A dense vector representation of text (or other data) in a continuous high-dimensional space where semantically similar items are closer together. Embedding models (OpenAI ada-002, Cohere, sentence-transformers) convert text to vectors of 256-3072 dimensions. Used in RAG for semantic search, in recommendation systems, and for clustering. Stored and queried via vector databases.
Empieza por la explicación más corta y útil antes de profundizar.
A dense vector representation of text (or other data) in a continuous high-dimensional space where semantically similar items are closer together. Embedding models (OpenAI ada-002, Cohere, sentence-transformers) convert text to vectors of 256-3072 dimensions. Used in RAG for semantic search, in recommendation systems, and for clustering. Stored and queried via vector databases.
Usa primero la analogía corta para razonar mejor sobre el término cuando aparezca en código, docs o prompts.
Piensa en esto como una pieza de la pila de contexto o inferencia usada en productos con agentes o LLMs.
Ubica el término dentro de la capa de Solana en la que vive para razonar mejor sobre él.
LLMs, RAG, embeddings, inferencia y primitivas orientadas a agentes.
Convierte el término de vocabulario en algo operacional para producto e ingeniería.
Se vuelve más útil cuando navegas por Ruta de Agentes y necesitas vocabulario aterrizado dentro de un flujo real de build.
Usa este bloque compacto cuando quieras dar contexto sólido a un agente o asistente sin volcar toda la página.
Embedding (embedding) Categoría: IA / ML Definición: A dense vector representation of text (or other data) in a continuous high-dimensional space where semantically similar items are closer together. Embedding models (OpenAI ada-002, Cohere, sentence-transformers) convert text to vectors of 256-3072 dimensions. Used in RAG for semantic search, in recommendation systems, and for clustering. Stored and queried via vector databases. Aliases: Vector Embedding Relacionados: RAG (Generación Aumentada por Recuperación), Vector Database
Usa contexto del glosario, relaciones entre términos, modelos mentales y builder paths para recibir respuestas estructuradas en vez de output genérico.
Opcional: pega código Anchor, Solana o Rust para que el Copilot mapee primitivas de vuelta al glosario.
El Copilot responderá usando el término actual, conceptos relacionados, modelos mentales y el grafo alrededor del glosario.
Estas ramas muestran qué conceptos toca este término directamente y qué existe una capa más allá de ellos.
An AI architecture that combines LLMs with external knowledge retrieval. Instead of relying solely on training data, RAG systems retrieve relevant documents from a knowledge base (using embeddings and vector search), then provide them as context to the LLM. RAG reduces hallucinations and enables up-to-date responses. Useful for blockchain documentation bots and developer assistants.
A database optimized for storing and querying high-dimensional vector embeddings using similarity search (cosine distance, dot product, Euclidean distance). Examples: Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Vector databases power RAG systems by quickly finding the most relevant documents for a given query embedding. Essential for AI-powered developer tools and documentation search.
Estos son los siguientes conceptos que vale la pena abrir si quieres que este término tenga más sentido dentro de un workflow real de Solana.
Estas entradas son fáciles de mezclar cuando lees rápido, haces prompting a un LLM o estás entrando en una nueva capa de Solana.
A database optimized for storing and querying high-dimensional vector embeddings using similarity search (cosine distance, dot product, Euclidean distance). Examples: Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Vector databases power RAG systems by quickly finding the most relevant documents for a given query embedding. Essential for AI-powered developer tools and documentation search.
Las entradas del glosario se vuelven útiles cuando están conectadas. Estos enlaces son el camino más corto hacia ideas adyacentes.
An AI architecture that combines LLMs with external knowledge retrieval. Instead of relying solely on training data, RAG systems retrieve relevant documents from a knowledge base (using embeddings and vector search), then provide them as context to the LLM. RAG reduces hallucinations and enables up-to-date responses. Useful for blockchain documentation bots and developer assistants.
A database optimized for storing and querying high-dimensional vector embeddings using similarity search (cosine distance, dot product, Euclidean distance). Examples: Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Vector databases power RAG systems by quickly finding the most relevant documents for a given query embedding. Essential for AI-powered developer tools and documentation search.
Usa estas rutas cuando quieras pasar de un lookup aislado a una exploración guiada.
Mapea el glosario al workflow agentic y a la pila de recuperación de contexto.
Estas entradas viven junto al término actual y ayudan a que la página se sienta parte de un grafo de conocimiento más amplio en lugar de un callejón sin salida.
A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.