{"id":437,"date":"2025-10-21T09:11:06","date_gmt":"2025-10-21T16:11:06","guid":{"rendered":"https:\/\/simpkins.social\/cliff\/?p=437"},"modified":"2025-10-24T14:08:09","modified_gmt":"2025-10-24T21:08:09","slug":"how-to-use-ai-studio-with-mealie-as-the-llm-recipe-parser","status":"publish","type":"post","link":"https:\/\/simpkins.social\/cliff\/blog\/2025\/10\/21\/how-to-use-ai-studio-with-mealie-as-the-llm-recipe-parser\/","title":{"rendered":"How to use AI Studio with Mealie as the LLM Recipe Parser"},"content":{"rendered":"\n

My family is trying Mealie.io<\/a> as our recipe management system, and I found an easy way to use Nebius AI Studio<\/a> with Mealie as the LLM Recipe Parser. This post documents the beginning of my journey, as this path hasn’t been documented yet.<\/p>\n\n\n\n

For now, I am only using AI Studio as the recipe parser within Mealie. The image-processing capabilities are still not working reliably yet, and I’ll update this post as I figure this part out.<\/em> <\/p>\n\n\n\n

What is Mealie.io?<\/h2>\n\n\n\n

As quick context, Mealie.io<\/a> is an opensource recipe management system that you can easily self-host. The software allows you to easily import and manage recipes for yourself and your friends\/family. This is particularly interesting for our family, as we have amassed quite the collection of recipes over the decades. We’re currently using Plan To Eat<\/a> to manage our recipes, but we much rather be able to manage our recipes locally rather than in a SaaS that holds us at their pricing and uptime whims.<\/p>\n\n\n\n

Installing the software locally was relatively easy using the official Docker images. I set our local instance up on my Synology NAS using Marius Bogdan’s guidance<\/a>, which included how to also install Portainer<\/a> to easily compose and manage the container’s backend. While Portainer<\/a> is new to me, I love how it simplifies the composition and management process.<\/p>\n\n\n\n

Why add an LLM to Mealie?<\/h2>\n\n\n\n

The Mealie recipe import process is fantastic and works most of the time for us, but it had issues importing the ingredients on a couple of food blog sites that we follow. Thankfully, Mealie provides the ability to connect the backend to an OpenAI-compatible LLM<\/a>.<\/p>\n\n\n\n

I selected AI Studio for four reasons: (1) I use Claude, but it doesn’t support OpenAI protocol; (2) I didn’t want to add the complexity of OpenRouter just for home recipe imports; (3) I didn’t feel like learning how to self-host an open LLM; and (4) I want a simple LLM experience – no compute management, no container management, etc. <\/p>\n\n\n\n

For these reasons, Nebius AI Studio<\/a> felt like a no-brainer – they provide a wide selection of inference models hosted on their own infrastructure at a much better price. For example, below are screenshots comparing a couple of models from Nebius to OpenAI’s [cheaper] models. Even for GPT nano, Nebius is half the cost and their models are fine-tunable. Additionally, I am interested in learning more about open LLM\/inference models, and this provides me with an easy onramp into that experience.<\/p>\n\n\n

\n
\"Screenshots<\/figure>\n<\/div>\n\n\n

So with an LLM provider in mind, let’s connect up AI Studio with Mealie as the LLM recipe parser.<\/p>\n\n\n\n

Connecting AI Studio to Mealie<\/h2>\n\n\n\n

It’s really easy to connect Nebius AI Studio to Mealie:<\/p>\n\n\n\n

    \n
  1. Select the LLM model that you want to use<\/li>\n\n\n\n
  2. Retrieve your API key for AI Studio<\/li>\n\n\n\n
  3. Update the Mealie configuration file<\/li>\n\n\n\n
  4. Validate that it works in Mealie<\/li>\n<\/ol>\n\n\n\n

    Step 1: Select your LLM model<\/h3>\n\n\n\n

    There are a lot of LLM options out there – I am starting out this week testing openai\/gpt-oss-20b<\/code> (for text only parsing) and google\/gemma-3-27b-it<\/code> (for image processing), but I’m going to be looking at a few additional models in the coming month.<\/p>\n\n\n\n

    For me, the choice will likely come down to how model performance and how fine-tuning progresses for this use case. So far, they are performing well without the need for fine-tuning, but I will update this as I learn more.<\/p>\n\n\n\n

    Step 2: Retrieve an API key from AI Studio<\/h3>\n\n\n\n

    To get an API key that you can use in Mealie, complete the following steps (and note that visual aid below to view the steps):<\/p>\n\n\n\n

      \n
    1. Login to Nebius AI Studio<\/li>\n\n\n\n
    2. Click the ‘Get API key’ button<\/li>\n\n\n\n
    3. In the popup dialog, provide a name for the API key (e.g., ‘Mealie App’)<\/li>\n\n\n\n
    4. Click ‘Create’ to create the API key<\/li>\n\n\n\n
    5. Copy the new API key using the clipboard icon (and save this somewhere safe!)<\/li>\n\n\n\n
    6. Click ‘Close’ to close the dialog<\/li>\n<\/ol>\n\n\n
      \n
      \"Screenshot<\/figure>\n<\/div>\n\n
      \n
      \"Screenshot<\/figure>\n<\/div>\n\n\n

