formatting

* Strip model name before training

* Handle options file for go2rtc option

* Make reviewed optional and add null to API call

* Send reviewed for dashboard

* Allow setting context size for openai compatible endpoints

* push empty go2rtc config to avoid homekit error in log

* Add option to set runtime options for LLM providers

* Docs

---------

Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>

docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run

@@ -54,8 +54,8 @@ function setup_homekit_config() {
     local config_path="$1"
 
     if [[ ! -f "${config_path}" ]]; then
-        echo "[INFO] Creating empty HomeKit config file..."
-        echo 'homekit: {}' > "${config_path}"
+        echo "[INFO] Creating empty config file for HomeKit..."
+        echo '{}' > "${config_path}"
     fi
 
     # Convert YAML to JSON for jq processing

docker/main/rootfs/usr/local/go2rtc/create_config.py

@@ -23,8 +23,28 @@ sys.path.remove("/opt/frigate")
 yaml = YAML()
 
 # Check if arbitrary exec sources are allowed (defaults to False for security)
-ALLOW_ARBITRARY_EXEC = os.environ.get(
-    "GO2RTC_ALLOW_ARBITRARY_EXEC", "false"
+allow_arbitrary_exec = None
+if "GO2RTC_ALLOW_ARBITRARY_EXEC" in os.environ:
+    allow_arbitrary_exec = os.environ.get("GO2RTC_ALLOW_ARBITRARY_EXEC")
+elif (
+    os.path.isdir("/run/secrets")
+    and os.access("/run/secrets", os.R_OK)
+    and "GO2RTC_ALLOW_ARBITRARY_EXEC" in os.listdir("/run/secrets")
+):
+    allow_arbitrary_exec = (
+        Path(os.path.join("/run/secrets", "GO2RTC_ALLOW_ARBITRARY_EXEC"))
+        .read_text()
+        .strip()
+    )
+# check for the add-on options file
+elif os.path.isfile("/data/options.json"):
+    with open("/data/options.json") as f:
+        raw_options = f.read()
+    options = json.loads(raw_options)
+    allow_arbitrary_exec = options.get("go2rtc_allow_arbitrary_exec")
+
+ALLOW_ARBITRARY_EXEC = allow_arbitrary_exec is not None and str(
+    allow_arbitrary_exec
 ).lower() in ("true", "1", "yes")
 
 FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}

docs/docs/configuration/genai/config.md

@@ -41,12 +41,12 @@ If you are trying to use a single model for Frigate and HomeAssistant, it will n
 
 The following models are recommended:
 
-| Model             | Notes                                                                |
-| ----------------- | -------------------------------------------------------------------- |
-| `qwen3-vl`        | Strong visual and situational understanding, higher vram requirement |
-| `Intern3.5VL`     | Relatively fast with good vision comprehension                       |
-| `gemma3`          | Strong frame-to-frame understanding, slower inference times          |
-| `qwen2.5-vl`      | Fast but capable model with good vision comprehension                |
+| Model         | Notes                                                                |
+| ------------- | -------------------------------------------------------------------- |
+| `qwen3-vl`    | Strong visual and situational understanding, higher vram requirement |
+| `Intern3.5VL` | Relatively fast with good vision comprehension                       |
+| `gemma3`      | Strong frame-to-frame understanding, slower inference times          |
+| `qwen2.5-vl`  | Fast but capable model with good vision comprehension                |
 
