after 1990 but before 2005 that's all about toys, and preferably is animated")    query='toys' 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='animated')]) limit=None    [Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'})]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,    vectara,    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={'lang': 'eng', 'offset': '0', 'len': '66', 'year': '1993', 'rating': '7.7', 'genre': 'science fiction', 'source': 'langchain'}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'})]PreviousTimescale Vector (Postgres) self-queryingNextWeaviateConnecting to Vectara from LangChainCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation
Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation ->: after 1990 but before 2005 that's all about toys, and preferably is animated")    query='toys' 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='animated')]) limit=None    [Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'})]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,    vectara,    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={'lang': 'eng', 'offset': '0', 'len': '66', 'year': '1993', 'rating': '7.7', 'genre': 'science fiction', 'source': 'langchain'}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'})]PreviousTimescale Vector (Postgres) self-queryingNextWeaviateConnecting to Vectara from LangChainCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.