How to combine results from multiple retrievers

The EnsembleRetriever supports ensembling of results from multiple retrievers. It is initialized with a list of BaseRetriever objects. EnsembleRetrievers rerank the results of the constituent retrievers 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 "hybrid 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.

Basic usage

Below we demonstrate ensembling of a BM25Retriever with a retriever derived from the FAISS vector store.

%pip install --upgrade --quiet  rank_bm25 > /dev/null

from langchain.retrievers import EnsembleRetriever
from langchain_community.retrievers import BM25Retriever
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]
)

API Reference:EnsembleRetriever | BM25Retriever | FAISS | OpenAIEmbeddings

docs = ensemble_retriever.invoke("apples")
docs

[Document(page_content='I like apples', metadata={'source': 1}),
 Document(page_content='You like apples', metadata={'source': 2}),
 Document(page_content='Apples and oranges are fruits', metadata={'source': 1}),
 Document(page_content='You like oranges', metadata={'source': 2})]

Runtime Configuration

We can also configure the individual retrievers at runtime using configurable fields. Below we update the "top-k" parameter for the FAISS retriever specifically:

from langchain_core.runnables import ConfigurableField

faiss_retriever = faiss_vectorstore.as_retriever(
    search_kwargs={"k": 2}
).configurable_fields(
    search_kwargs=ConfigurableField(
        id="search_kwargs_faiss",
        name="Search Kwargs",
        description="The search kwargs to use",
    )
)

ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, faiss_retriever], weights=[0.5, 0.5]
)

API Reference:ConfigurableField

config = {"configurable": {"search_kwargs_faiss": {"k": 1}}}
docs = ensemble_retriever.invoke("apples", config=config)
docs

[Document(page_content='I like apples', metadata={'source': 1}),
 Document(page_content='You like apples', metadata={'source': 2}),
 Document(page_content='Apples and oranges are fruits', metadata={'source': 1})]

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