agenticrag 0.1.3

Modular and extensible Agentic RAG framework

AgenticRAG

AgenticRAG is a modular framework for building customizable Retrieval-Augmented Generation systems. It provides a complete stack for ingesting, storing, retrieving, and acting on diverse data types through a unified interface.

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Key Features

• Modular Architecture: Easily customize any component while maintaining system compatibility
• Multi-format Data Support: Handle text, tables, and databases through specialized stores
• Flexible Data Ingestion: Import data from PDFs, CSVs, web pages, and external databases
• Intelligent Retrieval: Use semantic search, SQL queries, or table operations as needed
• Task-oriented Design: Perform question answering, chart generation, or custom operations
• Easy Extensibility: Create custom components with minimal boilerplate

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Installation

For Users

Install the stable release from PyPI:

pip install agenticrag

For Contributors

Clone the repository and install development dependencies:

git clone https://github.com/yourusername/agenticrag.git
cd agenticrag
pip install -r requirements.txt
pip install -e .

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Quick Start

from agenticrag import RAGAgent

# Initialize agent with default components
agent = RAGAgent(persistent_dir="./agenticrag_data")

# Load some data
agent.load_pdf("path/to/document.pdf", name="company_handbook")
agent.load_csv("path/to/data.csv", name="sales_data")

# Ask questions using the data
response = agent.invoke("What were our top selling products last quarter?")
print(response)

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Architecture Overview

The AgenticRAG system follows a modular, layered architecture where each component has specific responsibilities:

1. Data Storage Layer: Stores maintain structured representations of various data types
2. Data Ingestion Layer: Loaders and Connectors handle importing data into stores
3. Data Retrieval Layer: Retrievers access relevant information based on queries
4. Task Execution Layer: Tasks perform operations using retrieved context
5. Orchestration Layer: RAGAgent coordinates all components to fulfill user requests

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Documentation

For detailed documentation, check out:

• Full Documentation: Complete guide to architecture and components
• Examples: Jupyter notebooks showing various use cases

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Project Structure

├── agenticrag/                # Main package
│   ├── connectors/            # Database and API connectors
│   ├── loaders/               # Data ingestion components
│   ├── retrievers/            # Context retrieval components
│   ├── stores/                # Data storage components
│   ├── tasks/                 # Task execution components
│   ├── types/                 # Data models and type definitions
│   ├── utils/                 # Utility functions and helpers
│   └── rag_agent.py           # Main agent implementation
├── docs/                      # Documentation
├── examples/                  # Example notebooks
├── tests/                     # Unit and integration tests
├── ui/                        # Streamlit demo app
├── LICENSE                    # MIT License
├── pyproject.toml             # Package configuration
├── README.md                  # This file
└── requirements.txt           # Development dependencies

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Example Use Cases

Question Answering from Documents

from agenticrag import RAGAgent

agent = RAGAgent()
agent.load_pdf("research_paper.pdf", name="research")
agent.invoke("Summarize the key findings of the research paper")

Data Analysis with Visualizations

from agenticrag import RAGAgent
from agenticrag.retrievers import TableRetriever
from agenticrag.tasks import ChartGenerationTask

agent = RAGAgent(
    retrievers=[TableRetriever()],
    tasks=[ChartGenerationTask()]
)
agent.load_csv("Iris.csv")
agent.invoke("Create a scatter plot showing petel length and width of Iris")

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Contributing

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

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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License

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