Confessions of a RAG Pipeline Sufferer

I need to be honest with you: I hate transcriptions.

If you’ve ever tried to build a RAG system for your company — or even for a personal project — you know exactly what I’m talking about. You have an amazing video of a meeting where the CEO laid out the quarterly strategy. You have dashboard screenshots. You have audio from customer calls. All of it is informational gold.

But before any AI can “understand” that content, you need to go through a ritual I can only describe as digital bureaucracy: transcribe the audio, describe the images in text, convert everything to vectors… and pray that no important context was lost in translation.

I’ve lost entire nights building pipelines with multiple models just so an AI could retrieve information that was clearly visible in a 3-minute video. It’s frustrating. It’s inefficient. And, as of now, it’s unnecessary.

What Changed: Gemini Embedding 2

Last week (March 10, 2026), Google launched Gemini Embedding 2 in Public Preview — and I’m not exaggerating when I say this changes the game for anyone working with RAG.

The idea is elegantly simple: instead of needing separate pipelines for each media type, Gemini Embedding 2 maps text, images, videos, audio, and documents into the same vector space natively. All together. In the same mathematical representation. No intermediate conversions.

It’s Google’s first embedding model that truly does this — not with the “workaround” of previous models like CLIP, which aligned separate encoders at the end of the process. Gemini Embedding 2 is built on the Gemini architecture itself, which means multimodal understanding happens in the network’s intermediate layers, not as a patch at the end.

Why This Matters (In Practice, Not Theory)

I know “unified vector space” sounds like academic jargon. So let me translate to the real world.

Before Gemini Embedding 2, if you wanted an AI to find an answer within a meeting video, the pipeline went something like: transcribe the audio to text (losing tone, emphasis, and emotion), describe visual elements in text (losing subtle details), convert everything to vectors, and hope semantic search finds the right segment. Managing four or five different pipelines for audio, video, image, and text is a maintenance nightmare. And the latency? Brutal.

After Gemini Embedding 2, the flow is: send the video. Done. The AI “understands” directly what’s happening — what was said, what was shown, the tone of the conversation — without intermediaries.

Early access partners are already reporting concrete results. Sparkonomy, a creator economy platform, reported a 70% reduction in latency by eliminating intermediate inference steps. Everlaw, a legal tech firm, is using the model for litigation discovery — indexing images and videos alongside text documents to surface evidence that a text-only index would never find.

The Technical Details (For Those Who Care)

If you’re like me and enjoy looking under the hood, here goes:

The model generates 3,072-dimensional vectors by default, with support for smaller dimensions (1,536 and 768) via Matryoshka Representation Learning. In practice, this means you can do a fast, rough search with 768-dimension vectors, then refine the top results with the full 3,072. It’s like having a two-pass filter: speed first, precision second.

It supports up to 8,192 text tokens (4x the previous 2,048 limit), up to 6 images per request, 120 seconds of video (MP4 and MOV), and native audio — no transcription needed. It works in over 100 languages.

And the best part: you can send interleaved inputs in a single request. Text + image + audio, all together. This is what makes it possible to represent the true richness of a multimodal document without losing context.

Pricing? $0.25 per million tokens, with a free tier included. It ships with native integrations for LangChain, LlamaIndex, Haystack, Weaviate, Qdrant, and ChromaDB — basically the entire stack that matters for production RAG. The official notebooks are under Apache 2.0 license, so you can use and modify them commercially without royalties.

An Honest Warning

Not everything is perfect, and I want to be transparent about the limitations.

Migrating embeddings isn’t trivial. If you have a RAG system running on a different embedding model, you can’t just “mix” vectors. The vector spaces are different. You’ll need to re-index your entire dataset — which, depending on size, can be a project in itself.

Similarity thresholds will shift. Each model distributes vectors differently across latent space. That cosine similarity cutoff of 0.6 that worked well with your previous model may need recalibration to 0.7 or another value. I strongly recommend A/B testing before cutting over to production.

It’s still in Preview. Not GA (Generally Available) yet. For mission-critical applications, monitor closely before migrating everything.

The approach that makes the most sense, in my opinion, is to start with a shadow index — keep your production system running on the current model while re-indexing in parallel with Gemini Embedding 2 to compare results.

The Bigger Picture: The Embeddings Race

This launch doesn’t happen in a vacuum. The race for multimodal embeddings is one of the hottest fields in AI in 2026. Amazon Nova and Voyage Multimodal are direct competitors, and benchmarks show Gemini Embedding 2 outperforming both across text, image, and video.

And here’s what excites me most: RAG infrastructure is finally catching up with the promise. If you read my post about RAG without Vectors (PageIndex), you know that one of traditional RAG’s biggest problems is context loss from document “chunking.” Gemini Embedding 2 attacks this problem from two angles — with a 4x larger context window and by eliminating intermediate conversion.

It’s still not perfect RAG. But it’s a real leap, not an incremental one.

Conclusion: The Future Is Vectorized and Multimodal

AI is evolving from being just a “text reader” to becoming a world observer. Gemini Embedding 2 is a significant step in that direction, allowing machines to understand the complexity of human information in its original form — without translation, without transcription, without loss.

If your company is still spending time transcribing meetings so AI can read them, maybe it’s time to rethink the pipeline. The natively multimodal solution has arrived, it’s available, and the cost is accessible.

I’m personally already rewriting some of my RAG projects to test it. And I can guarantee one thing: the relief of not needing to build five separate pipelines for a semantic search is almost therapeutic.

If you work with RAG and haven’t looked into native multimodal embeddings yet, this is the moment.

Share if this was useful for your work:

The best embedding is one that understands the world as it is — not as a limited transcription of it.

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