 :::note
 
@@ -61,10 +61,10 @@ genai:
   provider: ollama
   base_url: http://localhost:11434
   model: minicpm-v:8b
-  provider_options:  # other Ollama client options can be defined
+  provider_options: # other Ollama client options can be defined
     keep_alive: -1
     options:
-        num_ctx: 8192  # make sure the context matches other services that are using ollama
+      num_ctx: 8192 # make sure the context matches other services that are using ollama
 ```
 
 ## Google Gemini
@@ -120,6 +120,23 @@ To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` env
 
 :::
 
+:::tip
+
+For OpenAI-compatible servers (such as llama.cpp) that don't expose the configured context size in the API response, you can manually specify the context size in `provider_options`:
+
+```yaml
+genai:
+  provider: openai
+  base_url: http://your-llama-server
+  model: your-model-name
+  provider_options:
+    context_size: 8192 # Specify the configured context size
+```
+
+This ensures Frigate uses the correct context window size when generating prompts.
+
+:::
+
 ## Azure OpenAI
 
 Microsoft offers several vision models through Azure OpenAI. A subscription is required.

docs/docs/configuration/reference.md

@@ -696,6 +696,9 @@ genai:
   # Optional additional args to pass to the GenAI Provider (default: None)
   provider_options:
     keep_alive: -1
+  # Optional: Options to pass during inference calls (default: {})
+  runtime_options:
+    temperature: 0.7
 
 # Optional: Configuration for audio transcription
 # NOTE: only the enabled option can be overridden at the camera level

docs/static/_headers

@@ -0,0 +1,8 @@
+https://:project.pages.dev/*
+  X-Robots-Tag: noindex
+
+https://:version.:project.pages.dev/*
+  X-Robots-Tag: noindex
+
+https://docs-dev.frigate.video/*
+  X-Robots-Tag: noindex

frigate/api/camera.py

@@ -848,9 +848,10 @@ async def onvif_probe(
                             try:
                                 if isinstance(uri, str) and uri.startswith("rtsp://"):
                                     if username and password and "@" not in uri:
-                                        # Inject URL-encoded credentials and add only the
-                                        # authenticated version.
-                                        cred = f"{quote_plus(username)}:{quote_plus(password)}@"
+                                        # Inject raw credentials and add only the
+                                        # authenticated version. The credentials will be encoded
+                                        # later by ffprobe_stream or the config system.
+                                        cred = f"{username}:{password}@"
                                         injected = uri.replace(
                                             "rtsp://", f"rtsp://{cred}", 1
                                         )
@@ -903,12 +904,8 @@ async def onvif_probe(
                     "/cam/realmonitor?channel=1&subtype=0",
                     "/11",
                 ]
-                # Use URL-encoded credentials for pattern fallback URIs when provided
-                auth_str = (
-                    f"{quote_plus(username)}:{quote_plus(password)}@"
-                    if username and password
-                    else ""
-                )
+                # Use raw credentials for pattern fallback URIs when provided
+                auth_str = f"{username}:{password}@" if username and password else ""
                 rtsp_port = 554
                 for path in common_paths:
                     uri = f"rtsp://{auth_str}{host}:{rtsp_port}{path}"
@@ -930,7 +927,7 @@ async def onvif_probe(
                         and uri.startswith("rtsp://")
                         and "@" not in uri
                     ):
-                        cred = f"{quote_plus(username)}:{quote_plus(password)}@"
+                        cred = f"{username}:{password}@"
                         cred_uri = uri.replace("rtsp://", f"rtsp://{cred}", 1)
                         if cred_uri not in to_test:
                             to_test.append(cred_uri)

frigate/api/defs/query/review_query_parameters.py

@@ -10,7 +10,7 @@ class ReviewQueryParams(BaseModel):
     cameras: str = "all"
     labels: str = "all"
     zones: str = "all"
-    reviewed: int = 0
+    reviewed: Union[int, SkipJsonSchema[None]] = None
     limit: Union[int, SkipJsonSchema[None]] = None
     severity: Union[SeverityEnum, SkipJsonSchema[None]] = None
     before: Union[float, SkipJsonSchema[None]] = None

frigate/api/review.py

@@ -144,6 +144,8 @@ async def review(
             (UserReviewStatus.has_been_reviewed == False)
             | (UserReviewStatus.has_been_reviewed.is_null())
         )
+    elif reviewed == 1:
+        review_query = review_query.where(UserReviewStatus.has_been_reviewed == True)
 
     # Apply ordering and limit
     review_query = (

frigate/config/camera/genai.py

@@ -26,3 +26,6 @@ class GenAIConfig(FrigateBaseModel):
     provider_options: dict[str, Any] = Field(
         default={}, title="GenAI Provider extra options."
     )
+    runtime_options: dict[str, Any] = Field(
+        default={}, title="Options to pass during inference calls."
+    )

frigate/genai/azure-openai.py

@@ -64,6 +64,7 @@ class OpenAIClient(GenAIClient):
                     },
                 ],
                 timeout=self.timeout,
+                **self.genai_config.runtime_options,
             )
         except Exception as e:
             logger.warning("Azure OpenAI returned an error: %s", str(e))

frigate/genai/gemini.py

@@ -35,10 +35,14 @@ class GeminiClient(GenAIClient):
             for img in images
         ] + [prompt]
         try:
+            # Merge runtime_options into generation_config if provided
+            generation_config_dict = {"candidate_count": 1}
+            generation_config_dict.update(self.genai_config.runtime_options)
+
             response = self.provider.generate_content(
                 data,
                 generation_config=genai.types.GenerationConfig(
-                    candidate_count=1,
+                    **generation_config_dict
                 ),
                 request_options=genai.types.RequestOptions(
                     timeout=self.timeout,

frigate/genai/ollama.py

@@ -58,11 +58,15 @@ class OllamaClient(GenAIClient):
             )
             return None
         try:
+            ollama_options = {
+                **self.provider_options,
+                **self.genai_config.runtime_options,
+            }
             result = self.provider.generate(
                 self.genai_config.model,
                 prompt,
                 images=images if images else None,
-                **self.provider_options,
+                **ollama_options,
             )
             logger.debug(
                 f"Ollama tokens used: eval_count={result.get('eval_count')}, prompt_eval_count={result.get('prompt_eval_count')}"

frigate/genai/openai.py

@@ -22,9 +22,14 @@ class OpenAIClient(GenAIClient):
 
     def _init_provider(self):
         """Initialize the client."""
-        return OpenAI(
-            api_key=self.genai_config.api_key, **self.genai_config.provider_options
-        )
+        # Extract context_size from provider_options as it's not a valid OpenAI client parameter
+        # It will be used in get_context_size() instead
+        provider_opts = {
+            k: v
+            for k, v in self.genai_config.provider_options.items()
+            if k != "context_size"
+        }
+        return OpenAI(api_key=self.genai_config.api_key, **provider_opts)
 
     def _send(self, prompt: str, images: list[bytes]) -> Optional[str]:
         """Submit a request to OpenAI."""
@@ -56,6 +61,7 @@ class OpenAIClient(GenAIClient):
                     },
                 ],
                 timeout=self.timeout,
+                **self.genai_config.runtime_options,
             )
             if (
                 result is not None
@@ -73,6 +79,16 @@ class OpenAIClient(GenAIClient):
         if self.context_size is not None:
             return self.context_size
 
+        # First check provider_options for manually specified context size
+        # This is necessary for llama.cpp and other OpenAI-compatible servers
+        # that don't expose the configured runtime context size in the API response
+        if "context_size" in self.genai_config.provider_options:
+            self.context_size = self.genai_config.provider_options["context_size"]
+            logger.debug(
+                f"Using context size {self.context_size} from provider_options for model {self.genai_config.model}"
+            )
+            return self.context_size
+
         try:
             models = self.provider.models.list()
             for model in models.data:

frigate/test/http_api/base_http_test.py

@@ -171,8 +171,8 @@ class BaseTestHttp(unittest.TestCase):
     def insert_mock_event(
         self,
         id: str,
