men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'})]# This example specifies a composite filterretriever.get_relevant_documents("What's a highly rated (above or equal 9) thriller film?")    query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='thriller'), Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=9)]) limit=None    [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 query and composite filterretriever.get_relevant_documents(    "What's a movie after 1990 but before 2005 that's all about dinosaurs, \    and preferably has a lot of action")    query='dinosaur' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='action')]) limit=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'})]Filter k‚ÄãWe can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm(    llm,     vector_store,     document_content_description,     metadata_field_info,     verbose=True,    enable_limit=True)# This example only specifies a relevant queryretriever.get_relevant_documents("What are two movies about dinosaurs?")    query='dinosaur' filter=None limit=2    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre':
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. ->: men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'})]# This example specifies a composite filterretriever.get_relevant_documents("What's a highly rated (above or equal 9) thriller film?")    query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='thriller'), Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=9)]) limit=None    [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 query and composite filterretriever.get_relevant_documents(    "What's a movie after 1990 but before 2005 that's all about dinosaurs, \    and preferably has a lot of action")    query='dinosaur' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='action')]) limit=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'})]Filter k‚ÄãWe can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm(    llm,     vector_store,     document_content_description,     metadata_field_info,     verbose=True,    enable_limit=True)# This example only specifies a relevant queryretriever.get_relevant_documents("What are two movies about dinosaurs?")    query='dinosaur' filter=None limit=2    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre':