Simplifying Vector Embeddings with Pinecone Integrated Inference Capabilities

What this post added

This post details Pinecone's integrated inference capabilities for generating vector embeddings. It explains the traditional embedding process and contrasts it with Pinecone's integrated approach, which collapses multiple steps into a single API call. The post highlights a customer issue where the `upsert_records()` method's automatic inclusion of the full text field in metadata exceeded the 40KB limit. A workaround is provided by directly calling the Inference API (`pc.inference.embed()`) to generate embeddings and then upserting them, offering greater control over metadata.