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# Source code for langchain.retrievers.parent_document_retriever

    import uuid
    from typing import List, Optional, Sequence

    from langchain_core.documents import Document
    from langchain_text_splitters import TextSplitter

    from langchain.retrievers import MultiVectorRetriever

    class ParentDocumentRetriever(MultiVectorRetriever):
        """Retrieve small chunks then retrieve their parent documents.

        When splitting documents for retrieval, there are often conflicting desires:

        1. You may want to have small documents, so that their embeddings can most
            accurately reflect their meaning. If too long, then the embeddings can
            lose meaning.
        2. You want to have long enough documents that the context of each chunk is
            retained.

        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:

            .. code-block:: python

                from langchain_community.embeddings import OpenAIEmbeddings
                from langchain_community.vectorstores import Chroma
                from langchain_text_splitters import RecursiveCharacterTextSplitter
                from langchain.storage import InMemoryStore

                # This text splitter is used to create the parent documents
                parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, add_start_index=True)
                # This text splitter is used to create the child documents
                # It should create documents smaller than the parent
                child_splitter = RecursiveCharacterTextSplitter(chunk_size=400, add_start_index=True)
                # 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,
                    parent_splitter=parent_splitter,
                )
        """  # noqa: E501

        child_splitter: TextSplitter
        """The text splitter to use to create child documents."""

        """The key to use to track the parent id. This will be stored in the
        metadata of child documents."""
        parent_splitter: Optional[TextSplitter] = None
        """The text splitter to use to create parent documents.
        If none, then the parent documents will be the raw documents passed in."""

        child_metadata_fields: Optional[Sequence[str]] = None
        """Metadata fields to leave in child documents. If None, leave all parent document
            metadata.
        """

        def add_documents(
            self,
            documents: List[Document],
            ids: Optional[List[str]] = None,
            add_to_docstore: bool = True,
        ) -> None:
            """Adds documents to the docstore and vectorstores.

            Args:
                documents: List of documents to add
                ids: Optional list of ids for documents. If provided should be the same
                    length as the list of documents. Can be provided if parent documents
                    are already in the document store and you don't want to re-add
                    to the docstore. If not provided, random UUIDs will be used as
                    ids.
                add_to_docstore: Boolean of whether to add documents to docstore.
                    This can be false if and only if `ids` are provided. You may want
                    to set this to False if the documents are already in the docstore
                    and you don't want to re-add them.
            """
            if self.parent_splitter is not None:
                documents = self.parent_splitter.split_documents(documents)
            if ids is None:
                doc_ids = [str(uuid.uuid4()) for _ in documents]
                if not add_to_docstore:
                    raise ValueError(
                        "If ids are not passed in, `add_to_docstore` MUST be True"
                    )
            else:
                if len(documents) != len(ids):
                    raise ValueError(
                        "Got uneven list of documents and ids. "
                        "If `ids` is provided, should be same length as `documents`."
                    )
                doc_ids = ids

            docs = []
            full_docs = []
            for i, doc in enumerate(documents):
                _id = doc_ids[i]
                sub_docs = self.child_splitter.split_documents([doc])
                if self.child_metadata_fields is not None:
                    for _doc in sub_docs:
                        _doc.metadata = {
                            k: _doc.metadata[k] for k in self.child_metadata_fields
                        }
                for _doc in sub_docs:
                    _doc.metadata[self.id_key] = _id
                docs.extend(sub_docs)
                full_docs.append((_id, doc))
            self.vectorstore.add_documents(docs)
            if add_to_docstore:
                self.docstore.mset(full_docs)