Advanced60 min
Building a RAG System
Create a production-ready Retrieval-Augmented Generation system
Last updated: 2025-01-13
Prerequisites
- LLM experience
- Vector database knowledge
- API development
1. Set Up Vector Database
Install and configure a vector database like Qdrant or Weaviate for storing document embeddings.
2. Create Embeddings
Generate embeddings for your documents using models like sentence-transformers.
3. Implement Retrieval
Build the retrieval mechanism to find relevant documents based on query similarity.
4. Integrate with LLM
Connect the retrieval system with your language model to generate contextual responses.