series of dreams within dreams within dreams and Inception reused the idea', metadata={'genre': None, 'rating': 8.6, 'year': 2006})]# This example specifies a query and a filterretriever.get_relevant_documents("Has Greta Gerwig directed any movies about women")    query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None    [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'genre': None, 'rating': 8.3, 'year': 2019})]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,    vectorstore,    document_content_description,    metadata_field_info,    enable_limit=True,    verbose=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={'genre': 'science fiction', 'rating': 7.7, 'year': 1993}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'rating': None, 'year': 1995})]PreviousVectaraNextSVMCreating a Weaviate vector storeCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from
Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from ->: series of dreams within dreams within dreams and Inception reused the idea', metadata={'genre': None, 'rating': 8.6, 'year': 2006})]# This example specifies a query and a filterretriever.get_relevant_documents("Has Greta Gerwig directed any movies about women")    query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None    [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'genre': None, 'rating': 8.3, 'year': 2019})]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,    vectorstore,    document_content_description,    metadata_field_info,    enable_limit=True,    verbose=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={'genre': 'science fiction', 'rating': 7.7, 'year': 1993}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'rating': None, 'year': 1995})]PreviousVectaraNextSVMCreating a Weaviate vector storeCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.