src/langchain_modules/span_retriever.py

import logging
import os
import uuid
from collections import defaultdict
from copy import copy
from enum import Enum
from typing import Any, Dict, Iterable, List, Optional, Sequence, Set, Tuple, Type, Union

from langchain_core.callbacks import (
    AsyncCallbackManagerForRetrieverRun,
    CallbackManagerForRetrieverRun,
)
from langchain_core.documents import BaseDocumentCompressor
from langchain_core.documents import Document as LCDocument
from langchain_core.retrievers import BaseRetriever
from pydantic import Field
from pytorch_ie.annotations import LabeledMultiSpan, LabeledSpan, MultiSpan, Span
from pytorch_ie.core.document import BaseAnnotationList
from pytorch_ie.documents import (
    TextBasedDocument,
    TextDocumentWithLabeledMultiSpans,
    TextDocumentWithLabeledSpans,
    TextDocumentWithSpans,
)

from ..utils import parse_config
from .pie_document_store import PieDocumentStore
from .serializable_store import SerializableStore
from .span_vectorstore import SpanVectorStore

logger = logging.getLogger(__name__)


METADATA_KEY_CHILD_ID2IDX = "child_id2idx"


class SpanNotFoundError(ValueError):
    def __init__(self, span_id: str, doc_id: Optional[str] = None, msg: Optional[str] = None):
        if msg is None:
            if doc_id is not None:
                msg = f"Span with id [{span_id}] not found in document [{doc_id}]"
            else:
                msg = f"Span with id [{span_id}] not found in the vectorstore"
        super().__init__(msg)
        self.span_id = span_id
        self.doc_id = doc_id


class DocumentNotFoundError(ValueError):
    def __init__(self, doc_id: str, msg: Optional[str] = None):
        msg = msg or f"Document with id [{doc_id}] not found in the docstore"
        super().__init__(msg)
        self.doc_id = doc_id


class SearchType(str, Enum):
    """Enumerator of the types of search to perform."""

    similarity = "similarity"
    """Similarity search."""
    similarity_score_threshold = "similarity_score_threshold"
    """Similarity search with a score threshold."""
    mmr = "mmr"
    """Maximal Marginal Relevance reranking of similarity search."""


class DocumentAwareSpanRetriever(BaseRetriever, SerializableStore):
    """Retriever for contextualized text spans, i.e. spans within text documents.
    It accepts spans as queries and retrieves spans with their containing document.
    Note that the query span (and its document) must already be in the retriever's
    store."""

    pie_document_type: Type[TextBasedDocument]
    """The name of the span annotation layer in the pie document."""
    use_predicted_annotations_key: str = "use_predicted_annotations"
    """Whether to use the predicted annotations or the gold annotations."""
    retrieve_from_same_document: bool = False
    """Whether to retrieve spans exclusively from the same document as the query span."""
    retrieve_from_different_documents: bool = False
    """Whether to retrieve spans exclusively from different documents than the query span."""

    # content from langchain_core.retrievers.MultiVectorRetriever
    vectorstore: SpanVectorStore
    """The underlying vectorstore to use to store small chunks
    and their embedding vectors"""
    docstore: PieDocumentStore
    """The storage interface for the parent documents"""
    id_key: str = "doc_id"
    """The key to use to track the parent id. This will be stored in the
    metadata of child documents."""
    search_kwargs: dict = Field(default_factory=dict)
    """Keyword arguments to pass to the search function."""
    search_type: SearchType = SearchType.similarity
    """Type of search to perform (similarity / mmr)"""

    # content taken from langchain_core.retrievers.ParentDocumentRetriever
    child_metadata_fields: Optional[Sequence[str]] = None
    """Metadata fields to leave in child documents. If None, leave all parent document
        metadata.
    """

    # re-ranking
    compressor: Optional[BaseDocumentCompressor] = None
    """Compressor for compressing retrieved documents."""
    compressor_context_size: int = 50
    """Size of the context to use around the query and retrieved spans when compressing."""
    compressor_query_context_size: Optional[int] = 10
    """Size of the context to use around the query when compressing. If None, will use the
    same value as `compressor_context_size`."""

