Initial upload for secondary development

chatlog_fastAPI/routers/ai.py

@@ -0,0 +1,116 @@
+from fastapi import APIRouter, Depends, HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from typing import Optional, Literal
+import aiosqlite, json, logging
+import httpx
+from database import get_db
+from config import settings
+from services.ai_client import get_openai_client
+from services.runtime_settings import get_ai_settings
+from services.media_parser import parse_media
+
+router = APIRouter(prefix="/api", tags=["ai"])
+log = logging.getLogger(__name__)
+
+
+async def _get_ai_client():
+    return await get_openai_client()
+
+
+class SummarizeRequest(BaseModel):
+    context: str          # 已组装好的对话文本（含媒体描述）
+    room_name: Optional[str] = ""
+    messages: Optional[list] = None   # 兼容旧调用，忽略
+
+
+class ParseRequest(BaseModel):
+    type: Literal["voice", "image", "video"]
+    key: str   # voice: ServerID string; image/video: md5
+
+
+@router.post("/ai/parse")
+async def ai_parse(body: ParseRequest):
+    """
+    通过 FastAPI 代理 AI 媒体解析：
+    - voice: 从 chatlog 下载音频 → DashScope Paraformer ASR 转文字
+    - image/video: 从 chatlog 下载媒体 → base64 → 视觉模型描述
+    """
+    try:
+        return await parse_media(body.type, body.key)
+    except HTTPException:
+        raise
+    except Exception as e:
+        log.error(f"[ai/parse] 媒体解析失败: {e}", exc_info=True)
+        raise HTTPException(500, f"媒体解析失败: {e}")
+
+
+@router.post("/ai/summarize/stream")
+async def summarize_stream(body: SummarizeRequest):
+    """
+    接收前端已处理好的对话上下文，调用 AI 模型流式输出总结。
+    前端负责先把媒体（图片/语音/视频）解析成文字再拼进 context。
+    """
+    _ai = await get_ai_settings()
+    if not _ai.get("ai_api_key"):
+        async def err_gen():
+            yield 'data: {"error": "AI 服务未配置，请在「设置」页面填入 AI API Key"}\n\n'
+        return StreamingResponse(err_gen(), media_type="text/event-stream")
+    if not _ai.get("ai_model"):
+        async def err_gen():
+            yield 'data: {"error": "知识总结模型未配置，请在「设置」页面填入模型名称（如 qwen-max）"}\n\n'
+        return StreamingResponse(err_gen(), media_type="text/event-stream")
+
+    context = body.context.strip()
+    if not context:
+        async def err_gen():
+            yield 'data: {"error": "对话内容为空"}\n\n'
+        return StreamingResponse(err_gen(), media_type="text/event-stream")
+
+    room = body.room_name or "会话"
+
+    system_prompt = (
+        "你是一位专业的对话分析助手。"
+        "请根据提供的聊天记录（可能包含图片描述、语音转文字、视频描述等多媒体内容）"
+        "生成一份结构清晰的 Markdown 总结。"
+        "总结应包含：主要话题、关键信息点、媒体内容要点、待办事项（如有）。"
+        "只输出 Markdown 格式内容，不要有任何额外说明。"
+    )
+    user_prompt = (
+        f"群聊：{room}\n\n"
+        f"以下是聊天记录（含多媒体内容描述）：\n\n"
+        f"{context[:12000]}\n\n"  # 限制 token 数
+        f"请生成总结："
+    )
+
+    async def generate():
+        try:
+            _client, _ai = await _get_ai_client()
+            stream = await _client.chat.completions.create(
+                model=_ai["ai_model"],
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt},
+                ],
+                stream=True,
+                temperature=0.3,
+            )
+            async for chunk in stream:
+                delta = chunk.choices[0].delta.content if chunk.choices else None
+                if delta:
+                    yield f"data: {json.dumps({'delta': delta}, ensure_ascii=False)}\n\n"
+            yield 'data: {"done": true}\n\n'
+        except Exception as e:
+            log.error(f"[summarize/stream] LLM 调用失败: {e}", exc_info=True)
+            yield f"data: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
+
+    return StreamingResponse(generate(), media_type="text/event-stream")
+
+
+@router.get("/tasks/{task_id}")
+async def get_task(task_id: int, db: aiosqlite.Connection = Depends(get_db)):
+    async with db.execute("SELECT * FROM ai_tasks WHERE id=?", (task_id,)) as cur:
+        row = await cur.fetchone()
+    if not row:
+        raise HTTPException(404, "not found")
+    return dict(row)