Having the correct data to support your use case is essential to a successful implementation of LLMs in any business. While most out-of-the-box LLMs are great at tasks, they can struggle with specific business problems. They didn’t train on the data for your business problem, so they don’t have adequate context to solve it. general Businesses often have a treasure trove of internal data and documents that could meet this need for specific context.
Our index — now a series of vector embeddings in memory — will be lost completely after we make our call to the OpenAI model and finish the workflow. Creating a vector index for our text isn’t , so we don’t want to have to recompute those results every time we call the model. It’s best to have a separate workflow where we persist the index to disk. Then, we can reference it at any time later.