    @classmethod
    def instantiate_from_config(
        cls, config: Dict[str, Any], overwrites: Optional[Dict[str, Any]] = None
    ) -> "DocumentAwareSpanRetriever":
        """Instantiate a retriever from a configuration dictionary."""
        from hydra.utils import instantiate

        return instantiate(config, **(overwrites or {}))

    @classmethod
    def instantiate_from_config_string(
        cls, config_string: str, format: str, overwrites: Optional[Dict[str, Any]] = None
    ) -> "DocumentAwareSpanRetriever":
        """Instantiate a retriever from a configuration string."""
        return cls.instantiate_from_config(
            parse_config(config_string, format=format), overwrites=overwrites
        )

    @classmethod
    def instantiate_from_config_file(
        cls, config_path: str, overwrites: Optional[Dict[str, Any]] = None
    ) -> "DocumentAwareSpanRetriever":
        """Instantiate a retriever from a configuration file."""
        with open(config_path, "r") as file:
            config_string = file.read()
        if config_path.endswith(".json"):
            return cls.instantiate_from_config_string(
                config_string, format="json", overwrites=overwrites
            )
        elif config_path.endswith(".yaml"):
            return cls.instantiate_from_config_string(
                config_string, format="yaml", overwrites=overwrites
            )
        else:
            raise ValueError(f"Unsupported file extension: {config_path}")

    @property
    def pie_annotation_layer_name(self) -> str:
        if issubclass(self.pie_document_type, TextDocumentWithSpans):
            return "spans"
        elif issubclass(self.pie_document_type, TextDocumentWithLabeledSpans):
            return "labeled_spans"
        elif issubclass(self.pie_document_type, TextDocumentWithLabeledMultiSpans):
            return "labeled_multi_spans"
        else:
            raise ValueError(
                f"Unsupported pie document type: {self.pie_document_type}. "
                "Must be one of TextDocumentWithSpans, TextDocumentWithLabeledSpans, "
                "or TextDocumentWithLabeledMultiSpans."
            )

    def _span_to_dict(self, span: Union[Span, MultiSpan]) -> dict:
        span_dict = {}
        if isinstance(span, Span):
            span_dict[self.vectorstore.SPAN_START_KEY] = span.start
            span_dict[self.vectorstore.SPAN_END_KEY] = span.end
            span_dict["type"] = "Span"
        elif isinstance(span, MultiSpan):
            starts, ends = zip(*span.slices)
            span_dict[self.vectorstore.SPAN_START_KEY] = starts
            span_dict[self.vectorstore.SPAN_END_KEY] = ends
            span_dict["type"] = "MultiSpan"
        else:
            raise ValueError(f"Unsupported span type: {type(span)}")
        if isinstance(span, (LabeledSpan, LabeledMultiSpan)):
            span_dict["label"] = span.label
            span_dict["score"] = span.score
        return span_dict

    def _dict_to_span(self, span_dict: dict) -> Union[Span, MultiSpan]:

        if span_dict["type"] == "Span":
            kwargs = dict(
                start=span_dict[self.vectorstore.SPAN_START_KEY],
                end=span_dict[self.vectorstore.SPAN_END_KEY],
            )
            if "label" in span_dict:
                kwargs["label"] = span_dict["label"]
                kwargs["score"] = span_dict["score"]
                return LabeledSpan(**kwargs)
            else:
                return Span(**kwargs)
        elif span_dict["type"] == "MultiSpan":
            starts = span_dict[self.vectorstore.SPAN_START_KEY]
            ends = span_dict[self.vectorstore.SPAN_END_KEY]
            slices = tuple((start, end) for start, end in zip(starts, ends))
            kwargs = dict(slices=slices)
            if "label" in span_dict:
                kwargs["label"] = span_dict["label"]
                kwargs["score"] = span_dict["score"]
                return LabeledMultiSpan(**kwargs)
            else:
                return MultiSpan(**kwargs)
        else:
            raise ValueError(f"Unsupported span type: {span_dict['type']}")

    def use_predicted_annotations(self, doc: LCDocument) -> bool:
        """Check if the document uses predicted spans."""
        return doc.metadata.get(self.use_predicted_annotations_key, True)

