article-tagger 0.4.1

RAG-based article tag generator using local embeddings and FAISS

Article Tagger

RAG-based article tag generator with hybrid scoring, active learning, and full agent integration.

Input an article, get the most relevant tags from your tag library — with confidence scores. Supports Chinese (Traditional/Simplified) natively.

100% accuracy on 30 test articles across 611 tags.

Install

# Core (CLI only — lightweight, ~500MB with model)
pip install article-tagger

# Full (API server + Web UI + export + visualization)
pip install article-tagger[all]

# Pick what you need
pip install article-tagger[api]       # REST API + Web UI server
pip install article-tagger[llm]       # Claude API reranker
pip install article-tagger[mcp]       # MCP server
pip install article-tagger[viz]       # t-SNE/UMAP visualization

# npm (agent integration)
npm install article-tagger-sdk        # TypeScript SDK
npx article-tagger-mcp                # MCP server for Claude Code / Cursor

Quick Start

# Zero-config demo (creates sample tags, builds index, tags an article)
bash quickstart.sh

Or step by step:

# 1. Build index from tag library
article-tagger build-index --tags 51標籤庫.json

# 2. Tag an article
article-tagger tag --text "她頭上有角，背後長著翅膀，尾巴尖尖的..."

# 3. Interactive mode
article-tagger interactive

# 4. Pre-load model for faster queries
article-tagger warmup

How It Works

Three-layer hybrid scoring:

1. Embedding search — sentence-transformers (multilingual) + FAISS vector similarity
2. Keyword boost — exact/partial match across Simplified/Traditional Chinese variants
3. Composite patterns — multi-feature detection (e.g., 角+翅膀 → 惡魔娘)

Plus:

• Active Learning — EMA-weighted feedback loop with dedup, converges over time
• Tag hierarchy — child tag matched → ancestors auto-boosted (with cycle detection)
• LLM reranker — optional Claude API second pass for precision
• Text augmentation — S/T Chinese variants + concept expansion in embedding space

Five Interfaces

Interface	Command	Use Case
CLI	article-tagger <cmd>	Human workflow, scripting
Agent CLI	atag <cmd>	Pure JSON, agent chaining
REST API	article-tagger serve	Web apps, microservices
MCP Server	article-tagger mcp-serve	Claude Code, Cursor
Web UI	localhost:8000/ui	Visual dashboard (Gradio)

CLI Commands

Command	Description
build-index	Build FAISS index from tag file (CSV/JSON)
tag	Tag single article (--rerank for LLM reranking)
tag-batch	Batch tag a directory
interactive	Interactive REPL with live feedback
watch	Watch directory, auto-tag new files
discover	New — Find new tag candidates from articles
warmup	New — Pre-load model + index into memory
analytics	Usage stats dashboard
quality	Tag library quality report
visualize	t-SNE/UMAP embedding visualization
cooccur	Tag co-occurrence analysis
weights	Active Learning weights
enrich	LLM auto-fill tag descriptions/categories
eval	Evaluate tagging accuracy (precision, recall, F1, MAP)
export	Multi-format export (CSV/Markdown/JSONL/Notion)
serve	Start REST API + Web UI
mcp-serve	Start MCP Server
state *	State management (export/import/incremental sync)
model *	Model management (list/switch/compare)
profile *	Config profiles (strict/broad/fast/custom)
daemon *	Daemon mode (start/stop/status/tag)

Agent-Native CLI (atag)

Pure JSON output, designed for agent chaining:

# Tag
atag tag "article text..."
atag tag -f article.txt

# Batch
atag batch -d ./articles/

# Feedback loop
atag feedback "標籤名" true

# Pipe-friendly
echo "article" | atag tag | jq '.tags[0].tag_name'

REST API

27 endpoints. Start with article-tagger serve.

Method	Path	Description
POST	/api/tag	Tag single article
POST	/api/tag-batch	Batch tag (up to 100)
GET/POST/PUT/DELETE	/api/tags[/{id}]	Tag CRUD
POST	/api/build-index	Upload tag file, rebuild index
POST	/api/feedback	Submit feedback (with dedup)
GET	/api/feedback	Get feedback + stats
POST	/api/feedback/undo	Undo last feedback
DELETE	/api/feedback/{id}	Delete feedback
GET	/api/history	Tagging history
GET	/api/history/search	Search history
DELETE	/api/history/{id}	Delete history
GET	/api/export	Export history (CSV/JSON)
GET	/api/analytics	Analytics dashboard
GET	/api/quality	Tag quality report
GET	/api/cooccurrence	Co-occurrence pairs
GET	/api/cooccurrence/suggest	Tag suggestions
GET	/api/weights	Active Learning weights
GET	/api/profiles	Config profiles
GET	/api/health	Health check

MCP Server (24 tools)

For Claude Code, Cursor, or any MCP-compatible agent:

{
  "mcpServers": {
    "article-tagger": {
      "command": "python",
      "args": ["-m", "article_tagger.mcp_server"]
    }
  }
}

Or via npm:

{
  "mcpServers": {
    "article-tagger": {
      "command": "npx",
      "args": ["article-tagger-mcp"]
    }
  }
}

