bring back dinosaurs and mayhem breaks loose", metadata={"year": 1993, "rating": 7.7, "genre": "action"}),    Document(page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...", metadata={"year": 2010,"genre": "thriller", "rating": 8.2}),    Document(page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them", metadata={"year": 2019, "rating": 8.3, "genre": "drama"}),    Document(page_content="Three men walk into the Zone, three men walk out of the Zone", metadata={"year": 1979, "rating": 9.9, "genre": "science fiction"}),    Document(        page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea',        metadata={"year": 2006, "genre": "thriller", 'rating': 9.0},    ),    Document(page_content="Toys come alive and have a blast doing so", metadata={"year": 1995, "genre": "animated", "rating": 9.3 }),]vector_store = Milvus.from_documents(    docs,    embedding=embeddings,    connection_args={"uri": 'Use your uri:)', "token":'Use your token:)'})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",    ),    AttributeInfo(        name="year",        description="The year the movie was released",        type="integer",    ),    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,
Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models. ->: bring back dinosaurs and mayhem breaks loose", metadata={"year": 1993, "rating": 7.7, "genre": "action"}),    Document(page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...", metadata={"year": 2010,"genre": "thriller", "rating": 8.2}),    Document(page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them", metadata={"year": 2019, "rating": 8.3, "genre": "drama"}),    Document(page_content="Three men walk into the Zone, three men walk out of the Zone", metadata={"year": 1979, "rating": 9.9, "genre": "science fiction"}),    Document(        page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea',        metadata={"year": 2006, "genre": "thriller", 'rating': 9.0},    ),    Document(page_content="Toys come alive and have a blast doing so", metadata={"year": 1995, "genre": "animated", "rating": 9.3 }),]vector_store = Milvus.from_documents(    docs,    embedding=embeddings,    connection_args={"uri": 'Use your uri:)', "token":'Use your token:)'})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",    ),    AttributeInfo(        name="year",        description="The year the movie was released",        type="integer",    ),    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,