refinire-rag 0.0.1

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refinire-rag

The refined RAG framework that makes enterprise-grade document processing effortless.

🌟 Why refinire-rag?

Traditional RAG frameworks are powerful but complex. refinire-rag refines the development experience with radical simplicity and enterprise-grade productivity.

→ Why refinire-rag? The Complete Story | → なぜrefinire-rag？完全版

⚡ 10x Simpler Development

# LangChain: 50+ lines of complex setup
# refinire-rag: 5 lines to production-ready RAG
manager = CorpusManager.create_simple_rag(doc_store, vector_store)
results = manager.process_corpus(["documents/"])
answer = query_engine.answer("How does this work?")

🏢 Enterprise-Ready Features Built-In

• Incremental Processing: Handle 10,000+ documents efficiently
• Japanese Optimization: Built-in linguistic processing
• Access Control: Department-level data isolation
• Production Monitoring: Comprehensive observability
• Unified Architecture: One pattern for everything

Overview

refinire-rag provides RAG (Retrieval-Augmented Generation) functionality as a sub-package of the Refinire library. The library follows a unified DocumentProcessor architecture with dependency injection for maximum flexibility and enterprise-grade capabilities.

Architecture

Use Case Classes (Refinire Steps)

• CorpusManager: Document loading, normalization, chunking, embedding generation, and storage
• QueryEngine: Document retrieval, re-ranking, and answer generation (inherits from Refinire Step)
• QualityLab: Evaluation data creation, automatic RAG evaluation, conflict detection, and report generation

DocumentProcessor Unified Architecture

All document processing components inherit from a single base class with consistent interface:

Document Processing Pipeline

• UniversalLoader: Multi-format document loading with parallel processing
• Normalizer: Dictionary-based term normalization and linguistic optimization
• TokenBasedChunker: Intelligent document chunking for optimal embedding
• DictionaryMaker: Term and abbreviation extraction with LLM integration
• GraphBuilder: Knowledge graph construction and relationship extraction
• VectorStoreProcessor: Embedding generation and vector storage

Quality & Evaluation

• TestSuite: Comprehensive evaluation pipeline execution
• Evaluator: Multi-metric aggregation and analysis
• ContradictionDetector: Automated conflict detection with NLI
• InsightReporter: Intelligent threshold-based reporting

Query Processing Components

• Retriever: Semantic and hybrid document search
• Reranker: Context-aware result re-ranking
• Reader: LLM-powered answer generation

Architecture Highlights

DocumentProcessor Unified Architecture

All document processing components inherit from a single base class with consistent process(document) -> List[Document] interface:

# Every processor follows the same pattern
normalizer = Normalizer(config)
chunker = TokenBasedChunker(config)
embedder = VectorStoreProcessor(config)

# Chain them together
pipeline = DocumentPipeline([normalizer, chunker, embedder])
results = pipeline.process_document(document)

Incremental Processing

Efficient handling of large document collections with automatic change detection:

# Only process new/changed files
incremental_loader = IncrementalLoader(document_store, cache_file=".cache.json")
results = incremental_loader.process_incremental(["documents/"])
# Skips unchanged files, processes only what's needed

Enterprise-Ready Features

• Multi-format document loading with parallel processing (detailed guide)
• Japanese text optimization with linguistic normalization
• Department-level data isolation patterns
• Comprehensive monitoring and error handling
• Production deployment ready configurations

🚀 Quick Start

Installation

pip install refinire-rag

30-Second RAG System

from refinire_rag import create_simple_rag

# One-liner enterprise RAG
rag = create_simple_rag("your_documents/")
answer = rag.query("How does this work?")
print(answer)

Production-Ready Setup

from refinire_rag.use_cases import CorpusManager, QueryEngine
from refinire_rag.storage import SQLiteDocumentStore, InMemoryVectorStore

# Configure storage
doc_store = SQLiteDocumentStore("corpus.db")
vector_store = InMemoryVectorStore()

# Build corpus with incremental processing
manager = CorpusManager.create_simple_rag(doc_store, vector_store)
results = manager.process_corpus(["documents/"])

# Query with confidence
query_engine = QueryEngine(retriever, reranker, reader)
result = query_engine.answer("What is our company policy on remote work?")

