= SelfQueryRetriever.from_llm(    llm, vector_store, document_content_description, metadata_field_info, verbose=True)Testing it out‚ÄãAnd now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents("What are some movies about dinosaurs")    query='dinosaur' filter=None limit=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'rating': 9.0, 'genre': 'thriller'})]# This example specifies a filterretriever.get_relevant_documents("What are some highly rated movies (above 9)?")    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=9) limit=None    [Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'})]# This example only specifies a query and a filterretriever.get_relevant_documents("I want to watch a movie about toys rated higher than 9")    query='toys' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=9) limit=None    [Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year':
Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models. ->: = SelfQueryRetriever.from_llm(    llm, vector_store, document_content_description, metadata_field_info, verbose=True)Testing it out‚ÄãAnd now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents("What are some movies about dinosaurs")    query='dinosaur' filter=None limit=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'rating': 9.0, 'genre': 'thriller'})]# This example specifies a filterretriever.get_relevant_documents("What are some highly rated movies (above 9)?")    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=9) limit=None    [Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'})]# This example only specifies a query and a filterretriever.get_relevant_documents("I want to watch a movie about toys rated higher than 9")    query='toys' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=9) limit=None    [Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year':