Merge a2b92caab0 into d036061e3f

README.md

@@ -12,6 +12,44 @@
 
 \[English\] | [简体中文](https://github.com/blakeblackshear/frigate/blob/dev/README_CN.md)
 
+---
+
+<p align="center">
+  <a href="https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=frigate">
+    <img src="docs/static/img/branding/atlas-cloud-logo.png" alt="Atlas Cloud" width="200">
+  </a>
+</p>
+
+<p align="center">
+  <b><a href="https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=frigate">Atlas Cloud</a></b> is an OpenAI-compatible inference platform that can power Frigate's
+  <a href="https://docs.frigate.video/configuration/genai/">Generative AI</a> features as a drop-in multimodal LLM backend.
+  Point the <code>atlas</code> provider at Atlas Cloud and use a vision-capable model
+  (such as <code>qwen/qwen3-vl-235b-a22b-thinking</code> or <code>Qwen/Qwen3-VL-235B-A22B-Instruct</code>)
+  to generate natural-language object and review descriptions from detection frames —
+  no local GPU required. See the <a href="https://docs.frigate.video/configuration/genai/">GenAI configuration docs</a>
+  to get started, or grab a <a href="https://www.atlascloud.ai/console/coding-plan">coding plan</a>.
+</p>
+
+<details>
+<summary>Vision-capable Atlas Cloud models for GenAI descriptions</summary>
+
+Frigate's GenAI features require a **vision-capable** model. Good multimodal choices on Atlas Cloud include:
+
+- `qwen/qwen3-vl-235b-a22b-thinking`
+- `Qwen/Qwen3-VL-235B-A22B-Instruct`
+- `qwen/qwen3-vl-30b-a3b-instruct`
+- `qwen/qwen3-vl-30b-a3b-thinking`
+- `qwen/qwen3-vl-8b-instruct`
+- `google/gemini-3.5-flash`
+- `google/gemini-3.1-pro-preview`
+
+The full, always-current model catalog is available at the
+[Atlas Cloud console](https://www.atlascloud.ai/console).
+
+</details>
+
+---
+
 A complete and local NVR designed for [Home Assistant](https://www.home-assistant.io) with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
 
 Use of a GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported [object detectors](https://docs.frigate.video/configuration/object_detectors/).

README_CN.md

@@ -12,6 +12,43 @@
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
+---
+
+<p align="center">
+  <a href="https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=frigate">
+    <img src="docs/static/img/branding/atlas-cloud-logo.png" alt="Atlas Cloud" width="200">
+  </a>
+</p>
+
+<p align="center">
+  <b><a href="https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=frigate">Atlas Cloud</a></b> 是一个兼容 OpenAI 接口的推理平台，可作为即插即用的多模态 LLM 后端，
+  为 Frigate 的<a href="https://docs.frigate.video/configuration/genai/">生成式 AI（Generative AI）</a>功能提供算力支持。
+  只需将 <code>atlas</code> provider 指向 Atlas Cloud，并选用一个支持视觉的模型
+  （例如 <code>qwen/qwen3-vl-235b-a22b-thinking</code> 或 <code>Qwen/Qwen3-VL-235B-A22B-Instruct</code>），
+  即可基于检测帧画面生成自然语言的物体描述与审查摘要，无需本地 GPU。
+  请参阅 <a href="https://docs.frigate.video/configuration/genai/">GenAI 配置文档</a>开始使用，
+  或了解 <a href="https://www.atlascloud.ai/console/coding-plan">coding plan</a>。
+</p>
+
+<details>
+<summary>适合做 GenAI 描述的 Atlas Cloud 多模态模型</summary>
+
+Frigate 的 GenAI 功能要求使用**支持视觉**的模型。Atlas Cloud 上推荐的多模态模型包括：
+
+- `qwen/qwen3-vl-235b-a22b-thinking`
+- `Qwen/Qwen3-VL-235B-A22B-Instruct`
+- `qwen/qwen3-vl-30b-a3b-instruct`
+- `qwen/qwen3-vl-30b-a3b-thinking`
+- `qwen/qwen3-vl-8b-instruct`
+- `google/gemini-3.5-flash`
+- `google/gemini-3.1-pro-preview`
+
+完整且实时更新的模型列表请见 [Atlas Cloud 控制台](https://www.atlascloud.ai/console)。
+
+</details>
+
+---
+
 一个完整的本地网络视频录像机（NVR），专为[Home Assistant](https://www.home-assistant.io)设计，具备 AI 目标/物体检测功能。使用 OpenCV 和 TensorFlow 在本地为 IP 摄像头执行实时物体检测。
 
 强烈推荐使用 GPU 或者 AI 加速器（例如[Google Coral 加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)等）。它们的运行效率远远高于现在的顶级 CPU，并且功耗也极低。

docs/docs/configuration/genai/config.md

@@ -386,3 +386,44 @@ genai:
 
 </TabItem>
 </ConfigTabs>
+
+### Atlas Cloud
+
+[Atlas Cloud](https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=frigate) is an OpenAI-compatible inference platform that serves a range of vision-capable models, so it can act as a drop-in multimodal backend for Frigate's Generative AI features. The `atlas` provider defaults its base URL to the Atlas Cloud endpoint, so a minimal config only needs your API key and a model.
+
+#### Supported Models
+
+You must use a vision capable model with Frigate. Recommended multimodal models on Atlas Cloud include `qwen/qwen3-vl-235b-a22b-thinking`, `Qwen/Qwen3-VL-235B-A22B-Instruct`, `qwen/qwen3-vl-30b-a3b-instruct`, and `google/gemini-3.5-flash`. The full, always-current catalog is available in the [Atlas Cloud console](https://www.atlascloud.ai/console).
+
+#### Get API Key
+
+To start using Atlas Cloud, create an API key from the [Atlas Cloud console](https://www.atlascloud.ai/console/api-keys).
+
+#### Configuration
+
+<ConfigTabs>
+<TabItem value="ui">
+
+1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
+   - Set **Provider** to `atlas`
+   - Set **API key** to your Atlas Cloud API key (or use an environment variable such as `{FRIGATE_ATLAS_API_KEY}`)
+   - Set **Model** to a vision-capable model (e.g., `qwen/qwen3-vl-235b-a22b-thinking`)
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+genai:
+  provider: atlas
+  api_key: "{FRIGATE_ATLAS_API_KEY}"
+  model: qwen/qwen3-vl-235b-a22b-thinking
+```
+
+</TabItem>
+</ConfigTabs>
+
+:::note
+
+The `atlas` provider points to `https://api.atlascloud.ai/v1` by default. To target a different OpenAI-compatible endpoint, set `base_url` explicitly.
+
+:::

frigate/config/camera/genai.py

@@ -12,6 +12,7 @@ __all__ = ["GenAIConfig", "GenAIProviderEnum", "GenAIRoleEnum"]
 class GenAIProviderEnum(str, Enum):
     openai = "openai"
     azure_openai = "azure_openai"
+    atlas = "atlas"
     gemini = "gemini"
     ollama = "ollama"
     llamacpp = "llamacpp"

frigate/genai/plugins/atlas.py

@@ -0,0 +1,71 @@
+"""Atlas Cloud Provider for Frigate AI.
+
+Atlas Cloud (https://www.atlascloud.ai) is an OpenAI-compatible inference
+platform that serves a range of vision-capable models. Because its chat
+completions API follows the OpenAI standard, this provider inherits all
+transport, vision, streaming, reasoning, and tool-calling logic from
+:class:`OpenAIClient` and only overrides what is Atlas-specific:
+
+- Client construction: defaults ``base_url`` to the Atlas Cloud endpoint
+  when the user has not set one explicitly, so a minimal config (provider +
+  api_key + model) works out of the box. A user-supplied ``base_url`` still
+  takes precedence.
+- Context size: the Atlas ``/models`` endpoint does not reliably surface a
+  per-model context window, so we fall back to a conservative default rather
+  than the model-name heuristic used by OpenAI. It can be overridden via
+  ``provider_options.context_size``.
+"""
+
+import logging
+from typing import Optional
+
+from openai import OpenAI
+
+from frigate.config import GenAIProviderEnum
+from frigate.genai import register_genai_provider
+from frigate.genai.plugins.openai import OpenAIClient
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_BASE_URL = "https://api.atlascloud.ai/v1"
+
+# Atlas serves large-context models, but its model listing does not expose a
+# per-model context window; default conservatively and let users override via
+# provider_options.context_size when they know their model's window.
+DEFAULT_CONTEXT_SIZE = 32000
+
+
+@register_genai_provider(GenAIProviderEnum.atlas)
+class AtlasClient(OpenAIClient):
+    """Generative AI client for Frigate using Atlas Cloud."""
+
+    def _init_provider(self) -> OpenAI:
+        """Initialize the OpenAI client pointed at Atlas Cloud.
+
+        Defaults ``base_url`` to the Atlas endpoint when the user has not set
+        one, then defers to the OpenAI implementation for everything else.
+        """
+        if not self.genai_config.base_url:
+            self.genai_config.base_url = DEFAULT_BASE_URL
+
+        return super()._init_provider()
+
+    def get_context_size(self) -> int:
+        """Return the context window for Atlas models.
+
+        A manually specified ``context_size`` in ``provider_options`` always
+        wins; otherwise fall back to a conservative default since Atlas does
+        not reliably surface per-model context windows.
+        """
+        if self.context_size is not None:
+            return self.context_size
+
+        provider_context_size: Optional[int] = self.genai_config.provider_options.get(
+            "context_size"
+        )
+        if provider_context_size is not None:
+            self.context_size = provider_context_size
+            return self.context_size
+
+        self.context_size = DEFAULT_CONTEXT_SIZE
+        return self.context_size