ragplus

Lightweight, production-ready RAG (Retrieval-Augmented Generation) toolkit with 100% offline capabilities.

Build powerful RAG applications with document loading, hybrid search, reranking, and semantic chunking - all without API keys or internet connectivity.

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🎯 Why ragplus?

• 🔒 100% Offline - No API keys, no internet required, perfect for air-gapped environments
• ⚡ Production-Ready - Proper error handling, type hints, comprehensive testing
• 🎨 Flexible - Plug in any LLM (OpenAI, Anthropic, local models, Ollama)
• 📦 Lightweight - Minimal dependencies, easy to integrate
• 🚀 High Performance - Hybrid search + reranking for 20-40% accuracy boost

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✨ Features

Core RAG Capabilities

• 🔹 Smart Document Loading - Auto-detects and loads PDF, DOCX, TXT files
• 🔹 Multiple Embedding Models - MiniLM, BGE (default), E5 (all offline)
• 🔹 Advanced Chunking - Fixed, sentence-based (with overlap), markdown-aware, heading-based, semantic
• 🔹 Vector Storage - In-memory with persistence and metadata filtering
• 🔹 Semantic Search - Cosine similarity with optional hybrid BM25

v0.2.2 Accuracy Improvements ⚡ NEW!

• 🎯 Enhanced BM25 Tokenization - Regex-based with stemming (+20-30% accuracy)
• 🚀 BGE-Base Default - Upgraded from MiniLM (+10-15% accuracy)
• 📊 Query/Passage Prefixes - Optimized for BGE/E5 models (+5-10% accuracy)
• 🔄 Chunking with Overlap - Better context preservation (+5-10% accuracy)
• 🧠 Semantic Chunking - Groups similar sentences together (+10-15% accuracy)
• Total Improvement: 50-70% better accuracy! 🎉

v0.2.1 Advanced Features

• 🔍 Hybrid Search - Combines BM25 keyword + embedding semantic search
• 🎯 Cross-Encoder Reranking - 20-40% accuracy improvement
• 💾 Persistent Storage - Cache embeddings for instant reloads
• 🏷️ Metadata Filtering - Filter by source, page, section, custom fields
• 📝 Token-Based Truncation - Accurate context limits with tiktoken
• 🤖 Auto-Loader - Handles file paths, bytes, and text automatically

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📦 Installation

pip install ragplus

Requirements: Python 3.9+

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🚀 Quick Start

Basic RAG Pipeline

from ragplus import rag_answer, Embedder

def my_llm(prompt: str) -> str:
    # Use any LLM: OpenAI, Anthropic, Ollama, local models
    return "Your LLM response here"

# Works with text, file paths, or PDF bytes
documents = "RAG combines retrieval with generation for better AI responses."
query = "What is RAG?"

answer = rag_answer(query, documents, llm_fn=my_llm)
print(answer)

Advanced Usage with All Features

from ragplus import rag_answer, Embedder

# Maximum accuracy configuration
answer = rag_answer(
    query="What are the key findings?",
    documents="report.pdf",  # or PDF bytes from upload
    llm_fn=my_llm,
    
    # Advanced features
    use_hybrid_search=True,      # BM25 + embeddings
    use_reranking=True,           # Cross-encoder reranking
    chunk_strategy="sentence",    # Better chunking
    max_tokens=4096,              # Token-based truncation
    persist_dir="./rag_cache",    # Cache for fast reloads
    
    # Fine-tuning
    k=10,                         # Retrieve top 10 chunks
    embedder=Embedder("bge-base") # High-accuracy embeddings
)

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📚 Documentation

Load Documents

from ragplus import load_document, load_pdf, load_docx

# Auto-detect format
text = load_document("report.pdf")

# Specific loaders
pdf_text = load_pdf("document.pdf")
docx_text = load_docx("report.docx")

Choose Embedding Models

from ragplus import Embedder

# High accuracy (recommended)
embedder = Embedder(model_name="bge-base")

# Fastest
embedder = Embedder(model_name="minilm")

# Best accuracy
embedder = Embedder(model_name="e5-large")

Hybrid Search (BM25 + Embeddings)

from ragplus import HybridRetriever, VectorStore, Embedder

embedder = Embedder()
store = VectorStore()
# ... add documents ...

hybrid = HybridRetriever(
    vectorstore=store,
    embedder=embedder,
    bm25_weight=0.3,        # 30% keyword matching
    embedding_weight=0.7     # 70% semantic similarity
)

results = hybrid.search("your query", k=5)

Semantic Chunking Strategies

from ragplus import chunk_text

# Markdown-aware (preserves structure)
chunks = chunk_text(text, strategy="markdown")

# Sentence-based (natural boundaries)
chunks = chunk_text(text, strategy="sentence")

# Heading-based (section-aware)
chunks = chunk_text(text, strategy="heading")

# Fixed-size (default)
chunks = chunk_text(text, size=500, overlap=50)

Persistent Vector Store

from ragplus import VectorStore

# Auto-save and auto-load
store = VectorStore(persist_dir="./my_index")
store.add_documents(texts, embeddings, doc_id="file1.pdf")

# Reload instantly on next run
store = VectorStore(persist_dir="./my_index")  # Auto-loads existing data

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🎓 Examples

See the examples/ directory:

• basic_rag.py - Simple RAG pipeline
• advanced_rag.py - All v0.2.1 features

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🔑 Keywords

RAG, Retrieval Augmented Generation, LLM, embeddings, vector search, semantic search, document QA, offline RAG, Python RAG, BM25, hybrid search, cross-encoder, reranking, PDF extraction, DOCX parsing, sentence transformers, BGE embeddings, E5 embeddings, local RAG, air-gapped AI, private AI, document intelligence

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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📄 License

MIT License - see LICENSE file for details.

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🌟 Star History

If you find ragplus useful, please consider giving it a star on GitHub!

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• PyPI: https://pypi.org/project/ragplus/

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