This repository provides a practical guide and implementation for building scalable Retrieval-Augmented Generation (RAG) systems using Qdrant as the retrieval engine and deploying the system on Google Cloud. RAG systems combine the capabilities of retrieval and generation models to produce high-quality, relevant text tailored to specific queries, making them valuable tools for enterprises to extract insights from large volumes of unstructured data.
- High Performance: Qdrant is optimized for fast vector similarity search, enabling rapid retrieval of relevant documents from large datasets.
- Scalability: Qdrant can handle massive datasets and high query volumes, making it suitable for enterprise-scale applications with growing data and user demands.
- Cloud Integration: Seamless integration with Google Cloud services, such as Cloud Run and Cloud Storage, simplifies deployment and data management.
- Cost-Effective: Qdrant's efficient storage and retrieval mechanisms help reduce infrastructure costs, making it an economical choice for enterprises.
- Python 3.7 or higher
- Google Cloud account
- OpenAI API key (for language model integration)
- Clone the repository:
git clone https://github.com/your-username/rag-qdrant-gcloud.git
- Install the required Python packages:
pip install PyPDF2 nltk fastembed qdrant-client[fastembed] openai
- Set up a Qdrant cluster on Google Cloud using the Qdrant Cloud service.
- Prepare your data by extracting text from documents (e.g., PDFs) and generating embeddings using FastEmbed.
- Start the Qdrant client and create a new collection with the appropriate vector parameters.
- Upload the generated embeddings and associated metadata to the Qdrant cluster.
- Generate text with a language model (e.g., GPT-4, Gemini Ultra) by passing the retrieved documents as context.
Contributions are welcome!
This project is licensed under the MIT License.