Update transformers requirement in /docker/main

Updates the requirements on [transformers](https://github.com/huggingface/transformers) to permit the latest version.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](https://github.com/huggingface/transformers/compare/v4.45.0...v4.48.0)

---
updated-dependencies:
- dependency-name: transformers
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

.github/workflows/ci.yml

@@ -7,7 +7,7 @@ on:
       - dev
       - master
     paths-ignore:
-      - 'docs/**'      
+      - "docs/**"
 
 # only run the latest commit to avoid cache overwrites
 concurrency:
@@ -24,6 +24,8 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -45,6 +47,8 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -71,21 +75,14 @@ jobs:
             rpi.tags=${{ steps.setup.outputs.image-name }}-rpi
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
-      - name: Build and push Rockchip build
-        uses: docker/bake-action@v3
-        with:
-          push: true
-          targets: rk
-          files: docker/rockchip/rk.hcl
-          set: |
-            rk.tags=${{ steps.setup.outputs.image-name }}-rk
-            *.cache-from=type=gha
   jetson_jp4_build:
     runs-on: ubuntu-latest
     name: Jetson Jetpack 4
     steps:
       - name: Check out code
         uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -112,6 +109,8 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -140,6 +139,8 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -165,6 +166,8 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -188,6 +191,8 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup

.github/workflows/dependabot-auto-merge.yaml

@@ -1,24 +0,0 @@
-name: dependabot-auto-merge
-on: pull_request
-
-permissions:
-  contents: write
-
-jobs:
-  dependabot-auto-merge:
-    runs-on: ubuntu-latest
-    if: github.actor == 'dependabot[bot]'
-    steps:
-      - name: Get Dependabot metadata
-        id: metadata
-        uses: dependabot/fetch-metadata@v2
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-      - name: Enable auto-merge for Dependabot PRs
-        if: steps.metadata.outputs.dependency-type == 'direct:development' && (steps.metadata.outputs.update-type == 'version-update:semver-minor' || steps.metadata.outputs.update-type == 'version-update:semver-patch')
-        run: |
-          gh pr review --approve "$PR_URL"
-          gh pr merge --auto --squash "$PR_URL"
-        env:
-          PR_URL: ${{ github.event.pull_request.html_url }}
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/pull_request.yml

@@ -3,7 +3,7 @@ name: On pull request
 on:
   pull_request:
     paths-ignore:
-      - 'docs/**'
+      - "docs/**"
 
 env:
   DEFAULT_PYTHON: 3.9
@@ -19,6 +19,8 @@ jobs:
       DOCKER_BUILDKIT: "1"
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x
@@ -38,6 +40,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x
@@ -52,6 +56,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 20.x
@@ -67,8 +73,10 @@ jobs:
     steps:
       - name: Check out the repository
         uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Set up Python ${{ env.DEFAULT_PYTHON }}
-        uses: actions/setup-python@v5.1.0
+        uses: actions/setup-python@v5.3.0
         with:
           python-version: ${{ env.DEFAULT_PYTHON }}
       - name: Install requirements
@@ -88,6 +96,8 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x

.github/workflows/release.yml

@@ -11,6 +11,8 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - id: lowercaseRepo
         uses: ASzc/change-string-case-action@v6
         with:
@@ -22,10 +24,13 @@ jobs:
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
       - name: Create tag variables
+        env:
+          TAG: ${{ github.ref_name }}
+          LOWERCASE_REPO: ${{ steps.lowercaseRepo.outputs.lowercase }}
         run: |
-          BUILD_TYPE=$([[ "${{ github.ref_name }}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
+          BUILD_TYPE=$([[ "${TAG}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
           echo "BUILD_TYPE=${BUILD_TYPE}" >> $GITHUB_ENV
-          echo "BASE=ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}" >> $GITHUB_ENV
+          echo "BASE=ghcr.io/${LOWERCASE_REPO}" >> $GITHUB_ENV
           echo "BUILD_TAG=${GITHUB_SHA::7}" >> $GITHUB_ENV
           echo "CLEAN_VERSION=$(echo ${GITHUB_REF##*/} | tr '[:upper:]' '[:lower:]' | sed 's/^[v]//')" >> $GITHUB_ENV
       - name: Tag and push the main image

.github/workflows/stale.yml

@@ -23,7 +23,9 @@ jobs:
           exempt-pr-labels: "pinned,security,dependencies"
           operations-per-run: 120
       - name: Print outputs
-        run: echo ${{ join(steps.stale.outputs.*, ',') }}
+        env:
+          STALE_OUTPUT: ${{ join(steps.stale.outputs.*, ',') }}
+        run: echo "$STALE_OUTPUT"
 
   # clean_ghcr:
   #   name: Delete outdated dev container images
@@ -38,4 +40,3 @@ jobs:
   #         account-type: personal
   #         token: ${{ secrets.GITHUB_TOKEN }}
   #         token-type: github-token
-

benchmark.py

@@ -61,7 +61,7 @@ def start(id, num_detections, detection_queue, event):
     object_detector.cleanup()
     print(f"{id} - Processed for {duration:.2f} seconds.")
     print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
-    print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
+    print(f"{id} - Average frame processing time: {mean(frame_times) * 1000:.2f}ms")
 
 
 ######

docker/hailo8l/requirements-wheels-h8l.txt

@@ -1,12 +1,12 @@
-appdirs==1.4.4
-argcomplete==2.0.0
-contextlib2==0.6.0.post1
-distlib==0.3.6
-filelock==3.8.0
-future==0.18.2
-importlib-metadata==5.1.0
-importlib-resources==5.1.2
-netaddr==0.8.0
-netifaces==0.10.9
-verboselogs==1.7
-virtualenv==20.17.0
+appdirs==1.4.*
+argcomplete==2.0.*
+contextlib2==0.6.*
+distlib==0.3.*
+filelock==3.8.*
+future==0.18.*
+importlib-metadata==5.1.*
+importlib-resources==5.1.*
+netaddr==0.8.*
+netifaces==0.10.*
+verboselogs==1.7.*
+virtualenv==20.17.*

docker/main/requirements-wheels.txt

@@ -37,7 +37,7 @@ openvino == 2024.3.*
 onnxruntime-openvino == 1.19.* ; platform_machine == 'x86_64'
 onnxruntime == 1.19.* ; platform_machine == 'aarch64'
 # Embeddings
-transformers == 4.45.*
+transformers == 4.48.*
 # Generative AI
 google-generativeai == 0.8.*
 ollama == 0.3.*

docker/tensorrt/Dockerfile.amd64

@@ -12,26 +12,11 @@ ARG TARGETARCH
 COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt
 RUN mkdir -p /trt-wheels && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
 
-# Build CuDNN
-FROM wget AS cudnn-deps
-
-ARG COMPUTE_LEVEL
-
-RUN apt-get update \
-    && apt-get install -y git build-essential
-
-RUN wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.1-1_all.deb \
-&& dpkg -i cuda-keyring_1.1-1_all.deb \
-&& apt-get update \
-&& apt-get -y install cuda-toolkit \
-&& rm -rf /var/lib/apt/lists/*
-
 FROM tensorrt-base AS frigate-tensorrt
 ENV TRT_VER=8.5.3
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
     pip3 install -U /deps/trt-wheels/*.whl && \
     ldconfig
-COPY --from=cudnn-deps /usr/local/cuda-12.6 /usr/local/cuda
 
 ENV LD_LIBRARY_PATH=/usr/local/lib/python3.9/dist-packages/tensorrt:/usr/local/cuda/lib64:/usr/local/lib/python3.9/dist-packages/nvidia/cufft/lib
 WORKDIR /opt/frigate/
@@ -42,7 +27,7 @@ FROM devcontainer AS devcontainer-trt
 
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
-COPY --from=cudnn-deps /usr/local/cuda-12.6 /usr/local/cuda
+COPY --from=trt-deps /usr/local/cuda-12.1 /usr/local/cuda
 COPY docker/tensorrt/detector/rootfs/ /
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \

docker/tensorrt/Dockerfile.base

@@ -24,6 +24,7 @@ ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
 
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
+COPY --from=trt-deps /usr/local/cuda-12.* /usr/local/cuda
 COPY docker/tensorrt/detector/rootfs/ /
 ENV YOLO_MODELS=""
 

docs/docs/configuration/advanced.md

@@ -174,7 +174,7 @@ NOTE: The folder that is set for the config needs to be the folder that contains
 
 ### Custom go2rtc version
 
-Frigate currently includes go2rtc v1.9.4, there may be certain cases where you want to run a different version of go2rtc.
+Frigate currently includes go2rtc v1.9.2, there may be certain cases where you want to run a different version of go2rtc.
 
 To do this:
 

docs/docs/configuration/autotracking.md

@@ -41,6 +41,7 @@ cameras:
     ...
     onvif:
       # Required: host of the camera being connected to.
+      # NOTE: HTTP is assumed by default; HTTPS is supported if you specify the scheme, ex: "https://0.0.0.0".
       host: 0.0.0.0
       # Optional: ONVIF port for device (default: shown below).
       port: 8000
@@ -49,6 +50,8 @@ cameras:
       user: admin
       # Optional: password for login.
       password: admin
+      # Optional: Skip TLS verification from the ONVIF server (default: shown below)
+      tls_insecure: False
       # Optional: PTZ camera object autotracking. Keeps a moving object in
       # the center of the frame by automatically moving the PTZ camera.
       autotracking:

docs/docs/configuration/camera_specific.md

@@ -156,7 +156,9 @@ cameras:
 
 #### Reolink Doorbell
 
-The reolink doorbell supports 2-way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
+The reolink doorbell supports two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
+
+Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
 
 ```yaml
 go2rtc:

docs/docs/configuration/genai.md

@@ -5,6 +5,8 @@ title: Generative AI
 
 Generative AI can be used to automatically generate descriptive text based on the thumbnails of your tracked objects. This helps with [Semantic Search](/configuration/semantic_search) in Frigate to provide more context about your tracked objects. Descriptions are accessed via the _Explore_ view in the Frigate UI by clicking on a tracked object's thumbnail.
 
+Requests for a description are sent off automatically to your AI provider at the end of the tracked object's lifecycle. Descriptions can also be regenerated manually via the Frigate UI.
+
 :::info
 
 Semantic Search must be enabled to use Generative AI.

docs/docs/configuration/index.md

@@ -203,14 +203,13 @@ detectors:
   ov:
     type: openvino
     device: AUTO
-    model:
-      path: /openvino-model/ssdlite_mobilenet_v2.xml
 
 model:
   width: 300
   height: 300
   input_tensor: nhwc
   input_pixel_format: bgr
+  path: /openvino-model/ssdlite_mobilenet_v2.xml
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 
 record:

docs/docs/configuration/live.md

@@ -23,13 +23,13 @@ If you are using go2rtc, you should adjust the following settings in your camera
 
 - Video codec: **H.264** - provides the most compatible video codec with all Live view technologies and browsers. Avoid any kind of "smart codec" or "+" codec like _H.264+_ or _H.265+_. as these non-standard codecs remove keyframes (see below).
 - Audio codec: **AAC** - provides the most compatible audio codec with all Live view technologies and browsers that support audio.
-- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes.
+- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
 
 The default video and audio codec on your camera may not always be compatible with your browser, which is why setting them to H.264 and AAC is recommended. See the [go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness) for codec support information.
 
 ### Audio Support
 
-MSE Requires AAC audio, WebRTC requires PCMU/PCMA, or opus audio. If you want to support both MSE and WebRTC then your restream config needs to make sure both are enabled.
+MSE Requires PCMA/PCMU or AAC audio, WebRTC requires PCMA/PCMU or opus audio. If you want to support both MSE and WebRTC then your restream config needs to make sure both are enabled.
 
