langchain.retrievers.docarray.DocArrayRetriever¶
class langchain.retrievers.docarray.DocArrayRetriever[source]¶
Bases: BaseRetriever
DocArray Document Indices retriever.
Currently, it supports 5 backends:
InMemoryExactNNIndex, HnswDocumentIndex, QdrantDocumentIndex,
ElasticDocIndex, and WeaviateDocumentIndex.
Parameters
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
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param content_field: str [Required]¶
param embeddings: langchain.schema.embeddings.Embeddings [Required]¶
param filters: Optional[Any] = None¶
param index: Any = None¶
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param search_field: str [Required]¶
param search_type: langchain.retrievers.docarray.SearchType = SearchType.similarity¶
param tags: Optional[List[str]] = None¶