vulkan: vision requests must bypass the fast chat-render path

The plain-Jinja fast path in generate_chat_stream renders the template
itself and generates via create_completion — which never invokes the
multimodal (mmproj) chat handler, so image_url parts were silently
dropped and vision models answered as if no image was sent. Streaming
vision requests now fall through to create_chat_completion.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014S8VtAvG499SsCbeESRK7V

codai/__init__.py

@@ -16,7 +16,7 @@
 
 # Canonical product version for CoderAI — single source of truth. Both the API
 # metadata and the admin web UI read from here.
-__version__ = "0.1.31"
+__version__ = "0.1.32"
 
 # Configure the CUDA caching allocator BEFORE torch is imported anywhere.
 # expandable_segments lets the allocator return freed pages to the driver even

codai/backends/vulkan.py

@@ -2224,7 +2224,17 @@ class VulkanBackend(ModelBackend):
         # via create_completion — avoids llama-cpp-python's slow per-call sandboxed
         # chat-template render. Falls through to create_chat_completion if the model
         # has no usable chat_template or rendering fails.
-        _prompt, _fmt_stops = self._render_chat_prompt(messages)
+        # NEVER take the fast path for vision requests: only create_chat_completion
+        # invokes the multimodal chat handler (mmproj) that actually embeds the
+        # images — a plain Jinja render would silently drop them and the model
+        # answers as if no image was sent.
+        _has_images = self.supports_vision and any(
+            isinstance(m.get('content'), list) and any(
+                isinstance(p, dict) and p.get('type') in ('image_url', 'input_image')
+                for p in m['content'])
+            for m in messages)
+        _prompt, _fmt_stops = ((None, None) if _has_images
+                               else self._render_chat_prompt(messages))
         if _prompt is not None:
             # Tokenize with add_bos=False (the chat template already emitted the BOS
             # text — same as llama-cpp's handler, which tokenizes with