外掛標籤
開發者團隊
原文外掛簡介
VecPost AI Search for Posts replaces WordPress’s default SQL LIKE search with vector-based semantic search. Instead of matching exact words, it understands the meaning of a search query.
Example: A user searching “heart workouts” will find your post titled “Best cardiovascular exercises” – even though no words overlap – because the meanings are similar.
How It Works
When you publish a post, the plugin sends its content to your chosen AI provider (OpenAI or Google Gemini) to generate a vector embedding – a list of numbers that represents the meaning of the text.
These numbers are stored in your database.
When a user searches, their query is also converted to numbers, and the plugin finds posts whose numbers are closest – meaning most semantically similar.
Features
Semantic search powered by OpenAI (text-embedding-3-small or text-embedding-3-large) or Google Gemini (gemini-embedding-001)
Hybrid re-ranking: combines semantic similarity with keyword matching for best results
Gutenberg block and shortcode [vecpost_semantic_search] for easy placement
Bulk indexer with progress bar for existing posts
WP-CLI support: wp vecpost-semantic-search index, wp vecpost-semantic-search status, wp vecpost-semantic-search search "query"
Configurable scoring thresholds via Settings -> VecPost – AI Semantic Search for Posts
Automatic re-indexing when you switch embedding models
Results cached via WordPress object cache (Redis/Memcached compatible)
Third-Party Services
This plugin sends post content to external AI APIs to generate embeddings. By using this plugin, you agree to the terms of service and privacy policies of your chosen provider:
OpenAI: https://openai.com/policies/privacy-policy | https://openai.com/policies/terms-of-use
Google Gemini: https://policies.google.com/privacy | https://ai.google.dev/terms
No data is sent without your API key being configured. Data is only transmitted when posts are published or during bulk indexing.
Performance Note
Semantic search requires loading all embeddings into PHP memory for comparison. This works well for sites with up to approximately 1,500 posts. For larger sites, a dedicated vector database (pgvector, Qdrant, or Pinecone) is recommended.
