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Quick Update on Inngest blogpost #2909

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Jan 7, 2025
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Quick Update on Inngest blogpost #2909

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4 changes: 2 additions & 2 deletions blog/2025-01-07-inngest-ai-workflows/index.mdx
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Expand Up @@ -42,7 +42,7 @@ Once those datasets are loaded into our Weaviate cluster, our workflow is trigge
Triggered by the above event, our Inngest agentic workflow goes through the following steps:


![An Inngest Workflow](./img/image1.png)
![An Inngest Workflow](./img/image3.png)

1. First, our workflow will retrieve some recipes from the `Thefoodprocessorcuisinetype` Weaviate collection.
2. Then, using the OpenAI `gpt-4` model, it will try to see if the food preferences contain some allergies.
Expand Down Expand Up @@ -258,7 +258,7 @@ export const generateMeal = inngest.createFunction(

Starting our dinner generator workflow with food preferences including `”no nuts”` triggers the `"query-ingredients-alternatives"` Weaviate query step based on the detection of the `"Allergies analysis"` LLM step.

![Architectural Diagram](./img/image3.png)
![Architectural Diagram](./img/image1.png)

The following steps of our Dinner generator workflow leverage Weaviate’s `collection.query.nearText()` and Inngest’s `step.ai.infer()` APIs similarly generate the menu, get the relevant wine pairing, and generate the shopping list.
Let’s now see our Dinner generator in action.
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