 ```yaml
 go2rtc:
@@ -138,3 +138,13 @@ services:
 :::
 
 See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.8.3#module-webrtc) for more information about this.
+
+### Two way talk
+
+For devices that support two way talk, Frigate can be configured to use the feature from the camera's Live view in the Web UI. You should:
+
+- Set up go2rtc with [WebRTC](#webrtc-extra-configuration).
+- Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)).
+- For the Home Assistant Frigate card, [follow the docs](https://github.com/dermotduffy/frigate-hass-card?tab=readme-ov-file#using-2-way-audio) for the correct source.
+
+To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)

docs/docs/configuration/object_detectors.md

@@ -144,7 +144,9 @@ detectors:
 
 #### SSDLite MobileNet v2
 
-An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model. Use the model configuration shown below when using the OpenVINO detector with the default model.
+An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.
+
+Use the model configuration shown below when using the OpenVINO detector with the default OpenVINO model:
 
 ```yaml
 detectors:
@@ -254,6 +256,7 @@ yolov4x-mish-640
 yolov7-tiny-288
 yolov7-tiny-416
 yolov7-640
+yolov7-416
 yolov7-320
 yolov7x-640
 yolov7x-320
@@ -282,6 +285,8 @@ The TensorRT detector can be selected by specifying `tensorrt` as the model type
 
 The TensorRT detector uses `.trt` model files that are located in `/config/model_cache/tensorrt` by default. These model path and dimensions used will depend on which model you have generated.
 
+Use the config below to work with generated TRT models:
+
 ```yaml
 detectors:
   tensorrt:
@@ -501,11 +506,12 @@ detectors:
   cpu1:
     type: cpu
     num_threads: 3
-    model:
-      path: "/custom_model.tflite"
   cpu2:
     type: cpu
     num_threads: 3
+
+model:
+  path: "/custom_model.tflite"
 ```
 
 When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
@@ -632,8 +638,6 @@ detectors:
   hailo8l:
     type: hailo8l
     device: PCIe
-    model:
-      path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
 
 model:
   width: 300
@@ -641,4 +645,5 @@ model:
   input_tensor: nhwc
   input_pixel_format: bgr
   model_type: ssd
+  path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
 ```

docs/docs/configuration/reference.md

@@ -52,7 +52,7 @@ detectors:
   # Required: name of the detector
   detector_name:
     # Required: type of the detector
-    # Frigate provided types include 'cpu', 'edgetpu', 'openvino' and 'tensorrt' (default: shown below)
+    # Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (default: shown below)
     # Additional detector types can also be plugged in.
     # Detectors may require additional configuration.
     # Refer to the Detectors configuration page for more information.
@@ -117,25 +117,27 @@ auth:
   hash_iterations: 600000
 
 # Optional: model modifications
+# NOTE: The default values are for the EdgeTPU detector.
+# Other detectors will require the model config to be set.
 model:
-  # Optional: path to the model (default: automatic based on detector)
+  # Required: path to the model (default: automatic based on detector)
   path: /edgetpu_model.tflite
-  # Optional: path to the labelmap (default: shown below)
+  # Required: path to the labelmap (default: shown below)
   labelmap_path: /labelmap.txt
   # Required: Object detection model input width (default: shown below)
   width: 320
   # Required: Object detection model input height (default: shown below)
   height: 320
-  # Optional: Object detection model input colorspace
+  # Required: Object detection model input colorspace
   # Valid values are rgb, bgr, or yuv. (default: shown below)
   input_pixel_format: rgb
-  # Optional: Object detection model input tensor format
+  # Required: Object detection model input tensor format
   # Valid values are nhwc or nchw (default: shown below)
   input_tensor: nhwc
-  # Optional: Object detection model type, currently only used with the OpenVINO detector
+  # Required: Object detection model type, currently only used with the OpenVINO detector
   # Valid values are ssd, yolox, yolonas (default: shown below)
   model_type: ssd
-  # Optional: Label name modifications. These are merged into the standard labelmap.
+  # Required: Label name modifications. These are merged into the standard labelmap.
   labelmap:
     2: vehicle
   # Optional: Map of object labels to their attribute labels (default: depends on model)
@@ -686,6 +688,7 @@ cameras:
     # to enable PTZ controls.
     onvif:
       # Required: host of the camera being connected to.
+      # NOTE: HTTP is assumed by default; HTTPS is supported if you specify the scheme, ex: "https://0.0.0.0".
       host: 0.0.0.0
       # Optional: ONVIF port for device (default: shown below).
       port: 8000
@@ -694,6 +697,8 @@ cameras:
       user: admin
       # Optional: password for login.
       password: admin
+      # Optional: Skip TLS verification from the ONVIF server (default: shown below)
+      tls_insecure: False
       # Optional: Ignores time synchronization mismatches between the camera and the server during authentication.
       # Using NTP on both ends is recommended and this should only be set to True in a "safe" environment due to the security risk it represents.
       ignore_time_mismatch: False
@@ -757,6 +762,8 @@ cameras:
         - cat
       # Optional: Restrict generation to objects that entered any of the listed zones (default: none, all zones qualify)
       required_zones: []
+      # Optional: Save thumbnails sent to generative AI for review/debugging purposes (default: shown below)
+      debug_save_thumbnails: False
 
 # Optional
 ui:

docs/docs/configuration/semantic_search.md

@@ -5,7 +5,7 @@ title: Using Semantic Search
 
 Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.
 
-Frigate has support for [Jina AI's CLIP model](https://huggingface.co/jinaai/jina-clip-v1) to create embeddings, which runs locally. Embeddings are then saved to Frigate's database.
+Frigate uses [Jina AI's CLIP model](https://huggingface.co/jinaai/jina-clip-v1) to create and save embeddings to Frigate's database. All of this runs locally.
 
 Semantic Search is accessed via the _Explore_ view in the Frigate UI.
 
@@ -19,7 +19,7 @@ For best performance, 16GB or more of RAM and a dedicated GPU are recommended.
 
 ## Configuration
 
-Semantic Search is disabled by default, and must be enabled in your config file before it can be used. Semantic Search is a global configuration setting.
+Semantic Search is disabled by default, and must be enabled in your config file or in the UI's Settings page before it can be used. Semantic Search is a global configuration setting.
 
 ```yaml
 semantic_search:
@@ -29,9 +29,9 @@ semantic_search:
 
 :::tip
 
-The embeddings database can be re-indexed from the existing tracked objects in your database by adding `reindex: True` to your `semantic_search` configuration. Depending on the number of tracked objects you have, it can take a long while to complete and may max out your CPU while indexing. Make sure to set the config back to `False` before restarting Frigate again.
+The embeddings database can be re-indexed from the existing tracked objects in your database by adding `reindex: True` to your `semantic_search` configuration or by toggling the switch on the Search Settings page in the UI and restarting Frigate. Depending on the number of tracked objects you have, it can take a long while to complete and may max out your CPU while indexing. Make sure to turn the UI's switch off or set the config back to `False` before restarting Frigate again.
 
-If you are enabling the Search feature for the first time, be advised that Frigate does not automatically index older tracked objects. You will need to enable the `reindex` feature in order to do that.
+If you are enabling Semantic Search for the first time, be advised that Frigate does not automatically index older tracked objects. You will need to enable the `reindex` feature in order to do that.
 
 :::
 
@@ -39,9 +39,9 @@ If you are enabling the Search feature for the first time, be advised that Friga
 
 The vision model is able to embed both images and text into the same vector space, which allows `image -> image` and `text -> image` similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in the database. When searching for tracked objects via text in the search box, Frigate will perform a `text -> image` similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an `image -> image` similarity search to retrieve the closest matching thumbnails.
 
-The text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Search page when clicking on the gray tracked object chip at the top left of each review item. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate tracked object descriptions.
+The text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Explore page when clicking on thumbnail of a tracked object. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate tracked object descriptions.
 
-Differently weighted CLIP models are available and can be selected by setting the `model_size` config option as `small` or `large`:
+Differently weighted versions of the Jina model are available and can be selected by setting the `model_size` config option as `small` or `large`:
 
 ```yaml
 semantic_search:
@@ -50,7 +50,7 @@ semantic_search:
 ```
 
 - Configuring the `large` model employs the full Jina model and will automatically run on the GPU if applicable.
-- Configuring the `small` model employs a quantized version of the model that uses less RAM and runs on CPU with a very negligible difference in embedding quality.
+- Configuring the `small` model employs a quantized version of the Jina model that uses less RAM and runs on CPU with a very negligible difference in embedding quality.
 
 ### GPU Acceleration
 
@@ -84,7 +84,7 @@ If the correct build is used for your GPU and the `large` model is configured, t
 
 ## Usage and Best Practices
 
-1. Semantic Search is used in conjunction with the other filters available on the Search page. Use a combination of traditional filtering and Semantic Search for the best results.
+1. Semantic Search is used in conjunction with the other filters available on the Explore page. Use a combination of traditional filtering and Semantic Search for the best results.
 2. Use the thumbnail search type when searching for particular objects in the scene. Use the description search type when attempting to discern the intent of your object.
 3. Because of how the AI models Frigate uses have been trained, the comparison between text and image embedding distances generally means that with multi-modal (`thumbnail` and `description`) searches, results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" setting to help find what you are looking for. Note that if you are generating descriptions for specific objects or zones only, this may cause search results to prioritize the objects with descriptions even if the the ones without them are more relevant.
 4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".

docs/docs/frigate/camera_setup.md

@@ -28,7 +28,7 @@ For the Dahua/Loryta 5442 camera, I use the following settings:
 - Encode Mode: H.264
 - Resolution: 2688\*1520
 - Frame Rate(FPS): 15
-- I Frame Interval: 30
+- I Frame Interval: 30 (15 can also be used to prioritize streaming performance - see the [camera settings recommendations](../configuration/live) for more info)
 
 **Sub Stream (Detection)**
 

docs/docs/frigate/installation.md

@@ -305,8 +305,15 @@ To install make sure you have the [community app plugin here](https://forums.unr
 
 ## Proxmox
 
-It is recommended to run Frigate in LXC, rather than in a VM, for maximum performance. The setup can be complex so be prepared to read the Proxmox and LXC documentation. Suggestions include:
+[According to Proxmox documentation](https://pve.proxmox.com/pve-docs/pve-admin-guide.html#chapter_pct) it is recommended that you run application containers like Frigate inside a Proxmox QEMU VM. This will give you all the advantages of application containerization, while also providing the benefits that VMs offer, such as strong isolation from the host and the ability to live-migrate, which otherwise isn’t possible with containers.
 
+:::warning
+
+If you choose to run Frigate via LXC in Proxmox the setup can be complex so be prepared to read the Proxmox and LXC documentation, Frigate does not officially support running inside of an LXC.
+
+:::
+
+ Suggestions include:
 - For Intel-based hardware acceleration, to allow access to the `/dev/dri/renderD128` device with major number 226 and minor number 128, add the following lines to the `/etc/pve/lxc/<id>.conf` LXC configuration:
   - `lxc.cgroup2.devices.allow: c 226:128 rwm`
   - `lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file`

docs/docs/troubleshooting/faqs.md

@@ -98,3 +98,11 @@ docker run -d \
   -p 8555:8555/udp \
   ghcr.io/blakeblackshear/frigate:stable
 ```
+
+### My RTSP stream works fine in VLC, but it does not work when I put the same URL in my Frigate config. Is this a bug?
+
+No. Frigate uses the TCP protocol to connect to your camera's RTSP URL. VLC automatically switches between UDP and TCP depending on network conditions and stream availability. So a stream that works in VLC but not in Frigate is likely due to VLC selecting UDP as the transfer protocol.
+
+TCP ensures that all data packets arrive in the correct order. This is crucial for video recording, decoding, and stream processing, which is why Frigate enforces a TCP connection. UDP is faster but less reliable, as it does not guarantee packet delivery or order, and VLC does not have the same requirements as Frigate.
+
+You can still configure Frigate to use UDP by using ffmpeg input args or the preset `preset-rtsp-udp`. See the [ffmpeg presets](/configuration/ffmpeg_presets) documentation.

docs/docs/troubleshooting/recordings.md

@@ -3,7 +3,15 @@ id: recordings
 title: Troubleshooting Recordings
 ---
 
-### WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...
+## I have Frigate configured for motion recording only, but it still seems to be recording even with no motion. Why?
+
+You'll want to:
+
+- Make sure your camera's timestamp is masked out with a motion mask. Even if there is no motion occurring in your scene, your motion settings may be sensitive enough to count your timestamp as motion.
+- If you have audio detection enabled, keep in mind that audio that is heard above `min_volume` is considered motion.
+- [Tune your motion detection settings](/configuration/motion_detection) either by editing your config file or by using the UI's Motion Tuner.
+
+## I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...
 
