Building an EmbeddingGemma 2 Multimodal RAG Pipeline

Embed your documents, images and audio into one index with EmbeddingGemma 2. Retrieve with a text query, then pass the hits to a generator model. Setting up an EmbeddingGemma 2 multimodal RAG pipeline lets your team query PDFs, presentation decks, product photos and voice recordings from a single search box.

Google announced EmbeddingGemma 2 on October 6, 2026 as an open-weights model family built on the Gemma 4 architecture. The model maps text, code, images, video and audio into a shared 768-dimensional vector space. It is an embedding model that transforms files into numerical vectors rather than a conversational generation model.

At Layer3Labs, we build and run AI systems inside other people's businesses, and we build custom agents and integrations that work over a business's own documents and files. Adding visual files and spoken recordings alongside text changes how teams find information. Text search cannot see diagrams, charts or recorded explanations unless someone first turns them into text.

The weights are available under the Apache 2.0 license on Hugging Face. Modular loading lets you start with a lightweight text and vision index before committing memory to audio files. Building the index takes three parts and four steps.

How to Build a Multimodal RAG Index With EmbeddingGemma 2

Multimodal RAG retrieves images, audio and text from one index, then hands the hits to a generator. It searches images, audio and text in one vector space, not only text parsed from them. A user submits a question in plain text, the pipeline retrieves matching document pages, photos or audio snippets, and a generator model drafts the answer.

Building the pipeline requires three core parts. First, you need an embedder to convert incoming files and search queries into numerical vectors. You load EmbeddingGemma 2, selecting only the encoders required for your specific media formats. Second, you store the resulting vectors in a vector database. Google names Qdrant for vector storage in its technical documentation. Third, you pass the retrieved context to a generator model such as Gemma 4. Any capable generator model can draft the final response from retrieved media.

The operational sequence follows four clear hops: index, embed query, retrieve, and generate. During ingestion, you convert files into embeddings and write them to your database. When a user asks a question, your embedder turns that prompt into a query vector using the SearchQuery task prompt. Your vector store retrieves the nearest document vectors, and your generator combines those retrieved passages to draft the answer. Build the 440M text and vision setup first. Starting with text and slide decks lets you test retrieval before you pay for audio processing.

Selecting Encoders and Dimensions for an EmbeddingGemma 2 Multimodal RAG Pipeline

EmbeddingGemma 2 uses modular loading so you only run the model parameters your data requires. The base setup processes text and code with 270 million parameters. Adding the vision encoder brings the model to 440 million parameters by adding 170 million parameters. The audio encoder adds 300 million parameters, creating a 570 million parameter text and audio setup. The full multimodal model totals 740 million parameters.

All four configurations load from the same model checkpoint and share the exact same 768-dimensional vector space. A query embedded with the lightweight 270M text-only setup matches directly against documents indexed with the full 740M model. Adding audio recordings to your index at a later date does not require recomputing your existing text or image embeddings. The shared space preserves compatibility across all configurations.

Matryoshka Representation Learning allows you to truncate output vectors from 768 dimensions down to 512, 256, or 128 dimensions. This feature enables up to 6x storage reduction. In bfloat16 precision, one million 768-dimension vectors consume roughly 1.5 GB of storage. Truncating those vectors to 128 dimensions shrinks storage to roughly 250 MB. Google recommends 768 or 512 dimensions for visual document retrieval or when search recall matters more than storage costs. Indexes constrained by memory can use 256 dimensions, which retains most quality on text and code and about 95% on image, video, and speech retrieval.

Running On-Device Retrieval When Data Cannot Leave Your Hardware

EmbeddingGemma 2 runs fully offline, which Google ties to privacy and lower latency. Google distributes on-device builds through LiteRT Community on Hugging Face. Running the index locally keeps customer files and recorded calls on your own hardware. Your search pipeline executes without sending company data over an external network.

On a Google Pixel 11 Pro with quantization, Google reports active memory usage of about 191 MB for the text-only weights. The full multimodal configuration uses about 567 MB of active RAM on the same device. Google's Pixel 11 Pro figures are its only published memory numbers. A community demo on Reddit shows image-text retrieval with the vision tower running in the browser on WebGPU. This demo is a community project, not a Google product.

