Embeddings
AI / MCPDefinition
Embeddings are dense numerical vector representations of text, images, or other data. They capture semantic meaning in a format that enables similarity search, clustering, and other machine learning operations.
How It Relates to CrawlForge
Embeddings are the bridge between raw text and machine understanding. When you convert a web page's content into an embedding, you can compare it with other documents to find similar content, build recommendation systems, or power semantic search.
CrawlForge extract_content provides clean text that produces higher-quality embeddings. Raw HTML with navigation, footers, and ads creates noisy embeddings that degrade search quality. By extracting only the meaningful content, CrawlForge improves downstream embedding performance.
Related CrawlForge Tools
Related Terms
Vector Database
A vector database is a specialized database designed to store and efficiently query high-dimensional vector embeddings. It enables fast similarity search across millions of embedded documents.
Retrieval-Augmented Generation (RAG)
RAG is an AI architecture that combines information retrieval with text generation. It first retrieves relevant documents from external sources, then uses them as context for the language model to generate accurate, grounded responses.
Large Language Model (LLM)
A large language model is a neural network trained on vast amounts of text data that can understand and generate human language. LLMs power AI assistants, code generators, and autonomous agents.
Structured Output
Structured output refers to data returned in a predictable, machine-readable format like JSON, rather than free-form text. It enables reliable downstream processing by AI agents and data pipelines.
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