Gemma 4's native OCR and document-parsing capabilities allow you to generate text embeddings directly from scanned documents, PDFs, and image-heavy files without a separate OCR preprocessing step, simplifying the ingestion pipeline for Zilliz Cloud document RAG systems.
Traditional document RAG pipelines require chaining multiple services: an OCR engine to extract text, a chunking library to split it, an embedding model to vectorize it, and then ingestion into a vector database. Gemma 4 collapses the first two steps — its multimodal architecture can process a PDF page image and extract structured text content natively, including charts, tables, handwriting, and multilingual text. The resulting text is then vectorized and stored in Zilliz Cloud.
For enterprise document management use cases — contracts, technical manuals, compliance filings — this means fewer failure points in the pipeline and better handling of documents that mix text, diagrams, and tables. Gemma 4's chart comprehension capability is particularly valuable for financial document RAG where embedded charts are often the most important content but are discarded by text-only OCR.
Zilliz Cloud's managed ingestion handles the downstream vector storage and indexing at scale, so teams can focus on Gemma 4's multimodal preprocessing logic rather than infrastructure management.
Related Resources
- Zilliz Cloud Managed Vector Database — managed vector infrastructure
- Retrieval-Augmented Generation — RAG fundamentals
- Vector Embeddings — embedding concepts
- Zilliz Cloud Pricing — plan options