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Hybrid Search

The standard search in LangChain is done by vector similarity. However, a number of vectorstores implementations (Astra DB, ElasticSearch, Neo4J, AzureSearch, ...) also support more advanced search combining vector similarity search and other search techniques (full-text, BM25, and so on). This is generally referred to as "Hybrid" search.

Step 1: Make sure the vectorstore you are using supports hybrid search

At the moment, there is no unified way to perform hybrid search in LangChain. Each vectorstore may have their own way to do it. This is generally exposed as a keyword argument that is passed in during similarity_search. By reading the documentation or source code, figure out whether the vectorstore you are using supports hybrid search, and, if so, how to use it.

Step 2: Add that parameter as a configurable field for the chain

This will let you easily call the chain and configure any relevant flags at runtime. See this documentation for more information on configuration.

Step 3: Call the chain with that configurable field

Now, at runtime you can call this chain with configurable field.

Code Example​

Let's see a concrete example of what this looks like in code. We will use the Cassandra/CQL interface of Astra DB for this example.

Install the following Python package:

!pip install "cassio>=0.1.7"

Get the connection secrets.

Initialize cassio:

import cassio

database_id="Your database ID",
token="Your application token",
keyspace="Your key space",

Create the Cassandra VectorStore with a standard index analyzer. The index analyzer is needed to enable term matching.

from cassio.table.cql import STANDARD_ANALYZER
from langchain_community.vectorstores import Cassandra
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vectorstore = Cassandra(

"In 2023, I visited Paris",
"In 2022, I visited New York",
"In 2021, I visited New Orleans",
API Reference:Cassandra | OpenAIEmbeddings

If we do a standard similarity search, we get all the documents:

vectorstore.as_retriever().invoke("What city did I visit last?")
[Document(page_content='In 2022, I visited New York'),
Document(page_content='In 2023, I visited Paris'),
Document(page_content='In 2021, I visited New Orleans')]

The Astra DB vectorstore body_search argument can be used to filter the search on the term new.

vectorstore.as_retriever(search_kwargs={"body_search": "new"}).invoke(
"What city did I visit last?"
[Document(page_content='In 2022, I visited New York'),
Document(page_content='In 2021, I visited New Orleans')]

We can now create the chain that we will use to do question-answering over

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import (
from langchain_openai import ChatOpenAI

This is basic question-answering chain set up.

template = """Answer the question based only on the following context:
Question: {question}
prompt = ChatPromptTemplate.from_template(template)

model = ChatOpenAI()

retriever = vectorstore.as_retriever()

Here we mark the retriever as having a configurable field. All vectorstore retrievers have search_kwargs as a field. This is just a dictionary, with vectorstore specific fields

configurable_retriever = retriever.configurable_fields(
name="Search Kwargs",
description="The search kwargs to use",

We can now create the chain using our configurable retriever

chain = (
{"context": configurable_retriever, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
chain.invoke("What city did I visit last?")

We can now invoke the chain with configurable options. search_kwargs is the id of the configurable field. The value is the search kwargs to use for Astra DB.

"What city did I visit last?",
config={"configurable": {"search_kwargs": {"body_search": "new"}}},
New York

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