Tools: tag_article, build_index, search_tags, list_tags, create_tag, update_tag, delete_tag, add_feedback, undo_feedback, delete_feedback, get_stats, get_weights, search_history, delete_history, export_history, get_analytics, get_quality_report, suggest_related_tags, discover_tags, export_state, import_state, switch_model, list_models, get_pipeline_info, list_profiles

TypeScript SDK

import { ArticleTaggerClient } from "article-tagger-sdk";

const client = new ArticleTaggerClient({ baseUrl: "http://localhost:8000" });

// Tag
const result = await client.tag("article text...", 5);
console.log(result.tags);

// Feedback
await client.addFeedback("article", "tag_name", true);

// CRUD
await client.createTag({ name: "新標籤", description: "描述", category: "分類" });
await client.updateTag("123", { description: "更新描述" });
await client.deleteTag("123");

// History & Analytics
const history = await client.searchHistory("keyword");
const analytics = await client.getAnalytics(30);

// Co-occurrence
const suggestions = await client.suggestTags(["標籤A", "標籤B"]);

Tag Discovery

Semi-automatic new tag candidate extraction:

# Scan articles for frequently mentioned concepts not in tag library
article-tagger discover ./articles/ --min-freq 3 --top 10

# JSON output for automation
article-tagger discover ./articles/ --json

Reports frequency, confidence score, source files, and similar existing tags.

Daemon Mode

Keep model in memory for millisecond-level tagging:

article-tagger daemon start
article-tagger daemon tag --text "..."   # ~10ms vs ~3s cold start
article-tagger daemon stop

State Migration

Transfer everything (index, feedback, history, weights) between machines:

# Full export/import
article-tagger state export -o bundle.tar.gz
article-tagger state import -f bundle.tar.gz

# Incremental sync
article-tagger state export-incr -o incr.tar.gz --since 2026-03-01T00:00:00
article-tagger state merge -f incr.tar.gz

Evaluation

# Create benchmark template
article-tagger eval --create-template benchmark.json

# Run evaluation (precision@k, recall@k, F1, MAP, NDCG)
article-tagger eval -b benchmark.json --top-k 5

Configuration

All settings via TAGGER_ prefixed environment variables:

Variable	Default	Description
TAGGER_MODEL_NAME	paraphrase-multilingual-MiniLM-L12-v2	Embedding model
TAGGER_TOP_K_RETURN	3	Default tags returned
TAGGER_SIMILARITY_THRESHOLD	0.1	Minimum score
TAGGER_MAX_TEXT_LENGTH	100000	Max article size (chars)
TAGGER_ENABLE_HIERARCHY	true	Tag hierarchy
TAGGER_ENABLE_SYNONYMS	true	Synonym expansion
TAGGER_ENABLE_RERANKER	false	LLM reranker
TAGGER_ENABLE_CACHE	true	Result + embedding cache
TAGGER_ANTHROPIC_API_KEY	—	For LLM reranker/enricher
TAGGER_API_KEY	—	REST API auth

Project Structure

src/article_tagger/
├── tagger.py           # Core facade — orchestrates all components
├── embedder.py         # sentence-transformers wrapper
├── indexer.py          # FAISS index (Flat/IVF auto-select)
├── booster.py          # Keyword matching + composite patterns
├── text_augmenter.py   # Tag text enrichment (S/T Chinese, concept expansion)
├── tag_discovery.py    # New tag candidate extraction
├── hierarchy.py        # Parent/child resolution + cycle detection
├── active_learning.py  # EMA-based feedback weights + dedup
├── reranker.py         # LLM reranking (Claude API)
├── cooccurrence.py     # Tag co-occurrence graph
├── pipeline.py         # Pre/post processor plugins
├── cache.py            # LRU cache + persistent embedding cache
├── store.py            # Feedback + history (JSON)
├── tag_loader.py       # CSV/JSON tag loader
├── tag_quality.py      # Duplicate/orphan/isolated detection
├── tag_enricher.py     # LLM tag library enrichment
├── evaluation.py       # Benchmark framework
├── analytics.py        # Usage analytics
├── profiles.py         # Config profiles
├── exporter.py         # Multi-format export
├── visualizer.py       # t-SNE/UMAP visualization
├── state_manager.py    # State export/import
├── config.py           # pydantic-settings
├── models.py           # Data models
├── cli.py              # Typer CLI (26 commands)
├── agent_cli.py        # Agent CLI — pure JSON (atag)
├── api.py              # FastAPI (27 endpoints)
├── mcp_server.py       # MCP Server (24 tools)
├── ui.py               # Gradio Web UI (6 tabs)
├── repl.py             # Interactive REPL
├── daemon.py           # Unix socket daemon
├── watcher.py          # Directory watch mode
└── middleware.py        # API middleware
packages/
├── sdk/                # TypeScript SDK (17 methods)
├── mcp-server/         # TypeScript MCP Server (11 proxied tools)
└── tool-schemas/       # OpenAI + Anthropic tool formats

Testing

# 206 tests covering all modules
pytest tests/ -v

# Skip model-loading tests (faster, avoids torch issues on some platforms)
SKIP_MODEL_TESTS=1 pytest tests/ -q

Docker

docker compose up --build
# API at localhost:8000, Web UI at localhost:8000/ui

License

MIT