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This template uses HyDE with RAG.

Hyde is a retrieval method that stands for Hypothetical Document Embeddings (HyDE). It is a method used to enhance retrieval by generating a hypothetical document for an incoming query.

The document is then embedded, and that embedding is utilized to look up real documents that are similar to the hypothetical document.

The underlying concept is that the hypothetical document may be closer in the embedding space than the query.

For a more detailed description, see the paper here.

Environment Setup​

Set the OPENAI_API_KEY environment variable to access the OpenAI models.


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 hyde

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

langchain app add hyde

And add the following code to your file:

from hyde.chain import chain as hyde_chain

add_routes(app, hyde_chain, path="/hyde")

(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/hyde")

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