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={'year': 1995, 'genre': 'animated'})]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 specifies a query with a LIMIT valueretriever.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, 'genre': 'science fiction', 'rating': 7.7}),     Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7})]PreviousSupabaseNextVectaraWhat is Timescale Vector?How to access Timescale VectorCreating a TimescaleVector vectorstoreCreating our self-querying retrieverSelf Querying Retrieval with Timescale VectorFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Timescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL.
Timescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL. ->: 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={'year': 1995, 'genre': 'animated'})]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 specifies a query with a LIMIT valueretriever.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, 'genre': 'science fiction', 'rating': 7.7}),     Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7})]PreviousSupabaseNextVectaraWhat is Timescale Vector?How to access Timescale VectorCreating a TimescaleVector vectorstoreCreating our self-querying retrieverSelf Querying Retrieval with Timescale VectorFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.