This tutorial covers how to use Neo4j with LangChain .
Neo4j is a graph database backed by vector store and can be deployed locally or on cloud.
To fully utilize Neo4j, you need to learn about Cypher, declarative query language.
This tutorial walks you through using CRUD operations with the Neo4jstoring , updating , deleting documents, and performing similarity-based retrieval .
Table of Contents
References
Environment Setup
[Note]
langchain-opentutorial is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials.
You can visit Neo4j Docker installation reference to check more detailed information.
[NOTE]
Neo4j also supports native deployment on macOS, Windows and Linux. Visit the Neo4j Official Installation guide reference for more details.
The Neo4j community edition only supports one database.
What is Neo4j?
Neo4j is a native graph database, which means it represents data as nodes and edges.
Nodes
label: tag to represent node role in a domain.
property: key-value pairs, e.g. name-John.
Edges
Represents relationship between two nodes.
Directional, which means it has start and end node.
property: like nodes, edge can have properties.
NoSQL
Neo4j does not require predefined schema allowing flexible data modeling.
Cypher
Neo4j uses Cypher, a declarative query language, to interact with the database.
Cypher expression resembles how humans think about relationships.
Prepare Data
This section guides you through the data preparation process .
This section includes the following components:
Data Introduction
Preprocess Data
Introduce Data
In this tutorial, we will use the fairy tale 📗 The Little Prince in PDF format as our data.
This material complies with the Apache 2.0 license .
The data is used in a text (.txt) format converted from the original PDF.
You can view the data at the link below.
Preprocessing Data
In this tutorial section, we will preprocess the text data from The Little Prince and convert it into a list of LangChain Document objects with metadata.
Each document chunk will include a title field in the metadata, extracted from the first line of each section.
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
import re
from typing import List
def preprocessing_data(content: str) -> List[Document]:
# 1. Split the text by double newlines to separate sections
blocks = content.split("\n\n")
# 2. Initialize the text splitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=500, # Maximum number of characters per chunk
chunk_overlap=50, # Overlap between chunks to preserve context
separators=["\n\n", "\n", " "], # Order of priority for splitting
)
documents = []
# 3. Loop through each section
for block in blocks:
lines = block.strip().splitlines()
if not lines:
continue
# Extract title from the first line using square brackets [ ]
first_line = lines[0]
title_match = re.search(r"\[(.*?)\]", first_line)
title = title_match.group(1).strip() if title_match else None
# Remove the title line from content
body = "\n".join(lines[1:]).strip()
if not body:
continue
# 4. Chunk the section using the text splitter
chunks = text_splitter.split_text(body)
# 5. Create a LangChain Document for each chunk with the same title metadata
for chunk in chunks:
documents.append(Document(page_content=chunk, metadata={"title": title}))
print(f"Generated {len(documents)} chunked documents.")
return documents
# Load the entire text file
with open("./data/the_little_prince.txt", "r", encoding="utf-8") as f:
content = f.read()
# Preprocessing Data
docs = preprocessing_data(content=content)
Generated 262 chunked documents.
Setting up Neo4j
This part walks you through the initial setup of Neo4j .
This section includes the following components:
Load Embedding Model
Load Neo4j Client
Create Index
Load Embedding Model
In the Load Embedding Model section, you'll learn how to load an embedding model.
This tutorial uses OpenAI'sAPI-Key for loading the model.
💡 If you prefer to use another embedding model, see the instructions below.
import os
from langchain_openai import OpenAIEmbeddings
embedding = OpenAIEmbeddings(model="text-embedding-3-large")
Load Neo4j Client
In the Load Neo4j Client section, we cover how to load the database client object using the Python SDK for Neo4j .
import time
import neo4j
# Create Database Client Object Function
def get_db_client(uri, username, password):
"""
Initializes and returns a VectorStore client instance.
This function loads configuration (e.g., API key, host) from environment
variables or default values and creates a client object to interact
with the Neo4j Python SDK.
Returns:
client:ClientType - An instance of the Neo4j client.
Raises:
ValueError: If required configuration is missing.
"""
client = neo4j.GraphDatabase.driver(uri=uri, auth=(username, password))
return client
# Get DB Client Object
uri = os.getenv("NEO4J_URI")
username = os.getenv("NEO4J_USERNAME")
password = os.getenv("NEO4J_PASSWORD")
client = get_db_client(uri, username, password)
Create Index
If you are successfully connected to Neo4j Aura, some basic indexes are already created.
But, in this tutorial we will create a new index with Neo4jIndexManager class.
from utils.neo4j import Neo4jIndexManager
# Create IndexManager Object
indexManger = Neo4jIndexManager(client)
# Create A New Index
index_name = "tutorial_index"
node_label = "tutorial_node"
# create a new index
try:
tutorial_index = indexManger.create_index(
embedding, index_name=index_name, metric="cosine", node_label=node_label
)
except Exception as e:
print("Index creation failed due to")
print(type(e))
print(str(e))
Index with name tutorial_index already exists.
