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.
Comece pela explicação mais curta e útil antes de aprofundar.
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.
Use primeiro a analogia curta para raciocinar melhor sobre o termo quando ele aparecer em código, docs ou prompts.
Pense nisso como uma peça da pilha de contexto ou inferência usada em produtos com agentes ou LLMs.
Coloque o termo dentro da camada de Solana em que ele vive para raciocinar melhor sobre ele.
LLMs, RAG, embeddings, inferência e primitivas voltadas a agentes.
Transforme o termo de vocabulário em algo operacional para produto e engenharia.
Fica mais útil quando você está navegando por Trilha de Agentes e precisa de vocabulário aterrado dentro de um fluxo real de build.
Use este bloco compacto quando quiser dar contexto aterrado para um agente ou assistente sem despejar a página inteira.
Embedding (embedding) Categoria: IA / ML Definição: 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 (Geração Aumentada por Recuperação), Vector Database
Use contexto do glossário, relações entre termos, modelos mentais e builder paths para receber respostas estruturadas em vez de output genérico.
Opcional: cole código Anchor, Solana ou Rust para o Copilot mapear primitivas de volta para termos do glossário.
O Copilot vai responder usando o termo atual, conceitos relacionados, modelos mentais e o grafo ao redor do glossário.
Esses ramos mostram quais conceitos esse termo toca diretamente e o que existe uma camada além deles.
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.
Estes são os próximos conceitos que valem abrir se você quiser que este termo faça mais sentido dentro de um workflow real de Solana.
Essas entradas são fáceis de misturar quando você lê rápido, faz prompting em um LLM ou está entrando em uma nova camada 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.
Entradas de glossário só ficam úteis quando estão conectadas. Esses links são o caminho mais curto para ideias adjacentes.
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.
Use essas trilhas quando quiser sair do lookup isolado e entrar em uma exploração guiada.
Mapeie o glossário para o workflow agentic e para a pilha de recuperação de contexto.
Essas entradas vivem ao lado do termo atual e ajudam a página a parecer parte de um grafo maior, não um beco sem saída.
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.