feat(validation): chunk screenshots by max_images limit + fix websocket crash

- analyze_dashboard_multimodal now splits screenshots into chunks
  of max_images (from provider config) and sends them in parallel
- Results merged: worst status, deduped issues by (severity, msg, loc)
- New helper methods: _deduplicate_issues, _merge_chunk_results, _call_llm_for_images
- Plugin passes db_provider.max_images to the LLM client
- Report UI shows 'Chunked ×N' badge when analysis used multiple chunks
- i18n: added 'chunked' / 'По частям' key to validation.json
- Fix: isinstance(StopIteration) -> isinstance(_ws_exc, StopIteration)
  which crashed the websocket and broke task execution mid-flight
- Fix: update test mocks (_FakeLLMClient, _FakeScreenshotService)

backend/src/app.py

@@ -634,8 +634,8 @@ async def websocket_endpoint(
                             extra={"task_id": task_id, "message": result.message},
                         )
                         await asyncio.sleep(2)
-        except (WebSocketDisconnect, StopIteration):
-            if isinstance(StopIteration):
+        except (WebSocketDisconnect, StopIteration) as _ws_exc:
+            if isinstance(_ws_exc, StopIteration):
                 # Task reached terminal state — close cleanly with code 1000
                 try:
                     await websocket.close(code=1000, reason="Task completed")

backend/src/plugins/llm_analysis/plugin.py

@@ -371,6 +371,7 @@ class DashboardValidationPlugin(PluginBase):
                     screenshot_paths=jpeg_paths,
                     logs=logs,
                     prompt_template=dashboard_prompt,
+                    max_images=db_provider.max_images,
                 )
             else:
                 # Fallback: text-only analysis if no screenshots
@@ -418,6 +419,7 @@ class DashboardValidationPlugin(PluginBase):
 
             result_payload = _json_safe_value(validation_result.model_dump())
             result_payload["execution_path"] = "multimodal"
+            result_payload["chunk_count"] = analysis.get("chunk_count") or analysis.get("chunks") or 1
             result_payload["screenshot_paths"] = webp_paths or jpeg_paths
             result_payload["logs_sent_to_llm"] = logs
             result_payload["logs_sent_count"] = len(logs)

backend/src/plugins/llm_analysis/service.py

@@ -1193,13 +1193,71 @@ class LLMClient:
         }
     # endregion LLMClient._estimate_payload_size
 
