399 lines
14 KiB
Python
399 lines
14 KiB
Python
# -*- coding: utf-8 -*-
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import time
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import httpx
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import json
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import anthropic
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def fetch_llm_generate_task(odoo_base_url="http://localhost:8069"):
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"""
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从 Odoo 接口获取待生成的 LLM 任务数据
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Args:
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odoo_base_url: Odoo 服务地址，默认 http://localhost:8069
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Returns:
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dict or None: 返回获取到的任务数据，包含以下字段：
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- id: 记录ID
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- final_prompt: 最终提示词
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- system_prompt: 系统提示词
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- user_prompt: 用户提示词
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如果没有待生成的任务，返回 None
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"""
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url = f"{odoo_base_url}/api/local-llm-generate/fetch"
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headers = {
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"Content-Type": "application/json"
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}
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payload = {}
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try:
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print(f"[INFO] 正在请求 Odoo 接口: {url}")
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response = httpx.post(
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url,
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headers=headers,
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json=payload,
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timeout=30
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)
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response.raise_for_status()
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data = response.json()
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# print(f"[INFO] 接口返回数据: {json.dumps(data, ensure_ascii=False, indent=2)}")
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# 检查是否返回了 {"data": null}（没有待生成任务）
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if isinstance(data, dict) and 'data' in data and data['data'] is None:
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print("[INFO] 当前没有待生成的 LLM 任务")
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exit(1)
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# 返回获取到的任务数据（包含 id, final_prompt, system_prompt, user_prompt）
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if isinstance(data, dict) and 'id' in data:
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print(f"[SUCCESS] 获取到任务数据, record_id={data['id']}")
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return data
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print(f"[WARNING] 返回数据格式不符合预期: {data}")
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return None
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except httpx.TimeoutException:
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print("[ERROR] 请求超时")
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return None
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except httpx.ConnectError as e:
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print(f"[ERROR] 连接错误: {e}")
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return None
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except httpx.HTTPStatusError as e:
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print(f"[ERROR] HTTP 错误: {e}")
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return None
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except Exception as e:
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print(f"[ERROR] 发生未知错误: {e}")
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return None
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def call_llm_generate(task_data, max_retries=3):
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"""
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调用本地 Ollama LLM 生成内容
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Args:
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task_data: 包含 final_prompt, system_prompt, user_prompt 的字典
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max_retries: 最大重试次数，默认 max_retries 次
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Returns:
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str: 生成的内容，如果失败返回 "failed"
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"""
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if task_data is None:
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error_msg = "[ERROR] task_data 为 None，无法调用 LLM"
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print(error_msg)
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return error_msg
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# 提取 task_data 中的字段
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final_prompt = task_data.get('final_prompt', '')
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system_prompt = task_data.get('system_prompt', '')
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user_prompt = task_data.get('user_prompt', '')
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record_id = task_data.get('id', 'unknown')
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print(f"[INFO] 开始为 record_id={record_id} 调用本地 Ollama")
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# Ollama API 地址
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ollama_url = "http://localhost:11434/v1/chat/completions"
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# 组装 messages
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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if final_prompt:
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messages.append({"role": "user", "content": final_prompt})
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# 请求体
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payload = {
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"model": "kimi-k2.5:cloud",
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"messages": messages,
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"stream": False
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}
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# 重试机制
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for attempt in range(1, max_retries + 1):
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try:
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print(f"[INFO] 第 {attempt} 次尝试调用 Ollama...")
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response = httpx.post(
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ollama_url,
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json=payload,
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timeout=600
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)
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response.raise_for_status()
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result = response.json()
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# 提取生成的内容
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if "choices" in result and len(result["choices"]) > 0:
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generated_content = result["choices"][0].get("message", {}).get("content", "")
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if generated_content:
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print(f"[SUCCESS] 第 {attempt} 次尝试成功，生成长度: {len(generated_content)} 字符")
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return generated_content
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else:
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print(f"[WARNING] 第 {attempt} 次尝试返回空内容")
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else:
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print(f"[WARNING] 第 {attempt} 次尝试返回格式异常: {result}")
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except httpx.TimeoutException:
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print(f"[ERROR] 第 {attempt} 次尝试超时")
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except httpx.ConnectError as e:
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print(f"[ERROR] 第 {attempt} 次尝试连接错误: {e}")
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except httpx.HTTPStatusError as e:
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print(f"[ERROR] 第 {attempt} 次尝试 HTTP 错误: {e}")
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except Exception as e:
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print(f"[ERROR] 第 {attempt} 次尝试发生未知错误: {e}")
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# 如果不是最后一次尝试，等待后继续
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if attempt < max_retries:
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import time
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print(f"[INFO] 等待 2 秒后重试...")
