docs = [
    Document(
        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",
        metadata="year": 1993, "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="year": 2010, "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="year": 2006, "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="year": 2019, "director": "Greta Gerwig", "rating": 8.3,
    ),
    Document(
        page_content="Toys come alive and have a blast doing so",
        metadata="year": 1995, "genre": "animated",
    ),
    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",
        ,
    ),
]
vectorstore = OpenSearchVectorSearch.from_documents(
    docs,
    embeddings,
    index_name="opensearch-self-query-demo",
    opensearch_url="http://localhost:9200",
)


from langchain.chains.query_constructor.base import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_openai import OpenAI

metadata_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
)