fanqingsong / rag-ollama-langchain

RAG Project with Ollama and LangChain via Gradio Interface

This repository hosts the implementation of a Retrieval-Augmented Generation (RAG) project leveraging the capabilities of Ollama to run open-source large language models (LLMs) locally, alongside LangChain for robust integration of language models with data retrieval functionalities. A Gradio interface is provided for easy and interactive user engagement.

Project Overview

Retrieval-Augmented Generation (RAG) combines the generative strengths of large language models with advanced information retrieval techniques to produce contextually rich and accurate outputs. This project showcases the power of RAG in various domains such as question answering, content creation, and data synthesis, by harnessing the latest in language modeling and retrieval technologies through Ollama and LangChain.

Features

• Ollama Integration: Uses Ollama to locally run various open-source large language models, including Llama 2 and Code Llama, offering state-of-the-art language understanding and generation.
• LangChain for Efficient Retrieval: Implements LangChain for effective retrieval of pertinent information from diverse data sources, enhancing the models' output with real-time data.
• Interactive Gradio Interface: Provides a Gradio-based web interface for real-time user interaction, making it straightforward for both technical and non-technical users to input queries and receive responses.
• Flexible Framework: Designed for easy extension across different use cases and data sets, facilitating broad applicability.

Getting Started

Prerequisites

• Python 3.8+ and pip installed on your system.

Installation

1. Clone the repository:

git clone https://github.com/your-username/rag-project-with-ollama-and-langchain.git
cd rag-project-with-ollama-and-langchain

2. Install required Python packages:

pip install -r requirements.txt

Running the Application

1. Start the Gradio web interface:

python app.py

Gradio will then launch the web server, providing a local URL to access the interactive interface.

Usage

• Open the Gradio interface URL in your web browser.
• Enter your query in the designated text field.
• Hit the "Submit" button to send your query.
• The generated response, enriched with retrieved information, will be displayed on the interface.

Contributing

Contributions are welcome! Feel free to submit pull requests with new features, bug fixes, or enhancements.

License

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

Acknowledgments

• The Ollama team for enabling local execution of open-source large language models.
• LangChain for their innovative approach to integrating language models with information retrieval systems.
• Gradio for their user-friendly interface for machine learning models.

Contact

For inquiries or discussions, please open an issue in the repository, and we will respond as soon as possible.