LLM embeddings are numerical representations generated by large language models (LLMs) like GPT and BERT, capturing semantic meaning to enable efficient text processing, similarity search, and retrieval. They are used in various applications such as search engines, recommendation systems, and AI agents. Embeddings can be fine-tuned for specific domains and integrated into solutions using tools like Couchbase Capella. The key components of LLMs include tokenization, embedding layers, attention mechanisms, and feedforward layers. Different types of embeddings serve various tasks and can be tailored for optimal performance based on the use case.
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What are LLM embeddings?How do embeddings work?Components of LLMsUnimodal vs. multimodal embeddingsTypes of embeddingsUse cases for LLM embeddingsHow to choose an embedding approachHow to embed data for LLMsKey takeaways and next stepsFAQSort: