🌟 LlamaIndex Query Engine 🌟

Welcome to the LlamaIndex Query Engine project! 🚀
This project demonstrates how to create and interact with a query engine using LlamaIndex. It builds an index from documents stored in a specified directory and allows you to query the indexed data. If the index already exists, it loads the index from persistent storage, ensuring fast and efficient retrieval.

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🚀 Features

• 📄 Document Indexing: Loads documents from a specified directory and creates an index.
• 💾 Persistent Storage: Saves the index to disk for later use, avoiding unnecessary re-building.
• 🔍 Query Engine: After building or loading the index, you can perform queries with natural language.
• 🔧 Customizable: Easily adapt the project for different datasets and query requirements.

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📋 Requirements

Before you get started, make sure you have the following:

• 🐍 Python 3.x: Make sure you have Python 3 installed.
• 🍃 Conda: We use Conda environments to manage dependencies.
• 📦 llama_index: This package is used for indexing and querying data.

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⚙️ Setup

0. Scratch

conda create -p venv python==3.12 -y
conda activate ./venv 
pip install -r requirements.txt


pip install ipykernel

1. Clone the repository

Start by cloning the project to your local machine:

git clone https://github.com/your-username/llamaindex-project.git
cd llamaindex-project

2. Create a Conda environment

To create a Conda environment and install the necessary dependencies, run:

conda env create -f environment.yml

This will create a new environment with all the required packages, including llama_index.

3. Activate the Conda environment

Once the environment is created, activate it using:

conda activate llamaindex-env

4. Install additional dependencies (if any)

If there are any extra dependencies, you can install them using:

pip install -r requirements.txt

This will install any missing packages that are required.

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🏃‍♂️ Usage

1. Prepare your data

Place your documents in the data folder. The SimpleDirectoryReader will automatically load all documents from this directory to build the index.

2. Running the script

Once your data is prepared, run the script to either create or load the index, and then perform a query.

python main.py

3. Code Breakdown

• Storage Check: First, the script checks if the index already exists in the ./storage directory.
• Create or Load Index: If the index doesn't exist, it loads documents from the data folder and creates a new index. If the index exists, it loads it from storage.
• Querying: Once the index is created or loaded, you can perform queries like "list some essentials topics we learn?".

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📂 Directory Structure

Here’s an overview of the project structure:

/llamaindex-project
│
├── /data                 # Folder for storing your documents to index
├── /storage              # Directory for storing the index
├── main.py               # Main Python script for indexing and querying
├── environment.yml       # Conda environment configuration
├── requirements.txt      # List of additional Python dependencies
└── README.md             # This file

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❓ Troubleshooting

• API Key Issues: If you encounter issues with API keys (e.g., for OpenAI), ensure that your keys are correctly set in the .env file or environment variables.
• Missing Dependencies: Make sure the Conda environment is activated and all required dependencies are installed.

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📝 License

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

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🤝 Contributing

We welcome contributions! Feel free to fork the repository, make changes, and open pull requests. If you have suggestions or encounter issues, please open an issue in the repository.