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", "rating": 9.9})
]
vectorstore = Weaviate.from_documents(
    docs, embeddings, weaviate_url="http://127.0.0.1:8080"
)
Creating our self-querying retriever#
Now 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 OpenAI
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains.query_constructor.base import AttributeInfo
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)
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/retrievers/examples/weaviate_self_query.html