feat: wire image uploads into AI assistant chat (vision support)

- Backend: ChatMessageRequest accepts upload_ids, endpoint fetches
  images from S3, base64-encodes them, passes to Claude as multimodal
  content blocks (vision API)
- Backend: add download_file() to storage_service for fetching from S3
- Frontend: handleSend collects completed upload IDs from pendingUploads
  and includes them in the sendChatMessage API call
- Frontend: prefill handler passes upload IDs from dashboard nav state
- Enables paste-screenshot → AI-sees-it flow end-to-end

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/app/api/endpoints/assistant_chat.py

@@ -10,9 +10,10 @@
   GET    /assistant/retention          — Get account retention settings
   PATCH  /assistant/retention          — Update retention settings (owner only)
 """
+import base64
 import logging
 from datetime import datetime, timezone, timedelta
-from typing import Annotated, Optional
+from typing import Annotated, Any, Optional
 from uuid import UUID
 
 from fastapi import APIRouter, Depends, HTTPException, Query, Request, status
@@ -26,6 +27,7 @@ from app.core.ai_quota_service import check_ai_quota, record_ai_usage, get_user_
 from app.models.user import User
 from app.models.account import Account
 from app.models.assistant_chat import AssistantChat
+from app.models.file_upload import FileUpload
 from app.schemas.assistant_chat import (
     ChatCreateRequest,
     ChatMessageRequest,
@@ -46,6 +48,42 @@ logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/assistant", tags=["assistant-chat"])
 
 
+VISION_CONTENT_TYPES = {"image/png", "image/jpeg", "image/gif", "image/webp"}
+
+
+async def _fetch_upload_images(
+    upload_ids: list[UUID],
+    account_id: UUID,
+    db: AsyncSession,
+) -> list[dict[str, Any]]:
+    """Fetch uploaded images from S3 and return as base64-encoded dicts for Claude vision."""
+    if not upload_ids or not settings.STORAGE_ENDPOINT:
+        return []
+
+    from app.services import storage_service
+
+    result = await db.execute(
+        select(FileUpload).where(
+            FileUpload.id.in_(upload_ids),
+            FileUpload.account_id == account_id,
+            FileUpload.content_type.in_(VISION_CONTENT_TYPES),
+        )
+    )
+    uploads = result.scalars().all()
+
+    images: list[dict[str, Any]] = []
+    for upload in uploads:
+        try:
+            file_data = storage_service.download_file(upload.storage_key)
+            images.append({
+                "media_type": upload.content_type,
+                "data": base64.b64encode(file_data).decode("ascii"),
+            })
+        except Exception:
+            logger.warning("Failed to fetch upload %s from S3", upload.id)
+    return images
+
+
 def _require_ai_enabled() -> None:
     if not settings.ai_enabled:
         raise HTTPException(
@@ -151,6 +189,9 @@ async def post_message(
     user_id = current_user.id
     account_id = current_user.account_id
 
+    # Fetch attached images from S3 (if any)
+    images = await _fetch_upload_images(data.upload_ids, account_id, db)
+
     try:
         ai_content, suggested_flows, chat = await assistant_chat_service.send_message(
             chat_id=chat_id,
@@ -158,6 +199,7 @@ async def post_message(
             account_id=account_id,
             message=data.message,
             db=db,
+            images=images or None,
         )
     except ValueError as e:
         raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(e))