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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.
<h4>How It Works</h4>
<ol>
<li>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.</li>
<li>These numbers are stored in your database.</li>
<li>When a user searches, their query is also converted to numbers, and the plugin finds posts whose numbers are closest – meaning most semantically similar.</li>
</ol>
<h4>Features</h4>
<ul>
<li>Semantic search powered by OpenAI (<code>text-embedding-3-small</code> or <code>text-embedding-3-large</code>) or Google Gemini (<code>gemini-embedding-001</code>)</li>
<li>Hybrid re-ranking: combines semantic similarity with keyword matching for best results</li>
<li>Gutenberg block and shortcode <code>[vecpost_semantic_search]</code> for easy placement</li>
<li>Bulk indexer with progress bar for existing posts</li>
<li>WP-CLI support: <code>wp vecpost-semantic-search index</code>, <code>wp vecpost-semantic-search status</code>, <code>wp vecpost-semantic-search search "query"</code></li>
<li>Configurable scoring thresholds via Settings -> VecPost – AI Semantic Search for Posts</li>
<li>Automatic re-indexing when you switch embedding models</li>
<li>Results cached via WordPress object cache (Redis/Memcached compatible)</li>
</ul>
<h4>Third-Party Services</h4>
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:
<ul>
<li>OpenAI: https://openai.com/policies/privacy-policy | https://openai.com/policies/terms-of-use</li>
<li>Google Gemini: https://policies.google.com/privacy | https://ai.google.dev/terms</li>
</ul>
No data is sent without your API key being configured. Data is only transmitted when posts are published or during bulk indexing.
<h4>Performance Note</h4>
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.

VecPost AI Search for Posts

AI-powered semantic search for WordPress posts. Understands meaning, not just keywords. Powered by OpenAI or Google Gemini.

Alessandro Lioce

Creator Assistant Hub is the core infrastructure plugin of the Creator Assistant ecosystem. It connects your WordPress site to OpenAI and Qdrant to automatically generate and store vector embeddings for your content, enabling semantic search and AI-powered features.
How it works
The Hub is the shared infrastructure layer for the Creator Assistant ecosystem. When a compatible add-on is active and activates a vectorizer, the plugin generates vector embeddings via the OpenAI API and stores them in your Qdrant vector database cluster whenever a post is published or updated. This makes your content available for semantic search and retrieval by Creator Assistant add-ons. Without an active add-on, the Hub runs in standby mode — no content is sent to external services.
Key features
<ul>
<li>Automatic post vectorization — when an add-on activates a vectorizer, embeddings are generated and stored whenever a post is published or updated. Without an active add-on, no vectorization takes place.</li>
<li>Chunk-based vectorization — splits post content into word-level segments and stores each chunk as a separate Qdrant point, enabling fine-grained retrieval (activated by add-ons via filter).</li>
<li>Summary-based vectorization — generates a short AI summary of each post via OpenAI Chat Completions, then embeds and stores a single point per post for high-level semantic search (activated by add-ons via filter).</li>
<li>Background batch vectorization — an hourly cron job automatically picks up any existing posts that have not yet been vectorized, so your entire content library is indexed without manual intervention.</li>
<li>OpenAI integration — uses the <code>text-embedding-3-small</code> model (1536 dimensions) via the OpenAI Embeddings API for all vectorizers; the Summary vectorizer additionally calls the OpenAI Chat Completions API (<code>gpt-4.1-nano</code>) to generate post summaries.</li>
<li>Qdrant integration — stores and retrieves vector embeddings from your Qdrant Cloud cluster. Two collections are used: <code>post_chunk</code> for chunk-level points and <code>post_summary</code> for post-level summary points.</li>
<li>Block editor panel — shows the vectorization status of each post directly in the block editor sidebar (document settings panel).</li>
<li>Post list column — adds a “Vector Status” column to the posts and pages admin list, showing the vectorization status of each entry at a glance.</li>
<li>Settings page — configure OpenAI API key, Qdrant endpoint and API key, and cache expiration from a dedicated admin page.</li>
<li>API response caching — configurable cache duration to reduce API usage and latency.</li>
<li>Site Health check — reports the Hub service status in the WordPress Site Health screen.</li>
<li>Qdrant keep-alive — sends a daily lightweight ping to prevent free-tier Qdrant Cloud clusters from being suspended.</li>
<li>WPML compatible — detects the post language via WPML hooks (when WPML is active) and includes it in the Qdrant payload for multilingual filtering.</li>
<li>Extensible — exposes actions and filters so add-ons can register additional post types, override vectorizer behaviour, customise Qdrant payloads, and add settings tabs.</li>
</ul>
Requirements
<ul>
<li>An <a href="https://platform.openai.com/" rel="nofollow ugc">OpenAI</a> account with a valid API key.</li>
<li>A <a href="https://cloud.qdrant.io/" rel="nofollow ugc">Qdrant Cloud</a> account with an active cluster (free tier is supported).</li>
<li>WordPress 6.6 or higher.</li>
<li>PHP 7.4 or higher.</li>
</ul>
External services
This plugin connects to two third-party services. By using this plugin you agree to their respective terms and privacy policies.
<ul>
<li>
OpenAI — used to generate vector embeddings from your post content via the Embeddings API. When the Summary vectorizer is active (enabled by an add-on), post content is also sent to the Chat Completions API to generate a short AI summary before embedding.
<ul>
<li>Service: <a href="https://platform.openai.com/" rel="nofollow ugc">https://platform.openai.com/</a></li>
<li>Privacy Policy: <a href="https://openai.com/policies/privacy-policy" rel="nofollow ugc">https://openai.com/policies/privacy-policy</a></li>
<li>Terms of Use: <a href="https://openai.com/policies/terms-of-use" rel="nofollow ugc">https://openai.com/policies/terms-of-use</a></li>
</ul>
</li>
<li>
Qdrant Cloud — used to store and query vector embeddings.
<ul>
<li>Service: <a href="https://cloud.qdrant.io/" rel="nofollow ugc">https://cloud.qdrant.io/</a></li>
<li>Privacy Policy: <a href="https://qdrant.tech/legal/privacy-policy/" rel="nofollow ugc">https://qdrant.tech/legal/privacy-policy/</a></li>
<li>Terms of Service: <a href="https://qdrant.tech/legal/terms_and_conditions/" rel="nofollow ugc">https://qdrant.tech/legal/terms_and_conditions/</a></li>
</ul>
</li>
</ul>
Data is only transmitted to these services after you explicitly accept the Creator Assistant Terms of Service in the plugin settings and provide your own API credentials. No data is transmitted automatically upon activation.

Creator Assistant Hub

The AI infrastructure for your WordPress content. Vectorize posts with OpenAI embeddings and store them in Qdrant for semantic search.

VecPost AI Search for Posts vs Creator Assistant Hub