Source code for langchain.retrievers.multi_vector

from enum import Enum
from typing import Any, Dict, List, Optional

from langchain_core.callbacks import (
    AsyncCallbackManagerForRetrieverRun,
    CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_core.stores import BaseStore, ByteStore
from langchain_core.vectorstores import VectorStore
from pydantic import Field, model_validator

from langchain.storage._lc_store import create_kv_docstore



[docs]
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."""




[docs]
class MultiVectorRetriever(BaseRetriever):
    """Retrieve from a set of multiple embeddings for the same document."""

    vectorstore: VectorStore
    """The underlying vectorstore to use to store small chunks
    and their embedding vectors"""
    byte_store: Optional[ByteStore] = None
    """The lower-level backing storage layer for the parent documents"""
    docstore: BaseStore[str, Document]
    """The storage interface for the parent documents"""
    id_key: str = "doc_id"
    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)"""

    @model_validator(mode="before")
    @classmethod
    def shim_docstore(cls, values: Dict) -> Any:
        byte_store = values.get("byte_store")
        docstore = values.get("docstore")
        if byte_store is not None:
            docstore = create_kv_docstore(byte_store)
        elif docstore is None:
            raise Exception("You must pass a `byte_store` parameter.")
        values["docstore"] = docstore
        return values

    def _get_relevant_documents(
        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
    ) -> List[Document]:
        """Get documents relevant to a query.
        Args:
            query: String to find relevant documents for
            run_manager: The callbacks handler to use
        Returns:
            List of relevant documents
        """
        if self.search_type == SearchType.mmr:
            sub_docs = self.vectorstore.max_marginal_relevance_search(
                query, **self.search_kwargs
            )
        elif self.search_type == SearchType.similarity_score_threshold:
            sub_docs_and_similarities = (
                self.vectorstore.similarity_search_with_relevance_scores(
                    query, **self.search_kwargs
                )
            )
            sub_docs = [sub_doc for sub_doc, _ in sub_docs_and_similarities]
        else:
            sub_docs = self.vectorstore.similarity_search(query, **self.search_kwargs)

        # We do this to maintain the order of the ids that are returned
        ids = []
        for d in sub_docs:
            if self.id_key in d.metadata and d.metadata[self.id_key] not in ids:
                ids.append(d.metadata[self.id_key])
        docs = self.docstore.mget(ids)
        return [d for d in docs if d is not None]

    async def _aget_relevant_documents(
        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
    ) -> List[Document]:
        """Asynchronously get documents relevant to a query.
        Args:
            query: String to find relevant documents for
            run_manager: The callbacks handler to use
        Returns:
            List of relevant documents
        """
        if self.search_type == SearchType.mmr:
            sub_docs = await self.vectorstore.amax_marginal_relevance_search(
                query, **self.search_kwargs
            )
        elif self.search_type == SearchType.similarity_score_threshold:
            sub_docs_and_similarities = (
                await self.vectorstore.asimilarity_search_with_relevance_scores(
                    query, **self.search_kwargs
                )
            )
            sub_docs = [sub_doc for sub_doc, _ in sub_docs_and_similarities]
        else:
            sub_docs = await self.vectorstore.asimilarity_search(
                query, **self.search_kwargs
            )

        # We do this to maintain the order of the ids that are returned
        ids = []
        for d in sub_docs:
            if self.id_key in d.metadata and d.metadata[self.id_key] not in ids:
                ids.append(d.metadata[self.id_key])
        docs = await self.docstore.amget(ids)
        return [d for d in docs if d is not None]