Appendix B. LLM-Powered Summarization
In Appendix B we added /search, giving the news reader the ability to find articles by using a fuzzy seach with vectors in stead of relying solely on keywords. The key infrastructure for that was Qdrant, a vector database that stores high-dimensional representations of each article, and Ollama, a local model server running nomic-embed-text to convert article text into those vectors. But returning a ranked list of article titles still puts the burden on the reader to figure out what matters. This appendix takes the next step: use those search results to generate a human-readable summary with a Large Language Model (LLM).
Qdrant will continue to provide retrieval, finding articles related to the topic we want to summarize. Ollama gains a second responsibility: alongside running nomic-embed-text for embedding, it also serves llama3, a text generation model that synthesizes retrieved articles into prose. The domain.Storage and domain.Searchable interfaces from Appendix B require no changes; the new summary handler consumes them directly. Our focus is to add a new summarizer package and a new HTTP handler leveraging LLMs with the ability to be flexible with our design for the future.