How to Build an Intelligent Document Summary System: An Architect's Guide

As an AI application architect, I often receive inquiries from indie developers and small technical teams: "I want to add an AI feature to my product, but I don't know where to start. Calling the Large Language Model (LLM) API looks simple, but it's full of pitfalls when you actually get down to it."

Today, through a specific scenario—building an "Intelligent Document Summary System"—we will break down how to elegantly implement an AI application. This is not just about writing a few lines of code to call the OpenAI interface; it is a comprehensive drill involving architecture design, cost control, and maintainability.

1. Business Pain Points: Submerged Information Value

Imagine this scenario: You are developing an internal knowledge base system for a law firm or an investment institution. Every day, clients and analysts upload a large volume of PDF research reports, Word contracts, and scanned financial reports. For the partners, they don't have time to read these dozens of pages word for word; they just need to know: "What are the risk points in this contract?" "What are the core revenue figures for this company?"

Traditional solutions usually face the following three major pain points:

1. Low information density, poor retrieval efficiency: Traditional full-text search (Ctrl+F) can only locate keywords and cannot understand semantics. To find a single piece of data, a user might have to leaf through the entire document.
2. Limited long-text processing: LLMs on the market all have context window limits. A 50-page PDF document, once converted to tokens, can easily exceed