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Ensemble Retriever

The EnsembleRetriever takes a list of retrievers as input and ensemble the results of their get_relevant_documents() methods and rerank the results based on the Reciprocal Rank Fusion algorithm.

By leveraging the strengths of different algorithms, the EnsembleRetriever can achieve better performance than any single algorithm.

The most common pattern is to combine a sparse retriever (like BM25) with a dense retriever (like embedding similarity), because their strengths are complementary. It is also known as β€œhybrid search”. The sparse retriever is good at finding relevant documents based on keywords, while the dense retriever is good at finding relevant documents based on semantic similarity.

%pip install --upgrade --quiet  rank_bm25 > /dev/null
from langchain.retrievers import BM25Retriever, EnsembleRetriever
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
doc_list_1 = [
"I like apples",
"I like oranges",
"Apples and oranges are fruits",

# initialize the bm25 retriever and faiss retriever
bm25_retriever = BM25Retriever.from_texts(
doc_list_1, metadatas=[{"source": 1}] * len(doc_list_1)
bm25_retriever.k = 2

doc_list_2 = [
"You like apples",
"You like oranges",

embedding = OpenAIEmbeddings()
faiss_vectorstore = FAISS.from_texts(
doc_list_2, embedding, metadatas=[{"source": 2}] * len(doc_list_2)
faiss_retriever = faiss_vectorstore.as_retriever(search_kwargs={"k": 2})

# initialize the ensemble retriever
ensemble_retriever = EnsembleRetriever(
retrievers=[bm25_retriever, faiss_retriever], weights=[0.5, 0.5]
docs = ensemble_retriever.invoke("apples")
[Document(page_content='You like apples', metadata={'source': 2}),
Document(page_content='I like apples', metadata={'source': 1}),
Document(page_content='You like oranges', metadata={'source': 2}),
Document(page_content='Apples and oranges are fruits', metadata={'source': 1})]

Runtime Configuration​

We can also configure the retrievers at runtime. In order to do this, we need to mark the fields as configurable

from langchain_core.runnables import ConfigurableField
faiss_retriever = faiss_vectorstore.as_retriever(
search_kwargs={"k": 2}
name="Search Kwargs",
description="The search kwargs to use",
ensemble_retriever = EnsembleRetriever(
retrievers=[bm25_retriever, faiss_retriever], weights=[0.5, 0.5]
config = {"configurable": {"search_kwargs_faiss": {"k": 1}}}
docs = ensemble_retriever.invoke("apples", config=config)

Notice that this only returns one source from the FAISS retriever, because we pass in the relevant configuration at run time