FindAstra — AI Semantic Search For WooCommerce Wordpress Plugin - Rating, Reviews, Demo & Download
Plugin Description
FindAstra replaces WooCommerce’s default keyword search with AI search that understands what shoppers mean. A search for “comfortable hiking shoes” finds your “Trail Running Sneakers Cushioned” product even when none of those words appear in the title or description.
Every published product is converted into a vector embedding that captures its semantic meaning. At search time the shopper’s query is embedded too, and FindAstra returns the closest matches by cosine similarity.
The search engine
This plugin runs the Local engine: embeddings are generated in the shopper’s browser with transformers.js (Xenova/bge-small-en-v1.5). No API keys, no signup, no monthly cost. The ~33 MB model is downloaded once and cached. There is no product limit — index your whole catalog.
Pro engines (optional)
FindAstra Pro — a separate, paid version available from findastra.com — adds two server-side engines, useful for very large catalogs where browser-side indexing becomes impractical:
- OpenAI —
text-embedding-3-small, server-side. Bring your own API key (~$2/year for an average store). - Hugging Face —
BAAI/bge-small-en-v1.5, orBAAI/bge-m3for multilingual catalogs (100+ languages). Bring your own token (free tier ~30k requests/month).
Pro also adds WPML / Polylang multilingual indexing. The free version on WordPress.org is fully functional on its own; none of its features are time-limited or locked.
Shopper-facing features
- Live autocomplete dropdown that auto-attaches to the theme’s existing search bar.
- Semantic results page — WooCommerce’s product archive template renders proper product cards with the semantic ranking.
- Fallback products when no match scores above the relevance gate, so shoppers never see an empty results page.
Admin features
- One-click setup under WooCommerce FindAstra.
- Encrypted credential storage — API keys never sit in plain text.
- Match-quality gating so weak queries gracefully trigger the fallback instead of returning low-relevance noise.
Developer features
[findastra]shortcode + classic sidebar widget + Gutenberg block.findastra/v1/*REST namespace.- Filter hooks for source text, gating thresholds, fallback notice text, and the free/paid capability matrix.
Pro
The free version is fully functional with the Local engine and no product limit. FindAstra Pro (from findastra.com) adds the server-side OpenAI and Hugging Face engines, WPML / Polylang multilingual indexing, and search analytics (recent searches, zero-result catalog gaps, daily totals).
See findastra.com/pricing for details.
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 embedding model (Xenova/bge-small-en-v1.5) from the Hugging Face model hub and caches it locally for subsequent visits. Only the model files are fetched; no store, product, shopper, or site data is ever sent. After the download, all embedding and 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
Screenshots
Live AI autocomplete. A shopper typing “comfortable hiking shoes” instantly sees Merino Wool Hiking Socks, Trail Running Sneakers, and Mountain Trek Boots, even though those exact words are not in the product titles.
Semantic results page. The same search renders standard WooCommerce product cards, ranked by meaning instead of keyword matching.