+    # region LLMClient._deduplicate_issues [TYPE Function] [C:2]
+    # @PURPOSE Deduplicate issues by (severity, message, location) while preserving order.
+    def _deduplicate_issues(self, issues: list[dict]) -> list[dict]:
+        seen: set[tuple[str, str, str]] = set()
+        result: list[dict] = []
+        for issue in issues:
+            key = (issue.get("severity", ""), issue.get("message", ""), issue.get("location", "") or "")
+            if key not in seen:
+                seen.add(key)
+                result.append(issue)
+        return result
+
+    # endregion LLMClient._deduplicate_issues
+
+    # region LLMClient._merge_chunk_results [TYPE Function] [C:2]
+    # @PURPOSE Merge multiple chunk analyses into one. Takes the worst status,
+    #   concatenates summaries, and deduplicates issues.
+    # @PRE chunks is a non-empty list of {status, summary, issues} dicts.
+    # @POST Returns a single merged dict.
+    def _merge_chunk_results(self, chunks: list[dict[str, Any]]) -> dict[str, Any]:
+        STATUS_ORDER = {"FAIL": 0, "WARN": 1, "PASS": 2, "UNKNOWN": 3}
+        worst_status = "UNKNOWN"
+        all_summaries: list[str] = []
+        all_issues: list[dict] = []
+
+        for i, chunk in enumerate(chunks):
+            s = chunk.get("status", "UNKNOWN")
+            if STATUS_ORDER.get(s, 3) < STATUS_ORDER.get(worst_status, 3):
+                worst_status = s
+            all_summaries.append(f"[Chunk {i + 1}/{len(chunks)}] {chunk.get('summary', 'No summary')}")
+            all_issues.extend(chunk.get("issues", []))
+
+        merged = {
+            "status": worst_status,
+            "summary": " | ".join(all_summaries),
+            "issues": self._deduplicate_issues(all_issues),
+            "chunks": len(chunks),
+        }
+        return merged
+
+    # endregion LLMClient._merge_chunk_results
+
+    # region LLMClient._call_llm_for_images [TYPE Function] [C:2]
+    # @PURPOSE Send a single chunk of images to the LLM and return parsed result.
+    async def _call_llm_for_images(
+        self, encoded_images: list[str], prompt: str
+    ) -> dict[str, Any]:
+        content: list[dict] = [{"type": "text", "text": prompt}]
+        for b64_img in encoded_images:
+            content.append({
+                "type": "image_url",
+                "image_url": {"url": f"data:image/jpeg;base64,{b64_img}"},
+            })
+        messages = [{"role": "user", "content": content}]
+        return await self.get_json_completion(messages)
+
+    # endregion LLMClient._call_llm_for_images
+
     # region LLMClient.analyze_dashboard_multimodal [TYPE Function] [C:3]
-    # @PURPOSE Path A: send multiple tab screenshots + logs to multimodal LLM.
+    # @PURPOSE Path A: send screenshots + logs to multimodal LLM, with chunking support.
     # @PRE screenshot_paths is a non-empty list of paths.
     # @POST Returns dict {status, summary, issues}.
-    # @SIDE_EFFECT Compresses images, calls external LLM API.
-    # @RATIONALE Multi-chunk: one screenshot per tab. All images sent in single content[] array.
-    #   Token budget estimated before send; quality reduced if >80% of model context window.
+    # @SIDE_EFFECT Compresses images, calls external LLM API (possibly multiple times for chunks).
+    # @RATIONALE Screenshots are split into chunks of max_images to respect provider image limits.
+    #   Each chunk is sent in parallel; results are merged via _merge_chunk_results.
     async def analyze_dashboard_multimodal(
         self,
         screenshot_paths: list[str],
@@ -1207,6 +1265,7 @@ class LLMClient:
         prompt_template: str = DEFAULT_LLM_PROMPTS["dashboard_validation_prompt"],
         max_width: int = 1024,
         image_quality: int = 60,
+        max_images: int | None = None,
     ) -> dict[str, Any]:
         with belief_scope("analyze_dashboard_multimodal"):
             if not screenshot_paths:
@@ -1248,19 +1307,45 @@ class LLMClient:
                             b64 = base64.b64encode(f.read()).decode("utf-8")
                         encoded_images.append(b64)
 
-            # 3. Build multimodal content array with all images
-            content: list[dict] = [{"type": "text", "text": prompt}]
-            for b64_img in encoded_images:
-                content.append({
-                    "type": "image_url",
-                    "image_url": {"url": f"data:image/jpeg;base64,{b64_img}"},
-                })
+            # 3. Chunk images if max_images is set
+            n_total = len(encoded_images)
+            chunk_size = max_images if (max_images and max_images > 0 and max_images < n_total) else n_total
 
-            messages = [{"role": "user", "content": content}]
+            if chunk_size < n_total:
+                logger.reason(
+                    f"[analyze_dashboard_multimodal] Chunking {n_total} images into "
+                    f"{ (n_total + chunk_size - 1) // chunk_size } chunks of {chunk_size}",
+                    extra={"src": "analyze_dashboard_multimodal", "total": n_total, "chunk_size": chunk_size},
+                )
 