 This error can be caused by a number of different issues. The first step in troubleshooting is to enable debug logging for recording. This will enable logging showing how long it takes for recordings to be moved from RAM cache to the disk.
 
@@ -40,6 +48,7 @@ On linux, some helpful tools/commands in diagnosing would be:
 On modern linux kernels, the system will utilize some swap if enabled. Setting vm.swappiness=1 no longer means that the kernel will only swap in order to avoid OOM. To prevent any swapping inside a container, set allocations memory and memory+swap to be the same and disable swapping by setting the following docker/podman run parameters:
 
 **Compose example**
+
 ```yaml
 version: "3.9"
 services:
@@ -54,6 +63,7 @@ services:
 ```
 
 **Run command example**
+
 ```
 --memory=<MAXRAM> --memory-swap=<MAXSWAP> --memory-swappiness=0
 ```

docs/package.json

@@ -17,15 +17,15 @@
     "write-heading-ids": "docusaurus write-heading-ids"
   },
   "dependencies": {
-    "@docusaurus/core": "^3.5.2",
-    "@docusaurus/preset-classic": "^3.5.2",
-    "@docusaurus/theme-mermaid": "^3.5.2",
-    "@docusaurus/plugin-content-docs": "^3.5.2",
-    "@mdx-js/react": "^3.0.1",
+    "@docusaurus/core": "^3.6.3",
+    "@docusaurus/preset-classic": "^3.6.3",
+    "@docusaurus/theme-mermaid": "^3.6.3",
+    "@docusaurus/plugin-content-docs": "^3.6.3",
+    "@mdx-js/react": "^3.1.0",
     "clsx": "^2.1.1",
-    "docusaurus-plugin-openapi-docs": "^4.1.0",
-    "docusaurus-theme-openapi-docs": "^4.1.0",
-    "prism-react-renderer": "^2.4.0",
+    "docusaurus-plugin-openapi-docs": "^4.3.1",
+    "docusaurus-theme-openapi-docs": "^4.3.1",
+    "prism-react-renderer": "^2.4.1",
     "raw-loader": "^4.0.2",
     "react": "^18.3.1",
     "react-dom": "^18.3.1"

frigate/api/app.py

@@ -21,13 +21,13 @@ from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryPa
 from frigate.api.defs.request.app_body import AppConfigSetBody
 from frigate.api.defs.tags import Tags
 from frigate.config import FrigateConfig
-from frigate.const import CONFIG_DIR
 from frigate.models import Event, Timeline
 from frigate.util.builtin import (
     clean_camera_user_pass,
     get_tz_modifiers,
     update_yaml_from_url,
 )
+from frigate.util.config import find_config_file
 from frigate.util.services import (
     ffprobe_stream,
     get_nvidia_driver_info,
@@ -134,9 +134,27 @@ def config(request: Request):
         for zone_name, zone in config_obj.cameras[camera_name].zones.items():
             camera_dict["zones"][zone_name]["color"] = zone.color
 
+    # remove go2rtc stream passwords
+    go2rtc: dict[str, any] = config_obj.go2rtc.model_dump(
+        mode="json", warnings="none", exclude_none=True
+    )
+    for stream_name, stream in go2rtc.get("streams", {}).items():
+        if stream is None:
+            continue
+        if isinstance(stream, str):
+            cleaned = clean_camera_user_pass(stream)
+        else:
+            cleaned = []
+
+            for item in stream:
+                cleaned.append(clean_camera_user_pass(item))
+
+        config["go2rtc"]["streams"][stream_name] = cleaned
+
     config["plus"] = {"enabled": request.app.frigate_config.plus_api.is_active()}
     config["model"]["colormap"] = config_obj.model.colormap
 
+    # use merged labelamp
     for detector_config in config["detectors"].values():
         detector_config["model"]["labelmap"] = (
             request.app.frigate_config.model.merged_labelmap
@@ -147,13 +165,7 @@ def config(request: Request):
 
 @router.get("/config/raw")
 def config_raw():
-    config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
-
-    # Check if we can use .yaml instead of .yml
-    config_file_yaml = config_file.replace(".yml", ".yaml")
-
-    if os.path.isfile(config_file_yaml):
-        config_file = config_file_yaml
+    config_file = find_config_file()
 
     if not os.path.isfile(config_file):
         return JSONResponse(
@@ -198,13 +210,7 @@ def config_save(save_option: str, body: Any = Body(media_type="text/plain")):
 
     # Save the config to file
     try:
-        config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
-
-        # Check if we can use .yaml instead of .yml
-        config_file_yaml = config_file.replace(".yml", ".yaml")
-
-        if os.path.isfile(config_file_yaml):
-            config_file = config_file_yaml
+        config_file = find_config_file()
 
         with open(config_file, "w") as f:
             f.write(new_config)
@@ -253,13 +259,7 @@ def config_save(save_option: str, body: Any = Body(media_type="text/plain")):
 
 @router.put("/config/set")
 def config_set(request: Request, body: AppConfigSetBody):
-    config_file = os.environ.get("CONFIG_FILE", f"{CONFIG_DIR}/config.yml")
-
-    # Check if we can use .yaml instead of .yml
-    config_file_yaml = config_file.replace(".yml", ".yaml")
-
-    if os.path.isfile(config_file_yaml):
-        config_file = config_file_yaml
+    config_file = find_config_file()
 
     with open(config_file, "r") as f:
         old_raw_config = f.read()

frigate/app.py

@@ -437,7 +437,7 @@ class FrigateApp:
             # pre-create shms
             for i in range(shm_frame_count):
                 frame_size = config.frame_shape_yuv[0] * config.frame_shape_yuv[1]
-                self.frame_manager.create(f"{config.name}_{i}", frame_size)
+                self.frame_manager.create(f"{config.name}_frame{i}", frame_size)
 
             capture_process = util.Process(
                 target=capture_camera,

frigate/comms/webpush.py

@@ -151,7 +151,7 @@ class WebPushClient(Communicator):  # type: ignore[misc]
         camera: str = payload["after"]["camera"]
         title = f"{', '.join(sorted_objects).replace('_', ' ').title()}{' was' if state == 'end' else ''} detected in {', '.join(payload['after']['data']['zones']).replace('_', ' ').title()}"
         message = f"Detected on {camera.replace('_', ' ').title()}"
-        image = f'{payload["after"]["thumb_path"].replace("/media/frigate", "")}'
+        image = f"{payload['after']['thumb_path'].replace('/media/frigate', '')}"
 
         # if event is ongoing open to live view otherwise open to recordings view
         direct_url = f"/review?id={reviewId}" if state == "end" else f"/#{camera}"

frigate/config/camera/genai.py

@@ -38,6 +38,10 @@ class GenAICameraConfig(BaseModel):
         default_factory=list,
         title="List of required zones to be entered in order to run generative AI.",
     )
+    debug_save_thumbnails: bool = Field(
+        default=False,
+        title="Save thumbnails sent to generative AI for debugging purposes.",
+    )
 
     @field_validator("required_zones", mode="before")
     @classmethod

frigate/config/camera/onvif.py

@@ -74,6 +74,7 @@ class OnvifConfig(FrigateBaseModel):
     port: int = Field(default=8000, title="Onvif Port")
     user: Optional[EnvString] = Field(default=None, title="Onvif Username")
     password: Optional[EnvString] = Field(default=None, title="Onvif Password")
+    tls_insecure: bool = Field(default=False, title="Onvif Disable TLS verification")
     autotracking: PtzAutotrackConfig = Field(
         default_factory=PtzAutotrackConfig,
         title="PTZ auto tracking config.",

frigate/config/camera/record.py

@@ -4,6 +4,7 @@ from typing import Optional
 from pydantic import Field
 
 from frigate.const import MAX_PRE_CAPTURE
+from frigate.review.types import SeverityEnum
 
 from ..base import FrigateBaseModel
 
@@ -101,3 +102,15 @@ class RecordConfig(FrigateBaseModel):
             self.alerts.pre_capture,
             self.detections.pre_capture,
         )
+
+    def get_review_pre_capture(self, severity: SeverityEnum) -> int:
+        if severity == SeverityEnum.alert:
+            return self.alerts.pre_capture
+        else:
+            return self.detections.pre_capture
+
+    def get_review_post_capture(self, severity: SeverityEnum) -> int:
+        if severity == SeverityEnum.alert:
+            return self.alerts.post_capture
+        else:
+            return self.detections.post_capture

frigate/config/camera/zone.py

@@ -85,7 +85,7 @@ class ZoneConfig(BaseModel):
             if explicit:
                 self.coordinates = ",".join(
                     [
-                        f'{round(int(p.split(",")[0]) / frame_shape[1], 3)},{round(int(p.split(",")[1]) / frame_shape[0], 3)}'
+                        f"{round(int(p.split(',')[0]) / frame_shape[1], 3)},{round(int(p.split(',')[1]) / frame_shape[0], 3)}"
                         for p in coordinates
                     ]
                 )

frigate/config/config.py

@@ -29,6 +29,7 @@ from frigate.util.builtin import (
 )
 from frigate.util.config import (
     StreamInfoRetriever,
+    find_config_file,
     get_relative_coordinates,
     migrate_frigate_config,
 )
@@ -67,7 +68,6 @@ logger = logging.getLogger(__name__)
 
 yaml = YAML()
 
-DEFAULT_CONFIG_FILE = "/config/config.yml"
 DEFAULT_CONFIG = """
 mqtt:
   enabled: False
@@ -594,35 +594,27 @@ class FrigateConfig(FrigateBaseModel):
                 if isinstance(detector, dict)
                 else detector.model_dump(warnings="none")
             )
-            detector_config: DetectorConfig = adapter.validate_python(model_dict)
-            if detector_config.model is None:
-                detector_config.model = self.model.model_copy()
-            else:
-                path = detector_config.model.path
-                detector_config.model = self.model.model_copy()
-                detector_config.model.path = path
+            detector_config: BaseDetectorConfig = adapter.validate_python(model_dict)
 