    def get_document(self, doc_id: str) -> LCDocument:
        """Get a document by its id."""
        documents = self.docstore.mget([doc_id])
        if len(documents) == 0 or documents[0] is None:
            raise DocumentNotFoundError(doc_id=doc_id)
        if len(documents) > 1:
            raise ValueError(f"Multiple documents found with id: {doc_id}")
        return documents[0]

    def get_span_document(self, span_id: str, with_vector: bool = False) -> LCDocument:
        """Get a span document by its id."""
        if with_vector:
            span_docs = self.vectorstore.get_by_ids_with_vectors([span_id])
        else:
            span_docs = self.vectorstore.get_by_ids([span_id])
        if len(span_docs) == 0 or span_docs[0] is None:
            raise SpanNotFoundError(span_id=span_id)
        if len(span_docs) > 1:
            raise ValueError(f"Multiple span documents found with id: {span_id}")
        return span_docs[0]

    def get_base_layer(
        self, pie_document: TextBasedDocument, use_predicted_annotations: bool
    ) -> BaseAnnotationList:
        """Get the base layer of the pie document."""

        if self.pie_annotation_layer_name not in pie_document:
            raise ValueError(
                f'The pie document must contain the annotation layer "{self.pie_annotation_layer_name}"'
            )
        layer = pie_document[self.pie_annotation_layer_name]
        return layer.predictions if use_predicted_annotations else layer

    def get_span_by_id(self, span_id: str) -> Union[Span, MultiSpan]:
        """Get a span annotation by its id."""
        span_doc = self.get_span_document(span_id)
        doc_id = span_doc.metadata[self.id_key]
        doc = self.get_document(doc_id)
        return self.get_span_from_doc_by_id(doc=doc, span_id=span_id)

    def get_span_from_doc_by_id(self, doc: LCDocument, span_id: str) -> Union[Span, MultiSpan]:
        """Get the span of a query."""
        base_layer = self.get_base_layer(
            self.docstore.unwrap(doc),
            use_predicted_annotations=self.use_predicted_annotations(doc),
        )
        span_idx = doc.metadata[METADATA_KEY_CHILD_ID2IDX].get(span_id)
        if span_idx is None:
            raise SpanNotFoundError(span_id=span_id, doc_id=doc.id)
        return base_layer[span_idx]

    def get_span_id2idx_from_doc(self, doc: Union[LCDocument, str]) -> Dict[str, int]:
        """Get all span ids from a document.

        Args:
            doc: Document or document id

        Returns:
            Dictionary mapping span ids to their index in the base layer.
        """

        if isinstance(doc, str):
            doc = self.get_document(doc)
        return doc.metadata[METADATA_KEY_CHILD_ID2IDX]

    def prepare_search_kwargs(
        self,
        span_id: str,
        doc_id_whitelist: Optional[List[str]] = None,
        doc_id_blacklist: Optional[List[str]] = None,
        kwargs: Optional[Dict[str, Any]] = None,
    ) -> Tuple[dict, LCDocument]:
        # get the span document
        query_span_doc = self.get_span_document(span_id, with_vector=True)
        query_doc_id = query_span_doc.metadata[self.id_key]
        query_doc = self.get_document(query_doc_id)

        # TODO: why do we do this? Just to be the same as the result of the search when doing compression?
        # add "pie_document" to the metadata
        query_span_doc.metadata[self.docstore.METADATA_KEY_PIE_DOCUMENT] = self.docstore.unwrap(
            query_doc
        )

        search_kwargs = copy(self.search_kwargs)
        search_kwargs.update(kwargs or {})

        query_span = self.get_span_from_doc_by_id(doc=query_doc, span_id=span_id)

        if self.retrieve_from_different_documents and self.retrieve_from_same_document:
            raise ValueError("Cannot retrieve from both same and different documents")

        if self.retrieve_from_same_document:
            if doc_id_whitelist is None:
                doc_id_whitelist = [query_doc_id]
            elif query_doc_id not in doc_id_whitelist:
                doc_id_whitelist.append(query_doc_id)

        if self.retrieve_from_different_documents:
            if doc_id_blacklist is None:
                doc_id_blacklist = [query_doc_id]
            elif query_doc_id not in doc_id_blacklist:
                doc_id_blacklist.append(query_doc_id)

        query_filter = self.vectorstore.construct_filter(
            query_span=query_span,
            metadata_doc_id_key=self.id_key,
            doc_id_whitelist=doc_id_whitelist,
            doc_id_blacklist=doc_id_blacklist,
        )
        if query_filter is not None:
            search_kwargs["filter"] = query_filter