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time.sleep(2)
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# 所有重试都失败了
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print(f"[FAILED] 经过 {max_retries} 次尝试后仍然失败")
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return "failed"
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def call_llm_generate_with_anthropic(task_data, max_retries=3):
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"""
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调用 MiniMax 的 Anthropic 兼容接口生成内容
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Args:
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task_data: 包含 final_prompt, system_prompt, user_prompt 的字典
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max_retries: 最大重试次数，默认 3 次（针对非529错误）
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Returns:
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str: 生成的内容，如果失败返回 "failed"
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"""
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if task_data is None:
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error_msg = "[ERROR] task_data 为 None，无法调用 LLM"
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print(error_msg)
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return error_msg
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# 提取 task_data 中的字段
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final_prompt = task_data.get('final_prompt', '')
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system_prompt = task_data.get('system_prompt', '')
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user_prompt = task_data.get('user_prompt', '')
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record_id = task_data.get('id', 'unknown')
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print(f"[INFO] 开始为 record_id={record_id} 调用 MiniMax Anthropic 接口")
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# 配置 Anthropic 客户端
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ANTHROPIC_API_KEY = "sk-cp-l_as8mjqPhsOIny9IFKZ8jzA92z1c0eRwchldhEf4KzQjs9cjVknV2o7VNCcvYUXsXFq7uF4aSgp2RxxmUHLXwPGKgIvzedM70_XUIXiBB3gu_UmLDQLfh4"
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ANTHROPIC_BASE_URL = "https://api.minimaxi.com/anthropic"
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client = anthropic.Anthropic(
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api_key=ANTHROPIC_API_KEY,
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base_url=ANTHROPIC_BASE_URL
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)
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# 针对 529 错误的专门重试机制
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MAX_RETRIES_529 = 30 # 529 错误最大重试次数
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RETRY_DELAY_529 = 10 # 529 错误重试间隔（秒）
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total_retry = 1
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for attempt_529 in range(1, MAX_RETRIES_529 + 1):
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# 普通错误的重试机制
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for attempt in range(1, max_retries + 1):
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try:
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print(f"[INFO] 第 {total_retry} 次尝试调用 MiniMax...")
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total_retry += 1
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# 构建消息列表
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messages = []
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# 如果有 final_prompt 或 user_prompt，作为用户消息
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user_content = final_prompt if final_prompt else user_prompt
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if user_content:
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messages.append({
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": user_content
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}
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]
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})
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# 调用 API（使用流式传输，因为操作可能超过 10 分钟）
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generated_content = ""
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with client.messages.stream(
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model="MiniMax-M2.7",
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system=system_prompt if system_prompt else "You are a helpful assistant.",
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messages=messages,
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max_tokens=32768
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) as stream:
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for text in stream.text_stream:
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generated_content += text
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# 检查是否有思考过程（流式模式下通常不会返回 thinking 块）
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if hasattr(stream, 'thinking') and stream.thinking:
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print(f"[DEBUG] 思考过程: {stream.thinking[:100]}...")
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if generated_content:
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print(f"[SUCCESS] 第 {attempt} 次尝试成功，生成长度: {len(generated_content)} 字符")
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return generated_content
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else:
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print(f"[WARNING] 第 {attempt} 次尝试返回空内容")
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except Exception as e:
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# 检查是否是 529 错误
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error_str = str(e)
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if "529" in error_str or "overloaded" in error_str.lower():
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print(f"[WARNING] 遇到服务器拥挤 (529) 错误: {e}")
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# 跳出内层循环，进入外层 529 重试逻辑
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break
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else:
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print(f"[ERROR] 第 {attempt} 次尝试发生错误: {e}")
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# 如果不是最后一次尝试，等待后继续（普通错误重试）
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if attempt < max_retries:
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print(f"[INFO] 等待 2 秒后重试...")
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time.sleep(2)
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else:
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# 内层循环正常结束（没有 break），说明没有遇到 529 错误且重试用完
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# 所有重试都失败了
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error_msg = f"[FAILED] 经过 {max_retries} 次尝试后仍然失败"
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print(error_msg)
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return error_msg
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# 执行到这里说明遇到了 529 错误，进行外层重试
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if attempt_529 < MAX_RETRIES_529:
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print(f"[INFO] 遇到服务器拥挤 (529)，第 {attempt_529}/{MAX_RETRIES_529} 次重试，等待 {RETRY_DELAY_529} 秒...")
