add note about network requirements in each section

docs/docs/configuration/audio_detectors.md

@@ -119,6 +119,12 @@ audio:
 
 Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
 
+:::info
+
+Audio transcription requires a one-time internet connection to download the Whisper or Sherpa-ONNX model on first use. Once cached, transcription runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
 Transcription accuracy also depends heavily on the quality of your camera's microphone and recording conditions. Many cameras use inexpensive microphones, and distance to the speaker, low audio bitrate, or background noise can significantly reduce transcription quality. If you need higher accuracy, more robust long-running queues, or large-scale automatic transcription, consider using the HTTP API in combination with an automation platform and a cloud transcription service.
 
 #### Configuration

docs/docs/configuration/bird_classification.md

@@ -9,6 +9,12 @@ import NavPath from "@site/src/components/NavPath";
 
 Bird classification identifies known birds using a quantized Tensorflow model. When a known bird is recognized, its common name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
 
+:::info
+
+Bird classification requires a one-time internet connection to download the classification model and label map from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
 ## Minimum System Requirements
 
 Bird classification runs a lightweight tflite model on the CPU, there are no significantly different system requirements than running Frigate itself.

docs/docs/configuration/custom_classification/object_classification.md

@@ -9,6 +9,12 @@ import NavPath from "@site/src/components/NavPath";
 
 Object classification allows you to train a custom MobileNetV2 classification model to run on tracked objects (persons, cars, animals, etc.) to identify a finer category or attribute for that object. Classification results are visible in the Tracked Object Details pane in Explore, through the `frigate/tracked_object_details` MQTT topic, in Home Assistant sensors via the official Frigate integration, or through the event endpoints in the HTTP API.
 
+:::info
+
+Training a custom object classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
 ## Minimum System Requirements
 
 Object classification models are lightweight and run very fast on CPU.

docs/docs/configuration/custom_classification/state_classification.md

@@ -9,6 +9,12 @@ import NavPath from "@site/src/components/NavPath";
 
 State classification allows you to train a custom MobileNetV2 classification model on a fixed region of your camera frame(s) to determine a current state. The model can be configured to run on a schedule and/or when motion is detected in that region. Classification results are available through the `frigate/<camera_name>/classification/<model_name>` MQTT topic and in Home Assistant sensors via the official Frigate integration.
 
+:::info
+
+Training a custom state classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
 ## Minimum System Requirements
 
 State classification models are lightweight and run very fast on CPU.

docs/docs/configuration/face_recognition.md

@@ -9,6 +9,12 @@ import NavPath from "@site/src/components/NavPath";
 
 Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known `person` is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
 
+:::info
+
+Face recognition requires a one-time internet connection to download detection and embedding models from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
 ## Model Requirements
 
 ### Face Detection

docs/docs/configuration/genai/config.md

@@ -193,6 +193,12 @@ To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` env
 
 Cloud providers run on remote infrastructure and require an API key for authentication. These services handle all model inference on their servers.
 
+:::info
+
+Cloud Generative AI providers require an active internet connection to send images and prompts for processing. Local providers like llama.cpp and Ollama (with local models) do not require internet. See [Network Requirements](/frigate/network_requirements#generative-ai) for details.
+
+:::
+
 ### Ollama Cloud
 
 Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).

docs/docs/configuration/license_plate_recognition.md

@@ -11,6 +11,12 @@ Frigate can recognize license plates on vehicles and automatically add the detec
 
 LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
 
+:::info
+
+License plate recognition requires a one-time internet connection to download OCR and detection models from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
 When a plate is recognized, the details are:
 
 - Added as a `sub_label` (if [known](#matching)) or the `recognized_license_plate` field (if unknown) to a tracked object.

docs/docs/configuration/live.md

@@ -21,6 +21,12 @@ The jsmpeg live view will use more browser and client GPU resources. Using go2rt
 | mse    | native                                | native     | yes (depends on audio codec) | yes             | iPhone requires iOS 17.1+, Firefox is h.264 only. This is Frigate's default when go2rtc is configured.                                                              |
 | webrtc | native                                | native     | yes (depends on audio codec) | yes             | Requires extra configuration. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature.                                          |
 
+:::info
+
+WebRTC may use an external STUN server for NAT traversal. MSE and HLS streaming do not require any internet access. See [Network Requirements](/frigate/network_requirements#webrtc-stun) for details.
+
+:::
+
 ### Camera Settings Recommendations
 
 If you are using go2rtc, you should adjust the following settings in your camera's firmware for the best experience with Live view:

docs/docs/configuration/notifications.md

@@ -11,6 +11,12 @@ import NavPath from "@site/src/components/NavPath";
 
 Frigate offers native notifications using the [WebPush Protocol](https://web.dev/articles/push-notifications-web-push-protocol) which uses the [VAPID spec](https://tools.ietf.org/html/draft-thomson-webpush-vapid) to deliver notifications to web apps using encryption.
 
