zzh
5a26ae0048
- 新增小红书前端模块（启动服务、内容分析、爆款创作、发布记录、技能卡片、拟人设置、AI 助手） - 新增 xhs-agent-desktop 独立桌面 Agent，对接 OMS 任务调度 - 登录页重构，整合微信登录与 OMS JWT 认证 - 订单、渠道、用户中心等页面与 API 增强 Co-authored-by: Cursor <cursoragent@cursor.com>
545 lines
17 KiB
Python
545 lines
17 KiB
Python
from __future__ import annotations
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import json
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import re
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import uuid
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from pathlib import Path
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from typing import Any
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import requests
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from xhs_bridge.ai_provider_catalog import (
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ZHIPU_CHAT_MODEL,
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ZHIPU_IMAGE_MODEL,
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ZHIPU_IMAGE_SIZE,
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ZHIPU_VISION_MODEL,
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default_model,
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is_vision_model,
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uses_zhipu_sdk,
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)
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from xhs_bridge.ai_providers_store import AiProvider
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from xhs_bridge import zhipu_request_log as zlog
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_JSON_BLOCK_RE = re.compile(r"\{[\s\S]*\}", re.MULTILINE)
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_SYSTEM_NOTE = (
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"你是 LIKE AI 小红书爆款内容创作助手。"
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"严格按用户要求输出 JSON，不要 markdown 代码块外的解释。"
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)
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_SYSTEM_ANALYSIS = (
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"你是 LIKE AI 内容分析助手。"
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"根据用户提供的爆款技能配方与笔记素材，输出清晰、可执行的中文分析（可用 Markdown 小标题）。"
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)
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_SYSTEM_IMAGE_PROMPT = (
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"你是小红书配图助手。"
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"根据笔记标题与正文，输出适合文生图模型使用的中文画面描述，简洁具体，单段，不超过 120 字。"
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)
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_SYSTEM_VIDEO_SCRIPT = (
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"你是小红书短视频策划助手。"
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"根据笔记信息输出视频分镜脚本（Markdown），包含镜头、画面、口播要点，便于用户拍摄或剪辑。"
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)
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def _chat_url(base_url: str) -> str:
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base = (base_url or "").strip().rstrip("/")
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if not base:
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raise ValueError("请求地址为空")
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if base.endswith("/chat/completions"):
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return base
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if base.endswith("/v1"):
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return f"{base}/chat/completions"
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return f"{base}/v1/chat/completions"
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def _resolve_model(provider: AiProvider, *, model: str | None = None) -> str:
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if (model or "").strip():
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return model.strip()
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secret = (provider.api_secret or "").strip()
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if uses_zhipu_sdk(provider.provider_type):
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if (
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secret
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and secret not in (ZHIPU_IMAGE_MODEL, ZHIPU_VISION_MODEL)
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and not secret.startswith("sk-")
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):
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return secret
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return default_model(provider.provider_type)
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if secret and not secret.startswith("sk-") and len(secret) < 80:
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return secret
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return default_model(provider.provider_type)
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def _zhipu_chat_completions_url(base_url: str) -> str:
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base = (base_url or "https://open.bigmodel.cn/api/paas/v4").strip().rstrip("/")
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if base.endswith("/chat/completions"):
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return base
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if base.endswith("/v4") or base.endswith("/v1"):
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return f"{base}/chat/completions"
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return f"{base}/chat/completions"
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def _build_vision_user_content(prompt: str, image_urls: list[str]) -> list[dict[str, Any]]:
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parts: list[dict[str, Any]] = []
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for url in image_urls:
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parts.append({"type": "image_url", "image_url": {"url": url}})
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parts.append({"type": "text", "text": prompt})
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return parts
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def _invoke_zhipu_vision(
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provider: AiProvider,
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prompt: str,
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image_urls: list[str],
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*,
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system_prompt: str,
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timeout: int,
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model: str | None = None,
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) -> str:
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api_key = (provider.api_key or "").strip()
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if not api_key:
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raise ValueError("「智谱」未配置 API Key")
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url = _zhipu_chat_completions_url(provider.base_url)
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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user_content: str | list[dict[str, Any]]
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if image_urls:
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user_content = _build_vision_user_content(prompt, image_urls)
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else:
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user_content = prompt
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resolved_model = _resolve_model(provider, model=model or ZHIPU_VISION_MODEL)
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payload = {
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"model": resolved_model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_content},
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],
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"temperature": 0.7,
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}
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title = zlog.log_request(
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provider,
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"chat/completions (图文)",
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{
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"url": url,
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"headers": {
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"Authorization": f"Bearer {zlog.mask_api_key(api_key)}",
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"Content-Type": "application/json",
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},
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"body": {
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**payload,
