Takeaways
By the end of this module, you will be able to:- Create a table in your database to store embeddings
- Add logic to chunk and create embeddings when creating resources
- Understand the relationship between resources and their embeddings
- Implement the complete embedding generation workflow
Module structure
3.1 Create Embeddings Table
Create a table called embeddings to store chunks with proper indexing.
3.2 Add Embedding Logic
Write logic to chunk content and generate embeddings using AI SDK.
3.3 Update Server Action
Integrate embedding generation into the resource creation workflow.
Prerequisites
Before starting this module, ensure you have:- PostgreSQL database running with pgvector extension
- Basic understanding of database schemas and relationships
- Familiarity with TypeScript and async/await patterns
What you’ll build
Embeddings Table
A table with proper indexing to store vector representations of your content
chunks.
Chunking Function
Logic to break down content into manageable pieces for embedding generation.
Embedding Generation
Functions to create embeddings using OpenAI’s text-embedding-ada-002 model.
Enhanced Workflow
Updated resource creation that automatically generates and stores
embeddings.