Introduction
Imagine your search understanding the meaning of queries. When a user types remote work best practices, the search would instantly return an article titled Telecommuting Strategies for Modern Teams because the system understands the conceptual relationship between terms. This is semantic search in action, technology that interprets meaning rather than just matching keywords.
In this article, we explain how to create a semantic search plugin for Strapi that integrates OpenAI to generate automatic embeddings, stores vectors, and offers clean REST APIs for fast and relevant searches. We also include information about Q2BSTUDIO, a custom software and application development company specializing in artificial intelligence, cybersecurity, and AWS and Azure cloud services to help you implement professional and secure solutions.
Prerequisites
Before starting, you will need an up-to-date Strapi 5 project, an OpenAI key with access to the embeddings endpoint, Node.js 18 or higher and npm, basic knowledge of JavaScript or TypeScript, and notions about plugin development in Strapi. We will use the text-embedding-ada-002 model, which generates 1536-dimensional vectors for text, making it cost-effective for large content libraries.
Plugin design overview
The plugin consists of services that manage three main responsibilities: Embedding Service, which connects to OpenAI and preprocesses text; Vector Service, which stores embeddings and calculates similarity using cosine; and Search Service, which orchestrates the search and exposes results ordered by relevance. Additionally, the plugin registers lifecycles in Strapi to automatically generate embeddings when creating or updating content and exposes REST endpoints for simple and multi-content searches.
Key implementation points
1. OpenAI client initialization: validate the key and prepare calls to create embeddings
2. Text preprocessing: remove HTML tags, normalize spaces, and truncate to a safe size to avoid exceeding token limits
3. Embedding generation: request the text-embedding-ada-002 model and return a 1536-dimensional vector along with metadata such as original length, processed length, model, and timestamp
4. Similarity calculation: use cosine similarity to compare vectors, as it prioritizes conceptual direction over magnitude, allowing identification of conceptually close articles even if they differ in term frequency
5. Storage: save the embedding in a JSON field of type embedding and metadata in embeddingMetadata for tracking and auditing
6. Lifecycles in Strapi: register beforeCreate and beforeUpdate hooks to extract text from configured fields, generate the embedding, and insert the vector into the payload that persists in the database
7. REST endpoints: create routes for search, multi-search, and stats with parameter validations, limits, and error handling to protect OpenAI usage and control costs
Embedding automation and text extraction
Automation ensures content creators do not need to worry about manually generating embeddings. The plugin extracts configurable text from fields such as title, content, summary, body, tags, and any custom fields defined in the configuration. For rich content or repeatable blocks, the content is serialized to obtain representative text. If the text is insufficient, generation is skipped to avoid useless embeddings.
Searching and ranking results
When receiving a query, the semantic search first generates the query embedding, then retrieves documents with embeddings applying filters and locale if applicable, calculates cosine similarity against each embedding, filters by a configured threshold, and returns results ordered by score with metadata about search time and query processing.
Multi-content search
Multi-content search runs parallel searches across multiple content types, consolidates results, adds a sourceContentType field to identify the origin, and returns a global list ordered by similarity, allowing comparison of articles, products, courses, and blog posts in a single ranking.
Performance and scaling considerations
For production environments, optimizations are recommended such as batch processing during mass imports, caching frequent embeddings to avoid repeated OpenAI calls, database indexes on filtering fields, and rate limiting to protect the OpenAI account. For very large datasets, consider integration with external vector databases that offer optimized proximity search.
Cost management
Monitor usage in the OpenAI dashboard and configure alerts. Each embedding has a low cost but scales with the number of documents. Implementing cache and avoiding unnecessary embedding regeneration reduces costs. For content that changes little, generate embeddings only on significant changes.
Testing and validation
Add embedding and embeddingMetadata fields to the content schema, restart Strapi, and check initialization and embedding generation logs. Create test articles and run requests to the REST endpoints to verify that results come with similarityScore and processing metadata.
Configuration and customization
The plugin allows defining in config plugins the content types to process and the fields per content type. This makes it easy to adapt text extraction to specific applications such as product catalogs, courses, or technical articles. Limit, threshold, and locale options allow fine-tuning relevance according to the use case.
Integration with Q2BSTUDIO
At Q2BSTUDIO, we offer implementation and customization services for solutions like this plugin. We are a custom software and application development company specializing in artificial intelligence, cybersecurity, and AWS and Azure cloud services. We can help you adapt semantic search to your business by integrating business intelligence services, Power BI solutions, AI agents, and secure architectures that meet compliance and performance requirements.
Recommended use cases
Recommendations for applying semantic search in companies include internal content search engines, product catalogs with personalized recommendations, customer support with relevant article search, discovery systems for e-learning, and unified search across technical documentation and blog posts. Combining embeddings with metadata, you can build recommendation pipelines and AI agents that act on semantic results.
Keywords and SEO
Relevant keywords we recommend including in the implementation and metadata to improve positioning: custom applications, custom software, artificial intelligence, cybersecurity, AWS and Azure cloud services, business intelligence services, AI for companies, AI agents, Power BI.
Demonstration and deployment
You can test the solution first in a development environment and then deploy to production with integrations to cloud providers like AWS and Azure using managed services for scaling and security. For large volumes, consider specialized storage and load balancers that allow responding to traffic spikes while maintaining low latency.
Next steps and extensions
Natural extensions include adding components in the admin panel to test queries in real time, support for webhooks to sync with external systems, multimodal embeddings for images and audio, integration with external vector databases, and creating recommendation engines and conversational agents based on content similarity.
Conclusion
Semantic search transforms the way users discover content. With Strapi and OpenAI, you can build a plugin that automates embedding generation, offers clear REST APIs, and allows customization by content type. If you need professional support, Q2BSTUDIO, as a custom software and application development company specializing in artificial intelligence, cybersecurity, AWS and Azure cloud services, and business intelligence services, can accompany you from prototype to production deployment, integrating AI agents and Power BI dashboards to obtain actionable insights and improve the user experience.