-        start_time: float = datetime.datetime.now().timestamp(),
-        end_time: float = datetime.datetime.now().timestamp() + 20,
+        start_time: float | None = None,
+        end_time: float | None = None,
         has_clip: bool = True,
         top_score: int = 100,
         score: int = 0,
@@ -180,6 +180,11 @@ class BaseTestHttp(unittest.TestCase):
         camera: str = "front_door",
     ) -> Event:
         """Inserts a basic event model with a given id."""
+        if start_time is None:
+            start_time = datetime.datetime.now().timestamp()
+        if end_time is None:
+            end_time = start_time + 20
+
         return Event.insert(
             id=id,
             label="Mock",
@@ -229,11 +234,16 @@ class BaseTestHttp(unittest.TestCase):
     def insert_mock_recording(
         self,
         id: str,
-        start_time: float = datetime.datetime.now().timestamp(),
-        end_time: float = datetime.datetime.now().timestamp() + 20,
+        start_time: float | None = None,
+        end_time: float | None = None,
         motion: int = 0,
     ) -> Event:
         """Inserts a recording model with a given id."""
+        if start_time is None:
+            start_time = datetime.datetime.now().timestamp()
+        if end_time is None:
+            end_time = start_time + 20
+
         return Recordings.insert(
             id=id,
             path=id,

frigate/test/http_api/test_http_event.py

@@ -96,16 +96,17 @@ class TestHttpApp(BaseTestHttp):
             assert len(events) == 0
 
     def test_get_event_list_limit(self):
+        now = datetime.now().timestamp()
         id = "123456.random"
         id2 = "54321.random"
 
         with AuthTestClient(self.app) as client:
-            super().insert_mock_event(id)
+            super().insert_mock_event(id, start_time=now + 1)
             events = client.get("/events").json()
             assert len(events) == 1
             assert events[0]["id"] == id
 
-            super().insert_mock_event(id2)
+            super().insert_mock_event(id2, start_time=now)
             events = client.get("/events").json()
             assert len(events) == 2
 
@@ -144,7 +145,7 @@ class TestHttpApp(BaseTestHttp):
             assert events[0]["id"] == id2
             assert events[1]["id"] == id
 
-            events = client.get("/events", params={"sort": "score_des"}).json()
+            events = client.get("/events", params={"sort": "score_desc"}).json()
             assert len(events) == 2
             assert events[0]["id"] == id
             assert events[1]["id"] == id2

frigate/test/http_api/test_http_review.py

@@ -196,6 +196,50 @@ class TestHttpReview(BaseTestHttp):
             assert len(response_json) == 1
             assert response_json[0]["id"] == id
 
+    def test_get_review_with_reviewed_filter_unreviewed(self):
+        """Test that reviewed=0 returns only unreviewed items."""
+        now = datetime.now().timestamp()
+
+        with AuthTestClient(self.app) as client:
+            id_unreviewed = "123456.unreviewed"
+            id_reviewed = "123456.reviewed"
+            super().insert_mock_review_segment(id_unreviewed, now, now + 2)
+            super().insert_mock_review_segment(id_reviewed, now, now + 2)
+            self._insert_user_review_status(id_reviewed, reviewed=True)
+
+            params = {
+                "reviewed": 0,
+                "after": now - 1,
+                "before": now + 3,
+            }
+            response = client.get("/review", params=params)
+            assert response.status_code == 200
+            response_json = response.json()
+            assert len(response_json) == 1
+            assert response_json[0]["id"] == id_unreviewed
+
+    def test_get_review_with_reviewed_filter_reviewed(self):
+        """Test that reviewed=1 returns only reviewed items."""
+        now = datetime.now().timestamp()
+
+        with AuthTestClient(self.app) as client:
+            id_unreviewed = "123456.unreviewed"
+            id_reviewed = "123456.reviewed"
+            super().insert_mock_review_segment(id_unreviewed, now, now + 2)
+            super().insert_mock_review_segment(id_reviewed, now, now + 2)
+            self._insert_user_review_status(id_reviewed, reviewed=True)
+
+            params = {
+                "reviewed": 1,
+                "after": now - 1,
+                "before": now + 3,
+            }
+            response = client.get("/review", params=params)
