Overview
Document Search is a retrieval workflow for finding useful passages across a private collection of notes and reference documents. It emphasizes trustworthy results: every answer stays connected to the source text that produced it.
Retrieval flow
Documents are split into sections that preserve headings and nearby context. Each section receives an embedding and searchable metadata such as its title, source, and updated date. A query retrieves semantic candidates, then applies lightweight ranking signals to surface the most relevant passages.
The interface shows the matched excerpts beside their source references. This makes it possible to inspect the evidence directly instead of treating a generated summary as the final authority.
Design priorities
The project favors understandable retrieval steps and visible sources over a complicated autonomous workflow. Evaluation focuses on whether the right passage appears early, whether citations remain accurate, and how the system behaves when the collection has no useful answer.
Technology
The workflow uses Python for document processing, embeddings and vector search for retrieval, and a small Next.js interface for queries and results.