FindAstra vs VecPost AI Search for Posts

FindAstra is AI product search for WooCommerce. It helps your store understand what shoppers actually mean, not just the exact words they type. Someone searching for “comfortable shoes for hiking” will find your “Trail Running Sneakers” even when the title never mentions comfort or hiking. Ordinary keyword search only matches products that literally contain the words a shopper types. So a customer who describes things in their own way lands on a “no results” page and leaves. FindAstra closes that gap. It reads the intent behind each search, puts the most relevant products first, and offers helpful suggestions instead of dead ends. And the free version runs completely on its own. There are no API keys to paste, no account to create, and no monthly fees. The AI runs right inside the shopper’s browser, so nothing about your catalog or your customers is sent to an outside server, and you can index your entire catalog with no product limit. What your shoppers get Answers, not just matches. Searches are understood by meaning, so shoppers reach the right products even when their wording differs from yours. Live suggestions as they type. A search-as-you-type dropdown attaches to your existing search bar, with no theme editing. A results page that makes sense. Your normal WooCommerce results page is simply re-ordered by relevance, keeping your theme’s design exactly as it is. No more empty pages. When nothing is a strong match, shoppers see popular products instead of a dead end. What you get as a store owner Set up in minutes. Activate, click index once, and AI search is live. No keys, no account, no configuration headaches. Private by default. Your products and your customers’ searches stay on your own site. Fits your store. Works with classic and block themes, and includes a search block, a shortcode, and a sidebar widget. Developers also get filter hooks and a REST endpoint to build on. How it works, in plain terms When you click index, FindAstra reads each product and builds a small understanding of what it is. When a shopper searches, it compares their wording to that understanding and lists the closest products first. The first time it runs, the browser downloads a small AI model (about 33 MB) and reuses it from then on, so searches stay fast. FindAstra Pro (optional) A separate paid version is available at findastra.com for stores that want more. It adds optional server-side engines for very large catalogs (using your own OpenAI or Hugging Face key), multilingual search for WPML and Polylang, and built-in search analytics. The free version here is complete on its own, and nothing in it is locked or time limited. External services Which external service (if any) FindAstra contacts depends on the search engine you choose during setup. Each is described below, including what data is sent and when. Hugging Face model hub (huggingface.co) Used by the default Local engine. The first time the Local engine runs (when you index products in wp-admin, or when a shopper performs a search), the browser downloads a roughly 33 MB open-source AI model from the Hugging Face model hub and caches it locally for later visits. Only the model files are fetched. No store, product, shopper, or site data is ever sent. After the download, all search runs entirely in the browser. Hugging Face terms of service: https://huggingface.co/terms-of-service . Privacy policy: https://huggingface.co/privacy OpenAI API (api.openai.com) Used only if you select the OpenAI engine and enter your own API key. At index time, the text of each product (its title and the fields you choose to include) is sent to OpenAI to generate an embedding; at search time, the shopper’s query text is sent. Requests are authenticated with the API key you provide and are made only while the OpenAI engine is the active provider. OpenAI terms of use: https://openai.com/policies/terms-of-use . Privacy policy: https://openai.com/policies/privacy-policy Hugging Face Inference API (router.huggingface.co) Used only if you select the Hugging Face engine and enter your own access token. The same data as the OpenAI engine (product text at index time, query text at search time) is sent to the Hugging Face Inference API to generate embeddings, authenticated with the token you provide, and only while the Hugging Face engine is the active provider. Hugging Face terms of service: https://huggingface.co/terms-of-service . Privacy policy: https://huggingface.co/privacy

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.