-                if "path" not in model_dict or len(model_dict.keys()) > 1:
-                    logger.warning(
-                        "Customizing more than a detector model path is unsupported."
-                    )
+            # users should not set model themselves
+            if detector_config.model:
+                detector_config.model = None
 
-            merged_model = deep_merge(
-                detector_config.model.model_dump(exclude_unset=True, warnings="none"),
-                self.model.model_dump(exclude_unset=True, warnings="none"),
-            )
+            model_config = self.model.model_dump(exclude_unset=True, warnings="none")
 
-            if "path" not in merged_model:
+            if detector_config.model_path:
+                model_config["path"] = detector_config.model_path
+
+            if "path" not in model_config:
                 if detector_config.type == "cpu":
-                    merged_model["path"] = "/cpu_model.tflite"
+                    model_config["path"] = "/cpu_model.tflite"
                 elif detector_config.type == "edgetpu":
-                    merged_model["path"] = "/edgetpu_model.tflite"
+                    model_config["path"] = "/edgetpu_model.tflite"
 
-            detector_config.model = ModelConfig.model_validate(merged_model)
-            detector_config.model.check_and_load_plus_model(
-                self.plus_api, detector_config.type
-            )
-            detector_config.model.compute_model_hash()
+            model = ModelConfig.model_validate(model_config)
+            model.check_and_load_plus_model(self.plus_api, detector_config.type)
+            model.compute_model_hash()
+            detector_config.model = model
             self.detectors[key] = detector_config
 
         return self
@@ -638,16 +630,13 @@ class FrigateConfig(FrigateBaseModel):
 
     @classmethod
     def load(cls, **kwargs):
-        config_path = os.environ.get("CONFIG_FILE", DEFAULT_CONFIG_FILE)
-
-        if not os.path.isfile(config_path):
-            config_path = config_path.replace("yml", "yaml")
+        config_path = find_config_file()
 
         # No configuration file found, create one.
         new_config = False
         if not os.path.isfile(config_path):
             logger.info("No config file found, saving default config")
-            config_path = DEFAULT_CONFIG_FILE
+            config_path = config_path
             new_config = True
         else:
             # Check if the config file needs to be migrated.

frigate/detectors/detector_config.py

@@ -194,6 +194,9 @@ class BaseDetectorConfig(BaseModel):
     model: Optional[ModelConfig] = Field(
         default=None, title="Detector specific model configuration."
     )
+    model_path: Optional[str] = Field(
+        default=None, title="Detector specific model path."
+    )
     model_config = ConfigDict(
         extra="allow", arbitrary_types_allowed=True, protected_namespaces=()
     )

frigate/detectors/plugins/deepstack.py

@@ -32,6 +32,7 @@ class DeepStack(DetectionApi):
         self.api_timeout = detector_config.api_timeout
         self.api_key = detector_config.api_key
         self.labels = detector_config.model.merged_labelmap
+        self.session = requests.Session()
 
     def get_label_index(self, label_value):
         if label_value.lower() == "truck":
@@ -51,7 +52,7 @@ class DeepStack(DetectionApi):
         data = {"api_key": self.api_key}
 
         try:
-            response = requests.post(
+            response = self.session.post(
                 self.api_url,
                 data=data,
                 files={"image": image_bytes},

frigate/detectors/plugins/rknn.py

@@ -136,17 +136,17 @@ class Rknn(DetectionApi):
     def check_config(self, config):
         if (config.model.width != 320) or (config.model.height != 320):
             raise Exception(
-                "Make sure to set the model width and height to 320 in your config.yml."
+                "Make sure to set the model width and height to 320 in your config."
             )
 
         if config.model.input_pixel_format != "bgr":
             raise Exception(
-                'Make sure to set the model input_pixel_format to "bgr" in your config.yml.'
+                'Make sure to set the model input_pixel_format to "bgr" in your config.'
             )
 
         if config.model.input_tensor != "nhwc":
             raise Exception(
-                'Make sure to set the model input_tensor to "nhwc" in your config.yml.'
+                'Make sure to set the model input_tensor to "nhwc" in your config.'
             )
 
     def detect_raw(self, tensor_input):

frigate/detectors/plugins/tensorrt.py

@@ -219,19 +219,19 @@ class TensorRtDetector(DetectionApi):
         ]
 
     def __init__(self, detector_config: TensorRTDetectorConfig):
-        assert (
-            TRT_SUPPORT
-        ), f"TensorRT libraries not found, {DETECTOR_KEY} detector not present"
+        assert TRT_SUPPORT, (
+            f"TensorRT libraries not found, {DETECTOR_KEY} detector not present"
+        )
 
         (cuda_err,) = cuda.cuInit(0)
-        assert (
-            cuda_err == cuda.CUresult.CUDA_SUCCESS
-        ), f"Failed to initialize cuda {cuda_err}"
+        assert cuda_err == cuda.CUresult.CUDA_SUCCESS, (
+            f"Failed to initialize cuda {cuda_err}"
+        )
         err, dev_count = cuda.cuDeviceGetCount()
         logger.debug(f"Num Available Devices: {dev_count}")
-        assert (
-            detector_config.device < dev_count
-        ), f"Invalid TensorRT Device Config. Device {detector_config.device} Invalid."
+        assert detector_config.device < dev_count, (
+            f"Invalid TensorRT Device Config. Device {detector_config.device} Invalid."
+        )
         err, self.cu_ctx = cuda.cuCtxCreate(
             cuda.CUctx_flags.CU_CTX_MAP_HOST, detector_config.device
         )

frigate/embeddings/maintainer.py

@@ -5,6 +5,7 @@ import logging
 import os
 import threading
 from multiprocessing.synchronize import Event as MpEvent
+from pathlib import Path
 from typing import Optional
 
 import cv2
@@ -217,16 +218,47 @@ class EmbeddingMaintainer(threading.Thread):
                             _, buffer = cv2.imencode(".jpg", cropped_image)
                             snapshot_image = buffer.tobytes()
 
+                    num_thumbnails = len(self.tracked_events.get(event_id, []))
+
                     embed_image = (
                         [snapshot_image]
                         if event.has_snapshot and camera_config.genai.use_snapshot
                         else (
-                            [thumbnail for data in self.tracked_events[event_id]]
-                            if len(self.tracked_events.get(event_id, [])) > 0
+                            [
+                                data["thumbnail"]
+                                for data in self.tracked_events[event_id]
+                            ]
+                            if num_thumbnails > 0
                             else [thumbnail]
                         )
                     )
 
+                    if camera_config.genai.debug_save_thumbnails and num_thumbnails > 0:
+                        logger.debug(
+                            f"Saving {num_thumbnails} thumbnails for event {event.id}"
+                        )
+
+                        Path(
+                            os.path.join(CLIPS_DIR, f"genai-requests/{event.id}")
+                        ).mkdir(parents=True, exist_ok=True)
+
+                        for idx, data in enumerate(self.tracked_events[event_id], 1):
+                            jpg_bytes: bytes = data["thumbnail"]
+
+                            if jpg_bytes is None:
+                                logger.warning(
+                                    f"Unable to save thumbnail {idx} for {event.id}."
+                                )
+                            else:
+                                with open(
+                                    os.path.join(
+                                        CLIPS_DIR,
+                                        f"genai-requests/{event.id}/{idx}.jpg",
+                                    ),
+                                    "wb",
+                                ) as j:
+                                    j.write(jpg_bytes)
+
                     # Generate the description. Call happens in a thread since it is network bound.
                     threading.Thread(
                         target=self._embed_description,
@@ -325,18 +357,25 @@ class EmbeddingMaintainer(threading.Thread):
         )
 
         if event.has_snapshot and source == "snapshot":
-            with open(
-                os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg"),
-                "rb",
-            ) as image_file:
+            snapshot_file = os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg")
+
+            if not os.path.isfile(snapshot_file):
+                logger.error(
+                    f"Cannot regenerate description for {event.id}, snapshot file not found: {snapshot_file}"
+                )
+                return
+
+            with open(snapshot_file, "rb") as image_file:
                 snapshot_image = image_file.read()
                 img = cv2.imdecode(
                     np.frombuffer(snapshot_image, dtype=np.int8), cv2.IMREAD_COLOR
                 )
 
                 # crop snapshot based on region before sending off to genai
+                # provide full image if region doesn't exist (manual events)
+                region = event.data.get("region", [0, 0, 1, 1])
                 height, width = img.shape[:2]
-                x1_rel, y1_rel, width_rel, height_rel = event.data["region"]
+                x1_rel, y1_rel, width_rel, height_rel = region
 
                 x1, y1 = int(x1_rel * width), int(y1_rel * height)
                 cropped_image = img[
@@ -350,7 +389,7 @@ class EmbeddingMaintainer(threading.Thread):
             [snapshot_image]
             if event.has_snapshot and source == "snapshot"
             else (
-                [thumbnail for data in self.tracked_events[event_id]]
+                [data["thumbnail"] for data in self.tracked_events[event_id]]
                 if len(self.tracked_events.get(event_id, [])) > 0
                 else [thumbnail]
             )

frigate/events/cleanup.py

@@ -121,8 +121,8 @@ class EventCleanup(threading.Thread):
 
             events_to_update = []
 
-            for batch in query.iterator():
-                events_to_update.extend([event.id for event in batch])
+            for event in query.iterator():
+                events_to_update.append(event.id)
                 if len(events_to_update) >= CHUNK_SIZE:
                     logger.debug(
                         f"Updating {update_params} for {len(events_to_update)} events"
@@ -256,8 +256,9 @@ class EventCleanup(threading.Thread):
 
         events_to_update = []
 
-        for batch in query.iterator():
-            events_to_update.extend([event.id for event in batch])
+        for event in query.iterator():
+            events_to_update.append(event.id)
+
             if len(events_to_update) >= CHUNK_SIZE:
                 logger.debug(
                     f"Updating {update_params} for {len(events_to_update)} events"
@@ -330,9 +331,8 @@ class EventCleanup(threading.Thread):
 
     def run(self) -> None:
         # only expire events every 5 minutes
-        while not self.stop_event.wait(1):
+        while not self.stop_event.wait(300):
             events_with_expired_clips = self.expire_clips()
-            return
 
             # delete timeline entries for events that have expired recordings
             # delete up to 100,000 at a time

frigate/events/maintainer.py

@@ -82,18 +82,23 @@ class EventProcessor(threading.Thread):
             )
 
             if source_type == EventTypeEnum.tracked_object:
+                id = event_data["id"]
                 self.timeline_queue.put(
                     (
                         camera,
                         source_type,
                         event_type,
-                        self.events_in_process.get(event_data["id"]),
+                        self.events_in_process.get(id),
                         event_data,
                     )
                 )
 
-                if event_type == EventStateEnum.start:
-                    self.events_in_process[event_data["id"]] = event_data
+                # if this is the first message, just store it and continue, its not time to insert it in the db
+                if (
+                    event_type == EventStateEnum.start
+                    or id not in self.events_in_process
+                ):
+                    self.events_in_process[id] = event_data
                     continue
 
                 self.handle_object_detection(event_type, camera, event_data)
@@ -123,10 +128,6 @@ class EventProcessor(threading.Thread):
         """handle tracked object event updates."""
         updated_db = False
 
