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Hybrid Search in Weaviate

This template shows you how to use the hybrid search feature in Weaviate. Hybrid search combines multiple search algorithms to improve the accuracy and relevance of search results.

Weaviate uses both sparse and dense vectors to represent the meaning and context of search queries and documents. The results use a combination of bm25 and vector search ranking to return the top results.


Connect to your hosted Weaviate Vectorstore by setting a few env variables in


You will also need to set your OPENAI_API_KEY to use the OpenAI models.

Get Started​

To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package hybrid-search-weaviate

If you want to add this to an existing project, you can just run:

langchain app add hybrid-search-weaviate

And add the following code to your file:

from hybrid_search_weaviate import chain as hybrid_search_weaviate_chain

add_routes(app, hybrid_search_weaviate_chain, path="/hybrid-search-weaviate")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at We can access the playground at

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/hybrid-search-weaviate")

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