RAG (Retriever-Augmented Generation) System Overview
main.py - Setting Up Qdrant Collection In this part, you are setting up the Qdrant client and creating a collection where embeddings will be stored: Initialize Qdrant Client : You connect to the Qdrant service using the API key and Qdrant Cloud URL. Create Collection : You define a new collection ( book_data ) in Qdrant with a specified vector size (1024) and distance metric (Cosine similarity). Result : You confirm that the collection is created successfully. from qdrant_client import QdrantClient from qdrant_client.models import Distance, VectorParams qdrant_client = QdrantClient( url = "https://your-qdrant-cloud-url" , api_key = "your-api-key" ) qdrant_client.create_collection( collection_name = "book_data" , # Name of the collection vectors_config =VectorParams( size = 1024 , distance =Distance.COSINE) # Vector size and distance metric ) print ( "Collection 'book_data' created successfully!" ) add_data.py - Addin...