-        # if this is the first message, just store it and continue, its not time to insert it in the db
-        if event_type == EventStateEnum.start:
-            self.events_in_process[event_data["id"]] = event_data
-
         if should_update_db(self.events_in_process[event_data["id"]], event_data):
             updated_db = True
             camera_config = self.config.cameras[camera]

frigate/ffmpeg_presets.py

@@ -50,16 +50,9 @@ class LibvaGpuSelector:
         return ""
 
 
-FPS_VFR_PARAM = (
-    "-fps_mode vfr"
-    if int(os.getenv("LIBAVFORMAT_VERSION_MAJOR", "59") or "59") >= 59
-    else "-vsync 2"
-)
-TIMEOUT_PARAM = (
-    "-timeout"
-    if int(os.getenv("LIBAVFORMAT_VERSION_MAJOR", "59") or "59") >= 59
-    else "-stimeout"
-)
+LIBAV_VERSION = int(os.getenv("LIBAVFORMAT_VERSION_MAJOR", "59") or "59")
+FPS_VFR_PARAM = "-fps_mode vfr" if LIBAV_VERSION >= 59 else "-vsync 2"
+TIMEOUT_PARAM = "-timeout" if LIBAV_VERSION >= 59 else "-stimeout"
 
 _gpu_selector = LibvaGpuSelector()
 _user_agent_args = [
@@ -71,8 +64,8 @@ PRESETS_HW_ACCEL_DECODE = {
     "preset-rpi-64-h264": "-c:v:1 h264_v4l2m2m",
     "preset-rpi-64-h265": "-c:v:1 hevc_v4l2m2m",
     FFMPEG_HWACCEL_VAAPI: f"-hwaccel_flags allow_profile_mismatch -hwaccel vaapi -hwaccel_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format vaapi",
-    "preset-intel-qsv-h264": f"-hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv -c:v h264_qsv",
-    "preset-intel-qsv-h265": f"-load_plugin hevc_hw -hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv -c:v hevc_qsv",
+    "preset-intel-qsv-h264": f"-hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv -c:v h264_qsv{' -bsf:v dump_extra' if LIBAV_VERSION >= 61 else ''}",  # https://trac.ffmpeg.org/ticket/9766#comment:17
+    "preset-intel-qsv-h265": f"-load_plugin hevc_hw -hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv{' -bsf:v dump_extra' if LIBAV_VERSION >= 61 else ''}",  # https://trac.ffmpeg.org/ticket/9766#comment:17
     FFMPEG_HWACCEL_NVIDIA: "-hwaccel cuda -hwaccel_output_format cuda",
     "preset-jetson-h264": "-c:v h264_nvmpi -resize {1}x{2}",
     "preset-jetson-h265": "-c:v hevc_nvmpi -resize {1}x{2}",

frigate/genai/ollama.py

@@ -38,6 +38,11 @@ class OllamaClient(GenAIClient):
 
     def _send(self, prompt: str, images: list[bytes]) -> Optional[str]:
         """Submit a request to Ollama"""
+        if self.provider is None:
+            logger.warning(
+                "Ollama provider has not been initialized, a description will not be generated. Check your Ollama configuration."
+            )
+            return None
         try:
             result = self.provider.generate(
                 self.genai_config.model,

frigate/plus.py

@@ -68,11 +68,13 @@ class PlusApi:
             or self._token_data["expires"] - datetime.datetime.now().timestamp() < 60
         ):
             if self.key is None:
-                raise Exception("Plus API not activated")
+                raise Exception(
+                    "Plus API key not set. See https://docs.frigate.video/integrations/plus#set-your-api-key"
+                )
             parts = self.key.split(":")
             r = requests.get(f"{self.host}/v1/auth/token", auth=(parts[0], parts[1]))
             if not r.ok:
-                raise Exception("Unable to refresh API token")
+                raise Exception(f"Unable to refresh API token: {r.text}")
             self._token_data = r.json()
 
     def _get_authorization_header(self) -> dict:
@@ -116,15 +118,6 @@ class PlusApi:
             logger.error(f"Failed to upload original: {r.status_code} {r.text}")
             raise Exception(r.text)
 
-        # resize and submit annotate
-        files = {"file": get_jpg_bytes(image, 640, 70)}
-        data = presigned_urls["annotate"]["fields"]
-        data["content-type"] = "image/jpeg"
-        r = requests.post(presigned_urls["annotate"]["url"], files=files, data=data)
-        if not r.ok:
-            logger.error(f"Failed to upload annotate: {r.status_code} {r.text}")
-            raise Exception(r.text)
-
         # resize and submit thumbnail
         files = {"file": get_jpg_bytes(image, 200, 70)}
         data = presigned_urls["thumbnail"]["fields"]

frigate/ptz/autotrack.py

@@ -2,7 +2,6 @@
 
 import copy
 import logging
-import os
 import queue
 import threading
 import time
@@ -29,11 +28,11 @@ from frigate.const import (
     AUTOTRACKING_ZOOM_EDGE_THRESHOLD,
     AUTOTRACKING_ZOOM_IN_HYSTERESIS,
     AUTOTRACKING_ZOOM_OUT_HYSTERESIS,
-    CONFIG_DIR,
 )
 from frigate.ptz.onvif import OnvifController
 from frigate.track.tracked_object import TrackedObject
 from frigate.util.builtin import update_yaml_file
+from frigate.util.config import find_config_file
 from frigate.util.image import SharedMemoryFrameManager, intersection_over_union
 
 logger = logging.getLogger(__name__)
@@ -136,7 +135,7 @@ class PtzMotionEstimator:
 
             try:
                 logger.debug(
-                    f"{camera}: Motion estimator transformation: {self.coord_transformations.rel_to_abs([[0,0]])}"
+                    f"{camera}: Motion estimator transformation: {self.coord_transformations.rel_to_abs([[0, 0]])}"
                 )
             except Exception:
                 pass
@@ -328,13 +327,7 @@ class PtzAutoTracker:
         self.autotracker_init[camera] = True
 
     def _write_config(self, camera):
-        config_file = os.environ.get("CONFIG_FILE", f"{CONFIG_DIR}/config.yml")
-
-        # Check if we can use .yaml instead of .yml
-        config_file_yaml = config_file.replace(".yml", ".yaml")
-
-        if os.path.isfile(config_file_yaml):
-            config_file = config_file_yaml
+        config_file = find_config_file()
 
         logger.debug(
             f"{camera}: Writing new config with autotracker motion coefficients: {self.config.cameras[camera].onvif.autotracking.movement_weights}"
@@ -478,7 +471,7 @@ class PtzAutoTracker:
                 self.onvif.get_camera_status(camera)
 
             logger.info(
-                f"Calibration for {camera} in progress: {round((step/num_steps)*100)}% complete"
+                f"Calibration for {camera} in progress: {round((step / num_steps) * 100)}% complete"
             )
 
         self.calibrating[camera] = False
@@ -697,7 +690,7 @@ class PtzAutoTracker:
                             f"{camera}: Predicted movement time: {self._predict_movement_time(camera, pan, tilt)}"
                         )
                         logger.debug(
-                            f"{camera}: Actual movement time: {self.ptz_metrics[camera].stop_time.value-self.ptz_metrics[camera].start_time.value}"
+                            f"{camera}: Actual movement time: {self.ptz_metrics[camera].stop_time.value - self.ptz_metrics[camera].start_time.value}"
                         )
 
                     # save metrics for better estimate calculations
@@ -990,10 +983,10 @@ class PtzAutoTracker:
             logger.debug(f"{camera}: Zoom test: at max zoom: {at_max_zoom}")
             logger.debug(f"{camera}: Zoom test: at min zoom: {at_min_zoom}")
             logger.debug(
-                f'{camera}: Zoom test: zoom in hysteresis limit: {zoom_in_hysteresis} value: {AUTOTRACKING_ZOOM_IN_HYSTERESIS} original: {self.tracked_object_metrics[camera]["original_target_box"]} max: {self.tracked_object_metrics[camera]["max_target_box"]} target: {calculated_target_box if calculated_target_box else self.tracked_object_metrics[camera]["target_box"]}'
+                f"{camera}: Zoom test: zoom in hysteresis limit: {zoom_in_hysteresis} value: {AUTOTRACKING_ZOOM_IN_HYSTERESIS} original: {self.tracked_object_metrics[camera]['original_target_box']} max: {self.tracked_object_metrics[camera]['max_target_box']} target: {calculated_target_box if calculated_target_box else self.tracked_object_metrics[camera]['target_box']}"
             )
             logger.debug(
-                f'{camera}: Zoom test: zoom out hysteresis limit: {zoom_out_hysteresis} value: {AUTOTRACKING_ZOOM_OUT_HYSTERESIS} original: {self.tracked_object_metrics[camera]["original_target_box"]} max: {self.tracked_object_metrics[camera]["max_target_box"]} target: {calculated_target_box if calculated_target_box else self.tracked_object_metrics[camera]["target_box"]}'
+                f"{camera}: Zoom test: zoom out hysteresis limit: {zoom_out_hysteresis} value: {AUTOTRACKING_ZOOM_OUT_HYSTERESIS} original: {self.tracked_object_metrics[camera]['original_target_box']} max: {self.tracked_object_metrics[camera]['max_target_box']} target: {calculated_target_box if calculated_target_box else self.tracked_object_metrics[camera]['target_box']}"
             )
 
         # Zoom in conditions (and)
@@ -1076,7 +1069,7 @@ class PtzAutoTracker:
                 pan = ((centroid_x / camera_width) - 0.5) * 2
                 tilt = (0.5 - (centroid_y / camera_height)) * 2
 
-            logger.debug(f'{camera}: Original box: {obj.obj_data["box"]}')
+            logger.debug(f"{camera}: Original box: {obj.obj_data['box']}")
             logger.debug(f"{camera}: Predicted box: {tuple(predicted_box)}")
             logger.debug(
                 f"{camera}: Velocity: {tuple(np.round(average_velocity).flatten().astype(int))}"
@@ -1186,7 +1179,7 @@ class PtzAutoTracker:
                     )
                     zoom = (ratio - 1) / (ratio + 1)
                     logger.debug(
-                        f'{camera}: limit: {self.tracked_object_metrics[camera]["max_target_box"]}, ratio: {ratio} zoom calculation: {zoom}'
+                        f"{camera}: limit: {self.tracked_object_metrics[camera]['max_target_box']}, ratio: {ratio} zoom calculation: {zoom}"
                     )
                     if not result:
                         # zoom out with special condition if zooming out because of velocity, edges, etc.

frigate/ptz/onvif.py

@@ -6,6 +6,7 @@ from importlib.util import find_spec
 from pathlib import Path
 
 import numpy
+import requests
 from onvif import ONVIFCamera, ONVIFError
 from zeep.exceptions import Fault, TransportError
 from zeep.transports import Transport
@@ -48,7 +49,11 @@ class OnvifController:
 
             if cam.onvif.host:
                 try:
-                    transport = Transport(timeout=10, operation_timeout=10)
+                    session = requests.Session()
+                    session.verify = not cam.onvif.tls_insecure
+                    transport = Transport(
+                        timeout=10, operation_timeout=10, session=session
+                    )
                     self.cams[cam_name] = {
                         "onvif": ONVIFCamera(
                             cam.onvif.host,
@@ -558,22 +563,26 @@ class OnvifController:
             if not self._init_onvif(camera_name):
                 return
 
-        if command == OnvifCommandEnum.init:
-            # already init
-            return
-        elif command == OnvifCommandEnum.stop:
-            self._stop(camera_name)
-        elif command == OnvifCommandEnum.preset:
-            self._move_to_preset(camera_name, param)
-        elif command == OnvifCommandEnum.move_relative:
-            _, pan, tilt = param.split("_")
-            self._move_relative(camera_name, float(pan), float(tilt), 0, 1)
-        elif (
-            command == OnvifCommandEnum.zoom_in or command == OnvifCommandEnum.zoom_out
-        ):
-            self._zoom(camera_name, command)
-        else:
-            self._move(camera_name, command)
+        try:
+            if command == OnvifCommandEnum.init:
+                # already init
+                return
+            elif command == OnvifCommandEnum.stop:
+                self._stop(camera_name)
+            elif command == OnvifCommandEnum.preset:
+                self._move_to_preset(camera_name, param)
+            elif command == OnvifCommandEnum.move_relative:
+                _, pan, tilt = param.split("_")
+                self._move_relative(camera_name, float(pan), float(tilt), 0, 1)
+            elif (
+                command == OnvifCommandEnum.zoom_in
+                or command == OnvifCommandEnum.zoom_out
+            ):
+                self._zoom(camera_name, command)
+            else:
+                self._move(camera_name, command)
+        except ONVIFError as e:
+            logger.error(f"Unable to handle onvif command: {e}")
 
     def get_camera_info(self, camera_name: str) -> dict[str, any]:
         if camera_name not in self.cams.keys():

frigate/record/maintainer.py

@@ -29,6 +29,7 @@ from frigate.const import (
     RECORD_DIR,
 )
 from frigate.models import Recordings, ReviewSegment
+from frigate.review.types import SeverityEnum
 from frigate.util.services import get_video_properties
 
 logger = logging.getLogger(__name__)
@@ -194,6 +195,7 @@ class RecordingMaintainer(threading.Thread):
                 ReviewSegment.select(
                     ReviewSegment.start_time,
                     ReviewSegment.end_time,
+                    ReviewSegment.severity,
                     ReviewSegment.data,
                 )
                 .where(
@@ -219,11 +221,15 @@ class RecordingMaintainer(threading.Thread):
             [r for r in recordings_to_insert if r is not None],
         )
 
+    def drop_segment(self, cache_path: str) -> None:
+        Path(cache_path).unlink(missing_ok=True)
+        self.end_time_cache.pop(cache_path, None)
+
     async def validate_and_move_segment(
         self, camera: str, reviews: list[ReviewSegment], recording: dict[str, any]
     ) -> None:
-        cache_path = recording["cache_path"]
-        start_time = recording["start_time"]
+        cache_path: str = recording["cache_path"]
+        start_time: datetime.datetime = recording["start_time"]
         record_config = self.config.cameras[camera].record
 
         # Just delete files if recordings are turned off
@@ -231,8 +237,7 @@ class RecordingMaintainer(threading.Thread):
             camera not in self.config.cameras
             or not self.config.cameras[camera].record.enabled
         ):
-            Path(cache_path).unlink(missing_ok=True)
-            self.end_time_cache.pop(cache_path, None)
+            self.drop_segment(cache_path)
             return
 
         if cache_path in self.end_time_cache:
@@ -260,24 +265,34 @@ class RecordingMaintainer(threading.Thread):
                 return
 
         # if cached file's start_time is earlier than the retain days for the camera
+        # meaning continuous recording is not enabled
         if start_time <= (
             datetime.datetime.now().astimezone(datetime.timezone.utc)
             - datetime.timedelta(days=self.config.cameras[camera].record.retain.days)
         ):
-            # if the cached segment overlaps with the events:
+            # if the cached segment overlaps with the review items:
             overlaps = False
             for review in reviews:
-                # if the event starts in the future, stop checking events
+                severity = SeverityEnum[review.severity]
+
+                # if the review item starts in the future, stop checking review items
                 # and remove this segment
-                if review.start_time > end_time.timestamp():
+                if (
+                    review.start_time - record_config.get_review_pre_capture(severity)
+                ) > end_time.timestamp():
                     overlaps = False
-                    Path(cache_path).unlink(missing_ok=True)
-                    self.end_time_cache.pop(cache_path, None)
                     break
 
-                # if the event is in progress or ends after the recording starts, keep it
-                # and stop looking at events
-                if review.end_time is None or review.end_time >= start_time.timestamp():
+                # if the review item is in progress or ends after the recording starts, keep it
+                # and stop looking at review items
+                if (
+                    review.end_time is None
+                    or (
+                        review.end_time
+                        + record_config.get_review_post_capture(severity)
+                    )
+                    >= start_time.timestamp()
+                ):
                     overlaps = True
                     break
 
@@ -296,7 +311,7 @@ class RecordingMaintainer(threading.Thread):
                     cache_path,
                     record_mode,
                 )
-            # if it doesn't overlap with an event, go ahead and drop the segment
+            # if it doesn't overlap with an review item, go ahead and drop the segment
             # if it ends more than the configured pre_capture for the camera
             else:
                 camera_info = self.object_recordings_info[camera]
@@ -307,9 +322,9 @@ class RecordingMaintainer(threading.Thread):
                     most_recently_processed_frame_time - record_config.event_pre_capture
                 ).astimezone(datetime.timezone.utc)
                 if end_time < retain_cutoff:
-                    Path(cache_path).unlink(missing_ok=True)
-                    self.end_time_cache.pop(cache_path, None)
+                    self.drop_segment(cache_path)
         # else retain days includes this segment
+        # meaning continuous recording is enabled
         else:
             # assume that empty means the relevant recording info has not been received yet
             camera_info = self.object_recordings_info[camera]
@@ -390,8 +405,7 @@ class RecordingMaintainer(threading.Thread):
 
         # check if the segment shouldn't be stored
         if segment_info.should_discard_segment(store_mode):
-            Path(cache_path).unlink(missing_ok=True)
-            self.end_time_cache.pop(cache_path, None)
+            self.drop_segment(cache_path)
             return
 
         # directory will be in utc due to start_time being in utc
@@ -435,7 +449,7 @@ class RecordingMaintainer(threading.Thread):
                     return None
                 else:
                     logger.debug(
-                        f"Copied {file_path} in {datetime.datetime.now().timestamp()-start_frame} seconds."
+                        f"Copied {file_path} in {datetime.datetime.now().timestamp() - start_frame} seconds."
                     )
 
                 try:

frigate/review/maintainer.py

@@ -256,7 +256,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                 elif object["sub_label"][0] in self.config.model.all_attributes:
                     segment.detections[object["id"]] = object["sub_label"][0]
                 else:
-                    segment.detections[object["id"]] = f'{object["label"]}-verified'
+                    segment.detections[object["id"]] = f"{object['label']}-verified"
                     segment.sub_labels[object["id"]] = object["sub_label"][0]
 
                 # if object is alert label
@@ -352,7 +352,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                 elif object["sub_label"][0] in self.config.model.all_attributes:
                     detections[object["id"]] = object["sub_label"][0]
                 else:
-                    detections[object["id"]] = f'{object["label"]}-verified'
+                    detections[object["id"]] = f"{object['label']}-verified"
                     sub_labels[object["id"]] = object["sub_label"][0]
 
                 # if object is alert label
@@ -527,7 +527,9 @@ class ReviewSegmentMaintainer(threading.Thread):
 
                         if event_id in self.indefinite_events[camera]:
                             self.indefinite_events[camera].pop(event_id)
-                            current_segment.last_update = manual_info["end_time"]
+
+                            if len(self.indefinite_events[camera]) == 0:
+                                current_segment.last_update = manual_info["end_time"]
                         else:
                             logger.error(
                                 f"Event with ID {event_id} has a set duration and can not be ended manually."

frigate/service_manager/multiprocessing.py

@@ -72,8 +72,7 @@ class BaseServiceProcess(Service, ABC):
                 running = False
             except TimeoutError:
                 self.manager.logger.warning(
-                    f"{self.name} is still running after "
-                    f"{timeout} seconds. Killing."
+                    f"{self.name} is still running after {timeout} seconds. Killing."
                 )
 
         if running:

frigate/stats/util.py

@@ -293,7 +293,7 @@ def stats_snapshot(
     for path in [RECORD_DIR, CLIPS_DIR, CACHE_DIR, "/dev/shm"]:
         try:
             storage_stats = shutil.disk_usage(path)
-        except FileNotFoundError:
+        except (FileNotFoundError, OSError):
             stats["service"]["storage"][path] = {}
             continue
 

frigate/storage.py

@@ -17,6 +17,8 @@ bandwidth_equation = Recordings.segment_size / (
     Recordings.end_time - Recordings.start_time
 )
 
+MAX_CALCULATED_BANDWIDTH = 10000  # 10Gb/hr
+
 
 class StorageMaintainer(threading.Thread):
     """Maintain frigates recording storage."""
@@ -52,6 +54,12 @@ class StorageMaintainer(threading.Thread):
                         * 3600,
                         2,
                     )
+
+                    if bandwidth > MAX_CALCULATED_BANDWIDTH:
+                        logger.warning(
+                            f"{camera} has a bandwidth of {bandwidth} MB/hr which exceeds the expected maximum. This typically indicates an issue with the cameras recordings."
+                        )
+                        bandwidth = MAX_CALCULATED_BANDWIDTH
                 except TypeError:
                     bandwidth = 0
 

frigate/test/test_config.py

@@ -75,11 +75,11 @@ class TestConfig(unittest.TestCase):
             "detectors": {
                 "cpu": {
                     "type": "cpu",
-                    "model": {"path": "/cpu_model.tflite"},
+                    "model_path": "/cpu_model.tflite",
                 },
                 "edgetpu": {
                     "type": "edgetpu",
-                    "model": {"path": "/edgetpu_model.tflite"},
+                    "model_path": "/edgetpu_model.tflite",
                 },
                 "openvino": {
                     "type": "openvino",

frigate/track/tracked_object.py

@@ -339,7 +339,7 @@ class TrackedObject:
                 box[2],
                 box[3],
                 self.obj_data["label"],
-                f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}",
+                f"{int(self.thumbnail_data['score'] * 100)}% {int(self.thumbnail_data['area'])}",
                 thickness=thickness,
                 color=color,
             )

frigate/util/config.py

@@ -13,7 +13,17 @@ from frigate.util.services import get_video_properties
 
 logger = logging.getLogger(__name__)
 
-CURRENT_CONFIG_VERSION = "0.15-0"
+CURRENT_CONFIG_VERSION = "0.15-1"
+DEFAULT_CONFIG_FILE = "/config/config.yml"
+
+
+def find_config_file() -> str:
+    config_path = os.environ.get("CONFIG_FILE", DEFAULT_CONFIG_FILE)
+
+    if not os.path.isfile(config_path):
+        config_path = config_path.replace("yml", "yaml")
+
+    return config_path
 
 
 def migrate_frigate_config(config_file: str):
@@ -67,6 +77,13 @@ def migrate_frigate_config(config_file: str):
             yaml.dump(new_config, f)
         previous_version = "0.15-0"
 
+    if previous_version < "0.15-1":
+        logger.info(f"Migrating frigate config from {previous_version} to 0.15-1...")
+        new_config = migrate_015_1(config)
+        with open(config_file, "w") as f:
+            yaml.dump(new_config, f)
+        previous_version = "0.15-1"
+
     logger.info("Finished frigate config migration...")
 
