rag-agent 0.3.1

RAG (Retrieval-Augmented Generation) pipeline implementation

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RAG Pipeline

A powerful Retrieval-Augmented Generation (RAG) pipeline implementation that supports multiple document types, embedding models, and LLM providers.

Features

• Multiple Document Support

  • PDF documents
  • Text files
  • HTML content

• Flexible LLM Integration

  • Ollama support (local models)
  • Hugging Face integration
  • Configurable model parameters

• Advanced Document Processing

  • Metadata extraction from file paths
  • Configurable text chunking
  • Duplicate detection
  • Progress tracking

• Vector Store Features

  • ChromaDB integration
  • Configurable embedding models
  • Multiple search strategies
  • Persistent storage

Installation

1. Clone the repository:

git clone https://github.com/yourusername/rag-pipeline.git
cd rag-pipeline

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

# install from source
pip install -e .
# or install latest release from PyPI
pip install rag-agent

4. Install Ollama (if using local models):

curl -fsSL https://ollama.com/install.sh | sh

Configuration

Create a .env file in the project root with your configuration:

# Document source settings
PIPELINE_SOURCES='{"/path/to/your/documents": ["pdf", "mhtml"]}'

# Text splitting settings
PIPELINE_CHUNK_SIZE=1000
PIPELINE_CHUNK_OVERLAP=200

# Vector store settings
PIPELINE_PERSIST_DIRECTORY=chroma_db
PIPELINE_COLLECTION_NAME=default_collection

# Embedding model settings
PIPELINE_EMBEDDING_MODEL=all-MiniLM-L6-v2
PIPELINE_EMBEDDING_MODEL_KWARGS={"device": "cuda"}

# LLM settings
PIPELINE_LLM_PROVIDER=ollama  # or 'huggingface'
PIPELINE_LLM_MODEL=mistral
PIPELINE_LLM_MODEL_KWARGS={"temperature": 0.3}
PIPELINE_LLM_API_KEY=your_api_key  # Required for Hugging Face

# Retrieval settings
PIPELINE_SEARCH_TYPE=similarity  # or 'mmr', 'similarity_score_threshold'
PIPELINE_K=5
PIPELINE_SCORE_THRESHOLD=0.5
PIPELINE_FETCH_K=20
PIPELINE_LAMBDA_MULT=0.5

Refer to pipeline config for more information.

Usage

Basic Usage

from rag_agent.pipeline import RAGPipeline, Settings

async def main():
    # Initialize with default settings
    async with RAGPipeline() as pipeline:
        # Load and process documents
        documents = await pipeline.load_documents()
        processed_docs = await pipeline.process_documents(documents)
        await pipeline.update_vectorstore(processed_docs)
        
        # Setup and run query
        await pipeline.setup_retrieval_chain()
        answer = await pipeline.run("Your question here")
        print(answer)

# Run the pipeline
import asyncio
asyncio.run(main())

Web UI

The project includes a modern web interface built with Panel that provides an interactive way to use the RAG pipeline:

Features

• Interactive Chat Interface

  • Real-time question answering
  • Context toggle for viewing retrieved documents
  • Chat history saving and export
  • Copy and clear functionality

• Configuration Management

  • Live configuration updates
  • Document source management
  • Embedding model settings
  • Text splitting parameters
  • Vector store configuration
  • Retrieval strategy options
  • LLM provider settings

• User Experience

  • Responsive layout with collapsible sidebar
  • Real-time notifications
  • Progress tracking
  • Error handling with detailed feedback

Installation

Install the UI dependencies:

pip install 'rag-agent[ui]'

Usage

Start the web interface:

python -m rag_agent.ui

Or specify a custom port:

python -m rag_agent.ui --port 8502

The interface will be available at http://localhost:8501 (or your specified port).

Custom Configuration

config = Settings(
    pipeline_source="/path/to/documents",
    pipeline_source_type="pdf",
    pipeline_llm_provider="ollama",
    pipeline_llm_model="mixtral",
    pipeline_llm_model_kwargs={"temperature": 0.3}
)

async with RAGPipeline(config) as pipeline:
    # ... rest of the code

Supported Models

All Ollama Models

• mistral (7B parameters)
• mixtral (8x7B parameters)
• llama2 (7B parameters)
• codellama (Code specialized)
• neural-chat (Chat optimized)
• dolphin-mixtral (Chat optimized)
• And many more...

Hugging Face Models (Experimental)

• mistralai/Mistral-7B-Instruct-v0.2
• meta-llama/Llama-2-7b-chat-hf
• malteos/gpt2-wechsel-german
• And many more...

Document Processing

The pipeline supports various document types and processing options:

PDF Processing

• Uses PyMuPDF for efficient PDF processing
• Extracts text and metadata
• Configurable processing modes

Text Processing

• Configurable chunk sizes
• Overlap control
• Metadata extraction

HTML Processing

• Clean HTML extraction
• Structured content handling
• Metadata preservation

iX Archive Scraper

The project includes a specialized scraper for the archive of iX magazine issues with the following features:

Features

• Automated Article Download

  • Downloads articles in multiple formats (PDF, MHTML)
  • Preserves article metadata and structure
  • Handles authentication and session management

• Parallel Processing

  • Concurrent article processing
  • Configurable thread pool
  • Progress tracking with tqdm

• Robust Error Handling

  • Automatic retries for failed downloads
  • Graceful cleanup on interruption
  • Detailed logging

Configuration

Add the following to your .env file for IX scraper configuration:

# IX Scraper settings
IX_SCRAPER_BASE_URL=https://www.heise.de
IX_SCRAPER_SIGN_IN_URL=https://www.heise.de/sso/login/
IX_SCRAPER_ARCHIVE_URL=https://www.heise.de/select/ix/archiv/
IX_SCRAPER_MAX_THREADS=10
IX_SCRAPER_MAX_CONCURRENT=10
IX_SCRAPER_TIMEOUT=30
IX_SCRAPER_RETRY_ATTEMPTS=5
IX_SCRAPER_OUTPUT_DIR=~/Downloads/ix
IX_SCRAPER_USERNAME=your_username
IX_SCRAPER_PASSWORD=your_password
IX_SCRAPER_OVERWRITE=false
IX_SCRAPER_EXPORT_FORMATS=["pdf"]

Refer to iX scraper config for more information.

Usage

# pip install -e .[scraper]
from rag_agent.scrappers.ix import IXScraper, Settings

async def main():
    # Initialize with default settings
    async with IXScraper() as scraper:
        # Run the scraper to export each article separately using export configuration
        await scraper.run()

# Run the scraper
import asyncio
asyncio.run(main())

or

# pip install -e .[scraper]
from rag_agent.scrappers.ix import IXDownloader, Settings

async def main():
    # Initialize with default settings
    async with IXDownloader() as scraper:
        # Run the scraper to download the issues in PDF format
        await scraper.run()

# Run the scraper
import asyncio
asyncio.run(main())

Export Formats

The scraper supports multiple export formats:

1. PDF Export

   • High-quality PDF output
   • Configurable page settings
   • Base64 encoded transfer

2. MHTML Export

   • Preserves web page structure
   • Includes all resources
   • Suitable for archival

WebDriver Configuration

The scraper uses Selenium WebDriver with configurable options:

• Headless mode support
• Custom user agent
• Resource optimization
• Security settings

Vector Store

The pipeline uses ChromaDB for vector storage with features like:

• Configurable embedding models
• Multiple search strategies
• Persistent storage
• Duplicate detection

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• LangChain for the RAG framework
• ChromaDB for vector storage
• Ollama for local LLM support
• Hugging Face for model hosting
• iX publishers