Vector Database
AI / MCPDefinition
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.
How It Relates to CrawlForge
Vector databases like Pinecone, Weaviate, and pgvector are essential components of RAG systems and semantic search. They store document embeddings and retrieve the most relevant ones based on vector similarity when a query comes in.
CrawlForge integrates into vector database workflows as the content ingestion layer. Use batch_scrape to collect pages at scale, extract_content to get clean text, and then embed and store the results in your vector database. This pipeline keeps your knowledge base current with fresh web data.
Related CrawlForge Tools
Related Terms
Embeddings
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.
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.
Structured Data
Structured data is information organized in a predefined format that makes it easy for machines to parse and understand. On the web, it typically refers to schema.org markup embedded in HTML pages.
Data Pipeline
A data pipeline is an automated sequence of steps that collects, processes, transforms, and delivers data from sources to destinations. It enables continuous data flow between systems without manual intervention.
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