Retrieve PDF pages with embeddings, rerank the small candidate set, and summarize only the passages that can change the answer.
For a long contract, report, or knowledge-base export, I would not send every extracted page to a model. I would preserve page provenance, index sensible chunks, retrieve broadly for the user's topic, rerank narrowly, and put a hard input budget around the final summary. That is the operationally boring answer, which is usually the right one when my team owns the pager.
The point isn't to build the most elaborate RAG graph. It is to reduce irrelevant context before generation while keeping enough evidence to explain where each sentence came from. The quality target is a useful topic-focused summary; the SLO target is a pipeline whose latency, token volume, and failure boundaries I can measure separately.
What signal says a PDF summarization pipeline needs semantic search, embeddings, and reranking?
Start with the user's intent. If they want a faithful synopsis of a six-page memo, retrieval probably adds machinery without much benefit. If they want the clauses about termination and liability from a 280-page contract, or the findings related to one business unit in an annual report, full-document summarization spends context on material that cannot affect the answer. Embedding search supplies recall: it finds chunks likely to be related to the selected topic. Reranking then supplies a more expensive, more precise ordering before generation.