        # get the vector of the reference span
        search_kwargs["embedding"] = query_span_doc.metadata[self.vectorstore.METADATA_VECTOR_KEY]
        return search_kwargs, query_span_doc

    def _prepare_query_for_compression(self, query_doc: LCDocument) -> str:
        return self._prepare_doc_for_compression(
            query_doc, context_size=self.compressor_query_context_size
        ).page_content

    def _prepare_doc_for_compression(
        self, doc: LCDocument, context_size: Optional[int] = None
    ) -> LCDocument:
        if context_size is None:
            context_size = self.compressor_context_size
        pie_doc: TextBasedDocument = self.docstore.unwrap(doc)
        text = pie_doc.text
        span_dict = doc.metadata[self.vectorstore.METADATA_SPAN_KEY]
        span_start = span_dict[self.vectorstore.SPAN_START_KEY]
        span_end = span_dict[self.vectorstore.SPAN_END_KEY]
        if isinstance(span_start, list):
            span_start = span_start[0]
        if isinstance(span_end, list):
            span_end = span_end[0]
        context_start = span_start - context_size
        context_end = span_end + context_size
        doc.page_content = text[max(0, context_start) : min(context_end, len(text))]
        # save the original relevance score and remove it because otherwise we will not be able to get
        # the reranking relevance score
        if "relevance_score" in doc.metadata:
            doc.metadata["relevance_score_without_reranking"] = doc.metadata.pop("relevance_score")
        return doc

    def _get_relevant_documents(
        self,
        query: str,
        *,
        run_manager: CallbackManagerForRetrieverRun,
        doc_id_whitelist: Optional[List[str]] = None,
        doc_id_blacklist: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[LCDocument]:
        """Get span documents relevant to a query span
        Args:
            query: The span id to find relevant spans for
            run_manager: The callbacks handler to use
        Returns:
            List of relevant span documents with metadata from the parent document
        """

        search_kwargs, query_span_doc = self.prepare_search_kwargs(
            span_id=query,
            kwargs=kwargs,
            doc_id_whitelist=doc_id_whitelist,
            doc_id_blacklist=doc_id_blacklist,
        )
        if self.search_type == SearchType.mmr:
            span_docs = self.vectorstore.max_marginal_relevance_search_by_vector(**search_kwargs)
        elif self.search_type == SearchType.similarity_score_threshold:
            sub_docs_and_similarities = self.vectorstore.similarity_search_with_score_by_vector(
                **search_kwargs
            )
            span_docs = [sub_doc for sub_doc, _ in sub_docs_and_similarities]
        else:
            span_docs = self.vectorstore.similarity_search_by_vector(**search_kwargs)

        # We do this to maintain the order of the ids that are returned
        doc_ids = []
        for span_doc in span_docs:
            if self.id_key not in span_doc.metadata:
                raise ValueError(f"Metadata must contain the key {self.id_key}")
            if span_doc.metadata[self.id_key] not in doc_ids:
                doc_ids.append(span_doc.metadata[self.id_key])
        docs = self.docstore.mget(doc_ids)
        doc_id2doc = dict(zip(doc_ids, docs))
        for span_doc in span_docs:
            doc = doc_id2doc[span_doc.metadata[self.id_key]]
            span_doc.metadata.update(doc.metadata)
            span_doc.metadata["attached_span"] = self.get_span_from_doc_by_id(
                doc=doc, span_id=span_doc.id
            )
            span_doc.metadata["query_span_id"] = query
        # filter out the query span doc
        span_docs_filtered = [
            span_doc for span_doc in span_docs if span_doc.id != query_span_doc.id
        ]
        if self.compressor is None:
            return span_docs_filtered
        if span_docs_filtered:
            prepared_docs = [
                self._prepare_doc_for_compression(sub_doc) for sub_doc in span_docs_filtered
            ]
            prepared_query = self._prepare_query_for_compression(query_span_doc)
            compressed_docs = self.compressor.compress_documents(
                documents=prepared_docs, query=prepared_query, callbacks=run_manager.get_child()
            )
            return list(compressed_docs)
        else:
            return []

