Embedding
An embedding is a dense numerical vector representation of text (or other data) that encodes semantic meaning such that similar concepts are positioned close together in vector space.
理解する Embedding
Embeddings are the bridge between human language and mathematical computation. A word like 'meeting' is meaningless to a computer as a string. As a 768 or 1536-dimensional vector, it can be compared mathematically to other vectors. Embeddings encode meaning so that 'meeting' and 'conference' are close in vector space, while 'meeting' and 'database' are far apart. The power of embeddings is semantic similarity search. Given a query like 'emails about the product launch,' an embedding model converts the query to a vector, then finds all stored email embeddings that are mathematically similar — surfacing relevant emails without requiring exact keyword matches. This captures semantics, not just text patterns. Embedding models are trained separately from language models and optimized specifically for representation quality. OpenAI's text-embedding-3 models, Cohere's embed models, and open-source models like sentence-transformers are popular choices. Embeddings are typically 768-3072 dimensional vectors. Applications using embeddings store content in a vector database (ChromaDB, Pinecone, Weaviate) that enables fast approximate nearest-neighbor search over large embedding collections.
GAIAの活用方法 Embedding
GAIA embeds all ingested content — emails, tasks, calendar events, documents — into ChromaDB, its vector database. When GAIA needs to find relevant context (e.g., 'what have we discussed about the Q4 budget?'), it converts the query to an embedding and searches ChromaDB for semantically similar content rather than keyword matching, surfacing relevant items regardless of exact phrasing.
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.
グラフベースメモリ
グラフベースメモリは、情報と関係を相互接続されたノードと関係として保存し、リッチなコンテキスト理解と相互作用全体での永続的な知識を可能にするAIメモリアーキテクチャです。