 
@@ -257,6 +274,21 @@ def migrate_015_0(config: dict[str, dict[str, any]]) -> dict[str, dict[str, any]
     return new_config
 
 
+def migrate_015_1(config: dict[str, dict[str, any]]) -> dict[str, dict[str, any]]:
+    """Handle migrating frigate config to 0.15-1"""
+    new_config = config.copy()
+
+    for detector, detector_config in config.get("detectors", {}).items():
+        path = detector_config.get("model", {}).get("path")
+
+        if path:
+            new_config["detectors"][detector]["model_path"] = path
+            del new_config["detectors"][detector]["model"]
+
+    new_config["version"] = "0.15-1"
+    return new_config
+
+
 def get_relative_coordinates(
     mask: Optional[Union[str, list]], frame_shape: tuple[int, int]
 ) -> Union[str, list]:
@@ -282,7 +314,7 @@ def get_relative_coordinates(
                             continue
 
                         rel_points.append(
-                            f"{round(x / frame_shape[1], 3)},{round(y  / frame_shape[0], 3)}"
+                            f"{round(x / frame_shape[1], 3)},{round(y / frame_shape[0], 3)}"
                         )
 
                     relative_masks.append(",".join(rel_points))
@@ -305,7 +337,7 @@ def get_relative_coordinates(
                     return []
 
                 rel_points.append(
-                    f"{round(x / frame_shape[1], 3)},{round(y  / frame_shape[0], 3)}"
+                    f"{round(x / frame_shape[1], 3)},{round(y / frame_shape[0], 3)}"
                 )
 
             mask = ",".join(rel_points)

frigate/util/services.py

@@ -390,12 +390,22 @@ def try_get_info(f, h, default="N/A"):
 
 
 def get_nvidia_gpu_stats() -> dict[int, dict]:
+    names: dict[str, int] = {}
     results = {}
     try:
         nvml.nvmlInit()
         deviceCount = nvml.nvmlDeviceGetCount()
         for i in range(deviceCount):
             handle = nvml.nvmlDeviceGetHandleByIndex(i)
+            gpu_name = nvml.nvmlDeviceGetName(handle)
+
+            # handle case where user has multiple of same GPU
+            if gpu_name in names:
+                names[gpu_name] += 1
+                gpu_name += f" ({names.get(gpu_name)})"
+            else:
+                names[gpu_name] = 1
+
             meminfo = try_get_info(nvml.nvmlDeviceGetMemoryInfo, handle)
             util = try_get_info(nvml.nvmlDeviceGetUtilizationRates, handle)
             enc = try_get_info(nvml.nvmlDeviceGetEncoderUtilization, handle)
@@ -423,7 +433,7 @@ def get_nvidia_gpu_stats() -> dict[int, dict]:
                 dec_util = -1
 
             results[i] = {
-                "name": nvml.nvmlDeviceGetName(handle),
+                "name": gpu_name,
                 "gpu": gpu_util,
                 "mem": gpu_mem_util,
                 "enc": enc_util,

frigate/video.py

@@ -113,7 +113,7 @@ def capture_frames(
         fps.value = frame_rate.eps()
         skipped_fps.value = skipped_eps.eps()
         current_frame.value = datetime.datetime.now().timestamp()
-        frame_name = f"{config.name}_{frame_index}"
+        frame_name = f"{config.name}_frame{frame_index}"
         frame_buffer = frame_manager.write(frame_name)
         try:
             frame_buffer[:] = ffmpeg_process.stdout.read(frame_size)

process_clip.py

@@ -208,7 +208,7 @@ class ProcessClip:
                 box[2],
                 box[3],
                 obj["id"],
-                f"{int(obj['score']*100)}% {int(obj['area'])}",
+                f"{int(obj['score'] * 100)}% {int(obj['area'])}",
                 thickness=thickness,
                 color=color,
             )
@@ -227,7 +227,7 @@ class ProcessClip:
             )
 
         cv2.imwrite(
-            f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg",
+            f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time * 1000000)}.jpg",
             current_frame,
         )
 
@@ -290,7 +290,7 @@ def process(path, label, output, debug_path):
         1 for result in results if result[1]["true_positive_objects"] > 0
     )
     print(
-        f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s)."
+        f"Objects were detected in {positive_count}/{len(results)}({positive_count / len(results) * 100:.2f}%) clip(s)."
     )
 
     if output:

web/src/components/filter/CameraGroupSelector.tsx

@@ -755,7 +755,11 @@ export function CameraGroupEdit({
                 <FormMessage />
                 {[
                   ...(birdseyeConfig?.enabled ? ["birdseye"] : []),
-                  ...Object.keys(config?.cameras ?? {}),
+                  ...Object.keys(config?.cameras ?? {}).sort(
+                    (a, b) =>
+                      (config?.cameras[a]?.ui?.order ?? 0) -
+                      (config?.cameras[b]?.ui?.order ?? 0),
+                  ),
                 ].map((camera) => (
                   <FormControl key={camera}>
                     <FilterSwitch

web/src/components/overlay/detail/ObjectLifecycle.tsx

@@ -477,7 +477,10 @@ export default function ObjectLifecycle({
                           </p>
                           {Array.isArray(item.data.box) &&
                           item.data.box.length >= 4
-                            ? (item.data.box[2] / item.data.box[3]).toFixed(2)
+                            ? (
+                                aspectRatio *
+                                (item.data.box[2] / item.data.box[3])
+                              ).toFixed(2)
                             : "N/A"}
                         </div>
                       </div>

web/src/components/overlay/detail/ReviewDetailDialog.tsx

@@ -74,6 +74,23 @@ export default function ReviewDetailDialog({
     return events.length != review?.data.detections.length;
   }, [review, events]);
 
+  const missingObjects = useMemo(() => {
+    if (!review || !events) {
+      return [];
+    }
+
+    const detectedIds = review.data.detections;
+    const missing = Array.from(
+      new Set(
+        events
+          .filter((event) => !detectedIds.includes(event.id))
+          .map((event) => event.label),
+      ),
+    );
+
+    return missing;
+  }, [review, events]);
+
   const formattedDate = useFormattedTimestamp(
     review?.start_time ?? 0,
     config?.ui.time_format == "24hour"
@@ -263,8 +280,25 @@ export default function ReviewDetailDialog({
               </div>
               {hasMismatch && (
                 <div className="p-4 text-center text-sm">
-                  Some objects that were detected are not included in this list
-                  because the object does not have a snapshot
+                  {(() => {
+                    const detectedCount = Math.abs(
+                      (events?.length ?? 0) -
+                        (review?.data.detections.length ?? 0),
+                    );
+                    const objectLabel =
+                      detectedCount === 1 ? "object was" : "objects were";
+
+                    return `${detectedCount} unavailable ${objectLabel} detected and included in this review item.`;
+                  })()}{" "}
+                  Those objects either did not qualify as an alert or detection
+                  or have already been cleaned up/deleted.
+                  {missingObjects.length > 0 && (
+                    <div className="mt-2">
+                      Adjust your configuration if you want Frigate to save
+                      tracked objects for the following labels:{" "}
+                      {missingObjects.join(", ")}
+                    </div>
+                  )}
                 </div>
               )}
               <div className="relative flex size-full flex-col gap-2">

web/src/components/overlay/detail/SearchDetailDialog.tsx

@@ -469,16 +469,43 @@ function ObjectDetailsTab({
         </div>
       </div>
       <div className="flex flex-col gap-1.5">
-        <div className="text-sm text-primary/40">Description</div>
-        <Textarea
-          className="h-64"
-          placeholder="Description of the tracked object"
-          value={desc}
-          onChange={(e) => setDesc(e.target.value)}
-        />
+        {config?.cameras[search.camera].genai.enabled &&
+        !search.end_time &&
+        (config.cameras[search.camera].genai.required_zones.length === 0 ||
+          search.zones.some((zone) =>
+            config.cameras[search.camera].genai.required_zones.includes(zone),
+          )) &&
+        (config.cameras[search.camera].genai.objects.length === 0 ||
+          config.cameras[search.camera].genai.objects.includes(
+            search.label,
+          )) ? (
+          <>
+            <div className="text-sm text-primary/40">Description</div>
+            <div className="flex h-64 flex-col items-center justify-center gap-3 border p-4 text-sm text-primary/40">
+              <div className="flex">
+                <ActivityIndicator />
+              </div>
+              <div className="flex">
+                Frigate will not request a description from your Generative AI
+                provider until the tracked object's lifecycle has ended.
+              </div>
+            </div>
+          </>
+        ) : (
+          <>
+            <div className="text-sm text-primary/40">Description</div>
+            <Textarea
+              className="h-64"
+              placeholder="Description of the tracked object"
+              value={desc}
+              onChange={(e) => setDesc(e.target.value)}
+            />
+          </>
+        )}
+
         <div className="flex w-full flex-row justify-end gap-2">
-          {config?.cameras[search.camera].genai.enabled && (
-            <div className="flex items-center">
+          {config?.cameras[search.camera].genai.enabled && search.end_time && (
+            <div className="flex items-start">
               <Button
                 className="rounded-r-none border-r-0"
                 aria-label="Regenerate tracked object description"
@@ -516,13 +543,16 @@ function ObjectDetailsTab({
               )}
             </div>
           )}
-          <Button
-            variant="select"
-            aria-label="Save"
-            onClick={updateDescription}
-          >
-            Save
-          </Button>
+          {((config?.cameras[search.camera].genai.enabled && search.end_time) ||
+            !config?.cameras[search.camera].genai.enabled) && (
+            <Button
+              variant="select"
+              aria-label="Save"
+              onClick={updateDescription}
+            >
+              Save
+            </Button>
+          )}
         </div>
       </div>
     </div>

web/src/components/settings/SearchSettings.tsx

@@ -46,7 +46,7 @@ export default function SearchSettings({
   const trigger = (
     <Button
       className="flex items-center gap-2"
-      aria-label="Search Settings"
+      aria-label="Explore Settings"
       size="sm"
     >
       <FaCog className="text-secondary-foreground" />

web/src/hooks/use-overlay-state.tsx

@@ -5,6 +5,7 @@ import { usePersistence } from "./use-persistence";
 export function useOverlayState<S>(
   key: string,
   defaultValue: S | undefined = undefined,
+  preserveSearch: boolean = true,
 ): [S | undefined, (value: S, replace?: boolean) => void] {
   const location = useLocation();
   const navigate = useNavigate();
@@ -15,7 +16,7 @@ export function useOverlayState<S>(
     (value: S, replace: boolean = false) => {
       const newLocationState = { ...currentLocationState };
       newLocationState[key] = value;
-      navigate(location.pathname + location.search, {
+      navigate(location.pathname + (preserveSearch ? location.search : ""), {
         state: newLocationState,
         replace,
       });

web/src/pages/Events.tsx

@@ -39,8 +39,11 @@ export default function Events() {
 
   const [showReviewed, setShowReviewed] = usePersistence("showReviewed", false);
 