Returning Neo4jDBManager object.
Created index information
('Index name: tutorial_index', 'Node label: tutorial_node', 'Similarity metric: COSINE', 'Embedding dimension: 3072', 'Embedding node property: embedding', 'Text node property: text')
Index creation successful. Return Neo4jDBManager object.
Index creation failed due to
name 'Neo4jCRUDManager' is not defined
Document Manager
To support the Langchain-Opentutorial , we implemented a custom set of CRUD functionalities for VectorDBs.
The following operations are included:
upsert : Update existing documents or insert if they don’t exist
upsert_parallel : Perform upserts in parallel for large-scale data
similarity_search : Search for similar documents based on embeddings
delete : Remove documents based on filter conditions
Each of these features is implemented as class methods specific to each VectorDB.
In this tutorial, you can easily utilize these methods to interact with your VectorDB.
We plan to continuously expand the functionality by adding more common operations in the future.
Create Instance
First, we create an instance of the Neo4j helper class to use its CRUD functionalities.
This class is initialized with the Neo4j Python SDK client instance, index name and the embedding model instance , both of which were defined in the previous section.
Now you can use the following CRUD operations with the crud_manager instance.
These instance allow you to easily manage documents in your Neo4j .
Upsert Document
Update existing documents or insert if they don’t exist
✅ Args
texts : Iterable[str] – List of text contents to be inserted/updated.
metadatas : Optional[List[Dict]] – List of metadata dictionaries for each text (optional).
ids : Optional[List[str]] – Custom IDs for the documents. If not provided, IDs will be auto-generated.
**kwargs : Extra arguments for the underlying vector store.
🔄 Return
None
from uuid import uuid4
# Create ID for each document
ids = [str(uuid4()) for _ in docs]
args = {
"texts": [doc.page_content for doc in docs[:2]],
"metadatas": [doc.metadata for doc in docs[:2]],
"ids": ids[:2],
# Add additional parameters if you need
}
crud_manager.upsert(**args)
Upsert Parallel Document
Perform upserts in parallel for large-scale data
✅ Args
texts : Iterable[str] – List of text contents to be inserted/updated.
metadatas : Optional[List[Dict]] – List of metadata dictionaries for each text (optional).
ids : Optional[List[str]] – Custom IDs for the documents. If not provided, IDs will be auto-generated.
batch_size : int – Number of documents per batch (default: 32).
workers : int – Number of parallel workers (default: 10).
**kwargs : Extra arguments for the underlying vector store.
🔄 Return
None
from uuid import uuid4
args = {
"texts": [doc.page_content for doc in docs],
"metadatas": [doc.metadata for doc in docs],
"ids": ids,
# Add additional parameters if you need
}
crud_manager.upsert_parallel(**args)
Similarity Search
Search for similar documents based on embeddings .
This method uses "cosine similarity" .
✅ Args
query : str – The text query for similarity search.
k : int – Number of top results to return (default: 10).
results : List[Document] – A list of LangChain Document objects ranked by similarity.
# Search by query
results = crud_manager.search(query="What is essential is invisible to the eye.", k=3)
for idx, result in enumerate(results):
print(f"Rank {idx+1}")
print(f"Contents : {result['text']}")
print(f"Metadata: {result['metadata']}")
print(f"Similarity Score: {result['score']}")
print()
Rank 1
Contents : And he went back to meet the fox.
"Goodbye," he said.
"Goodbye," said the fox. "And now here is my secret, a very simple secret: It is only with the heart that one can see rightly; what is essential is invisible to the eye."
"What is essential is invisible to the eye," the little prince repeated, so that he would be sure to remember.
"It is the time you have wasted for your rose that makes your rose so important."
Metadata: {'id': '148b9b3f-2231-4ebd-86d8-6aa841c4ac1b', 'title': 'Chapter 21', 'embedding': None}
Similarity Score: 0.755
Rank 2
Contents : "Yes," I said to the little prince. "The house, the stars, the desert-- what gives them their beauty is something that is invisible!"
"I am glad," he said, "that you agree with my fox."
Metadata: {'id': '62df5e3c-2668-4f5c-96ea-23c5b7a38351', 'title': 'Chapter 24', 'embedding': None}
Similarity Score: 0.748
Rank 3
Contents : "The men where you live," said the little prince, "raise five thousand roses in the same garden-- and they do not find in it what they are looking for."
"They do not find it," I replied.
"And yet what they are looking for could be found in one single rose, or in a little water."
"Yes, that is true," I said.
And the little prince added:
"But the eyes are blind. One must look with the heart..."
Metadata: {'id': 'ff93762d-6bde-44f4-b3d2-c6dc466b46a8', 'title': 'Chapter 25', 'embedding': None}
Similarity Score: 0.711
Delete Document
Remove documents based on filter conditions
✅ Args
ids : Optional[List[str]] – List of document IDs to delete. If None, deletion is based on filter.
