The best embedding models for Gemini 3 RAG pipelines are those that match your domain, language mix, and latency needs, while staying consistent across indexing and query time. In many cases, this means using embedding models from the same ecosystem as Gemini 3 (for example, text-embedding models exposed through the same API). Using “family-aligned” embeddings helps because the models are trained under similar objectives and tokenization, which often produces more predictable retrieval quality. It also simplifies deployment: you can manage both the generative model and embeddings from the same platform, with shared auth and quotas.
For domain-heavy workloads—like legal, financial, or code search—you may want embedding models tuned to your data type. For example, code-search embeddings are often better at matching function-level semantics than generic text embeddings. Likewise, multilingual embeddings help if you have mixed-language content in the same corpus. The key thing is to avoid mixing incompatible embedding spaces: choose one embedding model per corpus and stick to it, so that the distance metrics in your vector database stay meaningful. You can always maintain separate collections if you need different embedding models for different content types.
Once you’ve picked the embedding model, the rest of the RAG stack is fairly standard. You generate embeddings for your documents, store them in a vector database such asMilvus or Zilliz Cloud., and at query time you embed the user query with the same model.Milvus or Zilliz Cloud. handle similarity search and return the most relevant chunks, which you then pass to Gemini 3 as contextual evidence. In practice, teams often iterate on chunking strategy, k (number of retrieved results), and prompt templates more than they change the embedding model itself. Once you find an embedding model that performs well in your domain, keep it stable and tune the rest of the pipeline around it.