RAGBee FW

Lightweight Retrieval-Augmented-Generation framework for SMEs.

RAGBee helps you load documents → split → retrieve → generate
with any LLM (OpenAI, HF Inference API, local vLLM/Triton).
Ports & Adapters + DI give you clean extensibility; CLI lets you run the pipeline in three commands.

✨ Features

• Clean architecture — core.ports (contracts) + infrastructure.* (plug-ins)
• Dependency Injection — YAML config ⇒ container wires loader / splitter / retriever / LLM
• CLI (ragbee_cli) — ingest, ask, shell out of the box
• LLM agnostic — OpenAI, HuggingFace Hub, vLLM, Triton … or your own adapter
• Composable — embed in FastAPI, Telegram-bot, Streamlit, Airflow DAG
• MIT license — free for commercial use

🚀 Quick start

pip install ragbee-fw           # 1. install

ragbee_cli ingest config.yml    # 2. build index
ragbee_cli ask config.yml "Что такое RAG?"   # 3. ask
ragbee_cli shell config.yml     # …or interactive REPL

config.yml (minimal):

data_loader:
  type: file_loader
  path: ./data

text_chunker:
  type: recursive_splitter
  chunk_size: 500
  chunk_overlap: 100

retriever:
  type: bm25
  top_k: 3

llm:
  type: hf
  model_name: gpt-3.5-turbo
  token: ${env:HF_TOKEN}

🧑‍💻 Python API

from ragbee_fw import DIContainer, load_config

cfg = load_config("config.yml")
container = DIContainer(cfg)

# 1) build / update index
ingestion = container.build_ingestion_service()
ingestion.build_index()                # or .update_index()

# 2) answer questions
answer = container.build_answer_service()
print(answer.generate_answer("What is RAG?", top_k=3))

🗺 Architecture (Clean / Hexagonal)

┌───────────────────┐
│      CLI / UI     │  ←  FastAPI, Streamlit, Telegram Bot …
└─────────▲─────────┘
          │ adapter
┌─────────┴─────────┐
│    Application    │  ←  DI container, services
└─────────▲─────────┘
          │ ports
┌─────────┴─────────┐
│      Core         │  ←  pure dataclasses, protocols
└─────────▲─────────┘
          │ adapters
┌─────────┴─────────┐
│ Infrastructure    │  ←  FS-loader, Splitter, BM25, HF LLM …
└───────────────────┘

📚 Documentation

Docs & API — README

Examples — example/

🤝 Contributing

1. Fork → clone → poetry install

2. Format code with black . && isort .

3. Submit PR → CI will run lint & tests

See CONTRIBUTING.md for full guide.

📜 License

MIT © V.A. Shevchenko — free for any purpose, commercial or private.