Gemini Embedding 2 Changed RAG Forever — Multimodal AI Search Explained

Description

Google's Gemini Embedding 2 isn't just another model update — it's a complete rethink of how AI searches and understands information. For the first time, a single embedding model handles text, images, audio, and video in one unified vector space. Key topics covered: • What multimodal embeddings are and why they matter for RAG pipelines • How Gemini Embedding 2 outperforms previous text-only models on MTEB benchmarks • Real-world use cases: image search, cross-modal Q&A, and document understanding • How to integrate Gemini Embedding 2 into your own RAG system via the Vertex AI API • Comparison with OpenAI, Cohere, and open-source alternatives Whether you're building a product search engine, a document Q&A bot, or a multimodal knowledge base — this is the embedding model that changes what's possible. 👍 Like and subscribe for weekly AI engineering breakdowns. #GeminiEmbedding #RAG #MultimodalAI #GoogleAI #AIEngineering #VectorSearch #LLM #ArtificialIntelligence #MachineLearning #GoogleCloud