-            # 4. Call LLM
+            # Split into chunks
+            chunks: list[list[str]] = []
+            for i in range(0, n_total, chunk_size):
+                chunks.append(encoded_images[i:i + chunk_size])
+
+            # 4. Call LLM — parallel for multiple chunks, single for one
             try:
-                return await self.get_json_completion(messages)
+                if len(chunks) == 1:
+                    result = await self._call_llm_for_images(chunks[0], prompt)
+                else:
+                    tasks = [self._call_llm_for_images(chunk, prompt) for chunk in chunks]
+                    chunk_results = await asyncio.gather(*tasks, return_exceptions=True)
+
+                    # Filter out exceptions
+                    valid_results: list[dict] = []
+                    for i, cr in enumerate(chunk_results):
+                        if isinstance(cr, Exception):
+                            logger.error(f"[analyze_dashboard_multimodal] Chunk {i + 1}/{len(chunks)} failed: {cr!s}")
+                            valid_results.append({
+                                "status": "UNKNOWN",
+                                "summary": f"Chunk {i + 1} failed: {cr!s}",
+                                "issues": [],
+                            })
+                        else:
+                            valid_results.append(cr)
+
+                    result = self._merge_chunk_results(valid_results)
+                    result["chunk_count"] = len(chunks)
             except Exception as e:
                 logger.error(f"[analyze_dashboard_multimodal] Failed to get analysis: {e!s}")
                 return {
@@ -1268,6 +1353,8 @@ class LLMClient:
                     "summary": f"Failed to get response from LLM: {e!s}",
                     "issues": [{"severity": "UNKNOWN", "message": "LLM provider returned empty or invalid response"}],
                 }
+
+            return result
     # endregion LLMClient.analyze_dashboard_multimodal
 
     # region LLMClient.analyze_dashboard_text_batch [TYPE Function] [C:3]

backend/src/services/__tests__/test_llm_plugin_persistence.py

@@ -104,6 +104,10 @@ async def test_dashboard_validation_plugin_persists_task_and_environment_ids(
         async def capture_dashboard(self, _dashboard_id, _screenshot_path):
             return [], []
 
+        @staticmethod
+        def _cleanup_temp_files(_paths):
+            pass
+
     # endregion _FakeScreenshotService
 
     # region _FakeLLMClient [TYPE Class]
@@ -127,6 +131,22 @@ async def test_dashboard_validation_plugin_persists_task_and_environment_ids(
                 "issues": [],
             }
 
+        async def analyze_dashboard_multimodal(self, *_args, **_kwargs):
+            return {
+                "status": "PASS",
+                "summary": "Dashboard healthy",
+                "issues": [],
+            }
+
+        async def analyze_dashboard_text_batch(self, *_args, **_kwargs):
+            return {
+                "dashboards": [{
+                    "status": "PASS",
+                    "summary": "Dashboard healthy",
+                    "issues": [],
+                }],
+            }
+
     # endregion _FakeLLMClient
 
     # region _FakeNotificationService [TYPE Class]

frontend/src/lib/i18n/locales/en/validation.json

@@ -212,5 +212,6 @@
   "path_b": "Path B — Text-only",
   "no_issues": "No issues detected",
   "no_logs": "No logs available",
-  "no_screenshots": "No screenshots saved"
+  "no_screenshots": "No screenshots saved",
+  "chunked": "Chunked"
 }

frontend/src/lib/i18n/locales/ru/validation.json

@@ -212,5 +212,6 @@
   "path_b": "Путь B — Только текст",
   "no_issues": "Проблем не обнаружено",
   "no_logs": "Нет доступных логов",
-  "no_screenshots": "Нет сохраненных скриншотов"
+  "no_screenshots": "Нет сохраненных скриншотов",
+  "chunked": "По частям"
 }

frontend/src/routes/validation-tasks/[policyId]/runs/[runId]/+page.svelte

@@ -414,6 +414,11 @@
 							{#if dbPath === 'A'}
 								<span class="text-purple-600 font-medium">📸 {$t.validation?.path_a || 'Path A — Screenshot'}</span>
 								<span class="text-gray-400">| {$t.validation?.screenshots || 'Screenshots'} captured via browser automation</span>
+								{#if dashboard.chunk_count && dashboard.chunk_count > 1}
+									<span class="inline-flex items-center gap-1 px-2 py-0.5 rounded-full bg-amber-100 text-amber-700 text-xs font-medium">
+										{$t.validation?.chunked || 'Chunked'} ×{dashboard.chunk_count}
+									</span>
+								{/if}
 							{:else}
 								<span class="text-teal-600 font-medium">📝 {$t.validation?.path_b || 'Path B — Text-only'}</span>
 								<span class="text-gray-400">| {$t.validation?.issues || 'Issue'} detection via DOM extraction</span>