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"messages": [
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{"role": "system", "content": f"<{len(system_prompt)} 字符>"},
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{
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"role": "user",
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"content": (
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f"<图文 {len(image_urls)} 张 + 文本 {len(prompt)} 字符>"
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if image_urls
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else f"<文本 {len(prompt)} 字符>"
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),
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},
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],
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},
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"image_urls": image_urls,
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},
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)
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try:
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resp = requests.post(url, headers=headers, json=payload, timeout=timeout)
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if resp.status_code >= 400:
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zlog.log_error(title, f"HTTP {resp.status_code}: {resp.text[:500]}")
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raise ValueError(f"智谱图文分析失败 ({resp.status_code}): {resp.text[:500]}")
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data = resp.json()
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zlog.log_response(title, data)
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choices = data.get("choices") or []
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if not choices:
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raise ValueError("智谱 AI 返回无 choices")
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message = choices[0].get("message") or {}
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content = str(message.get("content") or "").strip()
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if not content:
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raise ValueError("智谱 AI 返回内容为空")
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return content
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except ValueError:
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raise
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except Exception as exc:
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zlog.log_error(title, exc)
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raise
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def _invoke_zhipu(
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provider: AiProvider,
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prompt: str,
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*,
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system_prompt: str,
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timeout: int,
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model: str | None = None,
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) -> str:
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try:
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from zai import ZhipuAiClient
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except ImportError as exc:
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raise ValueError("未安装 zai-sdk，请执行: pip install zai-sdk") from exc
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api_key = (provider.api_key or "").strip()
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if not api_key:
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raise ValueError("「智谱」未配置 API Key")
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kwargs: dict[str, Any] = {"api_key": api_key}
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base_url = (provider.base_url or "").strip()
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if base_url:
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kwargs["base_url"] = base_url.rstrip("/")
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client = ZhipuAiClient(**kwargs)
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resolved_model = _resolve_model(provider, model=model)
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt},
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]
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title = zlog.log_request(
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provider,
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"chat/completions",
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{
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"model": resolved_model,
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"messages": messages,
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"temperature": 0.7,
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"timeout": timeout,
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},
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)
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try:
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response = client.chat.completions.create(
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model=resolved_model,
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messages=messages,
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temperature=0.7,
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timeout=timeout,
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)
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zlog.log_response(title, response)
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choices = getattr(response, "choices", None) or []
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if not choices:
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raise ValueError("智谱 AI 返回无 choices")
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message = getattr(choices[0], "message", None)
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content = str(getattr(message, "content", None) or "").strip() if message else ""
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if not content:
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raise ValueError("智谱 AI 返回内容为空")
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return content
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except ValueError:
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raise
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except Exception as exc:
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zlog.log_error(title, exc)
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raise
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def _invoke_openai_compatible(
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provider: AiProvider,
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prompt: str,
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*,
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system_prompt: str,
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timeout: int,
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model: str | None = None,
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) -> str:
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url = _chat_url(provider.base_url)
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headers = {
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"Authorization": f"Bearer {provider.api_key.strip()}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": _resolve_model(provider, model=model),
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt},
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],
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"temperature": 0.7,
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}
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resp = requests.post(url, headers=headers, json=payload, timeout=timeout)
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if resp.status_code >= 400:
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raise ValueError(f"AI 请求失败 ({resp.status_code}): {resp.text[:500]}")
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data = resp.json()
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choices = data.get("choices") or []
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if not choices:
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raise ValueError("AI 返回无 choices")
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message = choices[0].get("message") or {}
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content = str(message.get("content") or "").strip()
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if not content:
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raise ValueError("AI 返回内容为空")
