Multi-Document Retrieval-Augmented Generation (multi_doc_rag)

A powerful web application for retrieving and generating information from multiple documents using advanced AI techniques.

Overview

The multi_doc_rag project leverages state-of-the-art retrieval-augmented generation methods to efficiently search and synthesize information from a large collection of documents. This approach enhances the ability to generate accurate and contextually relevant responses by incorporating information from multiple sources.

Features

• Multi-Document Retrieval: Efficiently retrieve relevant information from a vast collection of documents.
• Augmented Generation: Generate high-quality responses by integrating retrieved information.
• Scalability: Handle large datasets with ease.
• Customizable: Easily adapt the framework to specific use cases and datasets.

Installation

To run the multi_doc_rag web application, you need to have Streamlit installed. You can install Streamlit using the following command:

pip install poetry==1.8.5
poetry install

Usage

Here's a basic example of how to run the multi_doc_rag web application:

ollama serve
streamlit run app.py

Prerequisites

Before running the multi_doc_rag web application, ensure you have the following installed:

• Ollama: Required for serving the application.
• Poetry: For managing dependencies.

Alternatively, you can use an OpenAI key for utilizing OpenAI models from LangChain.

Packages Used

The multi_doc_rag project utilizes several key packages:

• LangChain: For building and managing the language model workflows.
• Ollama: For serving local llm models for the application.
• Streamlit: For creating the web interface.
• Chroma: For efficient document retrieval and storage (vector database).

Preview

Here are some preview images and a GIF demonstrating the multi_doc_rag web application in action:

Application Demo

Application Interface

Document Upload