Build and deploy a RAG app with Pinecone Serverless

LLMs are revolutionizing generative AI applications by acting as the kernel process in a new kind of operating system. They utilize context windows that can be loaded with information retrieved from external data sources, such as databases or vectorstores. Retrieval augmented generation (RAG) is a central concept in LLM app development, reducing hallucinations and adding context not present in training data. Vectorstores have gained popularity for production RAG applications due to their efficient storage and retrieval capabilities. However, several challenges exist between RAG demos and production applications. Pinecone Serverless addresses these issues by offering unlimited index capacity via cloud object storage and pay-as-you-go pricing. LangServe supports rapid deployment of any chain to a web service suitable for production, while LangSmith offers LLM observability that integrates seamlessly with LangServe. These tools work together to bridge the gap between prototyping and production in RAG applications.