# or Google's multimodalembedding@001 Model Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/or-google-s-multimodalembedding-001-model
> Markdown URL: https://aitinkerers.org/technologies/or-google-s-multimodalembedding-001-model.md
> Technology record last updated: 2026-03-23T08:42:50Z
> Generated: 2026-09-21T18:36:14Z

A Vertex AI foundation model that generates unified 1408-dimension vectors from text, image, and video inputs for cross-modal search.

Google's multimodalembedding@001 model (part of the Vertex AI ecosystem) maps diverse data types into a shared vector space. It processes text (up to 32 tokens), images (standard formats), and video (up to 120 seconds) to enable high-performance applications like semantic image retrieval and video content recommendation. By outputting a consistent 1408-dimensional embedding, it allows developers to calculate cosine similarity across different media formats without separate specialized encoders.

- Official technology site: https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Let's talk about Embeddings](https://cologne.aitinkerers.org/talks/rsvp_NX6I1fvuINY)

I will talk about why embeddings are such a great thing. They can do so many tasks that we set out a huge LLM to do, but in a much more efficient and cost saving way. There are tons of use cases for embeddings, and in this talk, I just want to give a simple insight into some use cases of embeddings, beside RAG. I want to cover (not sure if this is the final list yet): - RAG - Image Search - Image Classifier - Advanced Image Classifier with an added MLP Head - Text Matching across languages - Getting Clear Text Input for Customer Intention Analysis (Main Focus) - And a short example of how you can use that clear text input to improve what you are offering as a company. (Main Focus) As the 5 Minute Time slot is very narrow, I will likely focus on the Clear Text Input Analysis part, as I think that is quite a nice use case for embedding based, customer facing search. While I will not show a lot of code in this presentation, coding this yourself is so easy, that anyone could do it without seeing any code. It's more about the idea and concept for this usecase.

- Event context: AI Tinkerers Cologne 3: Demos, Code, and Architecture — 2026-03-05 — Cologne
- Public talk page: https://cologne.aitinkerers.org/talks/rsvp_NX6I1fvuINY

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