A practical guide to building RAG pipelines that retrieve text, images, video, and documents together using Google's Gemini Embedding 2 on Vertex AI, from architecture decisions to production deployment.
Most RAG implementations today are text-only: chunk documents, embed them, store vectors, retrieve on query. It works, but it misses the richest parts of your knowledge base — diagrams in technical docs, product images in catalogues, video walkthroughs in training materials, and audio from customer calls.
With Gemini Embedding 2, you can embed all of these into a single vector space. When a user asks a question, your RAG pipeline retrieves the most relevant text paragraphs, diagrams, video clips, and audio segments — giving your generation model dramatically richer context.
A multimodal RAG pipeline has four key stages: ingestion, embedding, storage, and retrieval. Each stage needs to handle multiple content types — which adds complexity compared to text-only pipelines, but Gemini Embedding 2 simplifies the embedding stage significantly by providing a single model for all modalities.
Text documents need chunking — we recommend semantic chunking over fixed-size chunks for better retrieval quality. Images should be extracted from PDFs and stored with their surrounding text context. Videos need frame extraction at key moments (scene changes, slide transitions) plus transcript alignment. Audio files need transcription with speaker diarization and timestamp mapping.
The critical insight: each chunk should carry metadata about its source document, modality, position, and relationships to other chunks. This metadata enables re-ranking and context assembly during retrieval.
This is where Gemini Embedding 2 simplifies everything. Previously, you'd need CLIP for images, a text embedding model for documents, and Whisper + text embeddings for audio. Now, a single API call to Gemini Embedding 2 handles all modalities and produces vectors in the same space — meaning a text query can directly match against an image or audio clip.
Use batch embedding for initial indexing — it's 60-70% cheaper than single-item calls. For real-time ingestion (new documents uploaded by users), use the streaming API with proper retry logic and rate limiting.
Store vectors in a database that supports metadata filtering alongside vector search. For Vertex AI-native setups, Vertex AI Vector Search provides tight integration. For flexibility, Pinecone or Weaviate offer excellent multimodal metadata support. For teams already on PostgreSQL, pgvector with HNSW indexing handles moderate scale well.
Key design decision: store content references (S3/GCS URLs) alongside vectors, not the raw content itself. This keeps your vector index lean while allowing the retrieval layer to fetch full content on demand.
Embed the user's query with Gemini Embedding 2, retrieve the top-k most similar chunks across all modalities, then assemble them into a prompt for Gemini's generation model. The generation model (Gemini 2.5 Pro or Flash) natively understands images and text in the prompt, so you can pass retrieved images directly alongside text chunks.
Implement hybrid retrieval — combine dense vector search with sparse keyword search (BM25) for better recall. Re-rank results using a cross-encoder or Gemini itself before passing to generation. This two-stage retrieval consistently outperforms single-stage vector search.
Monitor embedding drift — as your content changes, the distribution of vectors shifts and retrieval quality can degrade. Set up periodic evaluation with a golden test set of queries and expected results. Track precision@k and recall@k over time and re-index when quality drops below threshold.
Cost management is critical at scale. Gemini Embedding 2 API calls add up quickly with multimodal content. Cache embeddings aggressively — content that doesn't change doesn't need re-embedding. Use content hashing to detect changes and only re-embed modified content during incremental updates.
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Multimodal RAG (Retrieval-Augmented Generation) extends traditional text-only RAG by retrieving relevant content across multiple formats — text, images, video, audio, and documents — to provide richer context to the generation model. This produces more accurate and comprehensive AI-generated responses.
For Vertex AI-native deployments, Vertex AI Vector Search provides the tightest integration. Pinecone and Weaviate offer excellent managed options with metadata filtering. pgvector is ideal if you're already running PostgreSQL and want to avoid adding new infrastructure. Choice depends on scale, ops preferences, and existing stack.
Costs depend on content volume, query rate, and vector database choice. For a typical enterprise knowledge base (10K documents, 50K images, 1K videos), expect embedding costs of $50-200/month for ingestion and $100-500/month for query embedding and generation. Vector database costs range from $50-500/month depending on provider and scale.
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