ceylon-rag 0.2.1

No project description provided

Ceylon AI RAG Framework

A powerful, modular, and extensible Retrieval-Augmented Generation (RAG) framework built with Python, supporting multiple LLM providers, embedders, and document types.

🌟 Features

• Multiple Document Types: Support for various document formats including:

  • Text files (with extensive format support)
  • PDF documents
  • Images (with OCR capabilities)
  • Source code files

• Flexible Architecture:

  • Modular component design
  • Pluggable LLM providers (OpenAI, Ollama)
  • Extensible embedding providers
  • Vector store integration (LanceDB)

• Advanced RAG Capabilities:

  • Intelligent document chunking
  • Context-aware searching
  • Query expansion and reranking
  • Metadata enrichment
  • Source attribution

• Specialized RAG Implementations:

  • FolderRAG: Process and analyze entire directory structures
  • CodeAnalysisRAG: Specialized for source code understanding
  • SimpleRAG: Basic RAG implementation for text data
  • Support for custom RAG implementations

🚀 Getting Started

Installation

# Install via pip
pip install ceylon-rag

# Or install from source
git clone https://github.com/ceylonai/ceylon-rag.git
cd ceylon-rag
pip install -e .

Basic Usage

Here's a simple example using the framework:

import asyncio
from dotenv import load_dotenv
from ceylon_rag import SimpleRAG

async def main():
    # Load environment variables
    load_dotenv()

    # Configure the RAG system
    config = {
        "llm": {
            "type": "openai",
            "model_name": "gpt-4",
            "api_key": os.getenv("OPENAI_API_KEY")
        },
        "embedder": {
            "type": "openai",
            "model_name": "text-embedding-3-small",
            "api_key": os.getenv("OPENAI_API_KEY")
        },
        "vector_store": {
            "type": "lancedb",
            "db_path": "./data/lancedb",
            "table_name": "documents"
        }
    }

    # Initialize RAG
    rag = SimpleRAG(config)
    await rag.initialize()

    try:
        # Process your documents
        documents = await rag.process_documents("path/to/documents")
        
        # Query the system
        result = await rag.query("What are the main topics in these documents?")
        print(result.response)
        
    finally:
        await rag.close()

if __name__ == "__main__":
    asyncio.run(main())

🏗️ Architecture

Core Components

1. Document Loaders

   • TextLoader: Handles text-based files
   • PDFLoader: Processes PDF documents
   • ImageLoader: Handles images with OCR
   • Extensible base class for custom loaders

2. Embedders

   • OpenAI embeddings support
   • Ollama embeddings support
   • Modular design for adding new providers

3. LLM Providers

   • OpenAI integration
   • Ollama integration
   • Async interface for all providers

4. Vector Store

   • LanceDB integration
   • Efficient vector similarity search
   • Metadata storage and retrieval

Document Processing

The framework provides sophisticated document processing capabilities:

# Example: Processing a code repository
async def analyze_codebase():
    config = {
        "llm": {
            "type": "openai",
            "model_name": "gpt-4"
        },
        "embedder": {
            "type": "openai",
            "model_name": "text-embedding-3-small"
        },
        "vector_store": {
            "type": "lancedb",
            "db_path": "./data/lancedb",
            "table_name": "code_documents"
        },
        "chunk_size": 1000,
        "chunk_overlap": 200
    }

    rag = CodeAnalysisRAG(config)
    await rag.initialize()
    
    documents = await rag.process_codebase("./src")
    await rag.index_code(documents)
    
    result = await rag.analyze_code(
        "Explain the main architecture of this codebase"
    )
    print(result.response)

🔧 Advanced Configuration

File Exclusions

Configure file exclusions using patterns:

config = {
    # ... other config options ...
    "excluded_dirs": [
        "venv",
        "node_modules",
        ".git",
        "__pycache__"
    ],
    "excluded_files": [
        ".env",
        "package-lock.json"
    ],
    "excluded_extensions": [
        ".pyc",
        ".pyo",
        ".pyd"
    ],
    "ignore_file": ".ragignore"  # Similar to .gitignore
}

Chunking Configuration

Customize document chunking:

config = {
    # ... other config options ...
    "chunk_size": 1000,  # Characters per chunk
    "chunk_overlap": 200,  # Overlap between chunks
}

🤝 Contributing

Contributions are welcome! Please feel free to submit pull requests. For major changes, please open an issue first to discuss what you would like to change.

📄 License

MIT License

🙏 Acknowledgments

• OpenAI for GPT and embedding models
• Ollama for local LLM support
• LanceDB team for vector storage
• All contributors and users of the framework

---

📚 API Documentation

For detailed API documentation, please visit our API Documentation page.

🔗 Links

• GitHub Repository
• Issue Tracker
• Documentation