kite/.planning/codebase/ARCHITECTURE.md

ARCHITECTURE — document-scanner

Last updated: 2026-05-21

Summary

Document Scanner is a two-tier web application: a Vue 3 SPA communicates with a FastAPI backend via a Vite dev-proxy (or directly in production). The backend handles document ingestion, text extraction, AI-based classification, and flat-file persistence. AI provider selection is fully runtime-configurable via a provider pattern abstraction.

---

System Overview

Browser (Vue 3 SPA)
      │  HTTP/JSON + multipart
      ▼
FastAPI  (port 8000)
  ├── api/documents.py   – upload, list, get, delete, reclassify
  ├── api/topics.py      – CRUD for topic list
  ├── api/settings.py    – AI provider config + system prompt
  │
  ├── services/
  │   ├── extractor.py   – text extraction dispatch
  │   ├── classifier.py  – orchestrates AI call + topic creation
  │   └── storage.py     – flat-file JSON + filesystem persistence
  │
  └── ai/                – provider abstraction layer
      ├── base.py        – AIProvider ABC + ClassificationResult
      ├── __init__.py    – get_provider() factory
      ├── anthropic_provider.py
      ├── openai_provider.py
      ├── ollama_provider.py   (subclasses OpenAIProvider)
      └── lmstudio_provider.py (subclasses OpenAIProvider)
                │
                ▼
     External AI service (Anthropic API / OpenAI API /
                          Ollama / LM Studio — host.docker.internal)

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Request Flow — Document Upload + Classification

1. Frontend POSTs multipart/form-data to POST /api/documents/upload
2. documents.py saves the file to data/uploads/, calls extractor.extract_text()
3. Extracted text (truncated to 50,000 chars) is stored in data/metadata/<id>.json
4. If auto_classify=true, classifier.classify_document() is called: a. Loads current settings from data/settings.json → calls get_provider(settings) b. Passes document text + existing topics to provider.classify() c. Any suggested new topics are created via storage.add_topic() d. Document metadata is updated with assigned topics
5. Full document metadata JSON is returned to the frontend

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AI Provider Abstraction

• AIProvider (ABC in ai/base.py) defines three async methods:
  • classify(document_text, existing_topics, system_prompt) → ClassificationResult
  • suggest_topics(document_text, system_prompt) → list[str]
  • health_check() → bool
• get_provider(settings: dict) factory in ai/__init__.py reads settings["active_provider"] and instantiates the correct class
• OllamaProvider and LMStudioProvider extend OpenAIProvider (both expose OpenAI-compatible endpoints)
• Provider is re-instantiated on every request (stateless; no connection pooling)

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Data Persistence

All state is stored on the local filesystem — no database:

Store	Path	Format	Access
Uploaded files	data/uploads/<id>.<ext>	Original binary	Direct filesystem
Document metadata	data/metadata/<id>.json	JSON per document	filelock protected
Topic list	data/topics.json	{"topics": [...]}	filelock protected
Settings	data/settings.json	JSON object	filelock protected

filelock is used to prevent concurrent write corruption on JSON files.

---

Frontend Architecture

• Vue 3 SPA (Options API), Pinia stores, Vue Router 4
• Three Pinia stores (documents, topics, settings) act as the sole data access layer — components never call the API directly
• src/api/client.js is the single HTTP adapter (wraps fetch)
• Vite proxies /api/* to http://localhost:8000 in dev mode

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Key Patterns

• Provider Pattern — AI backends are interchangeable at runtime via settings
• Service Layer — extractor, classifier, storage are pure Python modules; no FastAPI coupling
• Pinia-as-Facade — stores encapsulate all async API calls; views stay declarative

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Constraints & Notable Decisions

• All CORS origins allowed (allow_origins=["*"]) — suitable for local dev, not production
• No authentication or user model
• Single-worker assumption for file locking (does not scale to multiple uvicorn workers)
• AI provider re-instantiated per request (no connection reuse)
• Data directory is volume-mounted in Docker; no backup or migration strategy

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Gaps / Unknowns

• No API versioning strategy visible
• Frontend has no error boundary or global error handling component
• No pagination on document list endpoint (could be a scaling concern)