The Service Layer: Where Separate Components Become a System

The provided code snippet outlines a Java-based implementation for a vector search service that integrates with an external embedding API and a PostgreSQL database. The key components of this system include:

1. Embedding Service: This part handles the interaction with an external embedding API, which converts text inputs into numerical vectors (embeddings). These embeddings are crucial for semantic similarity searches in vector spaces.

2. Database Operations: The code interacts directly with a PostgreSQL database to store and retrieve document metadata along with their corresponding embeddings. It uses raw SQL queries rather than JPA (Java Persistence API) due to the complexity of constructing dynamic search queries that include filters, score thresholds, and pagination.

3. QueryBuilder Class: This utility class is responsible for dynamically building SQL queries based on user input parameters such as metadata filters, score thresholds, and pagination requirements. It ensures safe parameter handling by avoiding direct string concatenation which could lead to SQL injection vulnerabilities.

4. Vector Search Query Construction:

   • The query construction process involves creating an inner subquery that selects documents matching certain criteria (e.g., status='READY', non-null embeddings).
   • An outer query then applies additional filtering based on user-defined metadata filters and score thresholds.
   • Both queries use parameter

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