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Surprise, your data warehouse can RAG
How to use your data warehouse's built-in features to simplify and potentially improve your RAG pipeline.
After a bit of research, it seemed like Pinecone was the easiest to get started with — creating an index, adding data to it, and searching for K nearest neighbors was all straightforwardly done using their Python client without the need to set up any additional infrastructure. Now, to guide our generation agent, we can take a prompt that a user has written, find three closest matches using the Pinecone index, and map that back to the actions that human testers performed. It turns out that all major data warehouse providers have recently been hard at work implementing LLM-related features to address the hype popularity of AI-adjacent topics.
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