Generate embeddings with Gemma 4, insert vectors into Zilliz Cloud collections, then query using managed semantic search APIs.
The integration workflow is straightforward:
1. Generate embeddings: Use Gemma 4 to embed documents and queries into vectors. Specify your target embedding dimension and choose extraction layer based on quality/speed requirements.
2. Connect to Zilliz Cloud: Create a Zilliz Cloud project and collection with appropriate schema matching Gemma 4's embedding dimensions. Zilliz Cloud handles all infrastructure, replication, and backups automatically.
3. Insert vectors: Batch insert embeddings into Zilliz Cloud along with document references and metadata. Zilliz Cloud automatically indexes vectors for efficient retrieval and replicates across multiple nodes for high availability.
4. Execute semantic search: Query Zilliz Cloud with a query embedding from Gemma 4. Zilliz Cloud returns the most similar vectors (and associated documents) instantly, even across billions of vectors.
5. Optional filtering: Combine vector similarity with metadata filters to narrow results by document type, date, language, or custom attributes.
Zilliz Cloud eliminates operational burden: no infrastructure provisioning, no index tuning, no backup management, no scaling decisions. Focus on generating quality embeddings; Zilliz Cloud ensures they're stored, searched, and maintained reliably with 99.9% uptime.
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