RAG-LLM Using AI Pipeline with Streamlit Interface 🚀

Welcome to the RAG-LLM Using AI Pipeline with Streamlit Interface repository! This project integrates Retrieval-Augmented Generation (RAG) with the Claude Sonet 3.5 LLM and the Pathway framework to provide insights into financial reports and tables. The system ingests data from Google Drive, processes both structured and unstructured formats in real time, and presents the results through a user-friendly Streamlit interface.

Check out the latest releases here!

Table of Contents

1. Introduction
2. Features
3. Technologies Used
4. Installation
5. Usage
6. Contributing
7. License
8. Contact

Introduction

In the world of finance, analyzing data efficiently is crucial. This project aims to streamline that process by combining advanced AI techniques with practical tools. By leveraging the power of RAG and the Claude Sonet 3.5 LLM, users can extract meaningful insights from complex financial documents. The integration with Google Drive allows for easy data access, while the Streamlit interface makes it simple to visualize results.

Features

• Real-time Data Processing: Ingest and analyze data from Google Drive instantly.
• Structured and Unstructured Format Handling: Process various types of financial documents.
• User-friendly Interface: Visualize insights through a Streamlit-powered dashboard.
• Integration with Claude Sonet 3.5 LLM: Utilize advanced language models for enhanced analysis.
• Retrieval-Augmented Generation: Combine traditional data retrieval with modern AI techniques for better results.

Technologies Used

This project employs a variety of technologies to achieve its goals:

• Python: The primary programming language for development.
• Streamlit: For creating the web interface.
• Claude Sonet 3.5 LLM: The language model for processing and generating text.
• Pathway Framework: To streamline the AI pipeline.
• Google Drive API: For data ingestion.
• Vector Database: For efficient data storage and retrieval.

Installation

To set up the project locally, follow these steps:

1. Clone the Repository:

   git clone https://github.com/Dono1901/RAG-LLM-using-AI-Pipeline-with-streamlit-interface.git

2. Navigate to the Project Directory:

   cd RAG-LLM-using-AI-Pipeline-with-streamlit-interface

3. Install Dependencies: Make sure you have Python 3.8 or higher installed. Then, run:

   pip install -r requirements.txt

4. Set Up Google Drive API: Follow the instructions in the Google Drive API documentation to set up your credentials.

5. Run the Application: Start the Streamlit server with:

   streamlit run app.py

Usage

After setting up the application, you can start using it:

1. Access the Interface: Open your web browser and go to http://localhost:8501.

2. Upload Financial Reports: Use the interface to upload your financial documents from Google Drive.

3. Analyze Data: The system will process the data and provide insights in real time.

4. Visualize Results: Explore the insights through the interactive dashboard.

Contributing

We welcome contributions! If you want to help improve this project, please follow these steps:

1. Fork the Repository.
2. Create a New Branch:

   git checkout -b feature/YourFeature

3. Make Your Changes.
4. Commit Your Changes:

   git commit -m "Add some feature"

5. Push to the Branch:

   git push origin feature/YourFeature

6. Open a Pull Request.

License

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

Contact

For any questions or feedback, feel free to reach out:

• Email: your.email@example.com
• Twitter: @yourhandle

Thank you for your interest in the RAG-LLM Using AI Pipeline with Streamlit Interface!

For the latest updates and releases, visit our Releases section.