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return content
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def _invoke_chat(
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provider: AiProvider,
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prompt: str,
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*,
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system_prompt: str,
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timeout: int = 120,
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model: str | None = None,
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image_urls: list[str] | None = None,
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) -> str:
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resolved = _resolve_model(provider, model=model)
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if (
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image_urls
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and uses_zhipu_sdk(provider.provider_type)
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and is_vision_model(resolved)
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):
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return _invoke_zhipu_vision(
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provider,
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prompt,
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image_urls,
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system_prompt=system_prompt,
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timeout=timeout,
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model=resolved,
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)
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if uses_zhipu_sdk(provider.provider_type):
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return _invoke_zhipu(
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provider, prompt, system_prompt=system_prompt, timeout=timeout, model=model
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)
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if not (provider.base_url or "").strip():
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raise ValueError(f"「{provider.type_label}」未配置请求地址")
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return _invoke_openai_compatible(
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provider, prompt, system_prompt=system_prompt, timeout=timeout, model=model
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)
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def _parse_content_json(text: str) -> dict[str, Any]:
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text = (text or "").strip()
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if not text:
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raise ValueError("AI 返回内容为空")
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try:
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data = json.loads(text)
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if isinstance(data, dict):
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return data
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except json.JSONDecodeError:
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pass
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match = _JSON_BLOCK_RE.search(text)
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if match:
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data = json.loads(match.group(0))
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if isinstance(data, dict):
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return data
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raise ValueError("无法解析 AI 返回的 JSON，请检查模型输出格式")
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def _normalize_note_payload(parsed: dict[str, Any]) -> dict[str, Any]:
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title = str(parsed.get("title") or "").strip()
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body = str(parsed.get("content") or parsed.get("body") or "").strip()
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tags_raw = parsed.get("tags") or []
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tags: list[str] = []
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if isinstance(tags_raw, list):
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tags = [str(t).strip().lstrip("#") for t in tags_raw if str(t).strip()]
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elif isinstance(tags_raw, str):
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tags = [t.strip().lstrip("#") for t in re.split(r"[,，\s]+", tags_raw) if t.strip()]
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if not title and not body:
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raise ValueError("AI 未返回有效的 title / content")
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return {"title": title, "content": body, "tags": tags}
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def generate_ai_text(
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provider: AiProvider,
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prompt: str,
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*,
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timeout: int = 180,
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system_prompt: str | None = None,
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model: str | None = None,
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image_urls: list[str] | None = None,
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) -> str:
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"""调用 AI 返回纯文本（用于分析等场景）。"""
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if not (provider.api_key or "").strip():
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raise ValueError(f"「{provider.type_label}」未配置 API Key")
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return _invoke_chat(
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provider,
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prompt,
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system_prompt=system_prompt or _SYSTEM_ANALYSIS,
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timeout=timeout,
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model=model,
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image_urls=image_urls,
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)
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def generate_note_content(
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provider: AiProvider,
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prompt: str,
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*,
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timeout: int = 120,
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model: str | None = None,
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) -> dict[str, Any]:
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"""按平台类型调用 AI 生成笔记 JSON。"""
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raw = generate_ai_text(
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provider, prompt, timeout=timeout, system_prompt=_SYSTEM_NOTE, model=model
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)
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return _normalize_note_payload(_parse_content_json(raw))
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def build_image_prompt_from_note(title: str, content: str) -> str:
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return (
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f"标题：{title or '（无）'}\n"
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f"正文：{content or '（无）'}\n\n"
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"请生成 1 条小红书封面配图的画面描述。"
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)
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def build_video_script_prompt(title: str, content: str) -> str:
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return (
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f"标题：{title or '（无）'}\n"
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f"正文/要点：{content or '（无）'}\n\n"
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"请输出 30～60 秒小红书视频的分镜脚本。"
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)
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def generate_image_description(
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provider: AiProvider,
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title: str,
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content: str,
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*,
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timeout: int = 120,
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model: str | None = None,
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) -> str:
