Gemini API File Search Multimodal RAG Planner
Gemini API File Search now supports multimodal retrieval, custom metadata, and PDF page-level citations. Use this planner to scope a demo that can answer from images and PDFs without hiding source evidence from the user.
Multimodal RAG fit planner
Score whether your first Gemini File Search demo should focus on images, PDFs, page citations, metadata filters, or a smaller retrieval slice.
95
fit score
Starter architecture
| Layer | What to build | Failure to avoid |
|---|---|---|
| Ingestion | Upload a controlled set of PDFs, screenshots, and images with stable IDs. | Dumping a whole drive before you know the retrieval shape. |
| Metadata | Attach department, status, date, access class, and product line labels. | Relying on filenames for permission and filtering logic. |
| Retrieval | Ask a narrow question, retrieve sources, then generate the answer. | Letting the model answer without showing retrieved evidence. |
| Answer UI | Show source file, page, snippet, and confidence language. | Burying citations in logs or only returning a paragraph. |
Implementation checklist
- Start with 20-100 representative files, not the full corpus.
- Define metadata keys before upload so filtering is testable.
- Create a fixed prompt set for image search, PDF citations, and mixed questions.
- Show page citations beside each answer when the source is a PDF.
- Separate RAG search from live transactional writes.
- Log query length, selected corpus, retrieval count, latency, and failure type without storing raw private files.