\n{self.final_prompt}"
+
+        # 获取微服务配置
+        ms_config = self.get_ms_config()
+        ms_url = ms_config.get('url', '')
+        if not ms_url:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'Error',
+                    'message': 'Microservice address not configured.',
+                    'type': 'danger',
+                    'sticky': False
+                }
+            }
+
+        # 组装请求数据
+        payload = {
+            'record_id': record_id,
+            'prompt': full_prompt,
+            'model_name': model_name,
+            'base_url': base_url,
+            'api_key': api_key,
+            'callback_url': callback_url,
+            'system_prompt': self.system_prompt,
+            'user_prompt': self.user_prompt,
+            'final_prompt': self.final_prompt,
+        }
+
+        # 发送请求到微服务
+        try:
+            httpx.post(f"{ms_url}:32004/api_alpha_generate_idea",
+                       json=payload, timeout=0.001)
+        except httpx.TimeoutException:
+            pass
+        except Exception as e:
+            self.status = 'failed'
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'Error',
+                    'message': f'Failed to send microservice:\n{e}.',
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+
+        self.status = 'posted_to_ms'
+
+    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': f'Nacos request failed:\n{e}.',
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+
+        ms_config = ms_config_resp.json()
+
+        return ms_config
+
+    def action_set_llm_received(self):
+        """手动设置 LLM 已返回状态
+        当手动录入 llm_generated_idea 后，调用此函数将状态改为 llm_received
+        """
+        if self.llm_generated_idea and self.status == 'generated_prompt':
+            self.status = 'llm_received'
+            return True
+        else:
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'Error',
+                    'message': 'llm_generated_idea 无数据或状态不是 Generated Prompt',
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+
+    def decode_template(self):
+        if not self.llm_generated_idea or self.status != 'llm_received':
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'Please use llm to generate the template first.',
+                    'message': 'llm_generated_idea no data or status != llm_received',
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+
+        if self.status == 'done':
+            return {
+                'type': 'ir.actions.client',
+                'tag': 'display_notification',
+                'params': {
+                    'title': 'message',
+                    'message': 'This idea has been generated, please do not repeat the operation.',
+                    'type': 'danger',
+                    'sticky': False,
+                }
+            }
+
+        llm_template = self.llm_generated_idea
+
+        data_sets_list = []
+        for dataset in self.needed_data_set_ids:
+            datasets_id = self.env['alpha.datasets'].search(
+                [('datasets_id', '=', dataset.name)], limit=1)
+            for datasets_line in datasets_id.line_ids:
+                data_sets_list.append({
+                    'id': datasets_line.data_field_name
+                })
+
+        result_data = decode_template.process(data_sets_list, llm_template)
+
+        if result_data['success']:
+            templates = result_data['templates']
+            expressions = result_data['expressions']
+
+            # 如果没有解出数据，不做任何操作
+            if len(templates) == 0 and len(expressions) == 0:
+                return {
+                    'type': 'ir.actions.client',
+                    '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')