Retrieval augmented generation using Elasticsearch and OpenAI - Part 25
Here's what that code does:Uses OpenAI's model to generate a response, Sends a conversation containing a system message and a user message to the model. The system message sets the assistant's role as 'helpful assistant'. The user message contains a question as specified in the original kNN query and some input text. The response from the model is stored in the summary.choices variable. Next steps. That was just one example of how to combine Elasticsearch with the power of OpenAI's models, to enable retrieval augmented generation. RAG allows you to avoid the costly and complex process of training or fine-tuning models, by leveraging out-of-the-box models, enhanced with additional context. Use this as a blueprint for your own experiments. To adapt the conversation for different use cases, customize the system message to define the assistant's behavior or persona. Adjust the user message to specify the task, such as summarization or question answering, along with the desired format of the response.