ragie

import os from ragie import Ragie from dotenv import load_dotenv load_dotenv() # Load environment variables from .env file RAGIE_API_KEY = os.environ.get("RAGIE_API_KEY", "") if not RAGIE_API_KEY: raise ValueError("RAGIE_API_KEY environment variable not set. Get one at https://secure.ragie.ai/api-keys") try: # Initialize the Ragie client using a context manager for resource cleanup with Ragie(auth=RAGIE_API_KEY) as ragie_client: # Create a raw document print("Creating a raw document...") document = ragie_client.documents.create_raw(request={ "data": "The quick brown fox jumps over the lazy dog. A common phrase used to test typewriters." }) print(f"Document created with ID: {document.id}") # Wait for the document to be ready (simplified polling for demonstration) print("Waiting for document to be ready...") while True: doc_status = ragie_client.documents.get(document_id=document.id) if doc_status.status == "ready": print(f"Document {document.id} is ready.") break # In a real application, you'd add a delay (e.g., time.sleep(1)) and/or timeout # Retrieve chunks based on a query print("Retrieving chunks...") retrieval = ragie_client.retrievals.retrieve(request={"query": "What is a common phrase?"}) if retrieval.chunks: for chunk in retrieval.chunks: print(f"Retrieved chunk: {chunk.text}") else: print("No chunks retrieved for the query.") except Exception as e: print(f"An error occurred: {e}") # Consider handling specific ragie.models.RagieError for API-related issues