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This template allows you to balance precise embeddings and context retention by splitting documents into smaller chunks and retrieving their original or larger text information.

Using a Neo4j vector index, the package queries child nodes using vector similarity search and retrieves the corresponding parent's text by defining an appropriate retrieval_query parameter.

Environment Setup​

You need to define the following environment variables


Populating with data​

If you want to populate the DB with some example data, you can run python The script process and stores sections of the text from the file dune.txt into a Neo4j graph database. First, the text is divided into larger chunks ("parents") and then further subdivided into smaller chunks ("children"), where both parent and child chunks overlap slightly to maintain context. After storing these chunks in the database, embeddings for the child nodes are computed using OpenAI's embeddings and stored back in the graph for future retrieval or analysis. Additionally, a vector index named retrieval is created for efficient querying of these embeddings.


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 neo4j-parent

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

langchain app add neo4j-parent

And add the following code to your file:

from neo4j_parent import chain as neo4j_parent_chain

add_routes(app, neo4j_parent_chain, path="/neo4j-parent")

(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/neo4j-parent")

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