mirror of
https://github.com/linyqh/NarratoAI.git
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342fc15f3b
新增OmniVoice语音合成引擎全流程支持，包含配置项、WebUI界面与服务实现 集成Tavily联网搜索能力，支持短剧剧情分析前自动检索剧情背景信息 新增多视频源剪辑支持，完善脚本校验规则并重构剪辑逻辑适配多视频路径 重构LLM剧情分析Prompt，优化输出格式适配多场景与联网检索结果 调整streamlit版本至1.56.0修复兼容性问题 新增相关单元测试与多语言翻译，更新配置示例文件
117 lines
3.4 KiB
Python
117 lines
3.4 KiB
Python
"""Tavily-powered web search helpers for plot analysis."""
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from __future__ import annotations
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import os
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from typing import Any
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import requests
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from loguru import logger
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TAVILY_API_BASE_URL = "https://api.tavily.com"
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DEFAULT_SEARCH_DEPTH = "basic"
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DEFAULT_MAX_RESULTS = 5
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DEFAULT_TIMEOUT = 20
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class TavilySearchError(RuntimeError):
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"""Raised when Tavily search cannot be completed."""
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def _trim_text(value: Any, max_chars: int) -> str:
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text = str(value or "").strip()
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if len(text) <= max_chars:
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return text
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return f"{text[:max_chars].rstrip()}..."
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def search_short_drama(
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short_name: str,
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api_key: str | None = None,
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*,
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search_depth: str = DEFAULT_SEARCH_DEPTH,
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max_results: int = DEFAULT_MAX_RESULTS,
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timeout: int = DEFAULT_TIMEOUT,
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) -> dict[str, Any]:
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"""Search web context for a short drama name with Tavily."""
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short_name = str(short_name or "").strip()
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if not short_name:
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raise TavilySearchError("短剧名称不能为空")
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api_key = (api_key or os.getenv("TAVILY_API_KEY") or "").strip()
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if not api_key:
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raise TavilySearchError("Tavily API Key 未配置")
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query = f"{short_name} 短剧 剧情 介绍 人物 结局"
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payload = {
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"query": query,
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"search_depth": search_depth or DEFAULT_SEARCH_DEPTH,
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"topic": "general",
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"max_results": max(1, min(int(max_results or DEFAULT_MAX_RESULTS), 10)),
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"include_answer": True,
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"include_raw_content": False,
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"include_images": False,
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}
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try:
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response = requests.post(
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f"{TAVILY_API_BASE_URL}/search",
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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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json=payload,
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timeout=timeout,
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)
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except requests.RequestException as exc:
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raise TavilySearchError(f"Tavily 请求失败: {exc}") from exc
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if response.status_code >= 400:
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message = _trim_text(response.text, 500)
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raise TavilySearchError(f"Tavily 请求失败: HTTP {response.status_code} {message}")
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try:
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data = response.json()
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except ValueError as exc:
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raise TavilySearchError("Tavily 返回内容不是有效 JSON") from exc
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logger.info(
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"Tavily 短剧检索完成: query={}, results={}",
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query,
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len(data.get("results") or []),
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)
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return data
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def format_search_context(search_data: dict[str, Any], *, max_chars: int = 6000) -> str:
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"""Format Tavily response into compact LLM context."""
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if not search_data:
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return ""
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lines = [
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"# Tavily 联网检索结果",
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f"检索 query: {search_data.get('query', '')}",
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]
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answer = _trim_text(search_data.get("answer"), 1200)
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if answer:
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lines.extend(["", "## 综合回答", answer])
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results = search_data.get("results") or []
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if results:
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lines.extend(["", "## 搜索来源"])
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for index, result in enumerate(results, start=1):
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title = _trim_text(result.get("title"), 120)
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url = _trim_text(result.get("url"), 240)
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content = _trim_text(result.get("content") or result.get("raw_content"), 700)
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lines.extend(
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[
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f"{index}. 标题: {title}",
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f" 来源: {url}",
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f" 摘要: {content}",
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]
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)
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return _trim_text("\n".join(lines).strip(), max_chars)
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