retrievers

Retriever class returns Documents given a text query.

It is more general than a vector store. A retriever does not need to be able to store documents, only to return (or retrieve) it. Vector stores can be used as the backbone of a retriever, but there are other types of retrievers as well.

Class hierarchy:

BaseRetriever --> <name>Retriever  # Examples: ArxivRetriever, MergerRetriever

Main helpers:

Document, Serializable, Callbacks,
CallbackManagerForRetrieverRun, AsyncCallbackManagerForRetrieverRun

Classes

retrievers.contextual_compression.ContextualCompressionRetriever	Retriever that wraps a base retriever and compresses the results.
retrievers.document_compressors.base.DocumentCompressorPipeline	Document compressor that uses a pipeline of Transformers.
retrievers.document_compressors.chain_extract.LLMChainExtractor	Document compressor that uses an LLM chain to extract the relevant parts of documents.
retrievers.document_compressors.chain_extract.NoOutputParser	Parse outputs that could return a null string of some sort.
retrievers.document_compressors.chain_filter.LLMChainFilter	Filter that drops documents that aren't relevant to the query.
retrievers.document_compressors.cross_encoder.BaseCrossEncoder()	Interface for cross encoder models.
retrievers.document_compressors.cross_encoder_rerank.CrossEncoderReranker	Document compressor that uses CrossEncoder for reranking.
retrievers.document_compressors.embeddings_filter.EmbeddingsFilter	Document compressor that uses embeddings to drop documents unrelated to the query.
retrievers.document_compressors.listwise_rerank.LLMListwiseRerank	Document compressor that uses Zero-Shot Listwise Document Reranking.
retrievers.ensemble.EnsembleRetriever	Retriever that ensembles the multiple retrievers.
retrievers.merger_retriever.MergerRetriever	Retriever that merges the results of multiple retrievers.
retrievers.multi_query.LineListOutputParser	Output parser for a list of lines.
retrievers.multi_query.MultiQueryRetriever	Given a query, use an LLM to write a set of queries.
retrievers.multi_vector.MultiVectorRetriever	Retrieve from a set of multiple embeddings for the same document.
retrievers.multi_vector.SearchType(value[, ...])	Enumerator of the types of search to perform.
retrievers.parent_document_retriever.ParentDocumentRetriever	Retrieve small chunks then retrieve their parent documents.
retrievers.re_phraser.RePhraseQueryRetriever	Given a query, use an LLM to re-phrase it.
retrievers.self_query.base.SelfQueryRetriever	Retriever that uses a vector store and an LLM to generate the vector store queries.
retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever	Retriever that combines embedding similarity with recency in retrieving values.

Functions

retrievers.document_compressors.chain_extract.default_get_input(...)	Return the compression chain input.
retrievers.document_compressors.chain_filter.default_get_input(...)	Return the compression chain input.
retrievers.ensemble.unique_by_key(iterable, key)	Yield unique elements of an iterable based on a key function.

Deprecated classes

retrievers.document_compressors.cohere_rerank.CohereRerank

Deprecated since version 0.0.30: Use langchain_cohere.CohereRerank instead.