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I walk you through a single, multimodal embedding model that handles text, images, tables —and even code —inside one vector space. In this short demo I show the install steps, run RAG retrieval benchmarks, and compare cost vs. traditional multi-model setups. If you’re building search or RAG pipelines, see how one all-in-one embedding can simplify your stack and boost accuracy.
LINKS:
Notebook: https://colab.research.google.com/drive/1TFK4KLqEnddmgyzgjO7oNNw7nZWsdR09#scrollTo=ktzbaWGoEO4f
https://jina.ai/news/jina-embeddings-v4-universal-embeddings-for-multimodal-multilingual-retrieval
https://jina.ai/news/late-chunking-in-long-context-embedding-models/
https://huggingface.co/blog/matryoshka
https://cohere.com/blog/embed-4
https://github.com/PromtEngineer/localGPT-Vision
https://huggingface.co/blog/manu/colpali
https://weaviate.io/developers/weaviate/tutorials/multi-vector-embeddings
Relevant Videos:
https://youtu.be/Ilf26xjT5is
https://youtu.be/hhMXE9-JUAc
https://youtu.be/V1VOdoEFaDw
https://youtu.be/bQL-yok_0qw
Website: https://engineerprompt.ai/
RAG Beyond Basics Course:
https://prompt-s-site.thinkific.com/courses/rag
Let’s Connect:
🦾 Discord: https://discord.com/invite/t4eYQRUcXB
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