    async def _aget_relevant_documents(
        self,
        query: str,
        *,
        run_manager: AsyncCallbackManagerForRetrieverRun,
        doc_id_whitelist: Optional[List[str]] = None,
        doc_id_blacklist: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[LCDocument]:
        """Asynchronously get span documents relevant to a query span
        Args:
            query: The span id to find relevant spans for
            run_manager: The callbacks handler to use
        Returns:
            List of relevant span documents with metadata from the parent document
        """
        search_kwargs, query_span_doc = self.prepare_search_kwargs(
            span_id=query,
            kwargs=kwargs,
            doc_id_whitelist=doc_id_whitelist,
            doc_id_blacklist=doc_id_blacklist,
        )
        if self.search_type == SearchType.mmr:
            span_docs = await self.vectorstore.amax_marginal_relevance_search_by_vector(
                **search_kwargs
            )
        elif self.search_type == SearchType.similarity_score_threshold:
            sub_docs_and_similarities = (
                await self.vectorstore.asimilarity_search_with_score_by_vector(**search_kwargs)
            )
            span_docs = [sub_doc for sub_doc, _ in sub_docs_and_similarities]
        else:
            span_docs = await self.vectorstore.asimilarity_search_by_vector(**search_kwargs)

        # We do this to maintain the order of the ids that are returned
        ids = []
        for span_doc in span_docs:
            if self.id_key not in span_doc.metadata:
                raise ValueError(f"Metadata must contain the key {self.id_key}")
            if span_doc.metadata[self.id_key] not in ids:
                ids.append(span_doc.metadata[self.id_key])
        docs = await self.docstore.amget(ids)
        doc_id2doc = dict(zip(ids, docs))
        for span_doc in span_docs:
            doc = doc_id2doc[span_doc.metadata[self.id_key]]
            span_doc.metadata.update(doc.metadata)
            span_doc.metadata["attached_span"] = self.get_span_from_doc_by_id(
                doc=doc, span_id=span_doc.id
            )
            span_doc.metadata["query_span_id"] = query
        # filter out the query span doc
        span_docs_filtered = [
            span_doc for span_doc in span_docs if span_doc.id != query_span_doc.id
        ]

        if self.compressor is None:
            return span_docs_filtered
        if docs:
            prepared_docs = [
                self._prepare_doc_for_compression(sub_doc) for sub_doc in span_docs_filtered
            ]
            prepared_query = self._prepare_query_for_compression(query_span_doc)
            compressed_docs = await self.base_compressor.acompress_documents(
                prepared_docs, query=prepared_query, callbacks=run_manager.get_child()
            )
            return list(compressed_docs)
        else:
            return []

    def create_span_documents(
        self, documents: List[LCDocument]
    ) -> Tuple[List[LCDocument], Dict[str, int]]:
        span_docs = []
        id2idx = {}
        for i, doc in enumerate(documents):
            pie_doc, metadata = self.docstore.unwrap_with_metadata(doc)
            base_layer = self.get_base_layer(
                pie_doc, use_predicted_annotations=self.use_predicted_annotations(doc)
            )
            if len(base_layer) == 0:
                logger.warning(f"No spans found in document {i} (id: {doc.id})")
            for idx, labeled_span in enumerate(base_layer):
                _metadata = {k: v for k, v in metadata.items() if k != METADATA_KEY_CHILD_ID2IDX}
                # save as dict to avoid serialization issues
                _metadata[self.vectorstore.METADATA_SPAN_KEY] = self._span_to_dict(labeled_span)
                new_doc = LCDocument(
                    id=str(uuid.uuid4()), page_content=pie_doc.text, metadata=_metadata
                )
                span_docs.append(new_doc)
                id2idx[new_doc.id] = idx
        return span_docs, id2idx

    def _split_docs_for_adding(
        self,
        documents: List[LCDocument],
        ids: Optional[List[str]] = None,
        add_to_docstore: bool = True,
    ) -> Tuple[List[LCDocument], List[Tuple[str, LCDocument]]]:
        if ids is None:
            doc_ids = [doc.id for doc 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

        if len(set(doc_ids)) != len(doc_ids):
            raise ValueError("IDs must be unique")

        docs = []
        full_docs = []
        for i, doc in enumerate(documents):
            _id = doc_ids[i]
            sub_docs, sub_doc_id2idx = self.create_span_documents([doc])
            if self.child_metadata_fields is not None:
                for sub_doc in sub_docs:
                    sub_doc.metadata = {k: sub_doc.metadata[k] for k in self.child_metadata_fields}
            for sub_doc in sub_docs:
                # Add the parent id to the child document id
                sub_doc.metadata[self.id_key] = _id
            docs.extend(sub_docs)
            doc.metadata[METADATA_KEY_CHILD_ID2IDX] = sub_doc_id2idx
            full_docs.append((_id, doc))

        return docs, full_docs

    def remove_missing_span_ids_from_document(
        self, document: LCDocument, span_ids: Set[str]
    ) -> LCDocument:
        """Remove invalid span ids from the span to idx mapping
        of the document.

        Args:
            document: Document to remove invalid span ids from
            span_ids: Set of valid span ids

        Returns:
            Document with invalid span ids removed
        """
        span_id2idx = document.metadata[METADATA_KEY_CHILD_ID2IDX]
        new_doc = copy(document)
        filtered_span_id2idx = {
            span_id: idx for span_id, idx in span_id2idx.items() if span_id in span_ids
        }
        new_doc.metadata[METADATA_KEY_CHILD_ID2IDX] = filtered_span_id2idx
        missed_span_ids = set(span_id2idx.keys()) - span_ids
        if len(missed_span_ids) > 0:
            layer = self.get_base_layer(
                self.docstore.unwrap(document),
                use_predicted_annotations=self.use_predicted_annotations(document),
            )
            resolved_missed_spans = [
                layer[span_id2idx[span_id]].resolve() for span_id in missed_span_ids
            ]
            logger.warning(
                f"Document {document.id} contains spans that can not be added to the "
                f"vectorstore because no vector could be calculated:\n{resolved_missed_spans}.\n"
                "These spans will be not queryable."
            )
        return document

    def add_documents(
        self,
        documents: List[LCDocument],
        ids: Optional[List[str]] = None,
        add_to_docstore: bool = True,
        **kwargs: Any,
    ) -> 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.
        """
        docs, full_docs = self._split_docs_for_adding(documents, ids, add_to_docstore)
        added_span_ids = self.vectorstore.add_documents(docs, **kwargs)
        full_docs = [
            (doc_id, self.remove_missing_span_ids_from_document(doc, set(added_span_ids)))
            for doc_id, doc in full_docs
        ]
        if add_to_docstore:
            self.docstore.mset(full_docs)

    async def aadd_documents(
        self,
        documents: List[LCDocument],
        ids: Optional[List[str]] = None,
        add_to_docstore: bool = True,
        **kwargs: Any,
    ) -> None:
        docs, full_docs = self._split_docs_for_adding(documents, ids, add_to_docstore)
        added_span_ids = await self.vectorstore.aadd_documents(docs, **kwargs)
        full_docs = [
            (doc_id, self.remove_missing_span_ids_from_document(doc, set(added_span_ids)))
            for doc_id, doc in full_docs
        ]
        if add_to_docstore:
            await self.docstore.amset(full_docs)

    def delete_documents(self, ids: List[str]) -> None:
        """Remove documents from the docstore and vectorstores.

        Args:
            ids: List of ids to remove
        """
        # get all child ids
        child_ids = []
        for doc in self.docstore.mget(ids):
            child_ids.extend(doc.metadata[METADATA_KEY_CHILD_ID2IDX])

        self.vectorstore.delete(child_ids)
        self.docstore.mdelete(ids)

    async def adelete_documents(self, ids: List[str]) -> None:
        """Asynchronously remove documents from the docstore and vectorstores.

