Building Multimodal Workflows with a Local LLM
LLM Applications Building Multimodal Workflows with a Local LLM Image inputs and structured outputs with Gemma 4 and Ollama Shuai Guo Aug 12, 2026 9 min read Share Generated by GPT-Image 2 Running an LLM locally is attractive when working with private data or building workflows that should run on our own machine. And those workflows no longer need to be limited to text. In this post, we’ll build such a workflow with Gemma 4 and Ollama.
- ▪LLM Applications Building Multimodal Workflows with a Local LLM Image inputs and structured outputs with Gemma 4 and Ollama Shuai Guo Aug 12, 2026 9 min read Share Generated by GPT-Image 2 Running an LLM locally is attractive when working w
- ▪And those workflows no longer need to be limited to text.
- ▪In this post, we’ll build such a workflow with Gemma 4 and Ollama.
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| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/building-multimodal-workflows-with-a-local-llm/ |
| Publication time | Wed, 12 Aug 2026 13:30:00 +0000 |
| Retrieval time | 2026-08-12T13:36:37.134Z |
| Last seen | 2026-08-12T13:36:37.134Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | t3DfEm3fyu28 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
LLM Applications Building Multimodal Workflows with a Local LLM Image inputs and structured outputs with Gemma 4 and Ollama Shuai Guo Aug 12, 2026 9 min read Share Generated by GPT-Image 2 Running an LLM locally is attractive when working with private data or building workflows that should run on our own machine. And those workflows no longer need to be limited to text. In this post, we’ll build such a workflow with Gemma 4 and Ollama. We’ll place Gemma 4’s multimodal capability inside a larger process, where downstream steps can consume its structured output. I recently took a trip to Finland, and I took quite some photos during my journey. In this post, I’ll show you how I use the workflow to analyze them, and then show how the same workflow can power a small application. 1.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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