bge-multilingual-gemma2

Architecture

Decoder-based (Gemma 2 9B) embedding model. The final hidden state is pooled into a dense sentence embedding and trained with contrastive (InfoNCE) loss over multilingual query-document pairs, including cross-lingual positives so a query in one language retrieves documents in another.

Key Capabilities

• •Dense text embeddings across 100+ languages
• •Strong cross-lingual retrieval (query and document in different languages)
• •Instruction-aware query encoding inherited from the LLM backbone
• •Long-context passages for document-level retrieval

Use Cases on Mixpeek

• •Multilingual document and metadata search over a global content library
• •Cross-lingual retrieval where an English query must surface non-English documents
• •Agent perception over international support tickets, contracts, or transcripts
• •First-stage recall feeding a reranker for precision

Use bge-multilingual-gemma2 on Mixpeek

Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

On Mixpeek, bge-multilingual-gemma2 runs as a managed extractor inside a processing pipeline. Point a bucket of feature extraction data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.