Deploying Gemma 4 alongside EmbeddingGemma 2 provides additional memory savings. Google states that Gemma 4 and EmbeddingGemma 2 share a tokenizer and audio encoder architecture, lowering the combined memory footprint.

• 191 MB active RAM: quantized text-only memory footprint reported by Google on a Pixel 11 Pro.
• 567 MB active RAM: quantized full multimodal footprint reported on the same device.
• Shared architecture: common tokenizer and audio encoder structure with Gemma 4, lowering the combined memory footprint.
• Offline execution: local inference runs without transmitting internal records to external services.

Hardware Requirements and What EmbeddingGemma 2 Costs

EmbeddingGemma 2 is released under the permissive Apache 2.0 license, allowing commercial deployment and self-hosting. Google makes the model weights free to download on Hugging Face and Kaggle. Google lists no per-token fee for the open weights. Your actual deployment expenses consist entirely of the hardware, power, or cloud server instances you choose to run.

Google publishes no hosted API pricing for EmbeddingGemma 2 in its launch materials. If Google publishes a hosted API price, compare it against your hosting cost. Google's only hardware figures are the Pixel 11 Pro RAM numbers above. Size a server with the Local AI Hardware Calculator to determine appropriate hosting specifications for your company's index volume.

Because the weights are open, your team avoids recurring licensing charges. Teams looking to run models without external dependencies can review how to run open-weights models or learn how to run Gemma locally. Evaluating self-hosted retrieval against fine-tuning is explored further in our guide on fine-tuning vs RAG.

Common Failure Modes in an EmbeddingGemma 2 Multimodal RAG System

Queries and documents must use the same dimension. If you store 256-dimension document vectors, truncate your query vectors to 256 too. Test your truncation settings before loading production files.

Truncating down to 128 dimensions degrades multimodal retrieval accuracy. Text and code maintain around 90% quality at 128 dimensions. Image, video, and speech retrieval drop to around 75%. Validate before using 128d for multimodal queries. For slide decks and audio files, maintain 256 or 512 dimensions in your production vector store.

Input length limits require careful preprocessing. The adapted Gemma 4 decoder provides an 8,192-token context window. Google notes that 8,192 tokens covers up to 5.5 minutes of audio, 29 images, or 58 video frames, or a mix of them. Each figure assumes one modality with no text. Files beyond these limits do not fit in a single embedding. Chunk long media before ingesting. Video is sampled at 1 frame per second by default. Ingest audio as 16 kHz mono in chunks under 5.5 minutes.

• Dimension consistency: truncate query vectors and document vectors to the identical length.
• 128d validation: avoid 128 dimensions for media files unless you verify accuracy on your own data.
• Audio limits: divide voice recordings into 16 kHz mono segments under 5.5 minutes.
• Video sampling: extract video frames at 1 frame per second to stay within token limits.
• Document chunking: split large slide decks and multi-page PDFs into separate segment embeddings.

Deploying Multimodal Search Across Business Documents and Files

A multimodal search index connects unstructured business media directly to automated generation workflows. Policy PDFs, slide decks, call recordings and product photos can sit in one index. Standard text search cannot parse diagrams, charts or recorded explanations unless someone first turns them into text. EmbeddingGemma 2 indexes these visual files and spoken recordings directly without requiring manual transcription.

Start your deployment with text and visual documents. Text and images are the cheapest inputs to index, so prove retrieval quality there before adding long audio. If your documents are regulated, such as clinical records, running retrieval on your own hardware keeps them off a hosted API. RAG for healthcare covers that case. For a deeper technical overview of the model architecture, see our breakdown of EmbeddingGemma 2 explained.

Putting this pipeline into production follows three concrete steps. Download the weights from Hugging Face. Configure a local Qdrant collection. Index a small batch of slide decks and policy PDFs, then check whether retrieval finds the right pages.

• Policy documents and slide decks: index presentation slides with graphics alongside internal manuals.
• Product photography: retrieve catalogue pictures using natural language descriptions.
• Customer call recordings: search spoken audio segments directly without prior speech-to-text conversion.
• Phased rollout: validate text and image retrieval before processing audio libraries.