Web Indexing

AI Summary: Web indexing is the process of parsing crawled web pages and storing tokens, entities, and relationships in a searchable database. In AI search engines, traditional inverted keyword indexes are augmented with high-dimensional vector embeddings for semantic retrieval.

Technical Definition

Web Indexing is the second phase of the search lifecycle (following crawling and preceding ranking/synthesis). During indexing, a search engine parses the HTML of crawled documents, strips boilerplate, identifies semantic entities, and enters tokens into an inverted index or vector database.

Two Indexing Paradigms

1. Inverted Keyword Index (Lexical): Maps words to document IDs. Highly efficient for exact string matches and structured queries (e.g., Lucene, Elasticsearch).
2. Dense Vector Embeddings (Semantic): Converts chunks of text into high-dimensional vector representations. AI engines perform nearest-neighbor search (k-NN) to match the conceptual meaning of user prompts regardless of exact vocabulary.

Indexing Signals to Monitor

• Status Codes: Only HTTP 200 OK pages are eligible for the primary index.
• Canonical Directives: Avoid self-inflicted indexing splits caused by differing canonical tags.
• Metadata: The robots meta tag (index, follow) must explicitly grant indexation permission.

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