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prompt = build_image_prompt_from_note(title, content)
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text_model = model
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if uses_zhipu_sdk(provider.provider_type) and text_model == ZHIPU_IMAGE_MODEL:
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text_model = ZHIPU_CHAT_MODEL
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return generate_ai_text(
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provider,
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prompt,
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timeout=timeout,
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system_prompt=_SYSTEM_IMAGE_PROMPT,
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model=text_model,
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).strip()
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def generate_video_script(
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provider: AiProvider,
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title: str,
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content: str,
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*,
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timeout: int = 180,
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model: str | None = None,
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) -> str:
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prompt = build_video_script_prompt(title, content)
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return generate_ai_text(
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provider,
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prompt,
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timeout=timeout,
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system_prompt=_SYSTEM_VIDEO_SCRIPT,
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model=model,
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).strip()
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def _images_api_url(base_url: str) -> str:
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base = (base_url or "https://open.bigmodel.cn/api/paas/v4").strip().rstrip("/")
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if base.endswith("/images/generations"):
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return base
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if base.endswith("/v4"):
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return f"{base}/images/generations"
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if base.endswith("/v1"):
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return f"{base}/images/generations"
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return f"{base}/v4/images/generations"
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def _download_image_url(url: str, dest: Path, *, timeout: int = 120) -> Path:
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resp = requests.get(url, timeout=timeout)
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resp.raise_for_status()
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dest.parent.mkdir(parents=True, exist_ok=True)
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dest.write_bytes(resp.content)
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return dest
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def generate_image_files(
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provider: AiProvider,
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image_prompt: str,
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output_dir: Path,
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*,
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count: int = 1,
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timeout: int = 180,
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model: str | None = None,
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) -> list[str]:
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"""文生图并保存到本地，返回绝对路径列表。智谱默认使用 glm-image。"""
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if not (provider.api_key or "").strip():
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raise ValueError(f"「{provider.type_label}」未配置 API Key")
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output_dir.mkdir(parents=True, exist_ok=True)
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count = max(1, min(count, 4))
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saved: list[str] = []
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if uses_zhipu_sdk(provider.provider_type):
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try:
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from zai import ZhipuAiClient
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except ImportError as exc:
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raise ValueError("未安装 zai-sdk，请执行: pip install zai-sdk") from exc
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kwargs: dict[str, Any] = {"api_key": provider.api_key.strip()}
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base_url = (provider.base_url or "").strip()
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if base_url:
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kwargs["base_url"] = base_url.rstrip("/")
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client = ZhipuAiClient(**kwargs)
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image_model = (model or "").strip() or ZHIPU_IMAGE_MODEL
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for _ in range(count):
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title = zlog.log_request(
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provider,
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"images/generations",
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{
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"model": image_model,
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"prompt": image_prompt,
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"size": ZHIPU_IMAGE_SIZE,
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"timeout": timeout,
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},
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)
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try:
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response = client.images.generations(
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model=image_model,
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prompt=image_prompt,
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size=ZHIPU_IMAGE_SIZE,
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timeout=timeout,
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)
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zlog.log_response(title, response)
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except Exception as exc:
|
||
zlog.log_error(title, exc)
|
||
raise ValueError(f"智谱文生图失败: {exc}") from exc
|
||
data = getattr(response, "data", None) or []
|
||
if not data:
|
||
raise ValueError("智谱文生图返回为空")
|
||
item = data[0]
|
||
url = str(getattr(item, "url", "") or "").strip()
|
||
if not url:
|
||
raise ValueError("智谱文生图未返回图片 URL")
|
||
name = f"xhs_{uuid.uuid4().hex[:10]}.png"
|
||
dl_title = zlog.log_request(provider, "下载生成图", {"url": url})
|
||
try:
|
||
path = _download_image_url(url, output_dir / name, timeout=timeout)
|
||
zlog.log_response(
|
||
dl_title,
|
||
{"saved_path": str(path.resolve()), "size_bytes": path.stat().st_size},
|
||
)
|
||
except Exception as exc:
|
||
zlog.log_error(dl_title, exc)
|
||
raise
|
||
saved.append(str(path.resolve()))
|
||
return saved
|
||
|
||
url = _images_api_url(provider.base_url)
|
||
headers = {
|
||
"Authorization": f"Bearer {provider.api_key.strip()}",
|
||
"Content-Type": "application/json",
|
||
}
|
||
image_model = (model or "").strip() or (provider.api_secret or "").strip() or "dall-e-3"
|
||
for _ in range(count):
|
||
payload = {"model": image_model, "prompt": image_prompt, "n": 1}
|
||
resp = requests.post(url, headers=headers, json=payload, timeout=timeout)
|
||
if resp.status_code >= 400:
|
||
raise ValueError(f"文生图失败 ({resp.status_code}): {resp.text[:400]}")
|
||
body = resp.json()
|
||
data = body.get("data") or []
|
||
if not data:
|
||
raise ValueError("文生图返回无 data")
|
||
item = data[0]
|
||
img_url = str(item.get("url") or "").strip()
|
||
b64 = str(item.get("b64_json") or "").strip()
|
||
name = f"xhs_{uuid.uuid4().hex[:10]}.png"
|
||
dest = output_dir / name
|
||
if img_url:
|
||
path = _download_image_url(img_url, dest, timeout=timeout)
|
||
elif b64:
|
||
import base64
|
||
|
||
dest.write_bytes(base64.b64decode(b64))
|
||
path = dest
|
||
else:
|
||
raise ValueError("文生图结果无 url / b64_json")
|
||
saved.append(str(path.resolve()))
|
||
return saved
|