-  const [recording, setRecording] =
-    useOverlayState<RecordingStartingPoint>("recording");
+  const [recording, setRecording] = useOverlayState<RecordingStartingPoint>(
+    "recording",
+    undefined,
+    false,
+  );
 
   useSearchEffect("id", (reviewId: string) => {
     axios

web/src/pages/Explore.tsx

@@ -328,12 +328,12 @@ export default function Explore() {
           <div className="flex max-w-96 flex-col items-center justify-center space-y-3 rounded-lg bg-background/50 p-5">
             <div className="my-5 flex flex-col items-center gap-2 text-xl">
               <TbExclamationCircle className="mb-3 size-10" />
-              <div>Search Unavailable</div>
+              <div>Explore is Unavailable</div>
             </div>
             {embeddingsReindexing && allModelsLoaded && (
               <>
                 <div className="text-center text-primary-variant">
-                  Search can be used after tracked object embeddings have
+                  Explore can be used after tracked object embeddings have
                   finished reindexing.
                 </div>
                 <div className="pt-5 text-center">
@@ -384,8 +384,8 @@ export default function Explore() {
               <>
                 <div className="text-center text-primary-variant">
                   Frigate is downloading the necessary embeddings models to
-                  support semantic searching. This may take several minutes
-                  depending on the speed of your network connection.
+                  support the Semantic Search feature. This may take several
+                  minutes depending on the speed of your network connection.
                 </div>
                 <div className="flex w-96 flex-col gap-2 py-5">
                   <div className="flex flex-row items-center justify-center gap-2">

web/src/pages/Settings.tsx

@@ -40,7 +40,7 @@ import UiSettingsView from "@/views/settings/UiSettingsView";
 
 const allSettingsViews = [
   "UI settings",
-  "search settings",
+  "explore settings",
   "camera settings",
   "masks / zones",
   "motion tuner",
@@ -175,7 +175,7 @@ export default function Settings() {
       </div>
       <div className="mt-2 flex h-full w-full flex-col items-start md:h-dvh md:pb-24">
         {page == "UI settings" && <UiSettingsView />}
-        {page == "search settings" && (
+        {page == "explore settings" && (
           <SearchSettingsView setUnsavedChanges={setUnsavedChanges} />
         )}
         {page == "debug" && (

web/src/types/frigateConfig.ts

@@ -142,6 +142,7 @@ export interface CameraConfig {
     password: string | null;
     port: number;
     user: string | null;
+    tls_insecure: boolean;
   };
   record: {
     enabled: boolean;

web/src/views/live/LiveCameraView.tsx

@@ -17,7 +17,12 @@ import {
   DropdownMenuItem,
   DropdownMenuTrigger,
 } from "@/components/ui/dropdown-menu";
-import { TooltipProvider } from "@/components/ui/tooltip";
+import {
+  Tooltip,
+  TooltipContent,
+  TooltipProvider,
+  TooltipTrigger,
+} from "@/components/ui/tooltip";
 import { useResizeObserver } from "@/hooks/resize-observer";
 import useKeyboardListener from "@/hooks/use-keyboard-listener";
 import { CameraConfig, FrigateConfig } from "@/types/frigateConfig";
@@ -29,6 +34,7 @@ import {
 import { CameraPtzInfo } from "@/types/ptz";
 import { RecordingStartingPoint } from "@/types/record";
 import React, {
+  ReactNode,
   useCallback,
   useEffect,
   useMemo,
@@ -518,6 +524,53 @@ export default function LiveCameraView({
   );
 }
 
+type TooltipButtonProps = {
+  label: string;
+  onClick?: () => void;
+  onMouseDown?: (e: React.MouseEvent) => void;
+  onMouseUp?: (e: React.MouseEvent) => void;
+  onTouchStart?: (e: React.TouchEvent) => void;
+  onTouchEnd?: (e: React.TouchEvent) => void;
+  children: ReactNode;
+  className?: string;
+};
+
+function TooltipButton({
+  label,
+  onClick,
+  onMouseDown,
+  onMouseUp,
+  onTouchStart,
+  onTouchEnd,
+  children,
+  className,
+  ...props
+}: TooltipButtonProps) {
+  return (
+    <TooltipProvider>
+      <Tooltip>
+        <TooltipTrigger asChild>
+          <Button
+            aria-label={label}
+            onClick={onClick}
+            onMouseDown={onMouseDown}
+            onMouseUp={onMouseUp}
+            onTouchStart={onTouchStart}
+            onTouchEnd={onTouchEnd}
+            className={className}
+            {...props}
+          >
+            {children}
+          </Button>
+        </TooltipTrigger>
+        <TooltipContent>
+          <p>{label}</p>
+        </TooltipContent>
+      </Tooltip>
+    </TooltipProvider>
+  );
+}
+
 function PtzControlPanel({
   camera,
   clickOverlay,
@@ -611,8 +664,8 @@ function PtzControlPanel({
     >
       {ptz?.features?.includes("pt") && (
         <>
-          <Button
-            aria-label="Move PTZ camera to the left"
+          <TooltipButton
+            label="Move camera left"
             onMouseDown={(e) => {
               e.preventDefault();
               sendPtz("MOVE_LEFT");
@@ -625,9 +678,9 @@ function PtzControlPanel({
             onTouchEnd={onStop}
           >
             <FaAngleLeft />
-          </Button>
-          <Button
-            aria-label="Move PTZ camera up"
+          </TooltipButton>
+          <TooltipButton
+            label="Move camera up"
             onMouseDown={(e) => {
               e.preventDefault();
               sendPtz("MOVE_UP");
@@ -640,9 +693,9 @@ function PtzControlPanel({
             onTouchEnd={onStop}
           >
             <FaAngleUp />
-          </Button>
-          <Button
-            aria-label="Move PTZ camera down"
+          </TooltipButton>
+          <TooltipButton
+            label="Move camera down"
             onMouseDown={(e) => {
               e.preventDefault();
               sendPtz("MOVE_DOWN");
@@ -655,9 +708,9 @@ function PtzControlPanel({
             onTouchEnd={onStop}
           >
             <FaAngleDown />
-          </Button>
-          <Button
-            aria-label="Move PTZ camera to the right"
+          </TooltipButton>
+          <TooltipButton
+            label="Move camera right"
             onMouseDown={(e) => {
               e.preventDefault();
               sendPtz("MOVE_RIGHT");
@@ -670,13 +723,13 @@ function PtzControlPanel({
             onTouchEnd={onStop}
           >
             <FaAngleRight />
-          </Button>
+          </TooltipButton>
         </>
       )}
       {ptz?.features?.includes("zoom") && (
         <>
-          <Button
-            aria-label="Zoom PTZ camera in"
+          <TooltipButton
+            label="Zoom in"
             onMouseDown={(e) => {
               e.preventDefault();
               sendPtz("ZOOM_IN");
@@ -689,9 +742,9 @@ function PtzControlPanel({
             onTouchEnd={onStop}
           >
             <MdZoomIn />
-          </Button>
-          <Button
-            aria-label="Zoom PTZ camera out"
+          </TooltipButton>
+          <TooltipButton
+            label="Zoom out"
             onMouseDown={(e) => {
               e.preventDefault();
               sendPtz("ZOOM_OUT");
@@ -704,45 +757,60 @@ function PtzControlPanel({
             onTouchEnd={onStop}
           >
             <MdZoomOut />
-          </Button>
+          </TooltipButton>
         </>
       )}
+
       {ptz?.features?.includes("pt-r-fov") && (
-        <>
-          <Button
-            className={`${clickOverlay ? "text-selected" : "text-primary"}`}
-            aria-label="Click in the frame to center the PTZ camera"
-            onClick={() => setClickOverlay(!clickOverlay)}
-          >
-            <TbViewfinder />
-          </Button>
-        </>
+        <TooltipProvider>
+          <Tooltip>
+            <TooltipTrigger asChild>
+              <Button
+                className={`${clickOverlay ? "text-selected" : "text-primary"}`}
+                aria-label="Click in the frame to center the camera"
+                onClick={() => setClickOverlay(!clickOverlay)}
+              >
+                <TbViewfinder />
+              </Button>
+            </TooltipTrigger>
+            <TooltipContent>
+              <p>{clickOverlay ? "Disable" : "Enable"} click to move</p>
+            </TooltipContent>
+          </Tooltip>
+        </TooltipProvider>
       )}
       {(ptz?.presets?.length ?? 0) > 0 && (
-        <DropdownMenu modal={!isDesktop}>
-          <DropdownMenuTrigger asChild>
-            <Button aria-label="PTZ camera presets">
-              <BsThreeDotsVertical />
-            </Button>
-          </DropdownMenuTrigger>
-          <DropdownMenuContent
-            className="scrollbar-container max-h-[40dvh] overflow-y-auto"
-            onCloseAutoFocus={(e) => e.preventDefault()}
-          >
-            {ptz?.presets.map((preset) => {
-              return (
-                <DropdownMenuItem
-                  key={preset}
-                  aria-label={preset}
-                  className="cursor-pointer"
-                  onSelect={() => sendPtz(`preset_${preset}`)}
+        <TooltipProvider>
+          <Tooltip>
+            <TooltipTrigger asChild>
+              <DropdownMenu modal={!isDesktop}>
+                <DropdownMenuTrigger asChild>
+                  <Button aria-label="PTZ camera presets">
+                    <BsThreeDotsVertical />
+                  </Button>
+                </DropdownMenuTrigger>
+                <DropdownMenuContent
+                  className="scrollbar-container max-h-[40dvh] overflow-y-auto"
+                  onCloseAutoFocus={(e) => e.preventDefault()}
                 >
-                  {preset}
-                </DropdownMenuItem>
-              );
-            })}
-          </DropdownMenuContent>
-        </DropdownMenu>
+                  {ptz?.presets.map((preset) => (
+                    <DropdownMenuItem
+                      key={preset}
+                      aria-label={preset}
+                      className="cursor-pointer"
+                      onSelect={() => sendPtz(`preset_${preset}`)}
+                    >
+                      {preset}
+                    </DropdownMenuItem>
+                  ))}
+                </DropdownMenuContent>
+              </DropdownMenu>
+            </TooltipTrigger>
+            <TooltipContent>
+              <p>PTZ camera presets</p>
+            </TooltipContent>
+          </Tooltip>
+        </TooltipProvider>
       )}
     </div>
   );

web/src/views/settings/SearchSettingsView.tsx

@@ -91,7 +91,7 @@ export default function SearchSettingsView({
       )
       .then((res) => {
         if (res.status === 200) {
-          toast.success("Search settings have been saved.", {
+          toast.success("Explore settings have been saved.", {
             position: "top-center",
           });
           setChangedValue(false);
@@ -128,7 +128,7 @@ export default function SearchSettingsView({
     if (changedValue) {
       addMessage(
         "search_settings",
-        `Unsaved search settings changes`,
+        `Unsaved Explore settings changes`,
         undefined,
         "search_settings",
       );
@@ -140,7 +140,7 @@ export default function SearchSettingsView({
   }, [changedValue]);
 
   useEffect(() => {
-    document.title = "Search Settings - Frigate";
+    document.title = "Explore Settings - Frigate";
   }, []);
 
   if (!config) {
@@ -152,7 +152,7 @@ export default function SearchSettingsView({
       <Toaster position="top-center" closeButton={true} />
       <div className="scrollbar-container order-last mb-10 mt-2 flex h-full w-full flex-col overflow-y-auto rounded-lg border-[1px] border-secondary-foreground bg-background_alt p-2 md:order-none md:mb-0 md:mr-2 md:mt-0">
         <Heading as="h3" className="my-2">
-          Search Settings
+          Explore Settings
         </Heading>
         <Separator className="my-2 flex bg-secondary" />
         <Heading as="h4" className="my-2">
@@ -221,7 +221,7 @@ export default function SearchSettingsView({
               <div className="text-md">Model Size</div>
               <div className="space-y-1 text-sm text-muted-foreground">
                 <p>
-                  The size of the model used for semantic search embeddings.
+                  The size of the model used for Semantic Search embeddings.
                 </p>
                 <ul className="list-disc pl-5 text-sm">
                   <li>