appendix B LLM-powered summarization
In appendix A, we added /search, giving the newsreader the ability to find articles by using fuzzy search with vectors instead 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: using 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 will gains second responsibility: alongside running nomic-embed-text for embedding, it will also serve llama3, a text generation model that synthesizes retrieved articles into prose. The domain.Storage and domain.Searchable interfaces from appendix A require no changes; the new summary handler consumes them directly. Our focus is to add a new summarizer package and a new HTTP handler that uses LLMs while keeping our design flexible for the future.