JOJO
97e1327987
- 索引覆盖检查改与 min(needed, 文件总数) 比较，修复每次列表请求 必触发两次全量重建的逻辑错误（重建索引日志刷屏根源） - OCRClient 的 httpx.Client/OpenAI 改为首次调用 VLM 时懒加载， 构造耗时 3675ms -> 2ms（此前每个对话级 terminal 创建都要付一次） - WebTerminal 复用父类 TerminalManager 仅注入广播回调，不再二次创建； close_all 在无终端时静默，消除 GC 路径的关闭终端噪音日志 - 新建对话版本控制初始化去重：create_new_conversation 返回 versioning_initialized 标记，路由层跳过重复初始化 - _update_index 读-改-写整体纳入 _io_lock，修复并发丢索引条目 进而引发连锁全量重建的隐患
127 lines
5.0 KiB
Python
127 lines
5.0 KiB
Python
"""视觉语言模型客户端（主智能体专用）。"""
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import base64
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import mimetypes
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from pathlib import Path
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from typing import Dict, List
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import httpx
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from openai import OpenAI
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from config import OCR_API_BASE_URL, OCR_API_KEY, OCR_MODEL_ID, OCR_MAX_TOKENS
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from modules.file_manager import FileManager
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class OCRClient:
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"""封装外部 VLM 调用逻辑。"""
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def __init__(self, project_path: str, file_manager: FileManager):
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self.project_path = Path(project_path).resolve()
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self.file_manager = file_manager
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# 懒加载：httpx.Client() 构造会加载 CA 证书并初始化 SSL 上下文，
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# 在 Windows（Defender 实时扫描）下可耗时数秒；每个对话级 terminal
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# 都会构造 OCRClient，因此推迟到首次真正调用 VLM 时再创建。
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self.http_client = None
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self.client = None
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self._client_ready = False
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self.model = OCR_MODEL_ID
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self.max_tokens = OCR_MAX_TOKENS or 4096
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# 默认大小上限（10MB），超出则警告并拒绝
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self.max_image_size = 10 * 1024 * 1024
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def _ensure_client(self):
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"""首次使用时创建 httpx / OpenAI 客户端（线程安全由 GIL 保证最坏情况重复创建一次）。"""
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if self._client_ready:
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return
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# 补全 base_url，兼容是否包含 /v1
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base_url = (OCR_API_BASE_URL or "").rstrip("/")
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if base_url and not base_url.endswith("/v1"):
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base_url = f"{base_url}/v1"
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# httpx 0.28 起不再支持 proxies 参数，显式传入 http_client 以避免默认封装报错
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self.http_client = httpx.Client()
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if OCR_API_KEY:
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self.client = OpenAI(
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api_key=OCR_API_KEY,
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base_url=base_url or None,
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http_client=self.http_client,
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)
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self._client_ready = True
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def _validate_image_path(self, path: str):
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"""复用 FileManager 的路径校验，确保在项目内。"""
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valid, error, full_path = self.file_manager._validate_path(path)
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if not valid:
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return False, error, None
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if not full_path.exists():
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return False, "文件不存在", None
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if not full_path.is_file():
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return False, "不是文件", None
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return True, "", full_path
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def vlm_analyze(self, path: str, prompt: str) -> Dict:
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"""使用大参数视觉语言模型分析图片：文字、物体、布局等。"""
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warnings: List[str] = []
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valid, error, full_path = self._validate_image_path(path)
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if not valid:
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return {"success": False, "error": error, "warnings": warnings}
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if not prompt or not str(prompt).strip():
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return {"success": False, "error": "prompt 不能为空", "warnings": warnings}
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if not OCR_API_KEY or not OCR_API_BASE_URL or not self.model:
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return {"success": False, "error": "VLM 配置缺失，请设置 OCR_API_BASE_URL / OCR_API_KEY / OCR_MODEL_ID", "warnings": warnings}
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self._ensure_client()
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if not self.client:
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return {"success": False, "error": "VLM 客户端初始化失败", "warnings": warnings}
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try:
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data = full_path.read_bytes()
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except Exception as exc:
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return {"success": False, "error": f"读取文件失败: {exc}", "warnings": warnings}
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size = len(data)
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if size <= 0:
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return {"success": False, "error": "文件为空，无法识别", "warnings": warnings}
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if size > self.max_image_size:
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return {
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"success": False,
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"error": f"图片过大({size}字节)，上限为{self.max_image_size}字节",
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"warnings": warnings,
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}
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mime_type, _ = mimetypes.guess_type(str(full_path))
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if not mime_type or not mime_type.startswith("image/"):
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warnings.append("无法确定图片类型，已按 JPEG 处理")
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mime_type = "image/jpeg"
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base64_image = base64.b64encode(data).decode("utf-8")
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data_url = f"data:{mime_type};base64,{base64_image}"
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try:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "image_url", "image_url": {"url": data_url}},
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{"type": "text", "text": prompt},
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],
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}
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],
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max_tokens=self.max_tokens,
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temperature=0,
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)
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content = response.choices[0].message.content if response.choices else ""
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return {"success": True, "content": content or "", "warnings": warnings}
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except Exception as exc:
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return {"success": False, "error": f"VLM 调用失败: {exc}", "warnings": warnings}
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def ocr_image(self, path: str, prompt: str) -> Dict:
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"""兼容旧名，转发到 vlm_analyze。"""
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return self.vlm_analyze(path, prompt)
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