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time.sleep(RETRY_DELAY_529)
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else:
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error_msg = f"[FAILED] 经过 {MAX_RETRIES_529} 次 529 重试后仍然失败"
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print(error_msg)
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return error_msg
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# 所有重试都失败了
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error_msg = f"[FAILED] 经过 {MAX_RETRIES_529} 次 529 重试后仍然失败"
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print(error_msg)
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return error_msg
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def upload_llm_result(odoo_base_url, upload_data):
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"""
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上传 LLM 生成结果到 Odoo
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Args:
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odoo_base_url: Odoo 服务地址
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upload_data: 包含以下字段的字典：
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- record_id: int, 记录ID
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- status: str, "success" 或 "failed"
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- result: str, 成功时的生成内容
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- error_message: str, 失败时的错误信息
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Returns:
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bool: 上传成功返回 True，失败返回 False
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"""
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# 构建上传结果的 API URL
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url = f"{odoo_base_url}/api/alpha-idea/result"
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# 设置请求头，指定内容类型为 JSON
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headers = {
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"Content-Type": "application/json"
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}
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try:
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# 打印上传信息日志
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print(f"[INFO] 正在上传结果到 Odoo: {url}")
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print(f"[INFO] 上传数据: {json.dumps(upload_data, ensure_ascii=False)}")
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# 发送 POST 请求，设置超时时间为 30 秒
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response = httpx.post(
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url,
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headers=headers,
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json=upload_data,
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timeout=30
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)
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# 如果 HTTP 状态码表示错误，则抛出异常
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response.raise_for_status()
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# 解析响应结果
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result = response.json()
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print(f"[INFO] 上传结果接口返回: {json.dumps(result, ensure_ascii=False)}")
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# 检查返回结果是否成功
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if isinstance(result, dict):
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# 判断返回状态是否为成功
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if result.get('status') == 'success' or result.get('success') is True:
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print(f"[SUCCESS] 结果上传成功")
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return True
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# 如果返回结果不符合预期，打印警告信息
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print(f"[WARNING] 结果上传可能失败: {result}")
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return False
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# 处理各种异常情况
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except httpx.TimeoutException:
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print("[ERROR] 上传结果超时")
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return False
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except httpx.ConnectError as e:
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print(f"[ERROR] 上传结果连接错误: {e}")
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return False
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except httpx.HTTPStatusError as e:
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print(f"[ERROR] 上传结果 HTTP 错误: {e}")
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return False
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except Exception as e:
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print(f"[ERROR] 上传结果发生未知错误: {e}")
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return False
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if __name__ == "__main__":
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# 配置 Odoo 服务地址
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ODOO_BASE_URL = "https://quantify.erhe.top"
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print("开始获取 LLM 生成任务...")
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# 这里我手动控制就行
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llm_select = 0
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while True:
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# 获取任务数据
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task_data = fetch_llm_generate_task(odoo_base_url=ODOO_BASE_URL)
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if not task_data:
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break
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if llm_select == 1:
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llm_generate_result = call_llm_generate(task_data)
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else:
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llm_generate_result = call_llm_generate_with_anthropic(task_data)
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if not llm_generate_result:
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continue
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print(llm_generate_result[:100])
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# 检查是否成功：如果结果以 [ERROR] 或 [FAILED] 开头，说明失败了
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is_failed = llm_generate_result.startswith('[ERROR]') or llm_generate_result.startswith('[FAILED]')
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if not is_failed:
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print("开始上传 LLM 生成结果...")
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upload_data = {
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'record_id': task_data['id'],
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'status': 'success',
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'result': llm_generate_result
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}
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else:
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print("开始上传 LLM 生成结果失败...")
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upload_data = {
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'record_id': task_data['id'],
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'status': 'failed',
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'result': '',
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'error_message': llm_generate_result
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}
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# 上传结果到 Odoo
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upload_success = upload_llm_result(ODOO_BASE_URL, upload_data)
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if upload_success:
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print("[SUCCESS] 当次流程完成")
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else:
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print("[FAILED] 结果上传失败")
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sleep_time = 60
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print(f"等待 {sleep_time} 秒后开始下一次循环...")
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time.sleep(sleep_time) |