https://github.com/bhavik-jikadara/qdrant-rag-app

This project is a Retrieval-Augmented Generation (RAG) application using the Qdrant vector database. Below is an overview of the project structure and steps to set up and run the application.
https://github.com/bhavik-jikadara/qdrant-rag-app

Last synced: 6 days ago
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This project is a Retrieval-Augmented Generation (RAG) application using the Qdrant vector database. Below is an overview of the project structure and steps to set up and run the application.

• Host: GitHub
• URL: https://github.com/bhavik-jikadara/qdrant-rag-app
• Owner: Bhavik-Jikadara
• Created: 2024-08-08T11:34:08.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-08T11:47:34.000Z (3 months ago)
• Last Synced: 2024-08-08T13:53:10.553Z (3 months ago)
• Language: Python
• Size: 113 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# RAG App using Qdrant VectorDB

This project is a Retrieval-Augmented Generation (RAG) application using the Qdrant vector database. Below is an overview of the project structure and steps to set up and run the application.

## Steps to Run the Application

* **Clone the Repository**: If you haven't already, clone the repository to your local machine:

    ```bash

    git clone https://github.com/Bhavik-Jikadara/qdrant-rag-app.git

    cd qdrant-rag-app

    ```

* **Set Up Virtual Environment**: Create a virtual environment to manage the project's dependencies:

    ```bash

    python -m venv venv

    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

    ```

* **Install Python Dependencies**: Install the required Python packages specified in requirements.txt:

    ```bash

    pip install -r requirements.txt

    ```

* **Install Docker and Run Qdrant VectorDB Using Docker**: Install Docker if you haven't already. Then, pull and run the Qdrant Docker container:

    ```bash

    docker pull qdrant/qdrant

    docker run -p 6333:6333 -v $(pwd):/qdrant qdrant/qdrant

    ```

This will start Qdrant and make it accessible on `http://localhost:6333`.

* **Configure Environment Variables**: Update the .env file with necessary environment variables such as Qdrant API keys or database URLs.

* **Ingest Data into Qdrant**: Use the ingest.py script to load data into the Qdrant vector database:

    ```bash

    python src/ingest.py

    ```

* **Run the Application**: Finally, run the main application using:

    ```bash

    python app.py

    ```