AdoptAutomate

EmbeddingGemma

Google's embedding model. Strong retrieval quality from a small footprint.

EmbeddingGemma is Google's open embedding model optimised for retrieval tasks. Run locally via Ollama, it produces high-quality embeddings for semantic search and RAG pipelines without cloud API dependencies.

An alternative to nomic-embed-text with different trade-offs in dimension size and retrieval accuracy. We run both and evaluate per use case. The Google lineage means good multilingual support, which matters for document collections that span languages.

aiembeddingslocalrag

Adoptnomic-embed-textLocal embedding model. Fast, free, good enough for production RAG.AdoptOllamaRun LLMs locally. Essential for RAG and embedding pipelines.AdoptLM StudioLocal model server with an OpenAI-compatible API. Drop-in replacement.AdoptDoclingDocument parsing that actually works. PDF, DOCX, images — all handled.AdoptGemmaGoogle's open-weight model. Best local reasoning at its size.

EmbeddingGemma

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