)
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)
retriever = SelfQueryRetriever.from_llm(llm, vectorstore, document_content_description, metadata_field_info, verbose=True)
Testing it out#
And now we can try actually using our retriever!
# This example only specifies a relevant query
retriever.get_relevant_documents("What are some movies about dinosaurs")
query='dinosaur' filter=None
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': ['action', 'science fiction'], 'rating': 7.7, 'year': 1993.0}),
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/retrievers/examples/self_query.html