gemini-embedding-001

Provides text embedding models for generating embeddings for words, phrases, sentences, and code. These foundational embeddings support advanced NLP tasks such as semantic search, classification, and clustering, offering more accurate and context-aware search results compared to keyword-based approaches. Building Retrieval Augmented Generation (RAG) systems is a common use case for embeddings. Embeddings play a crucial role in significantly enhancing model outputs, improving factual accuracy, coherence, and contextual richness. They enable efficient retrieval of relevant information from knowledge bases (represented as embeddings), which is then passed as additional background information in the input prompt to the language model, guiding it to generate more informed and accurate responses.