Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={            "year": 1979,            "rating": 9.9,            "director": "Andrei Tarkovsky",            "genre": "science fiction",            "rating": 9.9,        },    ),]username_or_org = "<USERNAME_OR_ORG>"vectorstore = DeepLake.from_documents(    docs, embeddings, dataset_path=f"hub://{username_or_org}/self_queery", overwrite=True,)    Your Deep Lake dataset has been successfully created!    /    Dataset(path='hub://adilkhan/self_queery', tensors=['embedding', 'id', 'metadata', 'text'])          tensor      htype      shape     dtype  compression      -------    -------    -------   -------  -------      embedding  embedding  (6, 1536)  float32   None           id        text      (6, 1)      str     None        metadata     json      (6, 1)      str     None          text       text      (6, 1)      str     None        Creating our self-querying retriever‚ÄãNow we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom 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,
Deep Lake is a multimodal database for building AI applications
Deep Lake is a multimodal database for building AI applications ->: Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={            "year": 1979,            "rating": 9.9,            "director": "Andrei Tarkovsky",            "genre": "science fiction",            "rating": 9.9,        },    ),]username_or_org = "<USERNAME_OR_ORG>"vectorstore = DeepLake.from_documents(    docs, embeddings, dataset_path=f"hub://{username_or_org}/self_queery", overwrite=True,)    Your Deep Lake dataset has been successfully created!    /    Dataset(path='hub://adilkhan/self_queery', tensors=['embedding', 'id', 'metadata', 'text'])          tensor      htype      shape     dtype  compression      -------    -------    -------   -------  -------      embedding  embedding  (6, 1536)  float32   None           id        text      (6, 1)      str     None        metadata     json      (6, 1)      str     None          text       text      (6, 1)      str     None        Creating our self-querying retriever‚ÄãNow we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom 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,