feat(vision): prioritize main model for image recognition with multi-provider fallback

- Add call_vision method to all bot implementations (DashScope, Claude,
  Gemini, ZhipuAI, MiniMax, Doubao, Moonshot, OpenAICompatibleBot)
  using each vendor's native multimodal API format
- Remove call_with_tools/call_vision from Bot base class to fix MRO
  shadowing issue with OpenAICompatibleBot mixin
- Refactor vision tool provider resolution: MainModel → other configured
  models (auto-discovered) → OpenAI → LinkAI, with automatic fallback
- Return actual model name used in call_vision responses
- Sync config.json API keys to .env bidirectionally on startup
- Fix bot instance cache to detect bot_type/use_linkai config changes
- Add SSE reconnection support for web console
- Preserve image path hints in Gemini text for correct vision tool calls
- Update docs/tools/vision.mdx

models/zhipuai/zhipuai_bot.py

@@ -2,6 +2,7 @@
 
 import time
 import json
+from typing import Optional
 
 from models.bot import Bot
 from models.zhipuai.zhipu_ai_session import ZhipuAISession
@@ -149,6 +150,40 @@ class ZHIPUAIBot(Bot, ZhipuAIImage):
             else:
                 return result
 
+    def call_vision(self, image_url: str, question: str,
+                    model: Optional[str] = None,
+                    max_tokens: int = 1000) -> dict:
+        """Analyze an image using ZhipuAI OpenAI-compatible SDK.
+        Always uses glm-5v-turbo — the text models (glm-5-turbo etc.) do not support vision.
+        """
+        try:
+            vision_model = "glm-5v-turbo"
+            response = self.client.chat.completions.create(
+                model=vision_model,
+                max_tokens=max_tokens,
+                messages=[{
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": question},
+                        {"type": "image_url", "image_url": {"url": image_url}},
+                    ],
+                }],
+            )
+            content = response.choices[0].message.content or ""
+            usage = response.usage
+            return {
+                "model": vision_model,
+                "content": content,
+                "usage": {
+                    "prompt_tokens": getattr(usage, "prompt_tokens", 0),
+                    "completion_tokens": getattr(usage, "completion_tokens", 0),
+                    "total_tokens": getattr(usage, "total_tokens", 0),
+                },
+            }
+        except Exception as e:
+            logger.error(f"[ZHIPU_AI] call_vision error: {e}")
+            return {"error": True, "message": str(e)}
+
     def call_with_tools(self, messages, tools=None, stream=False, **kwargs):
         """
         Call ZhipuAI API with tool support for agent integration