We also changed the type of the keyword gerne to a list of strings, where an LLM can use a new contain comparator to construct filters. We also provide the like comparator and arbitrary function support to filters, which will be introduced in next few cells.Now let's look at the data first.docs = [    Document(        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",        metadata={"date": "1993-07-02", "rating": 7.7, "genre": ["science fiction"]},    ),    Document(        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",        metadata={"date": "2010-12-30", "director": "Christopher Nolan", "rating": 8.2},    ),    Document(        page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",        metadata={"date": "2006-04-23", "director": "Satoshi Kon", "rating": 8.6},    ),    Document(        page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",        metadata={"date": "2019-08-22", "director": "Greta Gerwig", "rating": 8.3},    ),    Document(        page_content="Toys come alive and have a blast doing so",        metadata={"date": "1995-02-11", "genre": ["animated"]},    ),    Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={            "date": "1979-09-10",            "rating": 9.9,            "director": "Andrei Tarkovsky",            "genre": ["science fiction", "adventure"],            "rating": 9.9,        },    ),]vectorstore = MyScale.from_documents(    docs,    embeddings,)Creating our self-querying retriever‚ÄãJust like other retrievers... simple and nice.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [    AttributeInfo(        name="genre",
MyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain.
MyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain. ->: We also changed the type of the keyword gerne to a list of strings, where an LLM can use a new contain comparator to construct filters. We also provide the like comparator and arbitrary function support to filters, which will be introduced in next few cells.Now let's look at the data first.docs = [    Document(        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",        metadata={"date": "1993-07-02", "rating": 7.7, "genre": ["science fiction"]},    ),    Document(        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",        metadata={"date": "2010-12-30", "director": "Christopher Nolan", "rating": 8.2},    ),    Document(        page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",        metadata={"date": "2006-04-23", "director": "Satoshi Kon", "rating": 8.6},    ),    Document(        page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",        metadata={"date": "2019-08-22", "director": "Greta Gerwig", "rating": 8.3},    ),    Document(        page_content="Toys come alive and have a blast doing so",        metadata={"date": "1995-02-11", "genre": ["animated"]},    ),    Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={            "date": "1979-09-10",            "rating": 9.9,            "director": "Andrei Tarkovsky",            "genre": ["science fiction", "adventure"],            "rating": 9.9,        },    ),]vectorstore = MyScale.from_documents(    docs,    embeddings,)Creating our self-querying retriever‚ÄãJust like other retrievers... simple and nice.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [    AttributeInfo(        name="genre",