Generate embeddings with Gemma 4, insert vectors into Milvus collections, then query using Milvus semantic search APIs.
The integration workflow is straightforward:
1. Generate embeddings: Use Gemma 4 (running locally or on your server) to embed documents and queries into vectors. Specify your target embedding dimension and choose extraction layer based on quality/speed requirements.
2. Create Milvus collection: Define a collection schema with a vector field matching Gemma 4’s output dimension. For multimodal search, also include fields for original content, metadata, and document type.
3. Insert vectors: Batch insert embeddings into Milvus along with document references and metadata. Milvus automatically indexes vectors for efficient retrieval.
4. Execute semantic search: Query Milvus with a query embedding from Gemma 4. Milvus returns the most similar vectors (and associated documents) instantly.
5. Optional filtering: Combine vector similarity with metadata filters to narrow results by document type, date, language, or custom attributes.
Milvus handles indexing strategy automatically—it chooses appropriate index types based on your data size and hardware. For production systems, configure replication and backups to ensure reliability.
For real-time systems, update embeddings as documents change. Milvus supports efficient upserts and deletions, keeping your search index synchronized with source data.
Related Resources