Get up and running with Mixpeek in minutes
from mixpeek import Mixpeek import time # Initialize the Mixpeek client mp = Mixpeek(api_key="YOUR_API_KEY") # Create a namespace namespace = mp.namespaces.create( name="quickstart", description="My first Mixpeek project" ) namespace_id = namespace["namespace_id"] # Create a bucket bucket = mp.buckets.create( namespace_id=namespace_id, name="content-bucket", description="Storage for our content", schema={ "my_img": { "type" : "image" } } ) bucket_id = bucket["bucket_id"] # Create a collection collection = mp.collections.create( namespace_id=namespace_id, name="content-collection", description="Processed content documents", source={ "bucket": bucket_id } feature_extractors=[ {"feature_extractor_name": "scene_splitter"} ] ) collection_id = collection["collection_id"] # Upload an object object = mp.objects.create( bucket_id=bucket_id, name="content-bucket", files=[ { "my_img": "https://example.com/sample-image.jpg" } ] ) object_id = object["object_id"] # Wait for processing to complete print("Processing content, please wait...") time.sleep(30) # Adjust based on content size # Create a retriever retriever = mp.retrievers.create( namespace_id=namespace_id, name="content-retriever", description="Search across processed content", stages=[ { "name": "embedding_search", "type": "vector", "collection_id": collection_id, "index": "multimodal", "limit": 20 } ] ) retriever_id = retriever["retriever_id"] # Search for content results = mp.retrievers.execute( retriever_id=retriever_id, query={ "text": "show me images with people" } ) # Display results print("\nSearch Results:") for result in results["results"]: print(f"Document ID: {result['document_id']}") print(f"Score: {result['score']}") print(f"Content: {result['content']}") print("---")
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