        Args:
            ids: List of ids to remove
        """
        # get all child ids
        child_ids = []
        docs: List[LCDocument] = await self.docstore.amget(ids)
        for doc in docs:
            child_ids.extend(doc.metadata[METADATA_KEY_CHILD_ID2IDX])

        await self.vectorstore.adelete(child_ids)
        await self.docstore.amdelete(ids)

    def add_pie_documents(
        self,
        documents: Iterable[TextBasedDocument],
        use_predicted_annotations: bool,
        metadata: Optional[Dict[str, Any]] = None,
    ) -> None:
        """Add pie documents to the retriever.

        Args:
            documents: Iterable of pie documents to add
            use_predicted_annotations: Whether to use the predicted annotations or the gold annotations
            metadata: Optional metadata to add to each document
        """
        metadata = metadata or {}
        metadata = copy(metadata)
        metadata[self.use_predicted_annotations_key] = use_predicted_annotations
        docs = [self.docstore.wrap(doc, **metadata) for doc in documents]

        # delete any existing documents with the same ids (simply overwriting would keep the spans)
        new_docs_ids = [doc.id for doc in docs]
        existing_docs = self.docstore.mget(new_docs_ids)
        existing_doc_ids = [doc.id for doc in existing_docs]
        self.delete_documents(existing_doc_ids)

        self.add_documents(docs)

    def _save_to_directory(self, path: str, **kwargs) -> None:
        logger.info(f'Saving docstore and vectorstore to "{path}" ...')
        self.docstore.save_to_directory(os.path.join(path, "docstore"))
        self.vectorstore.save_to_directory(os.path.join(path, "vectorstore"))

    def _load_from_directory(self, path: str, **kwargs) -> None:
        logger.info(f'Loading docstore and vectorstore from "{path}" ...')
        self.docstore.load_from_directory(os.path.join(path, "docstore"))
        self.vectorstore.load_from_directory(os.path.join(path, "vectorstore"))


METADATA_KEY_RELATION_LABEL2TAILS_WITH_SCORES = "relation_label2tails_with_scores"


class DocumentAwareSpanRetrieverWithRelations(DocumentAwareSpanRetriever):
    """Retriever for related contextualized text spans, i.e. spans linked by relations
    to reference spans that are similar to the query span. It accepts spans as queries and
    retrieves spans with their containing document and the reference span."""

    relation_layer_name: str = "binary_relations"
    """The name of the relation annotation layer in the pie document."""
    relation_labels: Optional[List[str]] = None
    """The list of relation labels to consider."""
    span_labels: Optional[List[str]] = None
    """The list of span labels to consider."""
    reversed_relations_suffix: Optional[str] = None
    """Whether to consider reverse relations as well."""
    symmetric_relations: Optional[list[str]] = None
    """The list of relation labels that are symmetric."""

    def get_relation_layer(
        self, pie_document: TextBasedDocument, use_predicted_annotations: bool
    ) -> BaseAnnotationList:
        """Get the relation layer of the pie document."""
        if self.relation_layer_name not in pie_document:
            raise ValueError(
                f'The pie document must contain the annotation layer "{self.relation_layer_name}"'
            )
        layer = pie_document[self.relation_layer_name]
        return layer.predictions if use_predicted_annotations else layer

    def create_span_documents(
        self, documents: List[LCDocument]
    ) -> Tuple[List[LCDocument], Dict[str, int]]:
        span_docs = []
        id2idx = {}
        for i, doc in enumerate(documents):
            pie_doc, metadata = self.docstore.unwrap_with_metadata(doc)
            use_predicted_annotations = self.use_predicted_annotations(doc)
            base_layer = self.get_base_layer(
                pie_doc, use_predicted_annotations=use_predicted_annotations
            )
            if len(base_layer) == 0:
                logger.warning(f"No spans found in document {i} (id: {doc.id})")
            id2span = {str(uuid.uuid4()): span for span in base_layer}
            span2id = {span: span_id for span_id, span in id2span.items()}
            if len(id2span) != len(span2id):
                raise ValueError("Span ids and spans must be unique")
            relations = self.get_relation_layer(
                pie_doc, use_predicted_annotations=use_predicted_annotations
            )
            head2label2tails_with_scores: Dict[str, Dict[str, List[Tuple[str, float]]]] = (
                defaultdict(lambda: defaultdict(list))
            )