      Step 3: Update the Mealie configuration file<\/h3>\n\n\n\n

      Following the instructions for Mealie’s OpenAI integration<\/a>, I used the following steps to update Mealie running on my Synology NAS:<\/p>\n\n\n\n

        \n
      1. Open up your Portainer instance and connect to your local environment<\/li>\n\n\n\n
      2. Select Stacks from the left navigation pane<\/li>\n\n\n\n
      3. Select the stack you want to update – for example, ‘Mealie’<\/li>\n\n\n\n
      4. Within the Stack details page, click Editor<\/code> to edit the stack’s Docker Compose file<\/li>\n\n\n\n
      5. Add the OpenAI settings to the end of the environment<\/code> section of Compose file<\/li>\n\n\n\n
      6. Click the Update the stack<\/code> button to apply the changes<\/li>\n\n\n\n
      7. When prompted to confirm, click the Update button and Portainer will update Mealie’s stack<\/li>\n<\/ol>\n\n\n\n

        There are four environment configuration lines that you’ll need for AI Studio (replacing API_KEY<\/em> with your real API key from Step 2) to do recipe parsing using the LLM:<\/p>\n\n\n\n

        OPENAI_BASE_URL: https:\/\/api.studio.nebius.com\/v1\/\nOPENAI_API_KEY: API_KEY<\/em>\nOPENAI_MODEL: openai\/gpt-oss-20b\nOPENAI_ENABLE_IMAGE_SERVICES: false<\/code><\/pre>\n\n\n\n
        To use Mealie’s image services, we need to enable image services and add a new configuration line that enables us to increase the maximum number of tokens to support the extra processing.<\/p>\n\n\n\n
        OPENAI_BASE_URL: https:\/\/api.studio.nebius.com\/v1\/\nOPENAI_API_KEY: API_KEY<\/em>\nOPENAI_MODEL: google\/gemma-3-27b-it\nOPENAI_ENABLE_IMAGE_SERVICES: true\nOPENAI_CUSTOM_PARAMS: '{\"max_tokens\": 4096, \"temperature\": 0.5}' <\/code><\/pre>\n\n\n\n
        Below are screenshots of the Portainer experience, for those who want to follow along visually.<\/p>\n\n\n\n
        \"Screenshot<\/figure>\n\n\n
        \n
        \"Screenshot<\/figure>\n<\/div>\n\n
        \n
        \"Screenshot<\/figure>\n<\/div>\n\n
        \n
        \"Screenshot<\/figure>\n<\/div>\n\n\n
        Step 4: Validate that it works in Mealie<\/h3>\n\n\n\n
        Now point your browser to your Mealie instance (e.g., 192.168.1.120:9250) and do the following to verify that everything is wired up:<\/p>\n\n\n\n
          \n
        1. Once logged in as an admin user, click Settings <\/li>\n\n\n\n
        2. Select Admin Settings<\/code> from the fly-out menu<\/li>\n\n\n\n
        3. Verify that ‘OpenAI Ready’ has a green checkmark, marking it as working properly<\/li>\n<\/ol>\n\n\n
          \n
          \"Mealie<\/figure>\n<\/div>\n\n\n
          \n
          Once Mealie sees the OpenAI connection available, you can use the debug menu to validate the parsers work:<\/p>\n\n\n\n
            \n
          • OpenAI <\/em><\/strong>debugs that image processing works. The screenshot below shows a recipe tested using this debug option.<\/li>\n\n\n\n
          • Parser <\/em><\/strong>validates how text is processed. The primary test that you are looking for here is how the ingredients section is parsed.<\/li>\n<\/ul>\n<\/div>\n\n\n
            \n
            \"Mealie<\/figure>\n<\/div>\n\n\n
            Next Step – Getting image processing to work<\/h3>\n\n\n\n
            As mentioned earlier in the post – parsing recipes from an image is not yet working for me.<\/p>\n\n\n\n
              \n
            • The OpenAI image processer returns success (see screen capture above)<\/li>\n\n\n\n
            • The LLM successfully parses the recipe and returns a response (once I increased the maximum number of tokens) <\/li>\n\n\n\n
            • Beyond that, results are inconsistent – Some very basic recipe images process successfully, while others fail with this general ‘Something Went Wrong!’ message (see screenshot below)<\/li>\n<\/ul>\n\n\n
              \n
              \"Oops<\/figure>\n<\/div>\n\n\n
              I’m in the process of debugging Mealie to figure out what’s happening – my current thought is that the LLM is returning a different JSON structure than Mealie is expecting. If you have thoughts or suggestions, let me know.<\/p>\n\n\n\n
              Next Step – Fine tuning the LLM<\/h3>\n\n\n\n
              I also want to experiment with different models and fine-tuning to see if that makes a difference in the parsing process. Everything seems to “just work” at the moment, but the imports still require human review during the import\/parsing process. <\/p>\n\n\n\n
              That’s it – it’s time to start importing<\/h2>\n\n\n\n
              That’s all you need to do. Honestly, I was surprised at both how easy it was to add an LLM to Mealie and how much power an open LLM provides out-of-the-box.<\/p>\n\n\n\n
              <\/p>\n","protected":false},"excerpt":{"rendered":"
              My family is trying Mealie.io as our recipe management system, and I found an easy way to use Nebius AI Studio with Mealie as the LLM Recipe Parser. 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