Enterprise Features

# Incremental updates (90%+ time savings on large corpora)
incremental_loader = IncrementalLoader(document_store, cache_file=".cache.json")
results = incremental_loader.process_incremental(["documents/"])

# Department-level data isolation (Tutorial 5 pattern)
hr_rag = CorpusManager.create_simple_rag(hr_doc_store, hr_vector_store)
sales_rag = CorpusManager.create_simple_rag(sales_doc_store, sales_vector_store)

# Production monitoring
stats = corpus_manager.get_corpus_stats()

🏆 Framework Comparison

Feature	LangChain/LlamaIndex	refinire-rag	Advantage
Development Speed	Complex setup	5-line setup	90% faster
Enterprise Features	Custom development	Built-in	Ready out-of-box
Japanese Processing	Additional work	Optimized	Native support
Incremental Updates	Manual implementation	Automatic	90% time savings
Code Consistency	Component-specific APIs	Unified interface	Easier maintenance
Team Productivity	Steep learning curve	Single pattern	Faster onboarding

📚 Documentation

🎯 Tutorials

Learn how to build RAG systems step by step - from simple prototypes to enterprise deployment.

• Tutorial Overview
• Tutorial 1: Basic RAG Pipeline
• Tutorial 6: Incremental Document Loading

📖 API Reference

Detailed API documentation for each module.

• API Reference
• Document Processing Pipeline
• Corpus Management
• Query Engine

🏗️ Architecture & Design

System design philosophy and implementation details.

• Architecture Overview
• Design Philosophy
• Loader Implementation - Detailed document loading guide
• Requirements
• Function Specifications

Key Features

Flexible Document Model

• Minimal required metadata (4 fields)
• Completely flexible additional metadata
• Database-friendly design for search and lineage tracking

Parallel Processing

• Concurrent document loading with ThreadPoolExecutor/ProcessPoolExecutor
• Async support for high-throughput scenarios
• Progress tracking and error recovery

Extension-Based Architecture

• Universal loader delegates to specialized loaders by file extension
• Easy registration of custom loaders
• Subpackage support for advanced processing (Docling, Unstructured, etc.)

Metadata Enrichment

• Path-based metadata generation with pattern matching
• Automatic file type detection and classification
• Custom metadata generators for domain-specific requirements

Error Handling

• Comprehensive exception hierarchy
• Configurable error handling (fail-fast or skip-errors)
• Detailed error reporting and logging

Development

Running Tests

# Run all tests
python -m pytest tests/

# Run specific test categories
python -m pytest tests/unit/        # Unit tests
python -m pytest tests/integration/ # Integration tests

# Run examples
python examples/simple_rag_test.py

Project Structure

refinire-rag/
├── src/refinire_rag/          # Main package
│   ├── models/                # Data models
│   ├── loaders/              # Document loading system
│   ├── processing/           # Document processing pipeline
│   ├── storage/              # Storage systems
│   ├── use_cases/            # Use case classes
│   └── retrieval/            # Search and answer generation
├── docs/                     # Architecture documentation
├── examples/                 # Usage examples
└── tests/                    # Test suite
    ├── unit/                 # Unit tests
    └── integration/          # Integration tests

Contributing

This project follows the architecture defined in the documentation. When implementing new features:

1. Follow the DocumentProcessor interface patterns
2. Maintain dependency injection for testability
3. Add comprehensive error handling and logging
4. Include usage examples and tests
5. Update documentation for new features

📝 Documentation Languages

• 🇬🇧 English: Default file names (e.g., tutorial_01_basic_rag.md)
• 🇯🇵 Japanese: File names with _ja suffix (e.g., tutorial_01_basic_rag_ja.md)

🔗 Related Links

• Refinire Library - Parent workflow framework
• GitHub Repository
• Issue Tracker
• Discussions

License

[License information to be added]

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refinire-rag: Where enterprise RAG development becomes effortless.