Skip to main content

Configuring Generative AI

Configuration​

A Generative AI provider can be configured in the global config, which will make the Generative AI features available for use. There are currently 3 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI section below.

To use Generative AI, you must define a single provider at the global level of your Frigate configuration. If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with FRIGATE_.

Ollama​

warning

Using Ollama on CPU is not recommended, high inference times make using Generative AI impractical.

Ollama allows you to self-host large language models and keep everything running locally. It provides a nice API over llama.cpp. It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.

Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a Docker container available.

Parallel requests also come with some caveats. You will need to set OLLAMA_NUM_PARALLEL=1 and choose a OLLAMA_MAX_QUEUE and OLLAMA_MAX_LOADED_MODELS values that are appropriate for your hardware and preferences. See the Ollama documentation.

Supported Models​

You must use a vision capable model with Frigate. Current model variants can be found in their model library. Note that Frigate will not automatically download the model you specify in your config, Ollama will try to download the model but it may take longer than the timeout, it is recommended to pull the model beforehand by running ollama pull your_model on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.

tip

Each model is available in multiple parameter sizes (3b, 4b, 8b, etc.). Larger sizes are more capable of complex tasks and understanding of situations, but requires more memory and computational resources. It is recommended to try multiple models and experiment to see which performs best.

The following models are recommended:

ModelNotes
Intern3.5VLRelatively fast with good vision comprehension
gemma3Strong frame-to-frame understanding, slower inference times
qwen2.5vlFast but capable model with good vision comprehension
llava-phi3Lightweight and fast model with vision comprehension
note

You should have at least 8 GB of RAM available (or VRAM if running on GPU) to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.

Configuration​

genai:
  provider: ollama
  base_url: http://localhost:11434
  model: minicpm-v:8b
  provider_options:  # other Ollama client options can be defined
    keep_alive: -1

Google Gemini​

Google Gemini has a free tier allowing 15 queries per minute to the API, which is more than sufficient for standard Frigate usage.

Supported Models​

You must use a vision capable model with Frigate. Current model variants can be found in their documentation. At the time of writing, this includes gemini-1.5-pro and gemini-1.5-flash.

Get API Key​

To start using Gemini, you must first get an API key from Google AI Studio.

  1. Accept the Terms of Service
  2. Click "Get API Key" from the right hand navigation
  3. Click "Create API key in new project"
  4. Copy the API key for use in your config

Configuration​

genai:
  provider: gemini
  api_key: "{FRIGATE_GEMINI_API_KEY}"
  model: gemini-1.5-flash

OpenAI​

OpenAI does not have a free tier for their API. With the release of gpt-4o, pricing has been reduced and each generation should cost fractions of a cent if you choose to go this route.

Supported Models​

You must use a vision capable model with Frigate. Current model variants can be found in their documentation. At the time of writing, this includes gpt-4o and gpt-4-turbo.

Get API Key​

To start using OpenAI, you must first create an API key and configure billing.

Configuration​

genai:
  provider: openai
  api_key: "{FRIGATE_OPENAI_API_KEY}"
  model: gpt-4o
note

To use a different OpenAI-compatible API endpoint, set the OPENAI_BASE_URL environment variable to your provider's API URL.

Azure OpenAI​

Microsoft offers several vision models through Azure OpenAI. A subscription is required.

Supported Models​

You must use a vision capable model with Frigate. Current model variants can be found in their documentation. At the time of writing, this includes gpt-4o and gpt-4-turbo.

Create Resource and Get API Key​

To start using Azure OpenAI, you must first create a resource. You'll need your API key and resource URL, which must include the api-version parameter (see the example below). The model field is not required in your configuration as the model is part of the deployment name you chose when deploying the resource.

Configuration​

genai:
  provider: azure_openai
  base_url: https://example-endpoint.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview
  api_key: "{FRIGATE_OPENAI_API_KEY}"