+            assert response.status_code == 200
+            response_json = response.json()
+            assert len(response_json) == 1
+            assert response_json[0]["id"] == id_reviewed
+
     ####################################################################################################################
     ###################################  GET /review/summary Endpoint   #################################################
     ####################################################################################################################

frigate/util/classification.py

@@ -43,6 +43,7 @@ def write_training_metadata(model_name: str, image_count: int) -> None:
         model_name: Name of the classification model
         image_count: Number of images used in training
     """
+    model_name = model_name.strip()
     clips_model_dir = os.path.join(CLIPS_DIR, model_name)
     os.makedirs(clips_model_dir, exist_ok=True)
 
@@ -70,6 +71,7 @@ def read_training_metadata(model_name: str) -> dict[str, any] | None:
     Returns:
         Dictionary with last_training_date and last_training_image_count, or None if not found
     """
+    model_name = model_name.strip()
     clips_model_dir = os.path.join(CLIPS_DIR, model_name)
     metadata_path = os.path.join(clips_model_dir, TRAINING_METADATA_FILE)
 
@@ -95,6 +97,7 @@ def get_dataset_image_count(model_name: str) -> int:
     Returns:
         Total count of images across all categories
     """
+    model_name = model_name.strip()
     dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
 
     if not os.path.exists(dataset_dir):
@@ -126,6 +129,7 @@ class ClassificationTrainingProcess(FrigateProcess):
             "TF_KERAS_MOBILENET_V2_WEIGHTS_URL",
             "",
         )
+        model_name = model_name.strip()
         super().__init__(
             stop_event=None,
             priority=PROCESS_PRIORITY_LOW,
@@ -292,6 +296,7 @@ class ClassificationTrainingProcess(FrigateProcess):
 def kickoff_model_training(
     embeddingRequestor: EmbeddingsRequestor, model_name: str
 ) -> None:
+    model_name = model_name.strip()
     requestor = InterProcessRequestor()
     requestor.send_data(
         UPDATE_MODEL_STATE,
@@ -359,6 +364,7 @@ def collect_state_classification_examples(
         model_name: Name of the classification model
         cameras: Dict mapping camera names to normalized crop coordinates [x1, y1, x2, y2] (0-1)
     """
+    model_name = model_name.strip()
     dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
 
     # Step 1: Get review items for the cameras
@@ -714,6 +720,7 @@ def collect_object_classification_examples(
         model_name: Name of the classification model
         label: Object label to collect (e.g., "person", "car")
     """
+    model_name = model_name.strip()
     dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
     temp_dir = os.path.join(dataset_dir, "temp")
     os.makedirs(temp_dir, exist_ok=True)

frigate/util/services.py

@@ -540,9 +540,16 @@ def get_jetson_stats() -> Optional[dict[int, dict]]:
     try:
         results["mem"] = "-"  # no discrete gpu memory
 
-        with open("/sys/devices/gpu.0/load", "r") as f:
-            gpuload = float(f.readline()) / 10
-            results["gpu"] = f"{gpuload}%"
+        if os.path.exists("/sys/devices/gpu.0/load"):
+            with open("/sys/devices/gpu.0/load", "r") as f:
+                gpuload = float(f.readline()) / 10
+                results["gpu"] = f"{gpuload}%"
+        elif os.path.exists("/sys/devices/platform/gpu.0/load"):
+            with open("/sys/devices/platform/gpu.0/load", "r") as f:
+                gpuload = float(f.readline()) / 10
+                results["gpu"] = f"{gpuload}%"
+        else:
+            results["gpu"] = "-"
     except Exception:
         return None
 

web/public/locales/en/config/genai.json

@@ -15,6 +15,9 @@
     },
     "provider_options": {
       "label": "GenAI Provider extra options."
+    },
+    "runtime_options": {
+      "label": "Options to pass during inference calls."
     }
   }
-}
+}

web/src/components/card/ClassificationCard.tsx

@@ -164,7 +164,7 @@ export const ClassificationCard = forwardRef<
           )}
         >
           <div className="break-all smart-capitalize">
-            {data.name == "unknown"
+            {data.name.toLowerCase() == "unknown"
               ? t("details.unknown")
               : data.name.toLowerCase() == "none"
                 ? t("details.none")
