Muffakir 0.1.4

Arabic Retrieval-Augmented Generation Library

🧠 Muffakir RAG

Advanced Arabic Retrieval-Augmented Generation (RAG) Library

Muffakir RAG is a powerful Python library designed specifically for building sophisticated Retrieval-Augmented Generation (RAG) systems tailored to the Arabic language. It supports advanced document processing, semantic search, and intelligent answer generation powered by multiple LLM providers.

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✨ Key Features

• 🌟 Arabic Language Focus: Optimized for accurate processing of Arabic texts
• 🤖 Multi-Provider Support: Seamless integration with Together AI, OpenAI, Groq, and Open Router
• 📚 Advanced Document Processing: Handles PDF (including OCR for scanned documents), DOCX, TXT, and images with OCR support
• 🔍 Smart Retrieval: Multiple retrieval methods including hybrid and contextual search, with built-in reranking
• ⚡ Simple API: Intuitive interface for quick integration and usage
• 🛡️ Hallucination Check: Validates answers to reduce hallucinations
• 🔄 Query Transformer: Automatically optimizes user queries for better retrieval
• 🔄 Reranker: Enhances retrieval results through semantic similarity reranking

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🚀 Installation

pip install Muffakir

For development:

git clone https://github.com/yourusername/muffakir-rag.git
cd muffakir-rag
pip install -e .

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📖 Quick Start

from Muffakir import MuffakirRAG

config = {
    "data_dir": "path/to/your/documents",
    "llm_provider": "together",
    "api_key": "your_api_key_here",
    "embedding_model": "mohamed2811/Muffakir_Embedding",
    "k": 5,
    "query_transformer": True,
    "hallucination_check": True,
    "reranking": True
}

rag = MuffakirRAG(config)

response = rag.ask("What is the definition of artificial intelligence?")
print(response["answer"])

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🔧 Configuration Parameters

Core

Parameter	Description	Default	Required
data_dir	Path to documents folder	None	Yes
api_key	API key for LLM provider	None	Yes
llm_provider	Language model provider	"together"	Yes
embedding_model	Embedding model to use	"mohamed2811/Muffakir_Embedding"	No

Advanced Options

config = {
    # Basic
    "data_dir": "documents/",
    "api_key": "your_api_key",
    "llm_provider": "together",

    # LLM
    "llm_model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
    "llm_temperature": 0.0,
    "llm_max_tokens": 1000,

    # Text Processing
    "chunk_size": 600,
    "chunk_overlap": 200,
    "chunking_method": "recursive",

    # Retrieval
    "retrieval_method": "max_marginal_relevance_search",
    "k": 5,
    "fetch_k": 15,

    # Features
    "query_transformer": True,
    "hallucination_check": True,
    "reranking": True,
    "reranking_method": "semantic_similarity"
}

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🎯 Usage Examples

Search Similar Documents

similar_docs = rag.get_similar_documents(
    query="القانون الجنائي",
    k=3,
    method="similarity_search"
)

for doc in similar_docs:
    print(f"Source: {doc.metadata.get('source', 'N/A')}")
    print(f"Content: {doc.page_content[:200]}...")

Add New Documents

new_docs = ["path/to/new_doc1.pdf", "path/to/new_doc2.docx"]
success = rag.add_documents(new_docs)

if success:
    print("Documents added successfully!")

Customize Parameters for Asking

response = rag.ask(
    "Explain neural networks.",
    k=10,
    retrieval_method="hybrid",
    temperature=0.3
)

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🏗️ Project Structure

muffakir-rag/
├── Muffakir/              # Core module
├── TextProcessor/         # Document processing and chunking
├── LLMProvider/           # Language model interfaces
├── Embedding/             # Embedding models and management
├── Generation/            # Answer generation pipeline
├── VectorDB/              # Vector database management
├── Reranker/              # Semantic reranking
├── RAGPipeline/           # End-to-end RAG pipeline control
├── PromptManager/         # Prompt templates and management
└── QueryTransformer/      # Query optimization and transformation

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🔌 Supported Providers

Provider	Enum Name	Description
Together AI	TOGETHER	Together AI LLM provider
OpenAI	OPENAI	OpenAI GPT models
Groq	GROQ	Groq's AI platform
Open Router	OPENROUTER	Open Router API

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📄 Supported File Types

• PDF: Including OCR for scanned documents
• DOCX: Microsoft Word files
• TXT: Plain text files
• Images: Processed with Azure Computer Vision OCR

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📊 Performance

• ⚡ Fast processing of large-scale Arabic documents
• 🎯 Optimized for high accuracy on Arabic text
• 💾 Efficient memory and resource usage
• 🔄 Scalable to thousands of documents

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🤝 Contribution

Contributions are welcome! Please:

1. Fork the repository
2. Create a new feature branch
3. Add your improvements and tests
4. Submit a pull request for review

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Built with ❤️ for the Arabic community

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