1. Project Overview

Project Name: Proof DB Type: Historical Evidence Retrieval System (RAG-oriented backend)

This project is a backend-centric system designed to manage, index, and retrieve historical evidence (documents, archives, OCR text) with strong emphasis on:

• Evidence traceability
• Chunk-level retrieval
• Hybrid search (full-text + vector)
• Citation reconstruction

Unlike generic RAG systems, this project treats "evidence" as first-class structured objects, not just text.

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2. Core Concept

The system is divided into three conceptual layers:

• Proof DB → Data layer (MySQL + OpenSearch + Vector)
• Archive Cask → Frontend interface (not part of this task)
• Few-shot Engine → OCR (external, not part of this task)

Current scope: Proof DB only

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3. System Architecture (Backend Focus)

The backend follows a modular service architecture (not microservices yet, but clearly separated layers):

Components:

1. Ingestion Layer

   • Accepts raw Markdown archive documents
   • Pre-processes Markdown page markers such as <!-- DOCMASTER:PAGE 0001 -->
   • Splits documents into page-bounded vector chunks
   • Keeps list-style archive records and their COMMENT blocks together where possible
   • Extracts metadata, including page numbers
   • Enqueues missing archive metadata such as title, year, author, tags, and summary for async LLM enrichment

2. Storage Layer

   • MySQL → metadata, relations
   • OpenSearch → full-text index
   • Vector DB → embeddings (can be OpenSearch kNN or Qdrant)

3. Retrieval Layer

   • Full-text search (BM25)
   • Vector search (embedding similarity)
   • Hybrid search (fusion)

4. Evidence Layer

   • Maps chunk → page → article
   • Provides page-level citation traceability

👉 这是典型 backend architecture 分层设计（server + database + API协同） (DEV Community)

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4. Tech Stack

Backend Framework

• PHP 8+
• Webman (HTTP API)
• Workerman (async workers / background jobs)

Database

• MySQL (relational metadata)

Search Engine

• OpenSearch

  • Full-text search (BM25)
  • Optional vector search (kNN)

Vector Layer

• Option A: OpenSearch kNN
• Option B: Qdrant (preferred if scaling)

Data Flow Tools

• Custom chunking logic (PHP)
• Embedding via external API / local model
• Metadata enrichment via Redis queue + OpenAI-compatible chat completion API

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5. Data Model (CRITICAL)

Core Entities

Archive
 ├── archive_uid (ULID)
 ├── title
 ├── summary
 ├── source
 └── metadata

Page
 ├── page_number
 ├── block_count
 ├── chunk_count
 └── content_length

PageBlock (internal import structure)
 ├── block_uid
 ├── archive_uid
 ├── page_number
 └── content

Chunk
 ├── chunk_uid (archive_uid + sequence + short uid)
 ├── page_start
 ├── page_end
 ├── text
 ├── embedding_ref

Key Principle

• archive_uid 是档案级核心 ID，使用 ULID
• chunk_uid 是 chunk 级核心 ID，格式为 {archive_uid}_{chunk_index}_{short_uid}
• MySQL / OpenSearch / Vector DB 全部围绕 archive_uid 和 chunk_uid
• page_number 是证据定位的关键字段
• Chunk 是向量化和检索召回单位，不是精确 citation 单位
• 证据定位只需要定位到页码，因此 chunk 可以跨段落合并，但不能跨页

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6. Search Design

Full-text (OpenSearch)

• Indexed at chunk level

• Supports:

  • keyword match
  • phrase match

Vector Search

• embedding similarity

Hybrid Search

• BM25 + vector fusion
• rerank stage

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7. API Design (First Phase)

Ingestion

POST /api/articles/import

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Retrieval

POST /api/search/fulltext
POST /api/search/vector
POST /api/search/hybrid

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Evidence

GET /api/chunks/{chunk_uid}
GET /api/evidence/{chunk_uid}

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8. Design Philosophy (IMPORTANT)

• Evidence > Text
• Chunk > Document
• Traceability > Raw Retrieval
• Hybrid Search by default

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9. Non-goals (IMPORTANT)

• No frontend (Archive Cask handled later)

• No OCR (Few-shot Engine external)

• No heavy microservices (keep simple modular architecture first)

• Proof DB ≠ storage

• Proof DB = retrieval + meaning + traceability