@@ -336,7 +336,7 @@ export function GroupedClassificationCard({
                 <ContentTitle className="flex items-center gap-2 font-normal capitalize">
                   {classifiedEvent?.label && classifiedEvent.label !== "none"
                     ? classifiedEvent.label
-                    : t(noClassificationLabel)}
+                    : t(noClassificationLabel, { ns: i18nLibrary })}
                   {classifiedEvent?.label &&
                     classifiedEvent.label !== "none" &&
                     classifiedEvent.score !== undefined && (

web/src/components/overlay/chip/GenAISummaryChip.tsx

@@ -57,7 +57,7 @@ export function GenAISummaryDialog({
       !aiAnalysis ||
       (!aiAnalysis.potential_threat_level && !aiAnalysis.other_concerns)
     ) {
-      return "None";
+      return t("label.none", { ns: "common" });
     }
 
     let concerns = "";
@@ -74,7 +74,9 @@ export function GenAISummaryDialog({
           label = t("securityConcern", { ns: "views/events" });
           break;
         default:
-          label = THREAT_LEVEL_LABELS[threatLevel as ThreatLevel] || "Unknown";
+          label =
+            THREAT_LEVEL_LABELS[threatLevel as ThreatLevel] ||
+            t("details.unknown", { ns: "views/classificationModel" });
       }
       concerns = `• ${label}\n`;
     }
@@ -83,7 +85,7 @@ export function GenAISummaryDialog({
       concerns += `• ${c}\n`;
     });
 
-    return concerns || "None";
+    return concerns || t("label.none", { ns: "common" });
   }, [aiAnalysis, t]);
 
   // layout

web/src/components/player/PreviewThumbnailPlayer.tsx

@@ -342,7 +342,9 @@ export default function PreviewThumbnailPlayer({
                     default:
                       return (
                         THREAT_LEVEL_LABELS[threatLevel as ThreatLevel] ||
-                        "Unknown"
+                        t("details.unknown", {
+                          ns: "views/classificationModel",
+                        })
                       );
                   }
                 })()}

web/src/pages/Events.tsx

@@ -205,7 +205,7 @@ export default function Events() {
       cameras: reviewSearchParams["cameras"],
       labels: reviewSearchParams["labels"],
       zones: reviewSearchParams["zones"],
-      reviewed: 1,
+      reviewed: null, // We want both reviewed and unreviewed items as we filter in the UI
       before: reviewSearchParams["before"] || last24Hours.before,
       after: reviewSearchParams["after"] || last24Hours.after,
     };

web/src/utils/cameraUtil.ts

@@ -81,7 +81,8 @@ export async function detectReolinkCamera(
 export function maskUri(uri: string): string {
   try {
     // Handle RTSP URLs with user:pass@host format
-    const rtspMatch = uri.match(/rtsp:\/\/([^:]+):([^@]+)@(.+)/);
+    // Use greedy match for password to handle passwords with @
+    const rtspMatch = uri.match(/rtsp:\/\/([^:]+):(.+)@(.+)/);
     if (rtspMatch) {
       return `rtsp://${rtspMatch[1]}:${"*".repeat(4)}@${rtspMatch[3]}`;
     }

web/src/utils/dateUtil.ts

@@ -165,7 +165,7 @@ export const formatUnixTimestampToDateTime = (
       // Uppercase AM/PM for 12-hour formats
       if (date_format.includes("a") || date_format.includes("aaa")) {
         formatted = formatted.replace(/am|pm/gi, (match) =>
-          match.toUpperCase(),
+          i18n.t(`time.${match.toLowerCase()}`, { ns: "common" }).toUpperCase(),
         );
       }
       return formatted;
@@ -217,7 +217,7 @@ export const formatUnixTimestampToDateTime = (
       // Uppercase AM/PM in fallback
       if (options.hour12) {
         fallbackFormatted = fallbackFormatted.replace(/am|pm/gi, (match) =>
-          match.toUpperCase(),
+          i18n.t(`time.${match.toLowerCase()}`, { ns: "common" }).toUpperCase(),
         );
       }
       return fallbackFormatted;

web/src/views/classification/ModelSelectionView.tsx

@@ -266,7 +266,10 @@ function ModelCard({ config, onClick, onUpdate, onDelete }: ModelCardProps) {
       return undefined;
     }
 
-    const keys = Object.keys(dataset.categories).filter((key) => key != "none");
+    const keys = Object.keys(dataset.categories).filter(
+      (key) => key != "none" && key.toLowerCase() != "unknown",
+    );
+
     if (keys.length === 0) {
       return undefined;
     }

web/src/views/live/LiveDashboardView.tsx

@@ -114,6 +114,7 @@ export default function LiveDashboardView({
     {
       limit: 10,
       severity: "alert",
+      reviewed: 0,
       cameras: alertCameras,
     },
   ]);