Chaining Activities — from text to vectors

This post details a specific implementation of a data processing pipeline using Temporal Workflows and Activities. It showcases chaining multiple Activities for tasks like text embedding, vector caching (using Redis), vector storage (in Pinecone), and cleanup. It highlights the use of `case_id` for idempotency in Postgres and upsert semantics in Pinecone, along with an `embedded` flag for reconciliation. The post also demonstrates how to manage external dependencies like embedding models and vector databases through provider interfaces and discusses the importance of cache cleanup.