Using Stripe Docs in your RAG pipeline with LlamaIndex

Generative AI applications become extremely powerful when you augment them with up-to-date, domain-specific, or private data. This technique is called Retrieval Augmented Generation (RAG).

In this post we’ll build a Python script that uses StripeDocs Reader, a loader on LlamaIndex, that creates vector embeddings of Stripe's documentation in Pinecone. This allows a user to ask questions about Stripe Docs to an LLM, in this case OpenAI, and receive a generated response.

These techniques are similar to …

ai ai applications applications become build data documentation domain embeddings generative generative ai applications llamaindex pinecone pipeline private data python python script rag retrieval retrieval augmented generation stripe them tutorial vector vector embeddings