Introducing reranking to Pinecone Inference to simplify building accurate AI

What this post added

This post introduces reranking capabilities to Pinecone Inference. It details how reranking improves AI application accuracy by scoring documents for relevance to a query, reducing hallucination and cost by filtering irrelevant documents before LLM generation. The post explains the typical RAG pipeline stages and quantifies the potential cost savings (up to 85% with gpt4-o) and accuracy improvements. It also highlights the simplification of the AI development stack by integrating embedding and reranking models with the vector database via a single API. A Python SDK example demonstrates how to use the reranking functionality.