+:::info
+
+Push notifications require internet access from the Frigate server to the browser vendor's push service (e.g., Google FCM, Mozilla autopush). See [Network Requirements](/frigate/network_requirements#push-notifications) for details.
+
+:::
+
 ## Setting up Notifications
 
 In order to use notifications the following requirements must be met:

docs/docs/configuration/object_detectors.md

@@ -288,6 +288,12 @@ This detector is available for use with both Hailo-8 and Hailo-8L AI Acceleratio
 
 See the [installation docs](../frigate/installation.md#hailo-8) for information on configuring the Hailo hardware.
 
+:::info
+
+If no custom model is provided, the Hailo detector downloads a default model from the Hailo Model Zoo on first startup. Once cached, the model works fully offline. See [Network Requirements](/frigate/network_requirements#hardware-specific-detector-models) for details.
+
+:::
+
 ### Configuration
 
 When configuring the Hailo detector, you have two options to specify the model: a local **path** or a **URL**.
@@ -1793,6 +1799,12 @@ Hardware accelerated object detection is supported on the following SoCs:
 
 This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.3.2.
 
+:::info
+
+If no custom model is provided, the RKNN detector downloads a default model from GitHub on first startup. Once cached, the model works fully offline. See [Network Requirements](/frigate/network_requirements#hardware-specific-detector-models) for details.
+
+:::
+
 :::tip
 
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming NPU resources are available. An example configuration would be:
@@ -2176,6 +2188,12 @@ This implementation uses the [AXera Pulsar2 Toolchain](https://huggingface.co/AX
 
 See the [installation docs](../frigate/installation.md#axera) for information on configuring the AXEngine hardware.
 
+:::info
+
+The AXEngine detector downloads its default model from HuggingFace on first startup. Once cached, the model works fully offline. See [Network Requirements](/frigate/network_requirements#hardware-specific-detector-models) for details.
+
+:::
+
 ### Configuration
 
 When configuring the AXEngine detector, you have to specify the model name.

docs/docs/configuration/semantic_search.md

@@ -13,6 +13,12 @@ Frigate uses models from [Jina AI](https://huggingface.co/jinaai) to create and
 
 Semantic Search is accessed via the _Explore_ view in the Frigate UI.
 
+:::info
+
+Semantic search requires a one-time internet connection to download embedding models from HuggingFace. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
 ## Minimum System Requirements
 
 Semantic Search works by running a large AI model locally on your system. Small or underpowered systems like a Raspberry Pi will not run Semantic Search reliably or at all.

docs/docs/frigate/network_requirements.md

@@ -1,6 +1,6 @@
 ---
 id: network_requirements
-title: Network and Internet Requirements
+title: Network Requirements
 ---
 
 # Network Requirements

docs/docs/integrations/mqtt.md

@@ -5,6 +5,12 @@ title: MQTT
 
 These are the MQTT messages generated by Frigate. The default topic_prefix is `frigate`, but can be changed in the config file.
 
+:::info
+
+MQTT requires a network connection to your broker. This is typically local, but will require internet if using a cloud-hosted MQTT broker. See [Network Requirements](/frigate/network_requirements#mqtt) for details.
+
+:::
+
 ## General Frigate Topics
 
 ### `frigate/available`

docs/docs/integrations/plus.md

@@ -5,6 +5,12 @@ title: Frigate+
 
 For more information about how to use Frigate+ to improve your model, see the [Frigate+ docs](/plus/).
 
+:::info
+
+Frigate+ requires an active internet connection to communicate with `https://api.frigate.video` for model downloads, image uploads, and annotations. See [Network Requirements](/frigate/network_requirements#frigate) for details.
+
+:::
+
 ## Setup
 
 ### Create an account