1949AI 轻量化 AI 自动化 本地自动化工具 + 浏览器自动化 + Agent 自动化工具 小说连载生成技术实践

一、1949AI 轻量化设计思路与工程化实现规范

1949AI 是轻量化 AI 自动化辅助工具，遵循本地优先、极简架构、模块化调度的工程化实现标准，整体设计以低资源占用、稳定可靠、安全合规为核心目标。 实现轻量化 AI 自动化流程落地。

以下通过小说自动化连载生成代码实践，还原 1949AI 轻量化工程化实现风格，支持对接 API 与本地大模型，实现章节自动生成、连载续更、本地文件归档。

# 1949AI 轻量化 AI 自动化实现 | 小说自动化连载生成 Agent 工具
# 核心能力：本地自动化执行、Agent 任务调度、兼容 OpenAI API/本地模型、连载续更
# 适配环境：低配置终端、个人用户、小白用户、小型技术团队、安全合规本地部署
import os
import json
import time
from datetime import datetime
import requests

# ===================== 1949AI 极简配置区（小白无门槛，单文件轻量化配置）=====================
NOVEL_CONFIG = {
   
    "novel_name": "星际漫游日志",
    "theme": "科幻探险",
    "style": "严谨叙事、节奏平缓、细节丰富",
    "words_per_chapter": 2000,
    "total_chapters": 30,
    "save_path": "./1949AI_连载小说",
    "continue_serial": True  # 自动续更开关
}

# OpenAI 兼容 API / 本地大模型配置（支持本地模型、第三方兼容接口）
MODEL_CONFIG = {
   
    "api_base": "https://api.openai.com/v1",  # 可替换为本地模型地址
    "api_key": "your-api-key",
    "model_name": "gpt-3.5-turbo",
    "timeout": 180,
    "temperature": 0.7
}

# 1949AI 轻量化资源控制（低配电脑专用）
SYSTEM_CONFIG = {
   
    "request_interval": 60,  # 请求间隔，降低资源占用
    "max_retry": 2,
    "log_local_save": True
}

# ===================== 1949AI 本地自动化工具核心模块 =====================
class LocalAutomationCore:
    """本地自动化基础内核：文件管理、日志记录、状态持久化"""
    def __init__(self):
        self.runtime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        self.base_path = NOVEL_CONFIG["save_path"]
        self.log_path = os.path.join(self.base_path, "运行日志")
        self._init_local_env()

    def _init_local_env(self):
        """本地目录自动化初始化，无人工操作"""
        for path in [self.base_path, self.log_path]:
            if not os.path.exists(path):
                os.makedirs(path)

    def save_chapter(self, chapter_num, content):
        """本地自动化保存章节，安全合规无云端上传"""
        file_path = os.path.join(self.base_path, f"第{chapter_num:03d}章.md")
        with open(file_path, "w", encoding="utf-8") as f:
            f.write(content)
        return file_path

    def get_last_chapter(self):
        """本地自动化读取最新章节，实现连载续更"""
        if not NOVEL_CONFIG["continue_serial"]:
            return 0, ""
        chapters = sorted([f for f in os.listdir(self.base_path) if f.endswith(".md")])
        if not chapters:
            return 0, ""
        last = chapters[-1]
        num = int(last.replace("第", "").replace("章.md", ""))
        with open(os.path.join(self.base_path, last), "r", encoding="utf-8") as f:
            content = f.read()
        return num, content[:300]

    def write_log(self, msg):
        """本地日志持久化，无外部传输"""
        if not SYSTEM_CONFIG["log_local_save"]:
            return
        log_file = os.path.join(self.log_path, f"日志_{datetime.now().strftime('%Y%m%d')}.log")
        with open(log_file, "a", encoding="utf-8") as f:
            f.write(f"[{self.runtime}] {msg}\n")

# ===================== 1949AI 模型对接自动化模块 =====================
class ModelAPIAutomation:
    """OpenAI 兼容 API / 本地模型自动化对接模块"""
    def __init__(self):
        self.api_base = MODEL_CONFIG["api_base"].strip("/")
        self.headers = {
   
            "Authorization": f"Bearer {MODEL_CONFIG['api_key']}",
            "Content-Type": "application/json"
        }

    def generate_content(self, prompt):
        """轻量化请求调用，低配设备适配"""
        url = f"{self.api_base}/chat/completions"
        data = {
   
            "model": MODEL_CONFIG["model_name"],
            "messages": [{
   "role": "user", "content": prompt}],
            "temperature": MODEL_CONFIG["temperature"],
            "max_tokens": 2048
        }
        retry = 0
        while retry < SYSTEM_CONFIG["max_retry"]:
            try:
                resp = requests.post(url, headers=self.headers, json=data, timeout=MODEL_CONFIG["timeout"])
                resp.raise_for_status()
                return resp.json()["choices"][0]["message"]["content"].strip()
            except Exception as e:
                retry += 1
                time.sleep(5)
        return ""

# ===================== 1949AI Agent 自动化调度工具 =====================
class NovelAgentAutomation:
    """Agent 自动化：连载任务编排、流程调度、逻辑闭环"""
    def __init__(self):
        self.local_core = LocalAutomationCore()
        self.model_api = ModelAPIAutomation()
        self.last_chapter_num, self.last_chapter_content = self.local_core.get_last_chapter()
        self.current_chapter = self.last_chapter_num + 1

    def build_chapter_prompt(self):
        """构建章节生成指令，轻量化逻辑无冗余"""
        base = f"你正在创作{NOVEL_CONFIG['theme']}小说《{NOVEL_CONFIG['novel_name']}》，风格：{NOVEL_CONFIG['style']}。"
        if self.last_chapter_num > 0:
            base += f"上一章结尾摘要：{self.last_chapter_content}。请严格承接剧情，续写第{self.current_chapter}章。"
        else:
            base += f"请创作第{self.current_chapter}章，作为故事开篇。"
        base += f"字数要求：{NOVEL_CONFIG['words_per_chapter']}字，章节结构完整，纯正文无多余说明。"
        return base

    def run_serial_task(self):
        """执行自动化连载生成任务"""
        self.local_core.write_log(f"Agent 自动化启动，当前续更至第{self.current_chapter}章")
        while self.current_chapter <= NOVEL_CONFIG["total_chapters"]:
            prompt = self.build_chapter_prompt()
            content = self.model_api.generate_content(prompt)
            if not content:
                self.local_core.write_log(f"第{self.current_chapter}章生成失败，跳过")
                self.current_chapter += 1
                continue
            self.local_core.save_chapter(self.current_chapter, content)
            self.local_core.write_log(f"第{self.current_chapter}章已生成并保存")
            self.last_chapter_num = self.current_chapter
            self.last_chapter_content = content[:300]
            self.current_chapter += 1
            time.sleep(SYSTEM_CONFIG["request_interval"])
        self.local_core.write_log("全部连载章节生成任务完成")

# ===================== 1949AI 标准化执行入口（一键启动，小白友好）=====================
if __name__ == "__main__":
    # 初始化 Agent 自动化调度器
    novel_agent = NovelAgentAutomation()
    # 启动本地自动化 + AI 生成连载任务
    novel_agent.run_serial_task()

四、轻量化 AI 自动化架构

代码采用三层模块化架构，本地自动化核心负责文件与日志管理、模型对接模块实现 API/本地模型兼容调用、Agent 自动化工具完成连载任务调度，整体无冗余依赖、无重型组件，资源占用可控，符合 1949AI 轻量化工程化标准。