Embeddings
Embeddings are dense numerical vector representations of data, such as text, images, or audio, that capture semantic meaning and relationships in a high-dimensional space.
理解する Embeddings
When a machine learning model processes text, it needs to work with numbers, not words. Embeddings solve this by mapping words, sentences, or documents into lists of floating-point numbers, typically 768 to 4096 dimensions. The key property of embeddings is that semantically similar content ends up numerically close together in this vector space. 'Dog' and 'puppy' have embeddings close to each other. 'Schedule a meeting' and 'book a call' are near neighbors. This geometric property makes embeddings useful for semantic search, recommendation systems, clustering, and classification. By comparing the distance between embeddings, AI systems can find related content, identify duplicates, and understand conceptual relationships without explicit rules. Embedding models are trained separately from generation models. Popular embedding models include OpenAI's text-embedding-3-large, Cohere's embed-v3, and open-source models like nomic-embed-text. They produce fixed-size vectors regardless of input length, enabling efficient storage and retrieval in vector databases. In RAG systems, embeddings are the bridge between user queries and stored knowledge. The query is embedded, and the vector database finds the stored embeddings closest to it, retrieving relevant context for the LLM to use in its response.
GAIAの活用方法 Embeddings
GAIA generates embeddings for every email, task, calendar event, and document stored in your connected tools, then indexes them in ChromaDB. When you search for information or when GAIA needs context for a task, it embeds the query and retrieves the most semantically relevant stored content. This powers GAIA's ability to find information by meaning, not just keywords, across your entire digital workspace.
関連概念
ベクトル埋め込み
ベクトル埋め込みは、意味を捉える数値表現にテキスト、画像、またはその他のデータを変換し、機械が情報間の類似性と関係を理解できるようにします。
Vector Database
A vector database is a database system designed to store, index, and query high-dimensional vector embeddings at scale, enabling fast similarity search across large collections of embedded data.
意味論的検索
意味論的検索とは、クエリの背後にある意味と意図を理解し、キーワードの一致ではなく概念的な関連性に基づいて結果を返す検索手法です。
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique that enhances LLM responses by first retrieving relevant documents or data from an external knowledge base and injecting that context into the model's prompt.
