feat(audiobook): implement audiobook project management features

qwen3-tts-backend/core/llm_service.py

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+import json
+import logging
+from typing import Any, Dict
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+
+class LLMService:
+    def __init__(self, base_url: str, api_key: str, model: str):
+        self.base_url = base_url.rstrip("/")
+        self.api_key = api_key
+        self.model = model
+
+    async def chat(self, system_prompt: str, user_message: str) -> str:
+        url = f"{self.base_url}/chat/completions"
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        payload = {
+            "model": self.model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_message},
+            ],
+            "temperature": 0.3,
+        }
+
+        async with httpx.AsyncClient(timeout=120) as client:
+            resp = await client.post(url, json=payload, headers=headers)
+            if resp.status_code != 200:
+                logger.error(f"LLM API error {resp.status_code}: {resp.text}")
+                resp.raise_for_status()
+            data = resp.json()
+            return data["choices"][0]["message"]["content"]
+
+    async def chat_json(self, system_prompt: str, user_message: str) -> Any:
+        raw = await self.chat(system_prompt, user_message)
+        raw = raw.strip()
+        if raw.startswith("```"):
+            lines = raw.split("\n")
+            raw = "\n".join(lines[1:-1]) if len(lines) > 2 else raw
+        return json.loads(raw)
+
+    async def extract_characters(self, text: str) -> list[Dict]:
+        system_prompt = (
+            "你是一个专业的小说分析助手。请分析给定的小说文本，提取所有出现的角色（包括旁白narrator）。"
+            "只输出JSON，格式如下，不要有其他文字：\n"
+            '{"characters": [{"name": "narrator", "description": "第三人称叙述者", "instruct": "中年男声，语速平稳"}, ...]}'
+        )
+        user_message = f"请分析以下小说文本并提取角色：\n\n{text[:30000]}"
+        result = await self.chat_json(system_prompt, user_message)
+        return result.get("characters", [])
+
+    async def parse_chapter_segments(self, chapter_text: str, character_names: list[str]) -> list[Dict]:
+        names_str = "、".join(character_names)
+        system_prompt = (
+            "你是一个专业的有声书制作助手。请将给定的章节文本解析为对话片段列表。"
+            f"已知角色列表（必须从中选择）：{names_str}。"
+            "所有非对话的叙述文字归属于narrator角色。"
+            "只输出JSON数组，不要有其他文字，格式如下：\n"
+            '[{"character": "narrator", "text": "叙述文字"}, {"character": "角色名", "text": "对话内容"}, ...]'
+        )
+        user_message = f"请解析以下章节文本：\n\n{chapter_text}"
+        result = await self.chat_json(system_prompt, user_message)
+        if isinstance(result, list):
+            return result
+        return []