langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [    AttributeInfo(        name="genre",        description="The genre of the movie",        type="string or list[string]",    ),    AttributeInfo(        name="year",        description="The year the movie was released",        type="integer",    ),    AttributeInfo(        name="director",        description="The name of the movie director",        type="string",    ),    AttributeInfo(        name="rating", description="A 1-10 rating for the movie", type="float"    ),]document_content_description = "Brief summary of a movie"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, 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    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': ['action', 'science fiction'], 'rating': 7.7, 'year': 1993.0}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),     Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'director': 'Christopher Nolan', 'rating': 8.2, 'year': 2010.0})]# This example only specifies a filterretriever.get_relevant_documents("I want to watch a movie rated higher than 8.5")    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)    [Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams
Pinecone is a vector database with broad functionality.
Pinecone is a vector database with broad functionality. ->: langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [    AttributeInfo(        name="genre",        description="The genre of the movie",        type="string or list[string]",    ),    AttributeInfo(        name="year",        description="The year the movie was released",        type="integer",    ),    AttributeInfo(        name="director",        description="The name of the movie director",        type="string",    ),    AttributeInfo(        name="rating", description="A 1-10 rating for the movie", type="float"    ),]document_content_description = "Brief summary of a movie"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, 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    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': ['action', 'science fiction'], 'rating': 7.7, 'year': 1993.0}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),     Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'director': 'Christopher Nolan', 'rating': 8.2, 'year': 2010.0})]# This example only specifies a filterretriever.get_relevant_documents("I want to watch a movie rated higher than 8.5")    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)    [Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams