RAG-Docs-Chat

RAG-Docs-Chat is a modular and extensible framework for building question-answering systems using the LangChain library. This project demonstrates how to set up a retrieval-augmented generation (RAG) model to process and answer queries based on documents loaded from a PDF file.

Table of Contents

• Features
• Installation
• Usage
• Project Structure
• Configuration

Features

• Load and split documents from a PDF file.
• Embed document chunks using Ollama Embeddings.
• Retrieve relevant document chunks using FAISS.
• Generate responses using a ChatOpenAI model.
• Maintain conversation history with memory buffers.
• Stream responses in real-time.

Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)
• virtualenv (optional but recommended)

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/RAG-Docs-Chat.git
   cd RAG-Docs-Chat

2. Create a virtual environment (optional but recommended):

   python -m venv .venv
   source .venv/bin/activate   # On Windows, use `.venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Install Ollama:

   Download and install Ollama from the official website: Download Ollama here!

5. Set up the environment variables:

   Create a .env file in the root directory of the project and add the following variables:

   MODEL=llama3
   FILE_PATH=example_file.pdf
   EMBEDDINGS_MODEL=nomic-embed-text
   API_KEY=not-needed
   BASE_URL=http://localhost:11434/v1

Usage

To start the RAG-Docs-Chat, run the main.py script:

python main.py

You will be prompted to enter a message. The assistant will process the message, retrieve relevant context from the documents, and generate a concise response.

Project Structure

.
├── config.py
├── example_file.pdf
├── LICENSE
├── main.py
├── models.py
├── README.md
├── requirements.txt
└── utils.py

• config.py: Configuration settings for the project.
• example_file.pdf: An example PDF file used for loading documents.
• LICENSE: License information for the project.
• main.py: The main entry point of the application.
• models.py: Model-related functions, including initialization of the ChatOpenAI model and RAG chain.
• README.md: Comprehensive guide and information about the project.
• requirements.txt: List of dependencies required for the project.
• utils.py: Utility functions for loading documents, embeddings, memory, and retriever.

Configuration

The project uses environment variables for configuration. These variables are defined in the .env file:

• MODEL: The name of the language model to use.
• FILE_PATH: The path to the PDF file containing the documents.
• EMBEDDINGS_MODEL: The embeddings model to use.
• API_KEY: The API key for the language model.
• BASE_URL: The base URL for the language model's API.