[FEATURE] Embeddings Management

Problem

Developers need to manage vector embeddings for content indexing and similarity operations.

Goal

Provide embedding creation, deletion, and index management through the SDK.

Target Personas

• Developer teams
• DevOps teams

Demo Expectations

// What can be demonstrated if time allows
const embeddings = await client.ai.embeddings.create({
  query: 'content to embed',
  indexName: 'myIndex',
  fields: 'title,body'
});

const indexes = await client.ai.embeddings.listIndexes();
console.log(indexes.count); // Number of available indexes

User Stories

• As a developer, I want embeddings management for advanced AI features, so that I can build sophisticated content similarity and recommendation systems
• As a DevOps team member, I want to manage embedding indexes, so that I can monitor and maintain AI infrastructure effectively
• As a developer, I want to create and delete embeddings programmatically, so that I can manage content indexing as part of my application workflow

Acceptance Criteria

• Embedding creation with content and index specification
• Embedding deletion for individual items and bulk operations
• Index listing and management capabilities
• Support for custom fields and content filtering
• Integration with dotCMS content lifecycle
• Performance monitoring for embedding operations
• Database cleanup and index optimization tools

Links

• dotAI Embeddings API

No source files, tests, or code entry points are identified in the issue. Start with the linked dotAI Embeddings API documentation and define the SDK surface needed for creation, deletion, index management, filtering, lifecycle integration, monitoring, and cleanup; done means all listed acceptance criteria are addressed.