            for relation in relations:
                is_symmetric = (
                    self.symmetric_relations is not None
                    and relation.label in self.symmetric_relations
                )
                if self.relation_labels is None or relation.label in self.relation_labels:
                    head2label2tails_with_scores[span2id[relation.head]][relation.label].append(
                        (span2id[relation.tail], relation.score)
                    )
                    if is_symmetric:
                        head2label2tails_with_scores[span2id[relation.tail]][
                            relation.label
                        ].append((span2id[relation.head], relation.score))
                if self.reversed_relations_suffix is not None and not is_symmetric:
                    reversed_label = f"{relation.label}{self.reversed_relations_suffix}"
                    if self.relation_labels is None or reversed_label in self.relation_labels:
                        head2label2tails_with_scores[span2id[relation.tail]][
                            reversed_label
                        ].append((span2id[relation.head], relation.score))

            for idx, span in enumerate(base_layer):
                span_id = span2id[span]
                _metadata = {k: v for k, v in metadata.items() if k != METADATA_KEY_CHILD_ID2IDX}
                # save as dict to avoid serialization issues
                _metadata[self.vectorstore.METADATA_SPAN_KEY] = self._span_to_dict(span)
                relation_label2tails_with_scores = head2label2tails_with_scores[span_id]
                _metadata[METADATA_KEY_RELATION_LABEL2TAILS_WITH_SCORES] = dict(
                    relation_label2tails_with_scores
                )
                new_doc = LCDocument(id=span_id, page_content=pie_doc.text, metadata=_metadata)
                span_docs.append(new_doc)
                id2idx[span_id] = idx
        return span_docs, id2idx

    def _get_relevant_documents(
        self,
        query: str,
        return_related: bool = False,
        *,
        run_manager: CallbackManagerForRetrieverRun,
        **kwargs: Any,
    ) -> List[LCDocument]:
        """Get span documents relevant to a query span. We follow one hop of relations.

        Args:
            query: The span id to find relevant spans for
            return_related: Whether to return related spans
            run_manager: The callbacks handler to use
        Returns:
            List of relevant span documents with metadata from the parent document
        """
        similar_span_docs = super()._get_relevant_documents(
            query=query, run_manager=run_manager, **kwargs
        )
        if not return_related:
            return similar_span_docs

        related_docs = []
        for head_span_doc in similar_span_docs:
            doc_id = head_span_doc.metadata[self.id_key]
            doc = self.get_document(doc_id)
            query_span_id = head_span_doc.metadata["query_span_id"]

            for relation_label, tails_with_score in head_span_doc.metadata[
                METADATA_KEY_RELATION_LABEL2TAILS_WITH_SCORES
            ].items():
                for tail_id, relation_score in tails_with_score:
                    # in the case that we query against the same document,
                    # we don't want to return the same span as the query span
                    if tail_id == query_span_id:
                        continue

                    try:
                        attached_tail_span = self.get_span_from_doc_by_id(doc=doc, span_id=tail_id)
                    # this may happen if the tail span could not be added to the vectorstore, e.g. because
                    # the token span length is zero and no vector could be calculated
                    except SpanNotFoundError:
                        logger.warning(
                            f"Tail span with id [{tail_id}] not found in the vectorstore. Skipping."
                        )
                        continue

                    # TODO: handle via filter? see vectorstore.construct_filter
                    if self.span_labels is not None:
                        if not isinstance(attached_tail_span, (LabeledSpan, LabeledMultiSpan)):
                            raise ValueError(
                                "Span must must be a labeled span if span_labels is provided"
                            )
                        if attached_tail_span.label not in self.span_labels:
                            continue

                    related_docs.append(
                        LCDocument(
                            id=tail_id,
                            page_content="",
                            metadata={
                                "relation_score": relation_score,
                                "head_id": head_span_doc.id,
                                "relation_label": relation_label,
                                "attached_tail_span": attached_tail_span,
                                **head_span_doc.metadata,
                            },
                        )
                    )
        return related_docs