langchain.retrievers.parent_document_retriever.ParentDocumentRetriever¶
class langchain.retrievers.parent_document_retriever.ParentDocumentRetriever[source]¶
Bases: MultiVectorRetriever
Retrieve small chunks then retrieve their parent documents.
When splitting documents for retrieval, there are often conflicting desires:
You may want to have small documents, so that their embeddings can mostaccurately reflect their meaning. If too long, then the embeddings can
lose meaning.
You want to have long enough documents that the context of each chunk isretained.
The ParentDocumentRetriever strikes that balance by splitting and storing
small chunks of data. During retrieval, it first fetches the small chunks
but then looks up the parent ids for those chunks and returns those larger
documents.
Note that “parent document” refers to the document that a small chunk
originated from. This can either be the whole raw document OR a larger
chunk.
Examples
# Imports
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.storage import InMemoryStore
# This text splitter is used to create the parent documents
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
# This text splitter is used to create the child documents
# It should create documents smaller than the parent
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# The vectorstore to use to index the child chunks
vectorstore = Chroma(embedding_function=OpenAIEmbeddings())
# The storage layer for the parent documents
store = InMemoryStore()
# Initialize the retriever
retriever = ParentDocumentRetriever(
    vectorstore=vectorstore,
    docstore=store,
    child_splitter=child_splitter,