This project implements a Retrieval-Augmented Generation (RAG) system using the LangChain library. It is designed to work with documents in Markdown format, allowing querying and obtaining relevant information from a collection of documents.

Make sure you have Python version 3.10.10 installed on your system. Also, you will need pip to install dependencies.

To begin, clone this repository on your local system using the following command:

git clone https://github.com/yourusername/langchain-markdown-rag.git

Change to project directory:

cd langchain-markdown-rag

Create a Python virtual environment and activate it:

python3 -m venv venv
source venv/bin/activate  # On Windows use  `venv\Scripts\activate`

Once the virtual environment is activated, install the project dependencies:

pip install -r requirements.txt

Before running the application, you must configure the environment variables:

• Copy the file .env.example into .env:

cp .env.example .env

Open the .env file and enter your OpenAI API keys.

• Insert your markdown files in the project's markdown_folder directory. Make sure that all markdown files you wish to analyse are present in this directory before proceeding.

• Edit the main.py file to include the specific questions you wish to ask the system. You will find a designated area in the file where you can enter or edit questions.

After setting up the environment, you can run the project with:

 python main.py

• main.py: The main input file for running the RAG system.
• DocumentManager.py: It managed the loading and segmentation of Markdown documents.
• EmbeddingManager.py: Responsible for the creation and persistence of embeddings.
• ConversationalRetrievalAgent.py: It manages the conversation-based information retrieval system.