Source code for langchain_community.retrievers.docarray
from enum import Enum
from typing import Any, Dict, List, Optional, Union
import numpy as np
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.retrievers import BaseRetriever
from langchain_community.vectorstores.utils import maximal_marginal_relevance
[docs]class SearchType(str, Enum):
    """Enumerator of the types of search to perform."""
    similarity = "similarity"
    mmr = "mmr"
[docs]class DocArrayRetriever(BaseRetriever):
    """`DocArray Document Indices` retriever.
    Currently, it supports 5 backends:
    InMemoryExactNNIndex, HnswDocumentIndex, QdrantDocumentIndex,
    ElasticDocIndex, and WeaviateDocumentIndex.
    Args:
        index: One of the above-mentioned index instances
        embeddings: Embedding model to represent text as vectors
        search_field: Field to consider for searching in the documents.
            Should be an embedding/vector/tensor.
        content_field: Field that represents the main content in your document schema.
            Will be used as a `page_content`. Everything else will go into `metadata`.
        search_type: Type of search to perform (similarity / mmr)
        filters: Filters applied for document retrieval.
        top_k: Number of documents to return
    """
    index: Any
    embeddings: Embeddings
    search_field: str
    content_field: str
    search_type: SearchType = SearchType.similarity
    top_k: int = 1
    filters: Optional[Any] = None
    class Config: